From 8492067fa6872dd1d0e224b42aaec710fe4b6b5a Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Sat, 8 Mar 2014 12:22:37 +0530 Subject: [PATCH 01/13] Added Documentation regarding issue #1878 Added 'pca_notebook.ipynb' named python notebook in doc/ipython-notebooks/pca Implemented PCA on toy data for 2d to 1d and 3d to 2d projection. Implemented Eigenfaces for data compression and face recognition using att_face dataset. --- doc/ipython-notebooks/pca/pca_notebook.ipynb | 719 +++++++++++++++++++ 1 file changed, 719 insertions(+) create mode 100644 doc/ipython-notebooks/pca/pca_notebook.ipynb diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb new file mode 100644 index 00000000000..48cad6cedb9 --- /dev/null +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -0,0 +1,719 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:f5a08ebf445e92d5b42170c036c6da61b1e4d555d91b29483e006d7e34834339" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's already make it working!!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets generate the toy data.\n", + "import numpy as np\n", + "\n", + "\n", + "def randrange(n, vmin, vmax):\n", + " return (vmax-vmin)*np.random.rand(n)+vmin\n", + "\n", + "# number of data points:\n", + "n=100\n", + "\n", + "#generate random 2d data\n", + "xs = randrange(n,-20,+20)\n", + "ys = randrange(n,-20,+20)\n", + "\n", + "#subtract the mean from this\n", + "mxs=mean(xs)\n", + "xs = xs - np.tile(mxs,[n,1]).T[0]\n", + "mys=mean(ys)\n", + "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "\n", + "#form the data matrix\n", + "twoD_obsmatrix=np.array([xs, ys])\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the given data.\n", + "from matplotlib import pyplot\n", + "pyplot.ion() #to set the matplotlib's interactive mode on!\n", + "figure, axis = pyplot.subplots(1,1)\n", + "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "\n", + "def apply_pca_to_data(target_dims, data_matrix):\n", + " #from modshogun import RealFeatures\n", + " #from modshogun import PruneVarSubMean\n", + " train_features = RealFeatures(data_matrix)\n", + " submean = PruneVarSubMean(False)\n", + " submean.init(train_features)\n", + " submean.apply_to_feature_matrix(train_features)\n", + " preprocessor = PCA()\n", + " preprocessor.set_target_dim(target_dims)\n", + " preprocessor.init(train_features)\n", + " eigenvectors = preprocessor.get_transformation_matrix()\n", + " preprocessor.apply_to_feature_matrix(train_features)\n", + " return train_features,eigenvectors\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#apply pca\n", + "#the target_dims is made 1 for this 2d data.\n", + "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", + "eig1=eig\n", + "\n", + "#hence we need to take only that set of weights which corresponds to eig1.\n", + "y1=y[0,:]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# y is just the weights that each eigenvector carries. i.e\n", + "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", + "# dimension reduction).\n", + "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", + "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "\n", + "\n", + "\n", + "#we visualise this:\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-20, 20)\n", + "pyplot.ylim(-20, 20)\n", + "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", + "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", + "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", + "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", + "\n", + "\n", + "#we are plotting the projection here:\n", + "for i in range(n):\n", + " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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g4kRg7Vpda8KoRygdold3BWb9roeBxn1fGodod3d3rFq1Cu3atYOVlRX8/f1R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\nFvZth5cDH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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", + "\n", + "#we generate the height of the previous data.\n", + "zs=randrange(n, -5, +5)\n", + "mzs=mean(zs)\n", + "zs = zs - np.tile(mzs,[n,1]).T[0]\n", + "threeD_obsmatrix=array([xs, ys, zs])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#for plotting the 3d data, we import Axes3D from matplotlib module\n", + "from mpl_toolkits.mplot3d import Axes3D" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the 3d data\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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jCTnB0xufZkrlFKZUTUm7ndTwNDUWOBKJ9BPbbMears7tfcfdxw2rb2Bt+1qq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FQAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#again applying the previous methadology, we proceed by applying the pca\n", + "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", + "#are to be taken according to their eigenvalues.\n", + "\n", + "#1st eigenvactor\n", + "eig1=eig[:,0]\n", + "\n", + "#2nd eigenvactor\n", + "eig2=eig[:,1]\n", + "\n", + "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", + "\n", + "#1st set of weights\n", + "y1=y[0,:]\n", + "\n", + "#2nd set of weights\n", + "y2=y[1,:]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the output:\n", + "fig=pyplot.figure()\n", + "ax1=fig.add_subplot(111, projection='3d')\n", + "legend([p3],[\"2d projected data points\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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OtwQhMUekXtzROmOpF9Gsym+mWiCrra01fT5GCJG2/KEPC9X1Gj2GEVBFByCT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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "fig=pyplot.figure()\n", + "ax2=fig.add_subplot(111, projection='3d')\n", + "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax2.set_xlabel('x label')\n", + "ax2.set_ylabel('y label')\n", + "ax2.set_zlabel('z label')\n", + "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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tDlWW2IMPJbiHckd51pZKRIkw691ZfLXvK2Qpvg+/Ou5XnNk3N6Ec1GUQz49/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Ah6b+v1epODXVCQhZbWXkQs1tMajUYwtbUwKpUwmkmYrtCoIZAvdbWVlZVISEhw++86deqE0tJS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\n990n/FwqleLdd9/FvffeC57nM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TiAAAII4BYiQLoBBBBAALcQAdINIIAAAriFCJBuAAEE\nEMAtRIB0AwgggABuIQKkG0AAAQRwCxEg3QACCCCAW4j/Bw+B04wzDaeaAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough of the toy data! Lets work on something real.\n", + "\n", + "Here we will show the implementation of PCA for data compression and basic face recognition.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Eigenfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", + "\n", + "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", + "\n", + "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "\n", + "#lets start with data preparation.\n", + "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", + "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", + "\n", + "def get_imlist(path):\n", + " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", + " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", + "\n", + "#set path to the required folder.\n", + "path='../../gsoc/att_faces/att_aligned/'\n", + "\n", + "#set no. of rows that the images will be resized.\n", + "k1=100\n", + "#set no. of columns that the images will be resized.\n", + "k2=100\n", + "\n", + "filenames = get_imlist(path)\n", + "filenames = np.array(filenames)\n", + "# n is total number of images that has to be analysed.\n", + "n=len(filenames)\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# we will be using this often to visualise the images out there.\n", + "def showfig(image):\n", + " imgplot=plt.imshow(image, cmap='gray')\n", + " imgplot.axes.get_xaxis().set_visible(False)\n", + " imgplot.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import Image\n", + "from scipy import misc\n", + "\n", + "# to get a hang of the data, lets see some part of the dataset images.\n", + "fig = plt.figure()\n", + "\n", + "for i in range(49):\n", + " fig.add_subplot(7,7,i)\n", + " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", + " train_img=misc.imresize(train_img, [k1,k2])\n", + " showfig(train_img)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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Pw+12Y+vWrXzQCLc0mUx8SRMJe2M7JxVU4/E4otEozGYzHA4H8ygByN5wUSwW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\n7YXBYOBsjbLMTosK6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D5yuRwf4M8++yzK5TJGRkawurqKer2Ol156CSaTCcFgEAcOHEAk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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#read each and every image. flatten them to make row vectors and stack them together \n", + "#to form the observation matrix obs_matrix.\n", + "from modshogun import PCA\n", + "\n", + "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", + "train_img=misc.imresize(train_img, [k1,k2])\n", + "train_img=np.array(train_img, dtype='double')\n", + "train_img=train_img.flatten()\n", + "\n", + "for i in range(1,n):\n", + " temp=np.array(Image.open(filenames[i]).convert('L')) \n", + " temp=misc.imresize(temp, [k1,k2])\n", + " temp=np.array(temp, dtype='double')\n", + " temp=temp.flatten()\n", + " train_img=np.vstack([train_img,temp])\n", + " \n", + "obs_matrix=train_img.T\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Again applying the PCA on the data, the same way we were doing for the last \n", + "# two times.\n", + "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", + "\n", + "y,eig= apply_pca_to_data(100,obs_matrix)\n", + "res=y.get_feature_matrix()\n", + "#print res.shape" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets see how these eigenfaces/eigenvectors look like:\n", + "fig1 = plt.figure()\n", + "plt.title('top 20 eigenfaces')\n", + "\n", + "\n", + "for i in range(20):\n", + " a = fig1.add_subplot(5,4,i+1)\n", + " eigen_faces=eig[:,i].reshape([k1,k2])\n", + " showfig(eigen_faces)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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PLRuPx9rY2AiUA/JptZlMxtaJwMOnNJIh+fomYMlLS0tTjTf+9CaCGyCZXq9n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4AAAg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\nanBaGbNH86Oqqiq0t7djx44dc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+ "text": [ + "" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", + "# which when added provides us with a reconstruction of the original image.\n", + "\n", + "reconstructed_vector=np.mat(eig)*np.mat(res)\n", + "\n", + "#plot the reconstructed images to visualise it. \n", + "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", + "fig2=plt.figure()\n", + "for i in range(1,50):\n", + " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", + " fig2.add_subplot(7,7,i)\n", + " showfig(reconstructed_image)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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W3i/a4XBgbW0Nzz//vPTgGwwGkZKjI2daeOzYMSSTSUl5ecgPtgXS+QKP09Ev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\nPeMkdho7pKWVaFF7g1ABCelccc8N/4C/xB/gCgmBBBcgBKVVa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+ "text": [ + "" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Face Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets use this technique of eigenfaces to perform some basic face recognition.\n", + "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from modshogun import RealFeatures\n", + "from modshogun import EuclideanDistance\n", + "\n", + "#get the test image in the list test_file.\n", + "test_files= get_imlist('../../gsoc/att_faces/')\n", + "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", + "\n", + "\n", + "#we plot the test image , for which we have to identify a good match from the training images we already have\n", + "fig = plt.figure()\n", + "t_img=plt.imshow(test_img, cmap='gray')\n", + "plt.title('the test image for which a match is to be identified')\n", + "t_img.axes.get_xaxis().set_visible(False)\n", + "t_img.axes.get_yaxis().set_visible(False)\n", + "\n", + "\n", + "# we flatten out or test image just the way we have done for the other images\n", + "test_image=misc.imresize(test_img, [k1,k2])\n", + "test_image=test_image.flatten()\n", + "test_image=np.array(test_image, dtype='double')\n", + "test_image=np.mat(test_image).transpose()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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rRSDXZKVdTMyHThIxUdVJQKdJrP+VD0HKwYhAMz8/H4PBoKWKZ2aBiLYNkdey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+ "text": [ + "" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have to project our training images as well as the test image on the PCA subspace." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#projecting our training images into pca subspace\n", + "train_proj=np.dot(eig.T , obs_matrix)\n", + "\n", + "#projecting our test image into pca subspace\n", + "test_proj=np.dot(eig.T , test_image)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", + "#between test image and all the training images.\n", + "#The training image which produces the least distance measure with our test image \n", + "#is said to be the best match.\n", + "testfeat = RealFeatures(np.array(test_proj))\n", + "workfeat = RealFeatures(np.array(train_proj))\n", + "RaRb=EuclideanDistance(testfeat, workfeat)\n", + "\n", + "d=np.empty([n,1])\n", + "for i in range(n):\n", + " d[i]= RaRb.distance(0,i)\n", + " \n", + "\n", + "iden=np.array(Image.open(filenames[d.argmin()]))\n", + "i_img=plt.imshow(iden, cmap='gray')\n", + "plt.title('identified image from our set of training images')\n", + "i_img.axes.get_xaxis().set_visible(False)\n", + "i_img.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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p7nkIAd1VARDDJBTn5bFjdIl8Nws2eAGOOqcvXlb5m7QhCc50Db0OMsBjOEGW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+ "text": [ + "" + ] + } + ], + "prompt_number": 27 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file From 6a85f9fcd7effae7ba2a12abd455b8d00d51c992 Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Sat, 8 Mar 2014 20:46:58 +0530 Subject: [PATCH 02/13] cleared the output & added nbviewer link --- .../pca_notebook-checkpoint.ipynb | 587 ++++++++++++++++++ doc/ipython-notebooks/pca/pca_notebook.ipynb | 204 ++---- 2 files changed, 623 insertions(+), 168 deletions(-) create mode 100644 doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb diff --git a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb new file mode 100644 index 00000000000..99ea0bc1cd6 --- /dev/null +++ b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb @@ -0,0 +1,587 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's already make it working!!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets generate the toy data.\n", + "import numpy as np\n", + "from modshogun import *\n", + "\n", + "def randrange(n, vmin, vmax):\n", + " return (vmax-vmin)*np.random.rand(n)+vmin\n", + "\n", + "# number of data points:\n", + "n=100\n", + "\n", + "#generate random 2d data\n", + "xs = randrange(n,-20,+20)\n", + "ys = randrange(n,-20,+20)\n", + "\n", + "#subtract the mean from this\n", + "mxs=mean(xs)\n", + "xs = xs - np.tile(mxs,[n,1]).T[0]\n", + "mys=mean(ys)\n", + "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "\n", + "#form the data matrix\n", + "twoD_obsmatrix=np.array([xs, ys])\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the given data.\n", + "from matplotlib import pyplot\n", + "pyplot.ion() #to set the matplotlib's interactive mode on!\n", + "figure, axis = pyplot.subplots(1,1)\n", + "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "\n", + "def apply_pca_to_data(target_dims, data_matrix):\n", + " train_features = RealFeatures(data_matrix)\n", + " submean = PruneVarSubMean(False)\n", + " submean.init(train_features)\n", + " submean.apply_to_feature_matrix(train_features)\n", + " preprocessor = PCA()\n", + " preprocessor.set_target_dim(target_dims)\n", + " preprocessor.init(train_features)\n", + " eigenvectors = preprocessor.get_transformation_matrix()\n", + " preprocessor.apply_to_feature_matrix(train_features)\n", + " return train_features,eigenvectors\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#apply pca\n", + "#the target_dims is made 1 for this 2d data.\n", + "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", + "eig1=eig\n", + "\n", + "#hence we need to take only that set of weights which corresponds to eig1.\n", + "y1=y[0,:]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# y is just the weights that each eigenvector carries. i.e\n", + "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", + "# dimension reduction).\n", + "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", + "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "\n", + "\n", + "\n", + "#we visualise this:\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-20, 20)\n", + "pyplot.ylim(-20, 20)\n", + "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", + "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", + "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", + "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", + "\n", + "\n", + "#we are plotting the projection here:\n", + "for i in range(n):\n", + " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", + "\n", + "#we generate the height of the previous data.\n", + "zs=randrange(n, -5, +5)\n", + "mzs=mean(zs)\n", + "zs = zs - np.tile(mzs,[n,1]).T[0]\n", + "threeD_obsmatrix=array([xs, ys, zs])" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#for plotting the 3d data, we import Axes3D from matplotlib module\n", + "from mpl_toolkits.mplot3d import Axes3D" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the 3d data\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#again applying the previous methadology, we proceed by applying the pca\n", + "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", + "#are to be taken according to their eigenvalues.\n", + "\n", + "#1st eigenvactor\n", + "eig1=eig[:,0]\n", + "\n", + "#2nd eigenvactor\n", + "eig2=eig[:,1]\n", + "\n", + "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", + "\n", + "#1st set of weights\n", + "y1=y[0,:]\n", + "\n", + "#2nd set of weights\n", + "y2=y[1,:]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the output:\n", + "fig=pyplot.figure()\n", + "ax1=fig.add_subplot(111, projection='3d')\n", + "legend([p3],[\"2d projected data points\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "fig=pyplot.figure()\n", + "ax2=fig.add_subplot(111, projection='3d')\n", + "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax2.set_xlabel('x label')\n", + "ax2.set_ylabel('y label')\n", + "ax2.set_zlabel('z label')\n", + "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough of the toy data! Lets work on something real.\n", + "\n", + "Here we will show the implementation of PCA for data compression and basic face recognition.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Eigenfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", + "\n", + "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", + "\n", + "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "\n", + "#lets start with data preparation.\n", + "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", + "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", + "\n", + "def get_imlist(path):\n", + " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", + " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", + "\n", + "#set path to the required folder.\n", + "path='../../../data/att_dataset/training/'\n", + "\n", + "#set no. of rows that the images will be resized.\n", + "k1=100\n", + "#set no. of columns that the images will be resized.\n", + "k2=100\n", + "\n", + "filenames = get_imlist(path)\n", + "filenames = np.array(filenames)\n", + "# n is total number of images that has to be analysed.\n", + "n=len(filenames)\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# we will be using this often to visualise the images out there.\n", + "def showfig(image):\n", + " imgplot=plt.imshow(image, cmap='gray')\n", + " imgplot.axes.get_xaxis().set_visible(False)\n", + " imgplot.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import Image\n", + "from scipy import misc\n", + "\n", + "# to get a hang of the data, lets see some part of the dataset images.\n", + "fig = plt.figure()\n", + "\n", + "for i in range(49):\n", + " fig.add_subplot(7,7,i)\n", + " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", + " train_img=misc.imresize(train_img, [k1,k2])\n", + " showfig(train_img)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#read each and every image. flatten them to make row vectors and stack them together \n", + "#to form the observation matrix obs_matrix.\n", + "\n", + "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", + "train_img=misc.imresize(train_img, [k1,k2])\n", + "train_img=np.array(train_img, dtype='double')\n", + "train_img=train_img.flatten()\n", + "\n", + "for i in range(1,n):\n", + " temp=np.array(Image.open(filenames[i]).convert('L')) \n", + " temp=misc.imresize(temp, [k1,k2])\n", + " temp=np.array(temp, dtype='double')\n", + " temp=temp.flatten()\n", + " train_img=np.vstack([train_img,temp])\n", + " \n", + "obs_matrix=train_img.T\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Again applying the PCA on the data, the same way we were doing for the last \n", + "# two times.\n", + "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", + "\n", + "y,eig= apply_pca_to_data(100,obs_matrix)\n", + "res=y.get_feature_matrix()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets see how these eigenfaces/eigenvectors look like:\n", + "fig1 = plt.figure()\n", + "plt.title('top 20 eigenfaces')\n", + "\n", + "\n", + "for i in range(20):\n", + " a = fig1.add_subplot(5,4,i+1)\n", + " eigen_faces=eig[:,i].reshape([k1,k2])\n", + " showfig(eigen_faces)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", + "# which when added provides us with a reconstruction of the original image.\n", + "\n", + "reconstructed_vector=np.mat(eig)*np.mat(res)\n", + "\n", + "#plot the reconstructed images to visualise it. \n", + "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", + "fig2=plt.figure()\n", + "for i in range(1,50):\n", + " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", + " fig2.add_subplot(7,7,i)\n", + " showfig(reconstructed_image)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Face Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets use this technique of eigenfaces to perform some basic face recognition.\n", + "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#get the test image in the list test_file.\n", + "test_files= get_imlist('../../../data/att_dataset/testing/')\n", + "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", + "\n", + "#we plot the test image , for which we have to identify a good match from the training images we already have\n", + "fig = plt.figure()\n", + "t_img=plt.imshow(test_img, cmap='gray')\n", + "plt.title('the test image for which a match is to be identified')\n", + "t_img.axes.get_xaxis().set_visible(False)\n", + "t_img.axes.get_yaxis().set_visible(False)\n", + "\n", + "# we flatten out or test image just the way we have done for the other images\n", + "test_image=misc.imresize(test_img, [k1,k2])\n", + "test_image=test_image.flatten()\n", + "test_image=np.array(test_image, dtype='double')\n", + "test_image=np.mat(test_image).transpose()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have to project our training images as well as the test image on the PCA subspace." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#projecting our training images into pca subspace\n", + "train_proj=np.dot(eig.T , obs_matrix)\n", + "\n", + "#projecting our test image into pca subspace\n", + "test_proj=np.dot(eig.T , test_image)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", + "#between test image and all the training images.\n", + "#The training image which produces the least distance measure with our test image \n", + "#is said to be the best match.\n", + "testfeat = RealFeatures(np.array(test_proj))\n", + "workfeat = RealFeatures(np.array(train_proj))\n", + "RaRb=EuclideanDistance(testfeat, workfeat)\n", + "\n", + "d=np.empty([n,1])\n", + "for i in range(n):\n", + " d[i]= RaRb.distance(0,i)\n", + " \n", + "\n", + "iden=np.array(Image.open(filenames[d.argmin()]))\n", + "i_img=plt.imshow(iden, cmap='gray')\n", + "plt.title('identified image from our set of training images')\n", + "i_img.axes.get_xaxis().set_visible(False)\n", + "i_img.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb index 48cad6cedb9..99ea0bc1cd6 100644 --- a/doc/ipython-notebooks/pca/pca_notebook.ipynb +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:f5a08ebf445e92d5b42170c036c6da61b1e4d555d91b29483e006d7e34834339" + "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" }, "nbformat": 3, "nbformat_minor": 0, @@ -16,6 +16,13 @@ "Principal Component Analysis" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +57,7 @@ "input": [ "#lets generate the toy data.\n", "import numpy as np\n", - "\n", + "from modshogun import *\n", "\n", "def randrange(n, vmin, vmax):\n", " return (vmax-vmin)*np.random.rand(n)+vmin\n", @@ -73,8 +80,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 1 + "outputs": [] }, { "cell_type": "code", @@ -88,25 +94,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 2 + "outputs": [] }, { "cell_type": "code", @@ -115,8 +103,6 @@ "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", "\n", "def apply_pca_to_data(target_dims, data_matrix):\n", - " #from modshogun import RealFeatures\n", - " #from modshogun import PruneVarSubMean\n", " train_features = RealFeatures(data_matrix)\n", " submean = PruneVarSubMean(False)\n", " submean.init(train_features)\n", @@ -131,8 +117,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 3 + "outputs": [] }, { "cell_type": "code", @@ -144,8 +129,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 5 + "outputs": [] }, { "cell_type": "code", @@ -160,8 +144,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 6 + "outputs": [] }, { "cell_type": "code", @@ -198,17 +181,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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g4kRg7Vpda8KoRygdold3BWb9roeBxn1fGodod3d3rFq1Cu3atYOVlRX8/f1R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\nFvZth5cDH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jCTnB0xufZkrlFKZUTUm7ndTwNDUWOBKJ9BPbbMears7tfcfdxw2rb2Bt+1qq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OtwQhMUekXtzROmOpF9Gsym+mWiCrra01fT5GCJG2/KEPC9X1Gj2GEVBFByCT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tDlWW2IMPJbiHckd51pZKRIkw691ZfLXvK2Qpvg+/Ou5XnNk3N6Ec1GUQz49/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Ah6b+v1epODXVCQhZbWXkQs1tMajUYwtbUwKpUwmkmYrtCoIZAvdbWVlZVISEhw++86deqE0tJS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\n4ULs2bPHawP4AALwBoGcbgABBBDALUSgD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- "text": [ - "" - ] - } - ], - "prompt_number": 14 + "outputs": [] }, { "cell_type": "markdown", @@ -419,7 +350,7 @@ " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", "\n", "#set path to the required folder.\n", - "path='../../gsoc/att_faces/att_aligned/'\n", + "path='../../../data/att_dataset/training/'\n", "\n", "#set no. of rows that the images will be resized.\n", "k1=100\n", @@ -435,8 +366,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 15 + "outputs": [] }, { "cell_type": "code", @@ -450,8 +380,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 16 + "outputs": [] }, { "cell_type": "code", @@ -472,17 +401,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Pw+12Y+vWrXzQCLc0mUx8SRMJe2M7JxVU4/E4otEozGYzHA4H8ygByN5wUSwW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\n7YXBYOBsjbLMTosK6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D5yuRwf4M8++yzK5TJGRkawurqKer2Ol156CSaTCcFgEAcOHEAk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- "text": [ - "" - ] - } - ], - "prompt_number": 17 + "outputs": [] }, { "cell_type": "code", @@ -490,7 +409,6 @@ "input": [ "#read each and every image. flatten them to make row vectors and stack them together \n", "#to form the observation matrix obs_matrix.\n", - "from modshogun import PCA\n", "\n", "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", "train_img=misc.imresize(train_img, [k1,k2])\n", @@ -504,13 +422,11 @@ " temp=temp.flatten()\n", " train_img=np.vstack([train_img,temp])\n", " \n", - "obs_matrix=train_img.T\n", - "\n" + "obs_matrix=train_img.T\n" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 18 + "outputs": [] }, { "cell_type": "code", @@ -521,13 +437,11 @@ "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", "\n", "y,eig= apply_pca_to_data(100,obs_matrix)\n", - "res=y.get_feature_matrix()\n", - "#print res.shape" + "res=y.get_feature_matrix()\n" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 19 + "outputs": [] }, { "cell_type": "code", @@ -546,17 +460,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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PLRuPx9rY2AiUA/JptZlMxtaJwMOnNJIh+fomYMlLS0tTjTf+9CaCGyCZXq9n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4AAAg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\nanBaGbNH86Oqqiq0t7djx44dc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W3i/a4XBgbW0Nzz//vPTgGwwGkZKjI2daeOzYMSSTSUl5ecgPtgXS+QKP09Ev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\nHWiahvF4LImGeuTzrlYkQv8TQXECrd3Y4rIToTyWFxcHXzQCWVpaEoyU9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- "text": [ - "" - ] - } - ], - "prompt_number": 21 + "outputs": [] }, { "cell_type": "heading", @@ -617,14 +511,10 @@ "cell_type": "code", "collapsed": false, "input": [ - "from modshogun import RealFeatures\n", - "from modshogun import EuclideanDistance\n", - "\n", "#get the test image in the list test_file.\n", - "test_files= get_imlist('../../gsoc/att_faces/')\n", + "test_files= get_imlist('../../../data/att_dataset/testing/')\n", "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", "\n", - "\n", "#we plot the test image , for which we have to identify a good match from the training images we already have\n", "fig = plt.figure()\n", "t_img=plt.imshow(test_img, cmap='gray')\n", @@ -632,7 +522,6 @@ "t_img.axes.get_xaxis().set_visible(False)\n", "t_img.axes.get_yaxis().set_visible(False)\n", "\n", - "\n", "# we flatten out or test image just the way we have done for the other images\n", "test_image=misc.imresize(test_img, [k1,k2])\n", "test_image=test_image.flatten()\n", @@ -641,17 +530,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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rRSDXZKVdTMyHThIxUdVJQKdJrP+VD0HKwYhAMz8/H4PBoKWKZ2aBiLYNkdey\nrWzcO03Ps8qgfhQgipWxTTguBFwcI7pXixCB3sGd7RDRtv9NJpPmXtpayToz9jvLGdVJLh0YHiJu\n6+HA5i4TqrtU5bjjQWAl4KCTRSqqgFNgR3aZ7WPm5Iw4FU6jcmvyaLJqctPTSkanepHx0ItMhqc0\nBOyqD9VmgUKmdpNFKW2q+moTsiCCj7Mr9hFBwgOa9btCWdRPBCKy5ohotZFvuyPo1NRRgiCfdVbL\neEc9J5ClDVjMl7uBTof1dcxwtnRgeIhkqzVthQJCPyTUdw3Q6E7blO6V2iugY0C1q88M0tbvVNUo\nZD6Mu6PdUs+SqTqjESvjx9uJrEztIUB39Vb5UgWVWugxjzINkCG6aiubZ7YHmt7uiP1DVbUgEHRU\nzuXl5ZZdV6xXYKRxwB1CPkYyVZZli9hnzA6yEpVhMpk0J/NERJxxxhktW6HbDH3cuv2yk4PSgWFF\nyDz019UiB0INSgcqTjYOdAc6B0MewMC9yO5Jpjqr+x0YpLKThbjnmOorGRodMh5iw3wIAgTRzNhP\nhwfVcu5A8cntjib2FQGUfUXwZH9mXmsBJ5mvrnOhIMPlmGCZMtZYszuyDWppyiSzvr7eMH6NCy4K\nTI95dXK4dGBYkZq6w5Wd3uBsslPN8/AThslkYChAjYhGPaJDxbeLSaiec3LL9uXPaEIJlOnYcfAn\n82VbkJ1qMsrGKJZJkFEbqU1UR9UzC/txMNZfOmLIqhiqwpg+Z+xsB7JX1l82U1fTCToaAx6FoDq7\nI4VbL9X2BHEHUtVva2srIvYZpaITXCPgmO1Y4elJB4YVqQ0gTS56jskCBG6+I4EMSeJ2QlePBWxu\nJyTLY7kiDk4eTXzuYHEPtoelqP4++bkI6H633QmEaFNkOXyRcc8yz3Jku9Fux/+dDTKmkeWiGYDs\nlvVVO3o5mQb7MLuesUbVzdm0yqvrbD+WiQvN3t5eE26jMURbbgd6X7l0YFgRDiwOegHhaDRqPLMR\n+yqWAE2D2ncjUEX1MJrBYNBihHNzczEcDpt4QwIhwZATlKCsvwrlWVxcbGxherFRZvtzIBe4ibFp\nN0REtADGHQ8EBYb2cMIKDFU+vRRJdeHJ4cpD16bTacNACZ4ssz5ctLLQHjLKzISgZ7iAaFyQ4Uq4\nQDIf2kpp15TNMwNFb8vd3d04efJkREQr1tTHhOrjdshOcunAsCJu34lo7zaRvdCZAtVIMhtXoQVs\nVI3J+GjaCyizAAAgAElEQVQn1B5mhtjoHgceL4f+Uq2fTCat+rlNS2lE7Kt/qj/Zj/+udiM70nME\nITcf0B7HAHOq1AIOqrJkcbRVcgFifKPKSzCUkFnTFkfAYowm208ioOL1mo2RgOuOIWd4bkNUyM3W\n1laMRqMDDjWXji2ennRgWJFsAHPLnAdMS8VzNui2NQEhbYSMSfR7aD/kBPLJQnaiyaz/xeI0ibSj\ngUCWMSE9k8WskcH6cz6hmR5/J4iJ6YmpiiWLJQpoxuNxlFKaAww8HMjTU5q0o7qzwhkl7ZLsT29v\ntY9YGb3wbsKgmYFjQWWiTZTtpzb0PlBeAkONIYkzS//eyUHpwLAimf2MhynwSKuItk0o2/WgNAWa\nfiYhmRwdKr77JDP+U42LaB+qoMkWcQrMyURd9dOznLisv/5XfRmDyDoS7AiItXATAtloNGr9Tvsr\n7ZFklQ5M3i411psBIT3LXGQyhw7ByrUBbxPmMau9XF3P7mV/b21tNXveuSPJx0cnh0sHhoeIBjlV\nTJ0sQ+YXsc9e3JvID22FAjsPbOa5hYexQjIFqrG0S5IhSe1WPB/ZrRvsqYpTBZ3FuDI1j7ZEBwV3\nMo1Go5YdzUONCISM9SSIKF0HdWf7LI+XnSDti1uNuQmoI/bfP+OB0ZlanzFLivqCeakdRqNRLCws\nxNraWiwvLzfqvS9ync3wcOnAsCKZWqTJ54eS6p5MneRAzlRgf++xgNIBYBYgKv9ZNkOBF+2Qcvow\nPIhsJKuTJrCeYaiK8nPw5/8EQy+z0tdhtAQj2RNpk+WxY/pLO66r4SyPl8P7jQDN9nAGx4WM4OP9\nwoWTNtdZnu0MFPmMnHnj8Ti2trYa0wLT7+T0pQPDinAACwh914lsVxH7L1/ywa60aJjPXg1KIJRz\nxQ9x9UDuGluRV5uOBgGh1PDhcNia3Bkz9EnPSSiAkUdd7EaApWfo4RRI93rtU3b0LEFKYKvJzr3a\n9LqqbXSi92g0aswYBPednZ1W/lR71UbsKwJsxr75DBcQ1Ucne1ONZ1+R6WVB7SoL/yf75gnkk8kk\nTp482fRxVvYOIA+XDgwPEYEhD2TI1KbaQKsxQveaZnbEDATd3uX50C4pJhaxb+NTOYbDYSudDAwy\n+5d+Fzukl1V/fYsZ2RNtf1m+biejissQFHrcyarlGPKgcQEJ/7p9l/fRBhzRPg2cdc7UT6q9tGfy\nd9pB2bZZhEBmavHyjEajWF9fj8Fg0LRFxpw7qUsHhhXhwNnd3Y2tra3Y2tpqGKHbinzQcnJT9fVd\nJmSMHnidhUqQrZLZEXQ1qWhMJwOjoZ2gxDMPI+KA80XXNGE5ccVutFjIbiX7lqvPAkja5nQv2TJB\nROycjFBhQpz4ZLBUlSOidQoMQZPgT/DQd+WhOivgXmOAKixBS+USW9W9YtBuSiD7rKnxNJfQ1ri1\ntRXr6+sxNzcXa2trqZbSSV06MKwIJwdPonbbWhZ2wcHrrNAZHw8+oGrsbDAD35qhPaKt1vqEFlvj\n5HPgcNXQVS4JQ3joiSUDIyv1+jAfpaPyq6xcABjHR++5wN7BhA4QZ3IEYZbDVUuyMYIQbaDsA2eU\ntCtz8dDvDnKqj57hs3yeaWsx02EOij30BbqTunRgOEPobY3YPyxAYOLg4bY8Z4YMjqVtz4/0z4Aw\nm1C6nrFTgiEdHGIqHkIihsT3J0fsTzrGKnIxYHAynRVqNzlZWB5nQ1T9GRStRUTlcBsnVV2Vn/Y3\n9Zd7VpWXO1rYhlxAer1ewwT95B6Wl8DFvlKaDEdSfmSbHFdeZrdv6ig2MvzpdNocBitnimsIndSl\nA8OKkFlkaotPIq7w+u6s0MNkaCd0EMzKw3I5s+J1MgwyLk00gQTTlvpP5wPrzzyULyeaq+9kNg5k\nKo+H3ETsszVNdjqMWF/38pLh6j6Gr0hqbcsFwPPKbLXqPwINbZEZW2SoUmbuOB1RugRrtWdENOFf\nCgHzU206qUsHhhXhgPYVXiszgZC2NImYH4OnNTHcY0ynRw10Iw4eyEBxQzsnqdvVOEHFCPmbQFDM\nTLY5B2Pmrb9aAKTusQ6Zeujs189Y9HpQrReYMl5Sfcbv3h60pWb1IFix7Xxx4EKWnWJEzzrbu9fr\nNQw6Yv88RGf4BD1ffNk+XDRkQ9SntsB20pYODCviTItqcLb68zk948HTZENihVS53KnASeCAPCtv\nL6cmpTMsV3/514+xd4asiTorFIR1YZuyfhmbJRDJe+zMSiBGOyTzc6bo4ouGQNXL7nWSbVKhVGxD\n/u/tTBPBLDDO7uG1jBG7XTLTZLLFs5O2dGBYEYII1SGfoBI3pNODnIV/EAjdpkawyEJbMpXR7yO7\nIqBTbabKp7Q52QmqPOGG9aSaSMaoMjFtMjsvs/Kdn59v/U81j/Vg3lys5BEXEDoTdaDhdZVbzzFt\n35rIwyV8K6VeF0pnThaqw4VLaatf3HzAemdONKrP1FZolskW0E72pQPDimSqpsDCDyPIGJeDHmP8\n/FxCt41pgkmcnVB9cpbINJz5cFJTMhD28uhwU6XhebKcnMwsk7dpxhg1kVVHASpVaWdaBEX+xjo5\nQ8rA0RcGgjvrxrGg9lLfenm8j1x1V/gSQZL9wPZlX3ExyRZPer47Rnh60oFhRVxlzWxSEQcN8pwY\nzvz8RGuPJfSVmyDCaxLe7w4Tik8MTVRPswaGnPi0jWWgqfQZQ0mWRIBzhuO2QLFwtak+VKG3t7dj\nfn7/RUm03cre53Y8BylnYN6WWR9TzRVTZBvRecYx4zZH/SUYRuyfE1lb3HyB9IWl8yI/dunAsCKu\nNnEldrbAZzx2kLZBHt2fqcdKk+nXGIpPCre38V4HTf5lWln6bAcyy4zVeZ56XqDkqqP/pV1S4uqp\nLw6a+AIhsikBAkGW6qSYlC8CbJfDFiqmqe2DKkd2GpFAPAud8UWP393kQKF5g9+5C6cDxcOlA8OK\n0Hbktie3jRFgGE9IMBwMBrG8vNzssXWnACUDQ4nbnHQ/w1TIPnyC8Tmm7Y4UesjJJieTSUyn05b9\nkGxPwERmw10xzgY9LtBVRHqP9Tdjxzs7OwdsZK5aE3TVHt6+NCPo2VmLktotWzzlZOFuIzmmBIgs\nq/Jn36ncbJsMDMl6p9Npc87hYDCITk5POjCsCCcrmaEzJ92ra9n+Yp4UI6B0tXXWCu5gmNkJNWmp\nIrr65UzH1UO37dEul7VNRO4NzbzvLDNZmZeHeXq9vY2ckRIUCIJ+H21rLmS8mXkiY3LOoDlm3Pab\nLUgeLuXg5vXzsnlb7O7uNmC4srLSmBA6mS0dGM4Qqhu+04Cb5DWJ5TihXVDsUN+duSgfn6zurXYg\noFpHZknApmrv9znTOMzbSPZG9dLTk/DsQQ8HyVRkpeGAwDqLZbqtj7ZC1otATWbvkqm83uYZgBHE\nyBDZRzprcXt7u+kDHSahdtQnU8vJ9rysLL+ekaq+sLAQ4/E4JpNJy57aSV06MJwhnFRU71w0ARha\nkR3FlW2zIzNgeky3VrasDJka74zJGY+zTabtk5NgNDc315w9mAGb1Fa3yyk996wyP1fzWT9nTwRJ\n1tsZadZ+hzFxbkd0sM2AU2nSK6yj3+bm5qLf77dMB15eppcBXpYff9d49ePmeJRaJ7l0YFiRGph4\ncC7tdQynERDwbXk1cHN11ctRY4hZeiwjQU6Tmu8lVn4+ucVw3SNJUJCndHNzs2E/mZ1VbMQdBhkj\nIsNzMHMwd/XX24Pfs/bz9s/6vtYuak+W0fP2rY/yvkdEy4aqfObm5lq2adbLNYes7G7SkEZDT/os\n5t9JB4ZV0eDxTficzAzDYOhM7YDWTPX1Qe62IYmr1DXHiwOde3F5nzNCppOBCevK1xUwDEahMDxc\ndX5+vnEmsK40M7h6zN8ylZz31xaFWvjQLFDgffzuz9LUUVvMHJhLKS0Pr9/HPGbdk5Uzaxf2SeZ4\n6aQtHRhWxG2FHKQOKrKPub2Q7zqW/TCivlWN18iA3F6lvAlkBIRsclLlz1RqMldX2+fn5xsPuOo5\nmUya+3XKtPbCOiOh15Rs050NzsIj2qdLM0SGde71Dr5DhCdBqw7K8zA1V2VWuiwr8+WJOjrxm/ko\nX5oL5Gnn7553tiB6zCCBtsaQSylNn3RAeLh0YFgRqn2H2XIEiH6kf3Z+odvPOPFrDKHG4FgGsrcM\nDBlWQhsb02B9avY4mgXoNJJ9SqfeCAzVhr7HmOqcx96xvlSH9bvbHh0AZqnCGcNiH3gf+yLjJ23z\nefaXv/jey+cLzixArPW9mwsyQKfdkK8S7eSgdGBYEZ80ulZTyTIVmQHXHNAaqBFxgPn55HMW4+p0\nZieLaL/QiKob2Q3/OljUQl5Y5gwMyQ4FhnpPBz3WSpdvj3Pg82tsO7JI95pnwMB3oLC+tWdUJoGa\ng6XHRnI/Ms/AdFOG2kt95JqBM37vB95DMHTtRWWfTCYxGo1iPB53YHiIdGBYEVcded3VUzlKuAXP\nbYURB1d/jy8jwOh6bZJmICU1zdMUILq6GXHwoALmSbub8srahczYwVAsiY6lyWTSvD8lIlqsmU4E\nd6Cwfj7xPRjb2bx7p2tMMesjd05kLI3txPFBtZ4LnzuufIy5TbcWF0lgzBbc7e3t5mxDnkTUyUHp\nwLAi3M2QrcQaxDx8QTsNamAo4QDXb7THZSwk28NM0MrsjLwmsFB6MuTXDih1FdnDZlgunse4uLjY\nqMlKh+ra8vJyjEaj2Nraar1zpdc75XQZj8cH8mGfOPPVPb5N0J/nc9l9WZurXxxsXENwsOXWO33I\nPp3JZwueq7++KHChU55zc3MHXoSlEJvsLX2dtKUDw4pwBeeHk4hqop9CI1B0AJtlp3Nm6JNHeUa0\n39XB9Nyu5+oV1UufgD7ZncXofg8nkUOAaiI9mHt7e40TaW9vr9mfrbcNalFQKA7fuue2RGdqmXoo\n4fXst9pfb/fas+xDtjFVYdo59ZeMm+k4q+S1WWVm/9EUofz5vu9O6tKBYUWo2gjceBy+rvNoLj+n\n0FXYiHoQsa4JDGtqnAOU0naHiPIXKPFYKVfNODn5mzsJJEqPr+akR5igKJVZ3t7pdBrD4TBWVlZi\nNBrFxsZGE5BcSomVlZXWBJZKTTuhnBi67jF/BE7+jWi/9N372xcegldmShAj8z5m+7Ps3rYZ05x1\nPJovpA56PqbYB6PRqHk9bCe5dGBYEapJPHNQ4mAoYND/VLEpmZrk90hO57pPHJ9cbvMju9N1MieC\nAssppqNnBaoeLkIAEmgpDpHMZ3d3twkAH4/HrfbWs27vEkvU+1EWFhZa9kl6rwmM3v7+l5+avdb7\njf1HodpKJs6/3o9u72P/eJ6Z8DlX65Wu3ovSSV06MKyIBiNZHlVlj5vjxz3EEQd3CPiAdZtQRF0d\nc7WPk0yqsbMQt5UxT7IKigMjd064Q8bBkYxKQEZAlh2t1zsVrzcej1vXGK8pdkOmKNskmaPuy9rX\n1WmyR5dssVFbOVN0NVa/E9zJ9nUvzRzsn2wMOHOnFzoDUpZb41F2207q0oFhRVxN5orr9j23Efpq\nnrEO//B+5k82lU3eWmiGMwvek5XNmVQtfz1LVSwiVz95wKvAQIDKkBW1r4BMeU6n00a9luosx4u+\nK6xHTLLX6zXg6ACfLTT+1+vq97C+KmNtIXG1NVuYlEbmHHNhPtnvWf5cuDpv8mzpwLAiNcDib36Q\nq6vGPqEEiBykngdtSW4novPDvdr8rnsj2jF0nh7L5qEpnJz0Pqv+WRpZG/oBCs6QdE27WiaTyYGT\ngSJOgY9Ua7FBHkYwmUxarJz2RAGOtx+ZrQOU15XOIJoNvF9pu/XFw1k083N2WWvTWrvXQJTjsfMm\nz5YODGeIsy1XdxwQPfylxhoyoPUJVBvc/j1T/5wZOtPJ1CpXu6jGCQyzw0gJyFkeVPEi9sGQ39mG\nvV6vOfiBQOMnhjOecXt7O8bjcQsYGQTuNsXDFhDWIasPmbwvXM7Mvd68N2Ofh32nxpItchn71/WO\nGc6WDgwr4gBDm6BPZrJDtwlRnc7YJb9H7HsFOeiVnr67zcvDZXyiOdgSAPy6rnlws7+LhGqyAyLB\ngp5cXqd3W4Co3+fn5xtV2NVNBbgT4Bg6QsfLeDxudl8ojIdOFuXF0B43abANxejI4D0MKvvwPt9y\nqL5yx4ovxM4WaXvNxq6r3xHRgeEh0oFhRWqHB/gnsxdmEyIid4KQQbr4yu5/aUzXd543yL+er2/L\nY9pUaXWdR0tpccjqWit/dp02VN//HBEHTlvhfXrRkoBNdsPpdP8F6gru1keASEcL94uzfmyzjN1L\n3FyR9a/KTkDNwI75eltl48iZKcur36mddGrybOnAsCIeH0Y1K6K900Bsiau8gw7Pt8tW9NpE8B0p\ntGl5Pg5+GSPU/2IotFuyHGRIPrGdtbIss4CDdc1YqD/ncYRZu2iSK3yH/bW8vBwrKyuxtbUVGxsb\nsbW1FaPRqGWbJDCT9TvDJxvU9WyB0rPeFlTNCcAeysOFrdaOrplwYfT0eDBHJ7OlA8OKcCuVJpdC\nRCL2Q24UaJ3Z0SJmhz5Q9XF1WJPPAcvBxkGX9ysdpetslKCWqfHO9giirrq5Gq/rNdNAjW2zfEqv\n19s/H9Gvs22cycvhoj7SlsnRaNTEKMoeSvDJ1FcPmfJdMdzu5m2bMcTDTBk1Zso+pPpNcRD2Puok\nlw4MK5IBoWxTvV4vhsNh67gud57oWV13MNR9brsj2CpvreweosF0XL1zwMxsi2Rz2T5spqnyu4pX\ns3VJaDrQ96wtVB8Bjk6UZvm5GPFwB+0AUr+prmwLP2tSXmnZF/Ws10XpeBwpVXTa4gSsnpY7a2bZ\nJB1As/Zlf+i3bMGkbbFTk2dLB4YzRINIk1AfqWj0cLrDgeoVwShTI6nCuC3PWYJ+c7XcBzqZpqtf\nfk2gRkDKVEavF8vroMfnMjB19sS2y553h40vAAyMp8qoZ2nXlXlDzpVsu6WDIfuZ+cg+OT8/3xwy\nQdbI9vRFgXV2EMsWFr/uzLs2bjh2OqlLB4YVoTrEkz9cNdNEcvBxsOIOjIg2sPn2sVJKw0A18ZS3\nykYQc9aneyLigL2N4FljeAIPenwJPp6WX6O4zSoLBYqI1u9k5PpfgKK2IAPU87pHjDEDH/aD2KJs\niHKuuF12fn6+ObWc4Kp+2tnZaU4yF0CqLfxQW/ZLxvQyZu4aRMau/XkH2U4Olw4MZwiBgwcFUFX1\nAxJcfeFg1kSNaDMqhmzoGf2evWNZk3EWU2J5HKycFfqEoxHeQzrYLhkLyoBO5VW7ZF56B2+efDPL\nnudgJ/st7yVrXVpaarU71V+CIes5Pz8fg8GgBYbqR53hKO+2QLWU0jp9x80AvhCxvdjX7mX2vnUG\nWWOLNabZyb50YFgRTlgJJyCZigYtJzXtfZxArk7rN3o2pZqLYXpeYiA1ZwXVO/f4ej1UHpWXoO3P\nRLTjIfkM60R1XOXUoQwCUDIwsh0uOgQJX4zYT9yONxgMGtbmi4NiFF2VJMv3sjsY8tTyUkpjKlEf\ncgeNnlfoz8LCQgwGg5ZtUGkxjMj7MuLgK1tdeH+NEWbOlk72pQPD0xQNLDIVBxp+50nOmmjutdXA\n1XUCgnZhkAnyhBw5bZwxZjYzsgpXmZ3RKU9O5JoXkqDmtkiqsXNzc80xXTILOBiyfX3CM221JVVk\nmTJ4YK2zQvZDxowjogWirJMWIAIfwVZp7u3txXA4bAWB8zd3qqlM7Ae3/R7G6pxR6lpmjulktnRg\nWBGqKRH7oTQ6XUX3RLSBRAxBk0EDk+E3HOBUl5UWJzcnI0NEGNLjR4nJrub7gqleSmq2KKrA+u5C\n51IWisRnnKlkqqGEiwYBVgwzS4tqNQ/YdfBSXgI+gVNEtN4Rot+oNutZgpk8yf1+P6bTabPzRWVU\n/joxpva2PjJO1z7IjN0m6KYNlp/927HCw6UDwxnCyUzjtwMDQS0iUjBUGlR7MvVIQEKAcWaytLTU\nnBbNwG8xxoiD9rJM3A6nSeYsWP8TUHSNB7hmoOiT1pmg28nUNgJ1pkGPcA2cVS46UXgWo5eBKrK3\nl5wh7Ac9T1sxTR3+RkSJysP+pCOMeXpZvd29vVyc8Xes8PSkA8OKcDJHxAFQcfuYAEw7ISaTSeNJ\nFOhJRaR65qE7dNhwolCV1hl/8nByi5rvLImYvaWLnk63D/I7PbhKX9f9PRsEc9aNai498LqmPMUA\nPbxHz/PEGNaX11jOiP3jxLzvnL2S9RHAeY/SIKjpf9VNrzgQmLJ+rBMZmzQIndCT2WJ9QSagsoyd\nevzYpQPDijhwkDX4ZBKAReyrjpoEdH5ExIFBrvsJJmRkDmq9Xq9RtfTWOaUvMIyIhp1worgqrPRp\nr6RpgHUj++OkZPm5o8M/fEGUgqQFEL6d0YHaTRa0zzrose20+DAdqt20G6ptPEiegJyxLNp0ydyX\nlpZab6Rj2/PjfeLqc82U4N9nMcAOEE9POjCsiK/ezhLcSeAGff7m4OET0tmTT+iabWh7e7v13hUF\nAEdEDAaD6Pf7B0BcaSo97qpg+VlGnhtIL7fawcGczNABh0yODFRpuhPI1Vm1O22BBE+ybtr9GCPq\nYKhn6bFn3zMCwNVTt0NqhwvBXr+zLwTo/C5hnoxIYH7ZePW2yH7vpC4dGFYkY4UcYJxsVG2pMpNh\nOSBFHGQ+VP8IhprQnOh+1L2fCbi6utqEg7DsYmYEgSz2jWAmGyjBkJPTbYRKl+nLfOAqpe7xY9DY\ndg6IEQffDkhWyes1tZQOHnrrM2eL7x7J7HVKm6+MZf+SBTqwZXXLwCyzJ/o9HJunA6Kd7EsHhhXJ\n1GQOLrKPmlfV1StNKhrra0HbBEExL4/t4+8R0UxGbTVbXl6O4XDYYjy1E2JchXNVXnt4XX1zb7Tb\nVj3kRgxNOzZczWRwtgMWy+1s0EFPv9Px4d5yXaO338vsp724PZHtpnrQdKFtfmTIWTuRfXLMuGlG\n4guraxD+W6cqHy4dGFaE8XUOhBHtPbbuVXXnByepJgqZHOPivAxiZHoBkjtbVBYBojyaOvmZk5RM\nxRmQvKG0E3KC8UP1ViyGAeXOcgjizrT9PdNkhm4uYHuS/fg9Xr6IfdufgwRV85pNj2kSHP33UvaD\nsPWhvZj5Zgus30NA9GD97H6WK5MOEGdLB4YV4Z5YhnhEtB0LVJPF4AgQmnB6iTrjBJ1FSJxFKQ33\n0HLrmv5OJpPmAAIGbisvglFEm+EwHaqGmZoZsR8uop0dDhRilaWUFmtWviq/4vsIkpnq7mqf9wnN\nCFwofBFTfelJ9zQzZuX3ZuYPtYdsh6PR6MDi4wxR443mB194/D3YtXZh2fS/7utU5dnSgWFFsj23\nErcXanByGx3VUnoYBYT660b7TOUTsHICUm2UOkdnhw4xFRtjOIvnx0noJ7NIWAYHJTI8tR1V+J2d\nndY2NWejcogIIFlOCtV8fVc67CNftLLfWC9nm0qbaipZL9sgs+3JXCFTQK29faEVcPtYUznJEGv2\nQzcFdAB4+tKBYUU4cd2T7Cqbq44SMgSdoecnY1OVoyOGzhHGH3qIi8qjgwIERKPRKEaj0YHQDg8G\nJgg4K+OE9Zg/Mkv/vre314CxQm64g0L5RkSMx+MDi4MWEAKNAImLBxcKMi/2AVk8+87Bz0GQdt3M\n1pip7exLgSG9yjQreJvWbJ/Kk4tar9drbYNkeZQW607G3EldOjCsiAY4vaccnP6dgBKxH7LCMw85\n4H2HhoDR7YIepsPrPsjJNPTGONoWCRxez4ztEPwZtMx7fNFQwLlewkQPtJ6lai9WKxblYOiAzKPF\n+J2gpWfogPG2VlkcnFk//nWTAe9lO7JODHviPf68+oz3ZAtuxlRdrWfd+JeA3kkuHRhWRJNWjEKs\nrmY/02DWJCYAclCT8TmQCXwZtJ3FMrqnV5OBnlOpphkYMV8H+JpK54yS4OBMdjwet1727mqfXtak\nQGxvLzpVBILZ4apuguCuFpZR7esOFQ+ZYftkQMg2cWAhM9RvAnnZapmGdqWI0df6iIxXoUm0R/ti\nx7Hr/drJbOnAcIZwRc5sRLVVnuqc7qe3l+zEGZNsfpPJpGEYeo4OCD1Dwzm/u5OFzMXB0Ceysxwx\nEa+vh8zwtBYvr789UHUSCEwmk9ZEZzu604msm3XkPQ7yAhP2k5eHv7HP2U7ZGPE2c9B1ZkhzBsvp\n6j1BMVug2Pe87mDt93aSSweGFXE2Q68sBzSZh2/2p8F7NBq17ufgFAgKTMbjcWxubsbc3FwcOXIk\nFhYWGpDhKy51YIMmG7cEktUqD4bLcJK7+k01VvWgKu9p+kENAnF5wNVuspv6S+L9UFVXjeWJd+cT\nQXgymUS/34/V1dUGvMU4VQ4J7Y9Ze7gayjKp3G6X41hRSJT6KlOlPUi9Zv/zscWFxO21Pm6z8dtJ\nXTowrAhXV674WYgEY+wiDh6bL4eCDm7wFZ3xhJPJpHm1pdLVS9M5wehgIJOK2N9BwnKLpWX2skwN\nJyt0hsI03WbFxWFhYaFR7QaDQQyHwxgOhwd2tzBIPVtgGP/IstEZoROCtDBIbSawCsTYZ7Tjuhag\ndnLJGBkXSNp6a+r0rN9ZTuXnDrcsljUzfTDPDgxnSweGFXHVR+wlIg4MUp08redo4xLzkTOj3+83\nxn1OarE+sqTpdBqbm5vNWXhiYc5QI9o2MDJTZ31UAb2erK+EzFDC9tDElRqtEB8BlbYE6h3Gq6ur\nLTuo7IqKoePhBiwfWai+00FBWyTLygXCvdEESWfNtLURLFkmjgW1CwPiXR2uLT7+W8TBvcWu0rvX\nmYszpcY2OzkoHRhWhCoRWYt+i2jbCLlzgsZ/9xBrMmpSa4A6mAlUpFIyzlBgOxgMmnMNlQ4Dq7m1\nzdmPxEGQO288rs3ZnMritlHVQ23iau7i4mKsra3Fzs5ObG1ttVRzlpv18P2+DtoqI/dpkw0S3DK7\nWhuM2qEAACAASURBVA1Msu90xjhj9QM3aDqguLbB65moHB6C4ww2A7xamp20pQPDivT7/Yhon4+n\ngcv9xJocUos1+TSh/cAGTRQxPMbXybOoge4vFKLtbW1tLY4cORJra2vR6/UamyTZj947QmH6rt6K\neQn0Xe3O7II8obnX6zXH+4sBO3Ok2ry6uhrr6+stkNWJ0ZzU8/On3kHCLWnT6bQVthTR3jdMu6fA\nWoxc99A2qDoR2JUXzSEOmnQWkd2r//RxtVZpZ/mofGp73ivzCG2XNTshn/f8OjkoHRhWRPYuqiZu\ny+EAFpBxwLthnSxTbE9Gfh0GSiCSKirQ1GQcDAaxsrISKysrsba2FqWUFoMUGBNAWA73Luv/LIaR\nzJCMivnQjioA9nASpbGzs9MsHP1+P5aXl1vloBeWTJv1J6j2+/0WaHMhyuyZqgfr42ox+9i1Abe/\nqSzZQR1u9lAZJbNYnPKhFqIPGTt/47MchxmL7uSgdGBYETEsTXq3o5FZUSV0Y7/uj2hPJLEqsZvB\nYNACGoWNaPBz7y89s8PhMEopsbm52UwepTscDtMTnlkWsjIHw4j2+5fJ8Ojp5URcWVlp7TihjYtt\nJ7BbWVlp2T/1m0CVKrur7QRj3kdwVV25zzsLq+EWQH2o/mbxiwRSAR9tm9yB4zY+d9RI2EcqQ628\nHG9kiGxrmi86ZjhbOjCsiAYlJ4YPJk4WiQaob6j3XRURbVATmxkOhw2zIjASdAS+DITu9/stFV1b\nAN17zbIzWNpPpFZdyEToNffFQtd073g8boGF2tTNDcPhMJaWllptq8VBdlNfdAh+meNBDDQzBzgg\nss3c/sn7s7FBdq2wKLJCxlyyDTIgrDm1snGpts5Uak+ffdOB4WzpwLAiGtjuUXQbjMCEhnWeP6j7\nBoPBAQM+dyhIZAMjKDKMhvF/ip+TDU4Mi4wpCwWKOBgjyJOqI/Ynm8qqNHlYrIMkwc+ZCNkOJ+pg\nMGi1u0wOYrUqqzzMtJllXlnaan07o/Kn3ZeqNRcXnhGp8hPMqa4z7ImH7nJR5KLKdiBIurA9NRY5\nhsiUuXgzvRoD7eSgdGBYEU0gThzacQRGBBd9GHYiZudMSgM3e5WlROoiPdUMu9Hkkk1Rdk4960zH\n2QwPjvVXk5JVqHxitwQSgokm7d7eXgPwrh46gDo4CtAYTC6veimlxQzJbgmG6hstFgRDV5NZfgd1\nSQaCuk5mTSDkdzc5UGpsjQxPbcAFl33D8VfLpwPDw6UDwxnCiUNmpsmm3RCctDzgVQxPoS88vzCz\nG/mkowPDV36VT3+pBtGmR8YjFZ4g4h5Peq7pjFB6Umlrhnne1+v1DrwYi+ooQ2AI3LyuNqfKnKnx\nEoKGe5nZbgQHAqPaj+kxlKimGbjJga9JyGx4TJv96GVwJwiB1Vmu0oyIpp+YfyezpQPDilDNqxmw\nuReWthw9I3VPzgAHA58EmlxU+7jy+/1u0+M9BBefUARc/k+WRWdJBjoOhm5/48TOQI7psQ5U7ZV2\nZkPLVEa/hyy0Zo9z0GH6and6+Z1l63+3wfruHC87n5tlo8zK3ev1WvGcbg5hOZ3NdlKXDgwrMplM\nDqiKVI91UgqBTZM9Yv/YK9kEHfx88hMACFAR+wwkon3Qgxv+fTJkIEAPsqtiEfugq/sIiip7xL5N\nkflxsrJeNC1ImIfK43Y8Aq7bMjMQVDs5INKkwdAXgq23Q2YjVPvwL51KMj0QOFV/d7qwrspPz7BM\nLhwnXID1YflY7w4QD5cODCuys7PTsDtO0OyT2eci9tkhVT5NFAknhDOwbKXnJHG1m+qQszA9m01M\nz8tBOVMr6UXW75ldjosJJ6WDnPKVuLOKoFZje7SjOjA7+HgEAPOvsTi2PRcT9k0thIZ2zlni48HL\nQeEiyWdUDs+7k9nSgWFFHAT0vyaVbEMR+6AktTkiWgcqOIuiWpqpw243VP5zc3ONLY5M1NVK/qb8\n3b6XgZKHaLiKlam9nPC8j6xSDNZBQzZWgooDQMaoqYp7m0XEAebHMpLFqSyZKuoMl84l5ZGlPysd\nPXe64s+zTzwAXf3iYM1x1cls6cCwIj6odE0Dzr13AkO+uL2U0nJeaHBmtqRZtjlnpgQlP75Lz/At\nbb5lTvnVPLIEQ6pbytPBhayPqigBnaDMSSx7qwOK15vXvB0cDN3pkdkFWRYCOa9leTE/9afHW/o4\nYh1ki84+mXj7u1mC/SbxhY/pdFKXDgwrwpNQXO2IaO8moXOAYTDT6TR1mNC2JG8iQ2wks4CBoSSc\nFJqgfjiCg5ueyUJPWD/GWrrXVfezbrJbkW0SNDn5aYZwkHVw9Dbhd1dPWXavn9qvxpbZ3h7644Cj\n9Nz77eDm5gLuUlJ0goO2q/5c0FhGll/pqW31+2GA28kp6cCwIorZ444MioOWMwkGQNOLrAHNoGCC\nIdN0ydQtbdtzDzRVdx0x5k4YAqTn479rUpIFskwOlAJbV+Ey1ZvqNNuRaXmbZ+DtTJuByn5Qgntg\n+Zu3t4NczYSRgSdBMGPm+i3r6wzAsrZXuhnwUWPI8ulkXzowrAh3MswSqpW07/DgBoKT1B1XIzk5\nI+pbspzZuKpI+5xiIfUCeqWbqcqsi9dP5XUVXuUhCBA8BMDM07cpZs4oByCJ28xYvprK6QCaqcyZ\nicIBr1YfsnEGbjtTdclMJC5cFFhupumONY4hLkYaz53UpWuhishRoZ0PGUj4wNT/Ul8j4gCj4qRn\nWkzTGRklc0I4W6EdSxOUNiNPP5uYXj8xrYz9OJvzMpC5kKWpPt42NTBUGzmgZ/ewP7x+WR1Z90y8\nvd0pJhOJv5SqVg4HYhe/5s/5fQ6Gs+rSSS4dGFZkOBw2/wvQIg4yIVdPNRB9UghMOPHJRDR4BR6y\nVdacKRFxwCaWqa6eH50L7tl0dktgdpAmG3agYTvR8E/m6gzNFwRfaPSdiwjb1cNpCMhMQ0Cc7TtX\nXxPknHG6F5oMXHGn3lZ8lkyS93l4lMrFbZ3eTln7SLgwzWKonexLB4YVkWq5sLBw4LWeEfW4L1eb\nNMBdPRTwuUNEk8BDQDiwORFk1+RRTu7MccZJTyvr4vYyV/8J/m5v8wk3y97mbTZLaiDJNNUOWf/4\nIpXZR6nysp7ZLpIMWAiGCrLXVjw5TNwc4ZIxc2d4WVu4Ou+2Sda9k9nSgWFFBGY6eZlg5gARsW9P\noi2LqzmDY52xUdwWJCE4qhw6DIBn6clGKPukM5CMETK/TD1mufS7l8dDP1jXWSotF5DD+sOZtPeF\n8qT4QlADBgcV7tX2viZbVh/Lez8cDmMymTSMjn/dVMHyZio028gXKt+zTkbpaXYq8+lJB4YV0cRb\nWlpqqVYZS4g4ONEJGhy8tVANZ55uM3PDvkDIQ0d4FL5vxXLgoTrJetTAS2A6N5cfoVXzis7yYgog\nCEY1VuTOGQc1b9NMsr7jb96+fr+bDmgD7vf7MRgMYnl5uVlwJpNJCwQdDL38rId76WttmWktYoT0\nqHeAOFs6MJwhYodLS0utmLtMdeLKTNXEv89a9akGHzZ4M+dJzXZVm/ycXLrXA5UdBNQGdNAQHMiU\n3WvseWeqYY2ZkmXyObfZ1tTobPHx8qjcGWtz8GXe3JM+HA5bKvt0uv+SKoY3ef4sewZ6XFzVD7zu\nIO222g4ID5cODCuiAal4w8XFxcZ2GHFQTeOk85g7DuLs2dMZqA4AVHuVBkNpGKRbYziZGltTJZm/\nHDeKX8zURs8vAyS2dfbxZ3Vv7RleJxhlXnOmRXFzhmsDbA8HcbFDHpM2mUxasaC1hab2cduxWLnK\n5cHsztRrmkgnB6UDw4pQTdGk124J/R6RB+I6wDjb0d9Zxnxey9Rcn+Tci8xdLxkYsJycnO4plWRq\nohw3i4uLaWCvA6yr4s60MtUwy59pKz9Xsz1/36vs6bLeNDd4Xiwny8q66ZUFYoT9fr91rmEtP9aF\nQKhzCaVuezuqXRg+NZ2eOgxXi+MsM0Un+9KB4QwhABBoOOlqE9WBzVXl2iRzIKixNgfHubm5lvOk\nFo/oec1ig7PaheE5HqZS+/B5z8tDgLyNnFF5/TKvqS8amXfYTQ0Ze3epqfRk5/Is66g3Mnh6lzMt\nQv+rTyP2w6gIlP4cx47bKTtWeLh0YFgRTqKIdvgEV2hNGMa0ZYynZqNyVYt2MZ+wrl45uPHA2Sze\nLfM4cp8s2ZWnTaGKrkNGM293DQhVT9bhsPP9HBidZdFhoN/4fwaKBCVvX7ZLTT1mfT3gXItnv99v\nbemcm5trvZu6pirrXrWLNgEI5LjDie3j/eVqfyd16cCwIhlzkSoqJsT7PKjZJ1g22D19BwNnPJzg\nTF9AmO2CcEBVWWoM6rB28GcccKiy8ppPxiy+0sulfDLVl78723bwY/m4jzu7J2svz4ugman7+ssF\n1EN0dMSbO96ycaK0StkP0fFgcZlEPMSJtsWOHc6WDgwrUgMvD2rNmIIOYaBNSQMyOyG6ZudiOZQ2\nd52484ThNG63ZL0y8OP3jNVlamEGKLrXmZuecXWY4pM7a1/ex3plDC4DOh6O4Yy4xmbJ1D0fxpV6\n/6nP5XzjmwUlAkU/lUbt48H0EdHqe7ajGCkDvTNbaSe5dGB4iGiQu9oWcdAZosFHgz0POaWtJwND\npZWpPsyHgOjM0I8Ly4Tp6JMFBWd2Ri8T08nKmQGLnvN66292nf87yHm7eVsSyFRWOpdq6jzT9nRZ\nn1p51d/6LC0tRUS03hxIUFOZ9BzHmq55H1I0znwByu7t5KB0YFiRWoA11WT+pt0gEe0z/WiHI1Ok\n+kiAcCB0cXWZjFO2wmw3iD9PUOVrQrMdHiqP6izwVZn95UQZGOp5to3aSkzSy6n02R7O+HhvBpwO\n2ky3xoAdQFkGt+l5HKnS4YGv+siGyHcqa4cKx4fa1+2Q3g9csHSP2xIZipPtPOpkXzowrEimNnF1\ndluZBhuPb8ommiRzDDBv/UamlDFR3fdYTk1heTUheQJzNvEi2lvWvCzciiiwltDw73/VFt5GDoYE\nHDLvDLwyppc5Smr95M84IBMUa3GarDs1g+FwGLu7uzEej2MymTRgRRXXbYW+CLma7YuC8uQp6DWb\ncCf70oHhDMkmigavMxRX22qqXGYTY16ed1aODKD9yK4MSGnPdCB0oPMYOr71jfXLmKTy4qQVcGXO\nJwKjA7DKpHYTEAoE6IihqYL1ccDMwE91c29+Zov09nH7o9v0eBJ2v99P35+tSACFbunoON3Dd+pQ\njfZFWRsDCKRqv44ZzpYODCviLMLF1a+aIZ7p1SZ9Boj+P5/h765+uyonIaPKjv4i2GuicbsdwYH1\n8Trzu/8mMCQTFKBl9kiVm2qi6kEmy/agw8DbiuWq2Rsz4MvGQGYr9AWSzwqc5ERxhicwVOgWwVz9\noufUL73eqfM2aZ/Wvb7gdGE1h0sHhhXRydBcWTnBaSua5ZHUoBQI0K5G8cmpv66m8cBUB0MHVwfP\nmvNFDEMMheyDYNjr9VogKo+5AyBtqmRIKlPWjgxN0qR3W2IGatmCQ0ZYe85tjrOcNkw3MyEwHQfW\n7OQbqtdaIHVCkpwsYoMah9xqScY3Ho+bU4uyPuc7UbrTrmdL1zoV8QmiaxEHDxk4jD1mk9Ann09s\nXq+pasrD2SDLTsZUY4JSx2SA50ua9LszRKnWTNc/XqaI/dcHzAIQlYffycQzu6CrtsrjsbRrzX5L\nwKu9bsDr4n2epad6zc/Px2AwiOFwGP1+PyJOMcXJZBLj8bixNUqNJgj7C8ho2vCFtGOHs6UDw4pw\nRwUngwYUV/yI9nYo/u9gmakzPokjchCrqWDKn8J0ee5hxhrFNMROeKiERMyCbVEDQ99doeedIbFN\nHFAY/sL6UA12UJfwvllxdg5KBJKaU8T7zMGa93tfZcxc6vHy8nKsrq7GYDCIXq8Xk8mkOR9xfn4+\nVldXm9fQ7u7uxmg0avYfazxmiy29/J3Mlg4MK+KOEoJIxGx7kqTGFPi7q4285mwym2AZIGYqYjah\nHRAZp3g6bJdgSPAh0DlbJXtx1sj/uR2Ov5HdzmprB+nD7ue17D7VhcHQEfve7ux1BGwnZ4kLCwsx\nGAwiImIwGMTq6moDeOyP3d3dWFxcjLW1tSZsZjKZxPb2dqv9BLLcFOC22E5mSweGFakBXQ2AHLgc\nsMgG9TxX8Iy9OBh72hQHN1efnbFm13xvtX7PQEXAV0ppWJ/YHBmz7vVTt9UWvIftW2OPzsb1DK87\nUJNd8veaiuv9nY0B/+tjws0gfD/ywsJCrKysNDtT+v1+nHHGGbG8vNwAntIRI5etkK944JgQ+1d/\nacFhvGMns6UDw4rUAC/7PeKgV5ngQbWRRmxOGp+wupaBqrM8Z1kOZppUArsMEGsOBF4jYFNtpVNF\nzI+Ao++ZGuoAJckYpjNcLxsB1fsjq5MzftVnlq04+43lqtWH/SJgUxkXFxdjZWWlpQYrPx7FFtF+\n1arKK0cLAVd9XmOunRyUDgwrktngar9pZfaBSvsZ79XqTVuOJkAGbDX1T3Y+GdH9GX1XurNA0G2i\nSsMDol0Fkxczc85kIFUDFgcVAifLx7SyNiJjzX7P8mO91a7sB/Y7bcZsH996mKnzel4nC2kcyIGi\n9tze3m5CZtTm4/G4NdZUB9mCCfx0UpFld+rybOnAcIZwQmkwufpX86RmNjQNUAltg2SPGctjfgJd\nxgMq7dreW05kqt0Zk2E5CZYObhH7wcrurfY0I9qvRPAJ6oDmZoNsYWC7EARdRXYwZh7qH/YFwVBl\ndtB0yertzF7PLy4uNo4R5a//3dnlJ11LdnZ2YjweNztZMtOEs+8ODGdLB4YV0cDy1d0HulSUwWDQ\nUg8dlCL2Yw2ZvosGMScnJzAZaLYDgrszStmPM1OevoOD+aiMDvoqTwYyrJs+UsfJTlheAj73OGdg\nzHanmcB/IxBTdWRbe9+4Z1r19jccZuCc2Wg5XhxwBVZ8jasCr9n2rmFovPDEm+l0GqPRKE6ePBkb\nGxsxHo9bmgadf9RMOjCcLR0YzpAMsFwNdEbkrFCDmROMgJjZzjLG5BOLQEugFJCQBbI83I5Hj6ir\n8xLaGyP2QY/pCHTdsO+s0xcHsiVnVkzD28CByVkY24jiqjzz5CLkdc+YY3Zvxl7dNMCtk1S5fWuk\n2sXfabO7uxuTySTW19djfX29CboW0HLBcdNB50SZLR0YVoQDWH99YNPoLtWGTIJgw4me2e6coRAw\nOYHJkGRzUkiF8uJZirqXk002Jk08AhuBRTZJTUamR8dJxD7QSATMDEXR7+619oNHCTqZqkmQ9YXI\nrzkrdMbrKrc7pGrjwp9zwPY0M0boY8iBcGlpqfE6R5x67ejGxkY88sgj8cgjj8TJkydje3u7qQvL\nzvGQlbeTg9KBYUWyycf/qRYRaMhKMiM2Gc+sbXQsBye2fpP6q0NDOaloR1QZeDgDwYwG++3t7eYF\n6LJJar+sgrHF4nhiNPOapYoRSHmN8Y2sP+tOgPHf3Z6ZLR7ejuxPdwxxIfHyuy0uY7NZWcmwPf4v\naxfdr4VIjPDEiRPx6KOPxsmTJ2M0GrVsng6EStvHVCe5dGBYkWwl5YCWSkjwE0PkmYK0Q2XASFuV\n7lf+EQfPVaT3mYxPz/A0E9qP3Bala6PRKEajUYzH49ja2oqtra3Y3t5uQE4vRtdWMdn4lKe2iJHx\nZMxKZWLYCMFD4Sau4jm7o2pNFTADvBogsh/cZFGzBeoego/EwThjYCw3mbDXQWWi2WFvby+2trbi\nxIkT8fDDD8cjjzwSm5ubjXpMIOSiwsVzFtvt5JR0YFiRWQOaE4aTwO1pHKTZIZ7O9iLq2718kkn9\n0ZYs5euAxHdnSH0UI5xMJjEajWJzczO2trYaz2TtoNfd3d1YWlpqAFZ5ZUdSZXuLxapUVrad7vVj\n9DPzgO5zNkhQYp9l7ci+0X0OcrV2dxU3s2u6vZGglPWxmxnU3npF6PHjx1vqsTzIZJpktd72DL/q\nJJcODCviDM0Zim/Y13WqnxqEe3unXiZO47mfPKO0MpWZE4ZgoHSn02nD0Gif8klHdV5McHNzMzY3\nNxuVSyqxykWmqTP2eNyUPjxHkc85wxabcYAWWApc+/1+63mBDEE0oh1W444g9iGfcXCqMXMHWUqm\nBkdEqz6uEmfOGF9MVadSTplATpw4EePxOL785S/Hl7/85Xj00Udjc3OzxbAJhq4mq456u14ndenA\nsCI+kXxS1+xaAkNXV7SKcweKnskYBEGyNjFLKa3z7BwMXR0T+IgVignypUTD4bA5kCGiHYStiSYw\n1JFT7gBR+7jtSm+Ki2gfLEDmKABUPuwDMkh3DLiamgGp2/JcHa6p1hQuYATR7Hfm4/dEtHcYsYyM\nNdzZ2YmNjY14+OGH4/jx4wfUYwItxxH7RG3ZgeFs6cCwIm4cJ6vQtYiDwcYMonWPXnb8E22KTM8n\naeZIoHpFwJXNKQNkMcPJZNKoxHNzc82+WDlKlLcmmHuXB4NBixG6CuogoHIoHpOTXSxH5Sfrc+bm\ncZUUV4XddCGQzGI93dboaq2DKU8TZ53Foqn+cwzpr6vWKoMcWqPRqLHhrq+vx4kTJ2Jra6tls+Qn\nc8yoLATLTurSgWFFZq2iNeO3qzy0ExJQfMDyesZcmC7LoN+oOmrQM5ZNeZF1bG9vt+LTBoNBw/To\n1WVIjSadmKGn7yzJATHzOjugs55iu6e7r7bGpJ2Bev7OLpW3My7e72OEoMhYSm8Hxohmi9lkMmlM\nFxsbG7G+vt6Aor+Mnvm7+s0+7MDw9KQDw0MkU//0l8xDA46Tiu/opfqXqTNZ0DOfIyuhRzuifaIL\nVVl/O5uYB18ARbWXdkflwxhDXuenBjAR0WK8rK/yXVhYaICZjEqLyWAwOABiTL+m9tIGl6m9vM8d\nL2Rvqm+m7uo3LUhajLItdARDsXb1mT7b29uxsbHR7CzZ2NiIzc3Nxi7s+R6m9hIEO2/y4dKB4SHi\nkyBTn2nApxrngEN1i2pZZq+KyN+g52pRRHuXCdmb7ERiidPptAU8LJuDHlViPwyCZcpsZhnLcyDP\nALW2jZBlc1BU2v5x7yzLx/KTtfN31ov/E0S9zqxvxuplq5XnvZTSqMQCwePHj8f6+nrLw++7Rxz4\ns4WICxfv66QuHRhWhJNGIFZTAzWoZUsjm+NukCwPdwRQ5RaIZTYiZ3yj0ajJiw4Uf1ueVGTaMfkC\netVT390WlamXmbrInSm18vM9zzqrT6xVoK3DTXXSi7dJBoJUPVWmmprLBYUM1Ovo/c9rjAek6UEg\nq0MVptNpbGxsNO07nU5jPB7H+vp6E0i9sbHRnGLN/eMcb6q3ys9+cnbKRaWT2dKBYUVqAyhjFpyE\nEXFggmUhHZm90dkU86DdjpOALFQTjiqon16tCZuFZtTqqGtkY6x79nEVkQywpmbPz883QCjgVnlV\nBnm6tUVtFkDXVGPWJ2Pf/J4xXzIvmkrYn+64ktd/fX099vb2WmCoYGrFD9Jk4ItNpqlkdltffNgO\nneTSgWFFXMXhNf1fU285QN1p4g6NiPYBApmnk5OWwdxuC9rd3W08jtwZwh0xBGFX233y0XamuhBE\nMiDicxHthcHbjTY5qugCem0P5L5dmiPIELO2Utt6n9Xu5XVnY/LMqv35IiaCIccJ+1AgPzc312gR\n0+m0OXTh5MmTsbm5eaAvZpXL211SY76dmjxbOjCsiLMgvz7LpqTJTZB0m48DLUHHPYG1T1YuqVfc\nFug2Ok4kHizhbCljeGSnLIN7u53d+jUx1azdHGTFqrhouIc5U8OZJ/NwNVP94wBUMw34/8onMxmw\nHcneBe7aATQajWIymRwAazdFZDbArFz6zgWvA8PZ0oFhRXx19clNJuDg5cHV2UAkIGoykvFFxIEj\nmZiHM1aCnVRMqZd0iDBImoDNchA0XV0TENUmYMaUWScxKQcz95YLGKl2cr+u6unb9xzA/H+WxUGR\n/cwxwP9nMV3XAtg2/F99J3DUMVx0wLnaO51ODyxqGocU11A6Nfn0pQPDirgnkkzRbVO8ponFHQCc\n6D7IM9WaYECW5pPTn5X3VypZ5hSQrS0TAbmr1Z6Ge4m9jAR/t6NSTWe7OuBKeFCsA4musd0y8FOZ\ns0XDFxSKLxTsb1/EMvMJ24mefZpQZArwPGZpJnSQECyzMer16aQuHRhWJAvLcBWGwOYOFLIr2ugc\nDLOJLE8rA54z256E4Nnv95uy8z3J8/PzzUGhAkTfRkdWKxDib2RUDOXhR21A5qcyu5c9A1Le721E\ngKUtkB5m7obRd2dGBEOvoy8CvpuEdZhlrpB9Uf2+vLycAq+cWRnT9HSd7aq8XFx8zM0Cx07a0oFh\nRTykQeIMgOqKH87J467IpCg11Y4TnKu+s0x9pAJzkpJV6WzCwWDQssu5J1msg2EaZCsO5ln5VU4+\n6+qigMKdIDXQJ/tVe5MhZrY01acGAhmjY924CDlry1iyjx+yR7W5L2wO7MrX6+JASEZKhsty8/ca\n0+xkXzowrMgsG5GErEbb3LjrhCqRbEJkST6xPH0OfIJzxmb0vl2BmeL2CIweXK20BLhUJalSqq6u\nRvo9LD+BgCxF4EQmo7KxHHqezxHgCSoCVVdNsz51oHaQ81NveI/q5eq3jw1/Zm5urrHVRkTLkeJg\nq7LRZuvjwVl6Vk/uvskWiU4OSgeGFXGQyhhExL63Ljs8NSK3edVYleeniZGVi2mI9Um95unIvCc7\nQsz3T3MilrL/nudMxa+1UcTBd4oQIDJw5OR3sHN7HRcSt0Hyfi8Ty1VjSXo2AxsHOL/m9WQbacFS\n+04mkxS8s7L730w9z+rgi0LHDGdLB4YVEdPygU0QkYcvIloTnOCYeTslbvvJJrhPBDIxAYTiCRcX\nF2Nvb6/ZseG2KD/nUL8JdP08QuVHIOL/Spfl0/9us9N9WZB5r9drds9wwSBYsK04ydUW/iY8FTId\n/QAAIABJREFU/e9gJdbu9fBn6NDJFgLvQ/6WgY6bH8gSVXYufOwbloPlZBmc6Xq9OzlcOjCsiE9M\nZwIZKyKIKc7Pd0o4y+Hz/D3byRARjcGcqqzSj4jW7gYPi3EvN8Gbaj3ZoSYvFwSJs0nWo8YKax+1\nuYSssAYITL/m0HCG5CDh/TILRE5HzXS1mWCqNiaLrWkKGRv0tqT6W9MwvC06qUsHhhXJVB3aYsiu\nOBFp1CdwSTLPJMGPzJCgpHLItqb75TH22LksL1cP3R5KFZv3Mz+/7nFvzgjVlm7Qd9BQff09ymwf\nt30xPQr7gl7+w1id/658fX+1fqsxZIK7e+zdi+zglnn4CahsO44r/e/Hqnn5OqlLB4YVyVRkfucR\nTJzgPPKfv83KR8Lwm2xyuHeYu0doT1O6+p1qr5dJgdcCWrLDTAgIziKZLsGCi0UGhrqfgJ797g4e\npqXnnCl7Og7QLK/3eebgYL2yvmV+Wb/rWY0Tt3FmgMtFh+V21ugs3fuhA8TZ0oFhRXygn46XUodz\nal+wA4QPdJ/sykd/BQqc2CyPg1ymJgnc3Aur8rid0CeOq3HZBGTZ9LszSablQJQBegZSs9q+tiCw\nrbP2ckBkWdyrzXv9fr+WMTgyO75aNjMBuBy2QOke9n22oHdSlw4MK+KD3hlPTa3Siu8DkROcgCjx\n8AxOEg52npKs+6nCM8g4s4d5fdw+qHR0XxYHR7ZCEPI2ocMmY1tKy9Vg36pXU2+z9q8Bp8rp71Uh\naHnZ3O6ataEzNU+TQMdFgXGpWjRqjpoawGVAl41bV6k7yaUDw4pkbI6/UWVyT6GrVmQkGbvk/QQk\nP81GeclzzAmg+xlbSLXJT312W6J7ZVkO2vv4nMSBVOVlyA/brHb6C/NwlsM6ZWDsTFV5uXMn60Pa\nFt1xk31UXvZdpk5zDHFXj/etj7FMm2Dbu1Yh4RZHpsX276QuHRjOEJ+MZIUUGsqlAvE4dx6wqsMT\nJA5OmlQCDIbHSPjiJt2no644iWjTy1Qo1YX2KPdieygMGavKSzDl74ytcxVZDJcvpieono7tMvur\nuvE+B8gaw5v18bRPh7ll/cBzD13tztTYmt3V+8HF7Yaz7u3klHRgWBHfozqLKUa0mRRPbNaL3emh\n1f0CCYmzFAIiQXF+/tQLnASuNMa7Wu1gmLErgiGZIRmJG/szdVd10F+CJJ/R/UtLSzEcDhtAzPZD\nO5DVju7yyZ45VZgOv/Ovs0y3w2b1c7BhHpmDqaY91MQXXzJXZ70ZS2Y5OqlLB4YVyYJYa0K26ACW\nbcLn/xygupfsUOltb2+3tvqNRqMGpAiEYmJkZ1TjHRhVV9oqqdaJ7Qp0yUwYMKz0I+JA+TOHgvZJ\n19rTvcLZPRG5PZF9Rnul7qedU9cISjWG5ppCJtnCmTFThvz4mPDyZmYapsfnWQd9auE2nbSlA8OK\nZKu8D3T95e8CCb4T2E94kZCRyeniu054+oleKCTwc/V4aWkpVlZWmp0omgj6yNaY2dBYLh4S4bZJ\nn+C0/ZG17e2dOuper7jU89PptDnlWfZPnshdY+CZ/dBNDN5vGXtlP3gaZHu+vU9g4mYB9m/G9lzN\n1f989cIsoPLx5m2j3xkJ4BoBF7ZO6tKBYUV8hc6u+UocsQ9gGRh6yEhE23PKScmJmXl8J5NJ62DQ\nUkpzIk1E25guGyPTy4BEDFL36yMGKHBl+be2tqKUEsPhsAFaAroA3CenXvBEYM7A1vsgU3P9OQe4\nzPGQqb1Z/nSaZPZCB12CrwM779WY8LI8FsBy8HOmz0WwY4aHSweGFclW9xqTimjv4Z1OT73bYjKZ\ntMAwYh+kuG/Z7WDKazqdxtLSUkulVgyj2KTS3N7ebv7npNWe38XFxcY+J8AS61S59Xa/wWAQw+Gw\nebG8GMx4PG7Kocl24sSJJqRnMBi0jhHjqwd01uJwOIzhcBjLy8vNSS7+GlACpJ++QkAh4NXaj4tL\nxMETtV1ddoAScHl/u3eeJgEuRGwvpuflztRdlZH5MC6UKjDz4KLnu4k6qUsHhhWpAZ9Pipr6IoAi\nO4xob7yfn59vGBsZl1Rgn6wKQ1laWmrdywnLD/Pq9/uxsrLSvMqUtkgBgNTs5eXlBgjJDLnPen5+\nvgFJOnDETPVS+sFg0ITYDIfDWF1dbYCWr0bgBGY7ZY4f1d2ZGsNeaLPMGJerzv4/8yDo8qAH73P9\nTpXYGavKNcvuF9FeXDMbZPahndVB9LGyzv8fpQPDivgA4oDkRNSgzVQe2oY4iXRd2+WYjtt7ptNp\nywCu94L0+/0YDAbNXmIxMLEtpiuQW1tba8BJzFI2SNoch8NhA2ZSZZeXl5sDIMhSIyK2t7djY2Mj\nFhYWYjgcNmxkaWkpVldXGxVejLDf77ccPc7CqZr6FkEGlWfhJhHt2D+eqJ2xzpoqy/SUpswfKgeP\nQmPeCqcic8uYKj8sg48h9SPr74xWH4+rVHndptvJQenAsCKZvZDXXYXWPRz4YlR6D65PfqpWvhWO\nHmAOdLEzvVCdtiEBmlgbJ5PsdApj6fX2j83SQbCyF/KF7V4/t3Hqd72cXuq/1O6VlZXo9Xot1ZsH\nnbpdT2DIsxid9fR6vQOqpj/vLEwqJstOEMtCgAiWmYMk+xC8MhXWbYyugTiT1O90amXqtJfDx1pn\nMzxcOjCsCG08s353207E/js6pIaKHXocnSa9sx0NXm4dE7PMHCryxu7s7MTS0lJLvSJIOcuIOKXO\nSqV3ryzZ49bWVhMzKVukHCPZO4TJ6mRPFJtlvZRPdoK18uEiw/YQWJFJsuxMj+3rMZRuu6t9KGor\nZ/9qX48tdZuh+sPZoMrv13g6Ocefq8NcFNgeNdtqJ/vSgWFFfNX1VTW7pknLiay3n8nmxjSpLruX\nlipWZoOSvVHPzs2dejl5r9dr7Y0Wg9L7ecWqlK7Kt7W11Tp/UQ6ZnZ2dGI/HsbGxEdPpNPr9fvP7\naDSKhYWFWF5eblR0ls+9x+5BpufV5TDmlYWxZNe9vdmvzMc9wBkA+kKm9iX7J5OlecPLRwB34CIT\n5lhwFdrHntfTF5AODGdLB4YVycCP/xO0eI0DeW9vL0ajUSwuLsba2toBhuK7VDJ2ICbGMigfeWL1\nEQPc2dk58K6T9fX1KKXEeDxubH1iKQK7ubm52NnZiY2NjUbVHo1GMR6PGxZIB8ze3l6j/grwZJNU\nvowhFOA7OPhEdVbEdqcKTCCl7S7bxlhjeG5v9HwYT8jvBEOFUWmRIQg6k/fx4yEx/LAtmJbGlrcZ\n02YEA22NndSlA8OKcKWOOAiAs1gDGWKmKlON4z16hmm4LSsDRH1oo+z3+41tTiE4AjQFagtQJpNJ\njEajiDg1ycUSBZSa6AJXMVwFccu+yAMkVA/aHlnvTBXkdZ7FOAs8yZrFxnkaDNPOAFFpk1F6Htlz\nun+WilzbX+1jqWYr5rhyxpjZG7O6uu2xk7p0YHiIuLeY1yXO3Pi7VmmxNg8VidgPw9FgdyCM2D+E\n1Y+UkvNFe5VL2bfzyVmxvb3dciR4yA+31W1ubsb29nbrPEZN7KWlpcYbTLbnbCiiffoN28m97vrf\nJzYPnSWzcpWW/SKm64sGGZ2eYTuTTWWqeRYKozqqX1VutQUXCbdhktn6zhE/9iyrC/NyEwrb2sdY\nxwxnSweGM8TZjIszhkzNJRhOJpOUGUZEEwKhCUQbYhY0y2BpTT7G+BGkFD4joPDj5hl2wbpoUqrM\n/X6/iRNUObM20ST3svuk5qLgbafn3U7m9kAGOGeMy6/zeS5MDlLetxnLZDQAgYyM2VnhYZIthNkC\nwvv9u4Oi0ujAcLZ0YFgRHzhUYWsrNa/5qixnA4/eohoor6kLg2/JBmgzI2hJNCE16RUjGNH2aBJU\nCc4CSDLAfr/fCshWuXlk2SxAYjux7gIl1iljzxR6rlVef4bf3VMusPVwG9radC+dYmS+tCOSDWqX\nEFmf7uP4cFWY18kW/R6OM5Ypqx+DzjswnC0dGFaEoR4R+4OSkzKb5Nn1UkrzOgACBiedVGjamlyt\nyuxCHq9GABCYSf53e+fWnMqtROEGbHO3sZ04ecxz/v8/yluqdu04vsCADedh1xLfLFrgcx7PVldR\nxsOMRtKol1ZfpDmXVpIFCphczOV0SvY+HA7lZehMc3EW7QzL+4rfa32aMUuCp08mNZ+kM1Jn/TVT\nVL+pH9Ve9WfmJ/T28D7ZeHEg9zHEOtYmDTLL7LwmdWlgWBFfWRCRL8vLlDcb6Mr3O+fjEnvTx3PI\nWB4VjSyplhRN0PO0FzrsuY0Yz5fPcD6flxQanessk/XN1hZn/UkRkxPYsD2sG+8ln6sDkMpTQEb+\nPT6DbCLx/wmAWaTW/YMsn4EZMkJKLWiSTTAEuSwRm3XgePhvTfafTRoYVoTO6Zop4pIpla5XCsZm\nszkJrkihnY1SgQhmrhgqO+IYaHEG5HVn7p9vSc8XFTHVw1eOMDUo23xA53kf1fxw2Xe/zleF6K/6\nyQM5GWvy+2TpPdm9OVHxeZAV+j19csjcCJ5Ok5nC2TFKNubc5G7M8Lw0MKxIZmLpOE1NrknNZmiW\nt9vt4p9//imRXvq5NMCzdBqWz7rwHKXLULHIVBhA0X2YDkPfJBWOy8AiogeA2+22t+lsBmjOmgnw\nWR8TmPh75pNkPclAs5xGlcdVQAS5WhRa9yYQur+TDFv396yBjGFGnL5ulX3hwEcgpp81InpsX+f6\nOGzM8Lw0MKyIz76ZCeWmXzZb87sCKVIuBSLI9qigzmRqgCiWpt8JcDSL3fd1zkzN2AdBQWa/vwyd\n/cc6Z6BHAK7dW9+Zy8cyCIYCGpn0zqJZD5rbDnQZAOnjAYmM2dVMV7aNk6ozOAdCtoHRc/azTx5Z\nuY0ZnpcGhhXhQORg9lw0ztBuAlGkCPJZ6TxFl8lC9IY7bawwHA5PcgUdMDzaKVBUpDljHozG0uHu\nTI+gr2t8pQdBpmbm+oRC9uJBB7I1ga9SdtRP9MVxQhDj9teCkhlyhQr7TfflM2Kf8DUH/py9HZk5\nrL/0EfIcH3/0H9dYK/udk6g/9+y5NDlKA8Mz4iZGxsyyWTcDRKZniElpcwU6uan0Oi7lqZl0Xl8H\ncj+P5Wy3255iSvG1vlnnEczJwhyE/d41ZXSgZTvZd36fc21huhLXCTsIE2j8WRFUs8mGfU3A8Q/P\n8/I5CWTC+vmx7H+e70ywgeHXpYFhRRR9jDhdWeImGo+TTeh4xHFg0qzruq5sgKCXI2nJnJKoVY5Y\nzmazuQiGBIcMdAjIma/Jc+gIsudWabC9VExOBPJrZuCpfteH9fT+5X1qvjRNKmSE7ntUvZl0Ln8o\n66qPymP+JfvQ1wC7m4ObOpDp8jlF9Nceu+uALho+c7aXlgtdJE3q0sCwIrWB6Pl/ElduHiOYsPzd\nbhfr9bpn0kqpBcZ0kEsB6LvyOniir5v3EqaJOBjKVHeFq7WbpiXbxwnD2VBWrkdpCUSst/ojA4nM\nJcDnQgbp5iY/AkMmnquPGEH2jSgIxqoPx5Ovnc7YMr87q8v8oFl/8/ps/DU5lQaGFXG/EcX9RPqr\nAUlWVDNTda7eK+LbX+k8mqHuj3Iz1R34h8OhbMXF+mamfgaGHpF0VwDb51Fg/XUWS5PY7xtxTD5X\nPzCnL4v2EiTom+NHE08WCNHvarO/4pUAqOfC5Y8OiBJaBPwrv2NmApO9Z+OLwOpjSuDLgJVPYA0M\nz0sDw4q42fKV87MBd4kpapPUzWZTNlxwHx5Nbu5QQ/CliUgQyKKfBKvM7GSuoaTmjM/cBgRO+u7U\ndjJFZ7IOCjQD/fcM1Fzop/Wgi8pXgMbfZuiTBdd783ULl17FyX52cKIvkX3N8eNjJmPpPvH6pNmA\n8LI0MKwIlTwiD5DouA84N11UnhSP35Wiom2ztNRNSifGQt+XAi9UJC7nowgs3Y+l8wikqi+juK58\nzuR8NxSah1Re3Zf9QgB3EHXQYH3E9ATazlKdUTMaTPBUfVWOyswmD5rGvmbbI8MUB64swEKwlzBq\nzv99fNJXS3bMdCofl01yaWB4RmqmhZs+nPUvDThnSBE/FLLruvJCJa0KkZkps4r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p7nkIAd1VARDDJBTn5bFjdIl8Nws2eAGOOqcvXlb5m7QhCc50Db0OMsBjOEGW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- "text": [ - "" - ] - } - ], - "prompt_number": 27 + "outputs": [] } ], "metadata": {} From 18b0f5e17fe7a6b1b30f51ba75fc39c1795b48c1 Mon Sep 17 00:00:00 2001 From: Parijat Mazumdar Date: Sat, 15 Mar 2014 00:33:28 +0530 Subject: [PATCH 03/13] softmax function added --- src/shogun/machine/gp/LikelihoodModel.h | 3 +- src/shogun/machine/gp/SoftMaxLikelihood.cpp | 156 ++++++ src/shogun/machine/gp/SoftMaxLikelihood.h | 201 +++++++ .../machine/gp/SoftMaxLikelihood_unittest.cc | 495 ++++++++++++++++++ 4 files changed, 854 insertions(+), 1 deletion(-) create mode 100644 src/shogun/machine/gp/SoftMaxLikelihood.cpp create mode 100644 src/shogun/machine/gp/SoftMaxLikelihood.h create mode 100644 tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc diff --git a/src/shogun/machine/gp/LikelihoodModel.h b/src/shogun/machine/gp/LikelihoodModel.h index e9ef1a7b27c..f783f12655d 100644 --- a/src/shogun/machine/gp/LikelihoodModel.h +++ b/src/shogun/machine/gp/LikelihoodModel.h @@ -26,7 +26,8 @@ enum ELikelihoodModelType LT_GAUSSIAN=10, LT_STUDENTST=20, LT_LOGIT=30, - LT_PROBIT=40 + LT_PROBIT=40, + LT_SOFTMAX=50 }; /** @brief The Likelihood model base class. diff --git a/src/shogun/machine/gp/SoftMaxLikelihood.cpp b/src/shogun/machine/gp/SoftMaxLikelihood.cpp new file mode 100644 index 00000000000..b8d3e6281c8 --- /dev/null +++ b/src/shogun/machine/gp/SoftMaxLikelihood.cpp @@ -0,0 +1,156 @@ +#include + +#ifdef HAVE_EIGEN3 + +#include +#include + +using namespace shogun; +using namespace Eigen; + +CSoftMaxLikelihood::CSoftMaxLikelihood() : CLikelihoodModel() +{ +} + +CSoftMaxLikelihood::~CSoftMaxLikelihood() +{ +} + +SGVector CSoftMaxLikelihood::get_log_probability_f(const CLabels* lab, + SGVector func) const +{ + REQUIRE(lab, "Labels are required (lab should not be NULL)\n") + REQUIRE(lab->get_label_type()==LT_MULTICLASS, + "Labels must be type of CMulticlassLabels\n") + + SGVector labels=((CMulticlassLabels*) lab)->get_int_labels(); + for (int32_t i=0;i-1)&&(labels[i] eigen_f(func.vector,labels.vlen,func.vlen/labels.vlen); + VectorXd log_sum_exp=((eigen_f.array().exp()).rowwise().sum()).log(); + + SGVector ret=SGVector(labels.vlen); + Map eigen_ret(ret.vector,ret.vlen); + + for (int32_t i=0;i CSoftMaxLikelihood::get_log_probability_derivative_f( + const CLabels* lab, SGVector func, index_t i) const +{ + int32_t num_rows=lab->get_num_labels(); + int32_t num_cols=func.vlen/num_rows; + SGMatrix f=SGMatrix(func.vector,num_rows,num_cols,false); + + if (i==1) + return get_log_probability_derivative1_f(lab,f); + else if (i==2) + return get_log_probability_derivative2_f(f); + else + return get_log_probability_derivative3_f(f); +} + +SGVector CSoftMaxLikelihood::get_log_probability_derivative1_f( + const CLabels* lab, SGMatrix func) const +{ + REQUIRE(lab, "Labels are required (lab should not be NULL)\n") + REQUIRE(lab->get_label_type()==LT_MULTICLASS, + "Labels must be type of CMulticlassLabels\n") + REQUIRE(lab->get_num_labels()==func.num_rows, "Number of labels must match " + "number of vectors in function\n") + + SGVector labels=((CMulticlassLabels*) lab)->get_int_labels(); + for (int32_t i=0;i-1)&&(labels[i] ret=SGVector(func.num_rows*func.num_cols); + memcpy(ret.vector,func.matrix,func.num_rows*func.num_cols*sizeof(float64_t)); + + Map eigen_ret(ret.vector,func.num_rows,func.num_cols); + eigen_ret=eigen_ret.array().exp(); + eigen_ret=eigen_ret.cwiseQuotient(eigen_ret.rowwise().sum()*MatrixXd::Ones(1,func.num_cols)); + + MatrixXd y=MatrixXd::Zero(func.num_rows,func.num_cols); + + for (int32_t i=0;i CSoftMaxLikelihood::get_log_probability_derivative2_f(SGMatrix func) const +{ + SGVector ret=SGVector(func.num_rows*func.num_cols*func.num_cols); + Map eigen_ret(ret.vector,func.num_rows*func.num_cols,func.num_cols); + + Map eigen_f(func.matrix,func.num_rows,func.num_cols); + + MatrixXd f1= eigen_f.array().exp(); + f1=f1.cwiseQuotient(f1.rowwise().sum()*MatrixXd::Ones(1,f1.cols())); + + for (int32_t i=0;i CSoftMaxLikelihood::get_log_probability_derivative3_f(SGMatrix + func) const +{ + SGVector ret=SGVector(CMath::pow(func.num_cols,3)*func.num_rows); + + Map eigen_f(func.matrix,func.num_rows,func.num_cols); + + MatrixXd f1= eigen_f.array().exp(); + f1=f1.cwiseQuotient(f1.rowwise().sum()*MatrixXd::Ones(1,f1.cols())); + + for (int32_t i=0;i + +#ifdef HAVE_EIGEN3 + +#include +#include + +namespace shogun +{ + +/** @brief Class that models Soft-Max likelihood. + * + * softmax_i(f)=\frac{\exp{f_i}}{\sum\exp{f_i}} + * + */ +class CSoftMaxLikelihood : public CLikelihoodModel +{ +public: + /** default constructor */ + CSoftMaxLikelihood(); + + virtual ~CSoftMaxLikelihood(); + + /** returns the name of the likelihood model + * + * @return name SoftMaxLikelihood + */ + virtual const char* get_name() const { return "SoftMaxLikelihood"; } + + /** returns mean of the predictive marginal \f$p(y_*|X,y,x_*)\f$ + * + * NOTE: NOT IMPLEMENTED + * + * @param mu posterior mean of a Gaussian distribution + * \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the + * posterior marginal \f$p(f_*|X,y,x_*)\f$ + * @param s2 posterior variance of a Gaussian distribution + * \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the + * posterior marginal \f$p(f_*|X,y,x_*)\f$ + * @param lab labels \f$y_*\f$ + * + * @return final means evaluated by likelihood function + */ + virtual SGVector get_predictive_means(SGVector mu, + SGVector s2, const CLabels* lab=NULL) const + { + SG_ERROR("Not Implemented\n"); + return SGVector(); + } + + /** returns variance of the predictive marginal \f$p(y_*|X,y,x_*)\f$ + * + * NOTE: NOT IMPLEMENTED + * + * @param mu posterior mean of a Gaussian distribution + * \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the + * posterior marginal \f$p(f_*|X,y,x_*)\f$ + * @param s2 posterior variance of a Gaussian distribution + * \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the + * posterior marginal \f$p(f_*|X,y,x_*)\f$ + * @param lab labels \f$y_*\f$ + * + * @return final variances evaluated by likelihood function + */ + virtual SGVector get_predictive_variances(SGVector mu, + SGVector s2, const CLabels* lab=NULL) const + { + SG_ERROR("Not Implemented\n"); + return SGVector(); + } + + /** returns the logarithm of the point-wise likelihood \f$log(p(y_i|f_i))\f$ + * for each label \f$y_i\f$, an integer between 1 and C (ie. number of classes). + * + * One can evaluate log-likelihood like: \f$log(p(y|f)) = \sum_{i=1}^{n} + * log(p(y_i|f_i))\f$ + * + * @param lab labels \f$y_i\f$, an integer between 1 and C (ie. num of classes) + * @param func values of the function \f$f_i\f$ + * + * @return logarithm of the point-wise likelihood + */ + virtual SGVector get_log_probability_f(const CLabels* lab, + SGVector func) const; + + /** get derivative of log likelihood \f$log(p(y|f))\f$ with respect to + * location function \f$f\f$ + * + * @param lab labels \f$y_i\f$, an integer between 1 and C (ie. num of classes) + * @param func function location + * @param i index, choices are 1, 2, and 3 for first, second, and third + * derivatives respectively + * + * @return derivative + */ + virtual SGVector get_log_probability_derivative_f( + const CLabels* lab, SGVector func, index_t i) const; + + /** returns the zeroth moment of a given (unnormalized) probability + * distribution: + * + * NOTE: NOT IMPLEMENTED + * + * @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param lab labels \f$y_i\f$ + * + * @return log zeroth moment \f$log(Z_i)\f$ + */ + virtual SGVector get_log_zeroth_moments(SGVector mu, + SGVector s2, const CLabels* lab) const + { + SG_ERROR("Not Implemented\n"); + return SGVector(); + } + + /** returns the first moment of a given (unnormalized) probability + * distribution \f$q(f_i) = Z_i^-1 + * p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\f$, where \f$ Z_i=\int + * p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\f$. + * + * NOTE: NOT IMPLEMENTED + * + * @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param lab labels \f$y_i\f$ + * @param i index i + * + * @return first moment of \f$q(f_i)\f$ + */ + virtual float64_t get_first_moment(SGVector mu, + SGVector s2, const CLabels* lab, index_t i) const + { + SG_ERROR("Not Implemented\n"); + return -1.0; + } + + /** returns the second moment of a given (unnormalized) probability + * distribution \f$q(f_i) = Z_i^-1 + * p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\f$, where \f$ Z_i=\int + * p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\f$. + * + * NOTE: NOT IMPLEMENTED + * + * @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$ + * @param lab labels \f$y_i\f$ + * @param i index i + * + * @return the second moment of \f$q(f_i)\f$ + */ + virtual float64_t get_second_moment(SGVector mu, + SGVector s2, const CLabels* lab, index_t i) const + { + SG_ERROR("Not Implemented\n"); + return -1.0; + } + + /** return whether likelihood function supports multiclass classification + * + * @return true + */ + virtual bool supports_multiclass() const { return true; } + +private: + /** get 1st derivative of log likelihood \f$log(p(y|f))\f$ with respect to + * location function \f$f\f$ + * + * @param lab labels \f$y_i\f$, integers between 1 and C (ie. num of classes) + * @param func function (NxC where N is num vectors and C num classes) + * + * @return derivative (NxC matrix linearized in column major format) + */ + SGVector get_log_probability_derivative1_f(const CLabels* lab, SGMatrix func) const; + + /** get 2nd derivative of log likelihood \f$log(p(y|f))\f$ with respect to + * location function \f$f\f$ + * + * @param func function (NxC where N is num vectors and C num classes) + * + * @return derivative (NCxC matrix [N blocks of CxC matrices concatenated along column] + * linearized in column major format) + */ + SGVector get_log_probability_derivative2_f(SGMatrix func) const; + + /** get 3rd derivative of log likelihood \f$log(p(y|f))\f$ with respect to + * location function \f$f\f$ + * + * @param func function (NxC where N is num vectors and C num classes) + * + * @return derivative (NxCxCxC 4-d matrix linearized ie. Element(n,c1,c2,c3) = + * array[\f$n*C^{3}+c1*C^{2}+c2*C+c3\f$] where C is num_classes) + */ + SGVector get_log_probability_derivative3_f(SGMatrix func) const; +}; +} +#endif /* HAVE_EIGEN3 */ +#endif /* _SOFTMAXLIKELIHOOD_H_ */ diff --git a/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc b/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc new file mode 100644 index 00000000000..8e9f8282e51 --- /dev/null +++ b/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc @@ -0,0 +1,495 @@ +#include + +#ifdef HAVE_EIGEN3 + +#include +#include + +using namespace shogun; + +/* Results compare with softmax likelihood implementation in + * GPStuff toolbox + * http://becs.aalto.fi/en/research/bayes/gpstuff/install.html + */ + +TEST(SoftMaxLikelihood,get_log_probabilities_f) +{ + SGMatrix data(7,3); + + data(0,0)=-0.8095; + data(1,0)=-2.9443; + data(2,0)=1.4384; + data(3,0)=0.3252; + data(4,0)=-0.7549; + data(5,0)=1.3703; + data(6,0)=-1.7115; + + data(0,1)=-0.1022; + data(1,1)=-0.2414; + data(2,1)=0.3192; + data(3,1)=0.3129; + data(4,1)=-0.8649; + data(5,1)=-0.0301; + data(6,1)=-0.1649; + + data(0,2)=0.6277; + data(1,2)=1.0933; + data(2,2)=1.1093; + data(3,2)=-0.8637; + data(4,2)=0.0774; + data(5,2)=-1.2141; + data(6,2)=-1.1135; + + SGVector lab(7); + lab[0]=2; + lab[1]=1; + lab[2]=1; + lab[3]=0; + lab[4]=2; + lab[5]=0; + lab[6]=0; + + CMulticlassLabels* labels=new CMulticlassLabels(); + labels->set_int_labels(lab); + + CSoftMaxLikelihood* sml=new CSoftMaxLikelihood(); + SGVector data_vector=SGVector(data.matrix,data.num_rows*data.num_cols,false); + SGVector v=sml->get_log_probability_f(labels,data_vector); + + float64_t ep=0.0001; + EXPECT_NEAR(v[0],-0.5420,ep); + EXPECT_NEAR(v[1],-1.5823,ep); + EXPECT_NEAR(v[2],-1.8351,ep); + EXPECT_NEAR(v[3],-0.8296,ep); + EXPECT_NEAR(v[4],-0.6015,ep); + EXPECT_NEAR(v[5],-0.2791,ep); + EXPECT_NEAR(v[6],-2.0168,ep); + + SG_UNREF(sml); + SG_UNREF(labels); +} + +TEST(SoftMaxLikelihood,get_log_probability_derivative_first) +{ + SGMatrix data(7,3); + + data(0,0)=-0.8095; + data(1,0)=-2.9443; + data(2,0)=1.4384; + data(3,0)=0.3252; + data(4,0)=-0.7549; + data(5,0)=1.3703; + data(6,0)=-1.7115; + + data(0,1)=-0.1022; + data(1,1)=-0.2414; + data(2,1)=0.3192; + data(3,1)=0.3129; + data(4,1)=-0.8649; + data(5,1)=-0.0301; + data(6,1)=-0.1649; + + data(0,2)=0.6277; + data(1,2)=1.0933; + data(2,2)=1.1093; + data(3,2)=-0.8637; + data(4,2)=0.0774; + data(5,2)=-1.2141; + data(6,2)=-1.1135; + + SGVector lab(7); + lab[0]=2; + lab[1]=1; + lab[2]=1; + lab[3]=0; + lab[4]=2; + lab[5]=0; + lab[6]=0; + + CMulticlassLabels* labels=new CMulticlassLabels(); + labels->set_int_labels(lab); + + CSoftMaxLikelihood* sml=new CSoftMaxLikelihood(); + SGVector data_vector=SGVector(data.matrix,data.num_rows*data.num_cols,false); + SGVector v=sml->get_log_probability_derivative_f(labels,data_vector,1); + + float64_t ep=0.0001; + EXPECT_NEAR(v[0],-0.1382,ep); + EXPECT_NEAR(v[1],-0.0138,ep); + EXPECT_NEAR(v[2],-0.4887,ep); + EXPECT_NEAR(v[3],0.5638,ep); + EXPECT_NEAR(v[4],-0.2384,ep); + EXPECT_NEAR(v[5],0.2435,ep); + EXPECT_NEAR(v[6],0.8669,ep); + EXPECT_NEAR(v[7],-0.2803,ep); + EXPECT_NEAR(v[8],0.7945,ep); + EXPECT_NEAR(v[9],0.8404,ep); + EXPECT_NEAR(v[10],-0.4309,ep); + EXPECT_NEAR(v[11],-0.2136,ep); + EXPECT_NEAR(v[12],-0.1865,ep); + EXPECT_NEAR(v[13],-0.6249,ep); + EXPECT_NEAR(v[14],0.4184,ep); + EXPECT_NEAR(v[15],-0.7807,ep); + EXPECT_NEAR(v[16],-0.3517,ep); + EXPECT_NEAR(v[17],-0.1329,ep); + EXPECT_NEAR(v[18],0.4520,ep); + EXPECT_NEAR(v[19],-0.0571,ep); + EXPECT_NEAR(v[20],-0.2420,ep); + + SG_UNREF(sml); + SG_UNREF(labels); +} + +TEST(SoftMaxLikelihood,get_log_derivatives_second) +{ + SGMatrix data(7,3); + + data(0,0)=-0.8095; + data(1,0)=-2.9443; + data(2,0)=1.4384; + data(3,0)=0.3252; + data(4,0)=-0.7549; + data(5,0)=1.3703; + data(6,0)=-1.7115; + + data(0,1)=-0.1022; + data(1,1)=-0.2414; + data(2,1)=0.3192; + data(3,1)=0.3129; + data(4,1)=-0.8649; + data(5,1)=-0.0301; + data(6,1)=-0.1649; + + data(0,2)=0.6277; + data(1,2)=1.0933; + data(2,2)=1.1093; + data(3,2)=-0.8637; + data(4,2)=0.0774; + data(5,2)=-1.2141; + data(6,2)=-1.1135; + + SGVector lab(7); + lab[0]=2; + lab[1]=1; + lab[2]=1; + lab[3]=0; + lab[4]=2; + lab[5]=0; + lab[6]=0; + + CMulticlassLabels* labels=new CMulticlassLabels(); + labels->set_int_labels(lab); + + CSoftMaxLikelihood* sml=new CSoftMaxLikelihood(); + SGVector data_vector=SGVector(data.matrix,data.num_rows*data.num_cols,false); + SGVector v=sml->get_log_probability_derivative_f(labels,data_vector,2); + + float64_t ep=0.0001; + EXPECT_NEAR(v[0],-0.1191,ep); + EXPECT_NEAR(v[1],0.0387,ep); + EXPECT_NEAR(v[2],0.0804,ep); + EXPECT_NEAR(v[3],-0.0136,ep); + EXPECT_NEAR(v[4],0.0028,ep); + EXPECT_NEAR(v[5],0.0108,ep); + EXPECT_NEAR(v[6],-0.2499,ep); + EXPECT_NEAR(v[7],0.0780,ep); + EXPECT_NEAR(v[8],0.1719,ep); + EXPECT_NEAR(v[9],-0.2459,ep); + EXPECT_NEAR(v[10],0.1880,ep); + EXPECT_NEAR(v[11],0.0580,ep); + EXPECT_NEAR(v[12],-0.1816,ep); + EXPECT_NEAR(v[13],0.0509,ep); + EXPECT_NEAR(v[14],0.1306,ep); + EXPECT_NEAR(v[15],-0.1842,ep); + EXPECT_NEAR(v[16],0.1411,ep); + EXPECT_NEAR(v[17],0.0432,ep); + EXPECT_NEAR(v[18],-0.1154,ep); + EXPECT_NEAR(v[19],0.0832,ep); + EXPECT_NEAR(v[20],0.0322,ep); + EXPECT_NEAR(v[21],0.0387,ep); + EXPECT_NEAR(v[22],-0.2017,ep); + EXPECT_NEAR(v[23],0.1630,ep); + EXPECT_NEAR(v[24],0.0028,ep); + EXPECT_NEAR(v[25],-0.1633,ep); + EXPECT_NEAR(v[26],0.1604,ep); + EXPECT_NEAR(v[27],0.0780,ep); + EXPECT_NEAR(v[28],-0.1341,ep); + EXPECT_NEAR(v[29],0.0561,ep); + EXPECT_NEAR(v[30],0.1880,ep); + EXPECT_NEAR(v[31],-0.2452,ep); + EXPECT_NEAR(v[32],0.0573,ep); + EXPECT_NEAR(v[33],0.0509,ep); + EXPECT_NEAR(v[34],-0.1680,ep); + EXPECT_NEAR(v[35],0.1170,ep); + EXPECT_NEAR(v[36],0.1411,ep); + EXPECT_NEAR(v[37],-0.1517,ep); + EXPECT_NEAR(v[38],0.0106,ep); + EXPECT_NEAR(v[39],0.0832,ep); + EXPECT_NEAR(v[40],-0.2344,ep); + EXPECT_NEAR(v[41],0.1512,ep); + EXPECT_NEAR(v[42],0.0804,ep); + EXPECT_NEAR(v[43],0.1630,ep); + EXPECT_NEAR(v[44],-0.2433,ep); + EXPECT_NEAR(v[45],0.0108,ep); + EXPECT_NEAR(v[46],0.1604,ep); + EXPECT_NEAR(v[47],-0.1712,ep); + EXPECT_NEAR(v[48],0.1719,ep); + EXPECT_NEAR(v[49],0.0561,ep); + EXPECT_NEAR(v[50],-0.2280,ep); + EXPECT_NEAR(v[51],0.0580,ep); + EXPECT_NEAR(v[52],0.0573,ep); + EXPECT_NEAR(v[53],-0.1152,ep); + EXPECT_NEAR(v[54],0.1306,ep); + EXPECT_NEAR(v[55],0.1170,ep); + EXPECT_NEAR(v[56],-0.2477,ep); + EXPECT_NEAR(v[57],0.0432,ep); + EXPECT_NEAR(v[58],0.0106,ep); + EXPECT_NEAR(v[59],-0.0538,ep); + EXPECT_NEAR(v[60],0.0322,ep); + EXPECT_NEAR(v[61],0.1512,ep); + EXPECT_NEAR(v[62],-0.1834,ep); + + SG_UNREF(sml); + SG_UNREF(labels); +} + +TEST(SoftMaxLikelihood,get_log_derivatives_third) +{ + SGMatrix data(7,3); + + data(0,0)=-0.8095; + data(1,0)=-2.9443; + data(2,0)=1.4384; + data(3,0)=0.3252; + data(4,0)=-0.7549; + data(5,0)=1.3703; + data(6,0)=-1.7115; + + data(0,1)=-0.1022; + data(1,1)=-0.2414; + data(2,1)=0.3192; + data(3,1)=0.3129; + data(4,1)=-0.8649; + data(5,1)=-0.0301; + data(6,1)=-0.1649; + + data(0,2)=0.6277; + data(1,2)=1.0933; + data(2,2)=1.1093; + data(3,2)=-0.8637; + data(4,2)=0.0774; + data(5,2)=-1.2141; + data(6,2)=-1.1135; + + SGVector lab(7); + lab[0]=2; + lab[1]=1; + lab[2]=1; + lab[3]=0; + lab[4]=2; + lab[5]=0; + lab[6]=0; + + CMulticlassLabels* labels=new CMulticlassLabels(); + labels->set_int_labels(lab); + + CSoftMaxLikelihood* sml=new CSoftMaxLikelihood(); + SGVector data_vector=SGVector(data.matrix,data.num_rows*data.num_cols,false); + SGVector v=sml->get_log_probability_derivative_f(labels,data_vector,3); + + float64_t ep=0.0001; + EXPECT_NEAR(v[0],0.0862,ep); + EXPECT_NEAR(v[1],-0.0280,ep); + EXPECT_NEAR(v[2],-0.0581,ep); + EXPECT_NEAR(v[3],-0.0280,ep); + EXPECT_NEAR(v[4],-0.0170,ep); + EXPECT_NEAR(v[5],0.0450,ep); + EXPECT_NEAR(v[6],-0.0581,ep); + EXPECT_NEAR(v[7],0.0450,ep); + EXPECT_NEAR(v[8],0.0131,ep); + EXPECT_NEAR(v[9],-0.0280,ep); + EXPECT_NEAR(v[10],-0.0170,ep); + EXPECT_NEAR(v[11],0.0450,ep); + EXPECT_NEAR(v[12],-0.0170,ep); + EXPECT_NEAR(v[13],0.0886,ep); + EXPECT_NEAR(v[14],-0.0716,ep); + EXPECT_NEAR(v[15],0.0450,ep); + EXPECT_NEAR(v[16],-0.0716,ep); + EXPECT_NEAR(v[17],0.0266,ep); + EXPECT_NEAR(v[18],-0.0581,ep); + EXPECT_NEAR(v[19],0.0450,ep); + EXPECT_NEAR(v[20],0.0131,ep); + EXPECT_NEAR(v[21],0.0450,ep); + EXPECT_NEAR(v[22],-0.0716,ep); + EXPECT_NEAR(v[23],0.0266,ep); + EXPECT_NEAR(v[24],0.0131,ep); + EXPECT_NEAR(v[25],0.0266,ep); + EXPECT_NEAR(v[26],-0.0397,ep); + EXPECT_NEAR(v[27],0.0132,ep); + EXPECT_NEAR(v[28],-0.0028,ep); + EXPECT_NEAR(v[29],-0.0105,ep); + EXPECT_NEAR(v[30],-0.0028,ep); + EXPECT_NEAR(v[31],-0.0017,ep); + EXPECT_NEAR(v[32],0.0044,ep); + EXPECT_NEAR(v[33],-0.0105,ep); + EXPECT_NEAR(v[34],0.0044,ep); + EXPECT_NEAR(v[35],0.0060,ep); + EXPECT_NEAR(v[36],-0.0028,ep); + EXPECT_NEAR(v[37],-0.0017,ep); + EXPECT_NEAR(v[38],0.0044,ep); + EXPECT_NEAR(v[39],-0.0017,ep); + EXPECT_NEAR(v[40],0.0962,ep); + EXPECT_NEAR(v[41],-0.0945,ep); + EXPECT_NEAR(v[42],0.0044,ep); + EXPECT_NEAR(v[43],-0.0945,ep); + EXPECT_NEAR(v[44],0.0901,ep); + EXPECT_NEAR(v[45],-0.0105,ep); + EXPECT_NEAR(v[46],0.0044,ep); + EXPECT_NEAR(v[47],0.0060,ep); + EXPECT_NEAR(v[48],0.0044,ep); + EXPECT_NEAR(v[49],-0.0945,ep); + EXPECT_NEAR(v[50],0.0901,ep); + EXPECT_NEAR(v[51],0.0060,ep); + EXPECT_NEAR(v[52],0.0901,ep); + EXPECT_NEAR(v[53],-0.0961,ep); + EXPECT_NEAR(v[54],0.0056,ep); + EXPECT_NEAR(v[55],-0.0018,ep); + EXPECT_NEAR(v[56],-0.0039,ep); + EXPECT_NEAR(v[57],-0.0018,ep); + EXPECT_NEAR(v[58],-0.0531,ep); + EXPECT_NEAR(v[59],0.0549,ep); + EXPECT_NEAR(v[60],-0.0039,ep); + EXPECT_NEAR(v[61],0.0549,ep); + EXPECT_NEAR(v[62],-0.0510,ep); + EXPECT_NEAR(v[63],-0.0018,ep); + EXPECT_NEAR(v[64],-0.0531,ep); + EXPECT_NEAR(v[65],0.0549,ep); + EXPECT_NEAR(v[66],-0.0531,ep); + EXPECT_NEAR(v[67],0.0913,ep); + EXPECT_NEAR(v[68],-0.0382,ep); + EXPECT_NEAR(v[69],0.0549,ep); + EXPECT_NEAR(v[70],-0.0382,ep); + EXPECT_NEAR(v[71],-0.0166,ep); + EXPECT_NEAR(v[72],-0.0039,ep); + EXPECT_NEAR(v[73],0.0549,ep); + EXPECT_NEAR(v[74],-0.0510,ep); + EXPECT_NEAR(v[75],0.0549,ep); + EXPECT_NEAR(v[76],-0.0382,ep); + EXPECT_NEAR(v[77],-0.0166,ep); + EXPECT_NEAR(v[78],-0.0510,ep); + EXPECT_NEAR(v[79],-0.0166,ep); + EXPECT_NEAR(v[80],0.0676,ep); + EXPECT_NEAR(v[81],0.0314,ep); + EXPECT_NEAR(v[82],-0.0240,ep); + EXPECT_NEAR(v[83],-0.0074,ep); + EXPECT_NEAR(v[84],-0.0240,ep); + EXPECT_NEAR(v[85],-0.0260,ep); + EXPECT_NEAR(v[86],0.0500,ep); + EXPECT_NEAR(v[87],-0.0074,ep); + EXPECT_NEAR(v[88],0.0500,ep); + EXPECT_NEAR(v[89],-0.0426,ep); + EXPECT_NEAR(v[90],-0.0240,ep); + EXPECT_NEAR(v[91],-0.0260,ep); + EXPECT_NEAR(v[92],0.0500,ep); + EXPECT_NEAR(v[93],-0.0260,ep); + EXPECT_NEAR(v[94],0.0339,ep); + EXPECT_NEAR(v[95],-0.0079,ep); + EXPECT_NEAR(v[96],0.0500,ep); + EXPECT_NEAR(v[97],-0.0079,ep); + EXPECT_NEAR(v[98],-0.0420,ep); + EXPECT_NEAR(v[99],-0.0074,ep); + EXPECT_NEAR(v[100],0.0500,ep); + EXPECT_NEAR(v[101],-0.0426,ep); + EXPECT_NEAR(v[102],0.0500,ep); + EXPECT_NEAR(v[103],-0.0079,ep); + EXPECT_NEAR(v[104],-0.0420,ep); + EXPECT_NEAR(v[105],-0.0426,ep); + EXPECT_NEAR(v[106],-0.0420,ep); + EXPECT_NEAR(v[107],0.0846,ep); + EXPECT_NEAR(v[108],0.0950,ep); + EXPECT_NEAR(v[109],-0.0266,ep); + EXPECT_NEAR(v[110],-0.0684,ep); + EXPECT_NEAR(v[111],-0.0266,ep); + EXPECT_NEAR(v[112],-0.0292,ep); + EXPECT_NEAR(v[113],0.0558,ep); + EXPECT_NEAR(v[114],-0.0684,ep); + EXPECT_NEAR(v[115],0.0558,ep); + EXPECT_NEAR(v[116],0.0125,ep); + EXPECT_NEAR(v[117],-0.0266,ep); + EXPECT_NEAR(v[118],-0.0292,ep); + EXPECT_NEAR(v[119],0.0558,ep); + EXPECT_NEAR(v[120],-0.0292,ep); + EXPECT_NEAR(v[121],0.0962,ep); + EXPECT_NEAR(v[122],-0.0670,ep); + EXPECT_NEAR(v[123],0.0558,ep); + EXPECT_NEAR(v[124],-0.0670,ep); + EXPECT_NEAR(v[125],0.0112,ep); + EXPECT_NEAR(v[126],-0.0684,ep); + EXPECT_NEAR(v[127],0.0558,ep); + EXPECT_NEAR(v[128],0.0125,ep); + EXPECT_NEAR(v[129],0.0558,ep); + EXPECT_NEAR(v[130],-0.0670,ep); + EXPECT_NEAR(v[131],0.0112,ep); + EXPECT_NEAR(v[132],0.0125,ep); + EXPECT_NEAR(v[133],0.0112,ep); + EXPECT_NEAR(v[134],-0.0238,ep); + EXPECT_NEAR(v[135],-0.0945,ep); + EXPECT_NEAR(v[136],0.0724,ep); + EXPECT_NEAR(v[137],0.0221,ep); + EXPECT_NEAR(v[138],0.0724,ep); + EXPECT_NEAR(v[139],-0.0885,ep); + EXPECT_NEAR(v[140],0.0161,ep); + EXPECT_NEAR(v[141],0.0221,ep); + EXPECT_NEAR(v[142],0.0161,ep); + EXPECT_NEAR(v[143],-0.0382,ep); + EXPECT_NEAR(v[144],0.0724,ep); + EXPECT_NEAR(v[145],-0.0885,ep); + EXPECT_NEAR(v[146],0.0161,ep); + EXPECT_NEAR(v[147],-0.0885,ep); + EXPECT_NEAR(v[148],0.0951,ep); + EXPECT_NEAR(v[149],-0.0067,ep); + EXPECT_NEAR(v[150],0.0161,ep); + EXPECT_NEAR(v[151],-0.0067,ep); + EXPECT_NEAR(v[152],-0.0094,ep); + EXPECT_NEAR(v[153],0.0221,ep); + EXPECT_NEAR(v[154],0.0161,ep); + EXPECT_NEAR(v[155],-0.0382,ep); + EXPECT_NEAR(v[156],0.0161,ep); + EXPECT_NEAR(v[157],-0.0067,ep); + EXPECT_NEAR(v[158],-0.0094,ep); + EXPECT_NEAR(v[159],-0.0382,ep); + EXPECT_NEAR(v[160],-0.0094,ep); + EXPECT_NEAR(v[161],0.0477,ep); + EXPECT_NEAR(v[162],0.0847,ep); + EXPECT_NEAR(v[163],-0.0610,ep); + EXPECT_NEAR(v[164],-0.0236,ep); + EXPECT_NEAR(v[165],-0.0610,ep); + EXPECT_NEAR(v[166],0.0208,ep); + EXPECT_NEAR(v[167],0.0403,ep); + EXPECT_NEAR(v[168],-0.0236,ep); + EXPECT_NEAR(v[169],0.0403,ep); + EXPECT_NEAR(v[170],-0.0166,ep); + EXPECT_NEAR(v[171],-0.0610,ep); + EXPECT_NEAR(v[172],0.0208,ep); + EXPECT_NEAR(v[173],0.0403,ep); + EXPECT_NEAR(v[174],0.0208,ep); + EXPECT_NEAR(v[175],-0.0586,ep); + EXPECT_NEAR(v[176],0.0378,ep); + EXPECT_NEAR(v[177],0.0403,ep); + EXPECT_NEAR(v[178],0.0378,ep); + EXPECT_NEAR(v[179],-0.0780,ep); + EXPECT_NEAR(v[180],-0.0236,ep); + EXPECT_NEAR(v[181],0.0403,ep); + EXPECT_NEAR(v[182],-0.0166,ep); + EXPECT_NEAR(v[183],0.0403,ep); + EXPECT_NEAR(v[184],0.0378,ep); + EXPECT_NEAR(v[185],-0.0780,ep); + EXPECT_NEAR(v[186],-0.0166,ep); + EXPECT_NEAR(v[187],-0.0780,ep); + EXPECT_NEAR(v[188],0.0947,ep); + + SG_UNREF(sml); + SG_UNREF(labels); +} + +#endif /* HAVE_EIGEN3 */ From 24271f7124ec0fb9871be69c1977f7c4a58661f2 Mon Sep 17 00:00:00 2001 From: hwl596 Date: Sun, 16 Mar 2014 13:48:27 -0400 Subject: [PATCH 04/13] Update RandomKitchenSinksDotFeatures.h Fixed "bullet lists" errors and a subscript error in Doxygen documentation. --- .../features/RandomKitchenSinksDotFeatures.h | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/shogun/features/RandomKitchenSinksDotFeatures.h b/src/shogun/features/RandomKitchenSinksDotFeatures.h index be7e132a05b..93e17cd7a4d 100644 --- a/src/shogun/features/RandomKitchenSinksDotFeatures.h +++ b/src/shogun/features/RandomKitchenSinksDotFeatures.h @@ -20,17 +20,17 @@ namespace shogun * as mentioned in http://books.nips.cc/papers/files/nips21/NIPS2008_0885.pdf. * * The Random Kitchen Sinks algorithm expects: - * a dataset to work on - * a function phi such that |phi(x; a)| <= 1, the a's are the function parameters - * a probability distrubution p, from which to draw the a's - * the number of samples K to draw from p. + * - a dataset to work on + * - a function phi such that |phi(x; a)| <= 1, the a's are the function parameters + * - a probability distrubution p, from which to draw the a's + * - the number of samples K to draw from p. * * Then: - * it draws K a's from p - * it computes for each vector in the dataset - * Zi = [phi(Xi;a0), ..., phi(Xi;aK)] - * and then solves the empirical risk minimization problem for all Zi, either - * through least squares or through a linear SVM. + * 1. it draws K a's from p + * 2. it computes for each vector in the dataset + * Zi = [phi(Xi;a1), ..., phi(Xi;aK)] + * 3. it solves the empirical risk minimization problem for all Zi, either + * through least squares or through a linear SVM. * * This class implements the vector transformation on-the-fly whenever it is needed. * In order for it to work, the class expects the user to implement a subclass of From 18577fd2af26f94df33c878321e1d472d929a40c Mon Sep 17 00:00:00 2001 From: Thoralf Klein Date: Sun, 16 Mar 2014 22:55:34 +0100 Subject: [PATCH 05/13] Fixing memory leak in NCBM solver. --- src/shogun/structure/libncbm.cpp | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/src/shogun/structure/libncbm.cpp b/src/shogun/structure/libncbm.cpp index ddca4f82570..6928e0c79ea 100644 --- a/src/shogun/structure/libncbm.cpp +++ b/src/shogun/structure/libncbm.cpp @@ -321,7 +321,9 @@ BmrmStatistics svm_ncbm_solver( { BmrmStatistics ncbm; libqp_state_T qp_exitflag={0, 0, 0, 0}; - int32_t w_dim = machine->get_model()->get_dim(); + + CStructuredModel* model = machine->get_model(); + int32_t w_dim = model->get_dim(); maxCPs = _BufSize; BufSize = _BufSize; @@ -756,6 +758,17 @@ BmrmStatistics svm_ncbm_solver( LIBBMRM_FREE(icp_stats.ACPs); LIBBMRM_FREE(icp_stats.H_buff); + cp_ptr=CPList_head; + while(cp_ptr!=NULL) + { + bmrm_ll * cp_ptr_this=cp_ptr; + cp_ptr=cp_ptr->next; + LIBBMRM_FREE(cp_ptr_this); + cp_ptr_this=NULL; + } + + SG_UNREF(model); + return ncbm; } From 4a55113899d63fc41ea12b1b6e86e00ecfeacf7a Mon Sep 17 00:00:00 2001 From: hwl596 Date: Sun, 16 Mar 2014 23:02:50 -0400 Subject: [PATCH 06/13] Update RandomKitchenSinksDotFeatures.h Add more details on RKS algorithm. --- .../features/RandomKitchenSinksDotFeatures.h | 33 +++++++++++-------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/src/shogun/features/RandomKitchenSinksDotFeatures.h b/src/shogun/features/RandomKitchenSinksDotFeatures.h index 93e17cd7a4d..696039cbd43 100644 --- a/src/shogun/features/RandomKitchenSinksDotFeatures.h +++ b/src/shogun/features/RandomKitchenSinksDotFeatures.h @@ -16,25 +16,32 @@ namespace shogun { -/** @brief class that implements the Random Kitchen Sinks for the DotFeatures +/** @brief class that implements the Random Kitchen Sinks (RKS) for the DotFeatures * as mentioned in http://books.nips.cc/papers/files/nips21/NIPS2008_0885.pdf. * - * The Random Kitchen Sinks algorithm expects: - * - a dataset to work on - * - a function phi such that |phi(x; a)| <= 1, the a's are the function parameters - * - a probability distrubution p, from which to draw the a's - * - the number of samples K to draw from p. + * RKS input: + * - a dataset $\{x_i, y_i\}_{i=1,\dots,m}$ of $m$ points to work on + * - $\phi(x; w)$: a bounded feature function s.t. $|\phi(x; w)| \leq 1$, where $w$ is the function parameter + * - $p(w)$: a probability distrubution function, from which to draw the $w$ + * - $K$: the number of samples to draw from $p(w)$ + * - $C$: a scalar, which is chosen to be large enough in practice. * - * Then: - * 1. it draws K a's from p - * 2. it computes for each vector in the dataset - * Zi = [phi(Xi;a1), ..., phi(Xi;aK)] - * 3. it solves the empirical risk minimization problem for all Zi, either - * through least squares or through a linear SVM. + * RKS output: + * A function $\hat{f}(x) = \sum_{k=1}^{K} \phi(x; w_k)\alpha_k$ + * 1. Draw $w_1,\dots,w_K$ iid from $p(w)$ + * 2. Featurize the input: $z_i = [\phi(x_i; w_1),\dots,\phi(x_i; w_K)]^{\top}$ + * 3. With $w$ fixed, solve the empirical risk minimization problem: + * \begin{equation} + * \underset{\alpha \in \mathbf{R}^K}{\text{minimize}} \quad \frac{1}{m}\sum_{i=1}^{m} c(\alpha^{\top} z_i, y_i) + * \end{equation} + * \begin{equation} + * \text{s.t.} \quad \|\alpha\|_{\infty} \leq C/K. + * \end{equation} + * for vector $\alpha$, either through least squares when $c(y', y)$ is the quadratic loss or through a linear SVM when $c(y', y)$ is the hinge loss. * * This class implements the vector transformation on-the-fly whenever it is needed. * In order for it to work, the class expects the user to implement a subclass of - * CRKSFunctions and implement in there the functions phi and p and then pass an + * CRKSFunctions and implement in there the functions $\phi$ and $p$ and then pass an * instantiated object of that class to the constructor. * * Further useful resources, include : From 5b5334d4ef3dc26a649ea2b750ec7814ebd441d6 Mon Sep 17 00:00:00 2001 From: Parijat Mazumdar Date: Mon, 17 Mar 2014 17:20:35 +0530 Subject: [PATCH 07/13] licence added + log_sum_exp trick added --- src/shogun/machine/gp/SoftMaxLikelihood.cpp | 44 +++++++++++++++++-- src/shogun/machine/gp/SoftMaxLikelihood.h | 31 +++++++++++++ .../machine/gp/SoftMaxLikelihood_unittest.cc | 30 +++++++++++++ 3 files changed, 102 insertions(+), 3 deletions(-) diff --git a/src/shogun/machine/gp/SoftMaxLikelihood.cpp b/src/shogun/machine/gp/SoftMaxLikelihood.cpp index b8d3e6281c8..fd2d7012d90 100644 --- a/src/shogun/machine/gp/SoftMaxLikelihood.cpp +++ b/src/shogun/machine/gp/SoftMaxLikelihood.cpp @@ -1,3 +1,33 @@ +/* + * Copyright (c) The Shogun Machine Learning Toolbox + * Written (w) 2014 Parijat Mazumdar + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * The views and conclusions contained in the software and documentation are those + * of the authors and should not be interpreted as representing official policies, + * either expressed or implied, of the Shogun Development Team. + */ + #include #ifdef HAVE_EIGEN3 @@ -27,11 +57,19 @@ SGVector CSoftMaxLikelihood::get_log_probability_f(const CLabels* lab for (int32_t i=0;i-1)&&(labels[i] eigen_f(func.vector,labels.vlen,func.vlen/labels.vlen); - VectorXd log_sum_exp=((eigen_f.array().exp()).rowwise().sum()).log(); + + // log_sum_exp trick + VectorXd max_coeff=eigen_f.rowwise().maxCoeff(); + eigen_f=eigen_f-max_coeff*MatrixXd::Ones(1,eigen_f.cols()); + VectorXd log_sum_exp=((eigen_f.array().exp()).rowwise().sum()).array().log(); + log_sum_exp=log_sum_exp+max_coeff; + + // restore original matrix + eigen_f=eigen_f+max_coeff*MatrixXd::Ones(1,eigen_f.cols()); SGVector ret=SGVector(labels.vlen); Map eigen_ret(ret.vector,ret.vlen); @@ -72,7 +110,7 @@ SGVector CSoftMaxLikelihood::get_log_probability_derivative1_f( for (int32_t i=0;i-1)&&(labels[i] ret=SGVector(func.num_rows*func.num_cols); memcpy(ret.vector,func.matrix,func.num_rows*func.num_cols*sizeof(float64_t)); diff --git a/src/shogun/machine/gp/SoftMaxLikelihood.h b/src/shogun/machine/gp/SoftMaxLikelihood.h index 3a873224ca2..5bf0c10ecfb 100644 --- a/src/shogun/machine/gp/SoftMaxLikelihood.h +++ b/src/shogun/machine/gp/SoftMaxLikelihood.h @@ -1,3 +1,33 @@ +/* + * Copyright (c) The Shogun Machine Learning Toolbox + * Written (w) 2014 Parijat Mazumdar + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * The views and conclusions contained in the software and documentation are those + * of the authors and should not be interpreted as representing official policies, + * either expressed or implied, of the Shogun Development Team. + */ + #ifndef _SOFTMAXLIKELIHOOD_H_ #define _SOFTMAXLIKELIHOOD_H_ @@ -22,6 +52,7 @@ class CSoftMaxLikelihood : public CLikelihoodModel /** default constructor */ CSoftMaxLikelihood(); + /** destructor */ virtual ~CSoftMaxLikelihood(); /** returns the name of the likelihood model diff --git a/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc b/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc index 8e9f8282e51..4be5b718f7f 100644 --- a/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc +++ b/tests/unit/machine/gp/SoftMaxLikelihood_unittest.cc @@ -1,3 +1,33 @@ +/* + * Copyright (c) The Shogun Machine Learning Toolbox + * Written (w) 2014 Parijat Mazumdar + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * The views and conclusions contained in the software and documentation are those + * of the authors and should not be interpreted as representing official policies, + * either expressed or implied, of the Shogun Development Team. + */ + #include #ifdef HAVE_EIGEN3 From f1e6d43f130072c169ccdc254864cc6fbfd4ce89 Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Mon, 17 Mar 2014 23:46:52 +0530 Subject: [PATCH 08/13] pca ipython notebook revised --- data | 2 +- doc/ipython-notebooks/pca/pca_notebook.ipynb | 1164 ++++++++++++++---- 2 files changed, 921 insertions(+), 245 deletions(-) diff --git a/data b/data index 6615cf00763..464bbd7c277 160000 --- a/data +++ b/data @@ -1 +1 @@ -Subproject commit 6615cf007634595d459853bf4dc6f1a227d2450c +Subproject commit 464bbd7c2776b50e1271a4330cae8814faa340c3 diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb index 99ea0bc1cd6..e9aafb4bb87 100644 --- a/doc/ipython-notebooks/pca/pca_notebook.ipynb +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" + "signature": "sha256:a4af83329559fc62ad25f232e7026f28d4a0477cbf33bc4b68427c32ed11eff4" }, "nbformat": 3, "nbformat_minor": 0, @@ -10,73 +10,235 @@ "cells": [ { "cell_type": "heading", - "level": 3, + "level": 1, "metadata": {}, "source": [ - "Principal Component Analysis" + "Principal Component Analysis in Shogun" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "By Abhijeet Kislay (GitHub ID: kislayabhi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + "This notebook is about finding Principal Components of data. It's dimensional reduction capabilities are further utilised to show it's application in data compression, image processing and face recognition. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# import all shogun classes\n", + "from modshogun import *" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Some Formal Background (Skip if you just want code examples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + "PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.\n", + "\n", + "In machine learning problems data is often high dimensional - images, bag-of-word descriptions etc. In such cases we cannot expect the training data to densely populate the space, meaning that there will be large parts in which little is known about the data. Hence it is expected that only a small number of directions are relevant for describing the data to a reasonable accuracy.\n", + "\n", + "Whilst the data vectors may be very high dimensional, they will therefore typically lie closer to a much lower dimensional 'manifold'.\n", + "Here we concentrate on linear dimensional reduction techniques. In this approach a high dimensional datapoint $\\mathbf{x}$ is 'projected down' to a lower dimensional vector $\\mathbf{y}$ by:\n", + "$$\\mathbf{y}=\\mathbf{F}\\mathbf{x}+\\text{const}.$$\n", + "where the matrix $\\mathbf{F}\\in\\mathbb{R}^{\\text{M}\\times \\text{D}}$, with $\\text{M}<\\text{D}$. Here $\\text{M}=\\dim(\\mathbf{y})$ and $\\text{D}=\\dim(\\mathbf{x})$.\n", + "\n", + "From the above scenario, we infer that\n", + "\n", + "* The number of principal components to use is $\\text{M}$.\n", + "* The dimension of each data point is $\\text{D}$.\n", + "* The number of data points is $\\text{N}$.\n", + "\n", + "We express the approximation for datapoint $\\mathbf{x}^n$ as:$$\\mathbf{x}^n \\approx \\mathbf{c} + \\sum\\limits_{i=1}^{\\text{M}}y_i^n \\mathbf{b}^i \\equiv \\tilde{\\mathbf{x}}^n.$$\n", + "* Here the vector $\\mathbf{c}$ is a constant and defines a point in the hyperplane.\n", + "* The $\\mathbf{b}^i$ define vectors in the hyperplane (also known as 'principal component coefficients' or 'loadings').\n", + "* The $y_i^n$ are the low dimensional co-ordinates of the data.\n", + "\n", + "Hence our motive is to find the reconstruction $\\tilde{\\mathbf{x}}^n$ given the lower dimensional representation $\\mathbf{y}^n$(which has components $y_i^n,i = 1,...,\\text{M})$. For a data space of dimension $\\dim(\\mathbf{x})=\\text{D}$, we hope to accurately describe the data using only a small number$(\\text{M}\\ll \\text{D})$ of coordinates of $\\mathbf{y}$.\n", + "To determine the best lower dimensional representation it is convenient to use the square distance error between $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:$$\\text{E}(\\mathbf{B},\\mathbf{Y},\\mathbf{c})=\\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}[x_i^n - \\tilde{x}_i^n]^2.$$\n", + "* Here the best basis vectors are defined as $\\mathbf{B} = [\\mathbf{b}^1,...,\\mathbf{b}^\\text{M}]$ (defining $[\\text{B}]_{i,j} = b_i^j$).\n", + "* Corresponding low dimensional coordinates are defined as $\\mathbf{Y} = [\\mathbf{y}^1,...,\\mathbf{y}^\\text{N}].$\n", + "* Also, $x_i^n$ and $\\tilde{x}_i^n$ represents the coordinates of the data points for the original and the reconstructed data respectively.\n", + "* The optimal bias $\\mathbf{c}$ is given by the mean of the data $\\sum_n\\mathbf{x}^n/\\text{N}$.\n", + "\n", + "Therefore, for simplification purposes we centre our data, so as to set $\\mathbf{c}$ to zero. Now we concentrate on finding the optimal basis $\\mathbf{B}$( which has the components $\\mathbf{b}^i, i=1,...,\\text{M} $).\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Deriving the optimal linear reconstruction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + "To find the best basis vectors $\\mathbf{B}$ and corresponding low dimensional coordinates $\\mathbf{Y}$, we may minimize the sum of squared differences between each vector $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:\n", + "\n", + "$\\text{E}(\\mathbf{B},\\mathbf{Y}) = \\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}\\left[x_i^n - \\sum\\limits_{j=1}^{\\text{M}}y_j^nb_i^j\\right]^2 = \\text{trace} \\left( (\\mathbf{X}-\\mathbf{B}\\mathbf{Y})^T(\\mathbf{X}-\\mathbf{B}\\mathbf{Y}) \\right)$\n", + "\n", + "where $\\mathbf{X} = [\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n", + "Considering the above equation under the orthonormality constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$ ( i.e the basis vectors are mutually orthogonal and of unit length ), we differentiate it w.r.t $y_k^n$. The squared error $\\text{E}(\\mathbf{B},\\mathbf{Y})$ therefore has zero derivative when: \n", + "\n", + "$y_k^n = \\sum_i b_i^kx_i^n$\n", + "\n", + "By substituting this solution in the above equation, the objective becomes\n", + "\n", + "$\\text{E}(\\mathbf{B}) = (\\text{N}-1)\\left[\\text{trace}(\\mathbf{S}) - \\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)\\right],$\n", + "\n", + "where $\\mathbf{S}$ is the sample covariance matrix of the data.\n", + "To minimise equation under the constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$, we use a set of Lagrange Multipliers $\\mathbf{L}$, so that the objective is to minimize: \n", + "\n", + "$-\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)+\\text{trace}\\left(\\mathbf{L}\\left(\\mathbf{B}^T\\mathbf{B} - \\mathbf{I}\\right)\\right).$\n", + "\n", + "Since the constraint is symmetric, we can assume that $\\mathbf{L}$ is also symmetric. Differentiating with respect to $\\mathbf{B}$ and equating to zero we obtain that at the optimum \n", + "\n", + "$\\mathbf{S}\\mathbf{B} = \\mathbf{B}\\mathbf{L}$.\n", + "\n", + "This is a form of eigen-equation so that a solution is given by taking $\\mathbf{L}$ to be diagonal and $\\mathbf{B}$ as the matrix whose columns are the corresponding eigenvectors of $\\mathbf{S}$. In this case,\n", + "\n", + "$\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right) =\\text{trace}(\\mathbf{L}),$\n", + "\n", + "which is the sum of the eigenvalues corresponding to the eigenvectors forming $\\mathbf{B}$. Since we wish to minimise $\\text{E}(\\mathbf{B})$, we take the eigenvectors with the largest corresponding eigenvalues.\n", + "Whilst the solution to this eigen-problem is unique, this only serves to define the solution subspace since one may rotate and scale $\\mathbf{B}$ and $\\mathbf{Y}$ such that the value of the squared loss is exactly the same. The justification for choosing the non-rotated eigen solution is given by the additional requirement that the principal components corresponds to directions of maximal variance." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Maximum variance criterion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + "We aim to find that single direction $\\mathbf{b}$ such that, when the data is projected onto this direction, the variance of this projection is maximal amongst all possible such projections.\n", + "The projection of a datapoint onto a direction $\\mathbf{b}$ is $\\mathbf{b}^T\\mathbf{x}^n$ for a unit length vector $\\mathbf{b}$. Hence the sum of squared projections is: $$\\sum\\limits_{n}\\left(\\mathbf{b}^T\\mathbf{x}^n\\right)^2 = \\mathbf{b}^T\\left[\\sum\\limits_{n}\\mathbf{x}^n(\\mathbf{x}^n)^T\\right]\\mathbf{b} = (\\text{N}-1)\\mathbf{b}^T\\mathbf{S}\\mathbf{b} = \\lambda(\\text{N} - 1)$$ \n", + "which ignoring constants, is simply the negative of the equation for a single retained eigenvector $\\mathbf{b}$(with $\\mathbf{S}\\mathbf{b} = \\lambda\\mathbf{b}$). Hence the optimal single $\\text{b}$ which maximises the projection variance is given by the eigenvector corresponding to the largest eigenvalues of $\\mathbf{S}.$ The second largest eigenvector corresponds to the next orthogonal optimal direction and so on. This explains why, despite the squared loss equation being invariant with respect to arbitrary rotation of the basis vectors, the ones given by the eigen-decomposition have the additional property that they correspond to directions of maximal variance. These maximal variance directions found by PCA are called the $\\text{principal} $ $\\text{directions}.$\n", + "\n", + "There are two eigenvalue methods through which shogun can perform PCA namely\n", + "* Eigenvalue Decomposition Method.\n", + "* Singular Value Decomposition.\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "EGD vs SVD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The EGD viewpoint requires that one compute the eigenvalues and eigenvectors of the covariance matrix, which is the product of $\\mathbf{X}\\mathbf{X}^\\text{T}$, where $\\mathbf{X}$ is the data matrix. Since the covariance matrix is symmetric, the matrix is diagonalizable, and the eigenvectors can be normalized such that they are orthonormal:\n", + "\n", + "$\\mathbf{S}=\\frac{1}{\\text{N}-1}\\mathbf{X}\\mathbf{X}^\\text{T},$\n", + "\n", + "where the $\\text{D}\\times\\text{N}$ matrix $\\mathbf{X}$ contains all the data vectors: $\\mathbf{X}=[\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n", + "Writing the $\\text{D}\\times\\text{N}$ matrix of eigenvectors as $\\mathbf{E}$ and the eigenvalues as an $\\text{N}\\times\\text{N}$ diagonal matrix $\\mathbf{\\Lambda}$, the eigen-decomposition of the covariance $\\mathbf{S}$ is\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{X}^\\text{T}\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\tilde{\\mathbf{E}}=\\tilde{\\mathbf{E}}\\mathbf{\\Lambda},$\n", + "\n", + "where we defined $\\tilde{\\mathbf{E}}=\\mathbf{X}^\\text{T}\\mathbf{E}$. The final expression above represents the eigenvector equation for $\\mathbf{X}^\\text{T}\\mathbf{X}.$ This is a matrix of dimensions $\\text{N}\\times\\text{N}$ so that calculating the eigen-decomposition takes $\\mathcal{O}(\\text{N}^3)$ operations, compared with $\\mathcal{O}(\\text{D}^3)$ operations in the original high-dimensional space. We then can therefore calculate the eigenvectors $\\tilde{\\mathbf{E}}$ and eigenvalues $\\mathbf{\\Lambda}$ of this matrix more easily. Once found, we use the fact that the eigenvalues of $\\mathbf{S}$ are given by the diagonal entries of $\\mathbf{\\Lambda}$ and the eigenvectors by\n", + "\n", + "$\\mathbf{E}=\\mathbf{X}\\tilde{\\mathbf{E}}\\mathbf{\\Lambda}^{-1}$\n", + "\n", + "\n", + "\n", + "\n", + "* On the other hand, applying SVD to the data matrix $\\mathbf{X}$ follows like:\n", + "\n", + "$\\mathbf{X}=\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}$\n", + "\n", + "where $\\mathbf{U}^\\text{T}\\mathbf{U}=\\mathbf{I}_\\text{D}$ and $\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}_\\text{N}$ and $\\mathbf{\\Sigma}$ is a diagonal matrix of the (positive) singular values. We assume that the decomposition has ordered the singular values so that the upper left diagonal element of $\\mathbf{\\Sigma}$ contains the largest singular value.\n", + "\n", + "Attempting to construct the covariance matrix $(\\mathbf{X}\\mathbf{X}^\\text{T})$from this decomposition gives:\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)^\\text{T}$\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{V}\\mathbf{\\Sigma}\\mathbf{U}^\\text{T}\\right)$\n", + "\n", + "and since $\\mathbf{V}$ is an orthogonal matrix $\\left(\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}\\right),$\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T}=\\left(\\mathbf{U}\\mathbf{\\Sigma}^\\mathbf{2}\\mathbf{U}^\\text{T}\\right)$\n", + "\n", + "Since it is in the form of an eigen-decomposition, the PCA solution given by performing the SVD decomposition of $\\mathbf{X}$, for which the eigenvectors are then given by $\\mathbf{U}$, and corresponding eigenvalues by the square of the singular values.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "PCA on 2D data" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 1: Get some data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's already make it working!!" + "We will generate the toy data by adding orthogonal noise to a set of points lying on an arbitrary 2d line. We expect PCA to recover this line, which is a one-dimensional linear sub-space." ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets generate the toy data.\n", - "import numpy as np\n", - "from modshogun import *\n", - "\n", - "def randrange(n, vmin, vmax):\n", - " return (vmax-vmin)*np.random.rand(n)+vmin\n", - "\n", - "# number of data points:\n", + "#number of data points.\n", "n=100\n", "\n", - "#generate random 2d data\n", - "xs = randrange(n,-20,+20)\n", - "ys = randrange(n,-20,+20)\n", + "#generate a random 2d line(y1 = mx1 + c)\n", + "m=np.random.randint(1,10)\n", + "c=np.random.randint(1,10)\n", + "x1=np.random.random_integers(-20,20,n)\n", + "y1=m*x1+c\n", + "\n", + "#generate the noise.\n", + "noise=np.random.random_sample([n])*np.random.random_integers(-35,35,n)\n", "\n", - "#subtract the mean from this\n", - "mxs=mean(xs)\n", - "xs = xs - np.tile(mxs,[n,1]).T[0]\n", - "mys=mean(ys)\n", - "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "#make the noise orthogonal to the line y=mx+c and add it.\n", + "x=x1 + noise*m/np.sqrt(1+np.square(m))\n", + "y=y1 + noise/np.sqrt(1+np.square(m))\n", "\n", - "#form the data matrix\n", - "twoD_obsmatrix=np.array([xs, ys])\n" + "twoD_obsmatrix=np.array([x,y])" ], "language": "python", "metadata": {}, @@ -86,195 +248,517 @@ "cell_type": "code", "collapsed": false, "input": [ - "#lets visualise the given data.\n", + "#to visualise the data we must plot it.\n", "from matplotlib import pyplot\n", - "pyplot.ion() #to set the matplotlib's interactive mode on!\n", - "figure, axis = pyplot.subplots(1,1)\n", - "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + "pylab.rcParams['figure.figsize'] = 7, 7 \n", + "figure,axis=pyplot.subplots(1,1)\n", + "pyplot.xlim(-50,50)\n", + "pyplot.ylim(-50,50)\n", + "axis.plot(twoD_obsmatrix[0,:],twoD_obsmatrix[1,:],'o',color='green',markersize=6)\n", + "\n", + "#the line from which we generated the data is plotted in red\n", + "axis.plot(x1[:],y1[:],linewidth=0.3,color='red')\n", + "pyplot.title('One-Dimensional sub-space with noise')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 2: Subtract the mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For PCA to work properly, we must subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. So, all the $x$ values have $\\bar{x}$ subtracted, and all the $y$ values have $\\bar{y}$ subtracted from them, where:$$\\bar{\\mathbf{x}} = \\frac{\\sum\\limits_{i=1}^{n}x_i}{n}$$ $\\bar{\\mathbf{x}}$ denotes the mean of the $x_i^{'s}$" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "#Preprocessor PCA performs principial component analysis on input\n", + "#feature vectors/matrices.\n", + "train_features = RealFeatures(twoD_obsmatrix)\n", + "preprocessor = PCA()\n", "\n", - "def apply_pca_to_data(target_dims, data_matrix):\n", - " train_features = RealFeatures(data_matrix)\n", - " submean = PruneVarSubMean(False)\n", - " submean.init(train_features)\n", - " submean.apply_to_feature_matrix(train_features)\n", - " preprocessor = PCA()\n", - " preprocessor.set_target_dim(target_dims)\n", - " preprocessor.init(train_features)\n", - " eigenvectors = preprocessor.get_transformation_matrix()\n", - " preprocessor.apply_to_feature_matrix(train_features)\n", - " return train_features,eigenvectors\n", - "\n" + "#since we are projecting down the 2d data, the target dim is 1. But here the exhaustive method is detailed by\n", + "#setting the target dimension to 2 to visualize both the eigen vectors.\n", + "#However, in future examples we will get rid of this step by implementing it directly.\n", + "preprocessor.set_target_dim(2)\n", + "\n", + "#When the init method in PCA is called with proper\n", + "#feature matrix X (with say N number of vectors and D feature dimension), a\n", + "#transformation matrix is computed and stored internally.It inherenty also\n", + "#centralizes the data by subtracting the mean from it.\n", + "preprocessor.init(train_features)\n", + "\n", + "#get the mean for the respective dimensions.\n", + "mean_datapoints=preprocessor.get_mean()\n", + "mean_x=mean_datapoints[0]\n", + "mean_y=mean_datapoints[1]" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 3: Calculate the covariance matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand the relationship between 2 dimension we define $\\text{covariance}$. It is a measure to find out how much the dimensions vary from the mean $with$ $respect$ $to$ $each$ $other.$$$cov(X,Y)=\\frac{\\sum\\limits_{i=1}^{n}(X_i-\\bar{X})(Y_i-\\bar{Y})}{n-1}$$\n", + "A useful way to get all the possible covariance values between all the different dimensions is to calculate them all and put them in a matrix.\n", + "\n", + "Example: For a 3d dataset with usual dimensions of $x,y$ and $z$, the covariance matrix has 3 rows and 3 columns, and the values are this:\n", + "$$\\mathbf{S} = \\quad\\begin{pmatrix}cov(x,x)&cov(x,y)&cov(x,z)\\\\cov(y,x)&cov(y,y)&cov(y,z)\\\\cov(z,x)&cov(z,y)&cov(z,z)\\end{pmatrix}$$\n", + "\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the eigenvectors $e^1,....e^M$ of the covariance matrix $\\mathbf{S}$." + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "#apply pca\n", - "#the target_dims is made 1 for this 2d data.\n", - "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + "#Step 3 and step 4 are directly implemented by the PCA preprocessor of Shogun toolbar.\n", + "\n", + "#The transformation matrix is essentially a DxM matrix, the columns of which correspond\n", + "#to the eigenvectors of the covariance matrix(XX') having top M eigenvalues.\n", + "E = preprocessor.get_transformation_matrix()\n", + "\n", + "#Get all the eigenvalues returned by PCA.\n", + "eig_value=preprocessor.get_eigenvalues()\n", + "\n", + "e1 = E[:,0]\n", + "e2 = E[:,1]\n", + "eig_value1 = eig_value[0]\n", + "eig_value2 = eig_value[1]" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Steps 5: Choosing components and forming a feature vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets visualize the eigenvectors and decide upon which to choose as the $principle$ $component$ of the data set." + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", - "eig1=eig\n", + "#find out the M eigenvectors corresponding to top M number of eigenvalues and store it in E\n", + "#Here M=1\n", "\n", - "#hence we need to take only that set of weights which corresponds to eig1.\n", - "y1=y[0,:]\n", - "\n" + "#slope of e1 & e2\n", + "m1=e1[1]/e1[0]\n", + "m2=e2[1]/e2[0]\n", + "\n", + "#generate the two lines\n", + "x1=range(-50,50)\n", + "x2=x1\n", + "y1=np.multiply(m1,x1)\n", + "y2=np.multiply(m2,x2)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the data along with those two eigenvectors\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-50, 50)\n", + "pyplot.ylim(-50, 50)\n", + "axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n", + "axis.plot(x1[:], y1[:], linewidth=0.7, color='black')\n", + "axis.plot(x2[:], y2[:], linewidth=0.7, color='blue')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"black\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"blue\")\n", + "legend([p1,p2],[\"1st eigenvector\",\"2nd eigenvector\"])\n", + "pyplot.title('Eigenvectors selection')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above figure, the blue line is a good fit of the data. It shows the most significant relationship between the data dimensions.\n", + "It turns out that the eigenvector with the $highest$ eigenvalue is the $principle$ $component$ of the data set.\n", + "Form the matrix $\\mathbf{E}=[\\mathbf{e}^1,...,\\mathbf{e}^M].$\n", + "Here $\\text{M}$ represents the target dimension of our final projection" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# y is just the weights that each eigenvector carries. i.e\n", - "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", - "# dimension reduction).\n", - "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", - "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "#The eigenvector corresponding to higher eigenvalue(i.e eig_value2) is choosen(i.e e2).\n", + "#E is the feature vector.\n", + "E=e2" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step6: Deriving the new data set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the final step in PCA. Once we have choosen the components(eigenvectors) that we wish to keep in our data and formed a feature vector, we simply take the vector and multiply it on the left of the original dataset.\n", + "The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by \n", "\n", + "$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$\n", "\n", + "Here the $\\mathbf{E}^T$ is the matrix with the eigenvectors in rows, with the most significant eigenvector at the top. The mean adjusted data, with data items in each column, with each row holding a seperate dimension is multiplied to it." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#This can be performed by shogun's PCA preprocessor as follows:\n", + "#The transformation matrix that we got after init is used to transform all D-dimensional feature\n", + "#matrices (with D feature dimensions) supplied via apply_to_feature_matrix methods.This tranformation\n", + "#outputs the M-Dimensional approximation of all these input vectors and matrices (where M<=min(D,N)).\n", + "yn=preprocessor.apply_to_feature_matrix(train_features)\n", "\n", - "#we visualise this:\n", + "#Since, here we are manually trying to find the eigenvector corresponding to top eigenvalue\n", + "#The 2nd row of yn is choosen as it corresponds to the required eigenvector e2.\n", + "yn1=yn[1,:]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 5 and Step 6 can be applied directly with Shogun's PCA preprocessor(from next example). It has been done manually here to show the exhaustive nature of Principal Component Analysis." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x_new=(yn1 * E[0])+np.tile(mean_x,[n,1]).T[0]\n", + "y_new=(yn1 * E[1])+np.tile(mean_y,[n,1]).T[0]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new data is plotted below" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ "figure, axis = pyplot.subplots(1,1)\n", - "pyplot.xlim(-20, 20)\n", - "pyplot.ylim(-20, 20)\n", - "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", - "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", - "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "pyplot.xlim(-50, 50)\n", + "pyplot.ylim(-50, 50)\n", + "\n", + "axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n", + "axis.plot(x_new, y_new, 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('PCA Projection of 2D data into 1D subspace')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")\n", + "\n", + "#add some legend for information\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", "\n", - "\n", - "#we are plotting the projection here:\n", + "#plot the projections in red:\n", "for i in range(n):\n", - " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + " axis.plot([x[i],x_new[i]],[y[i],y_new[i]] , color='red')" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "PCA on a 3d data." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step1: Get some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We generate points from a plane and then add random noise orthogonal to it. The general equation of a plane is: $$\\text{a}\\mathbf{x}+\\text{b}\\mathbf{y}+\\text{c}\\mathbf{z}+\\text{d}=0$$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#number of points\n", + "n=100\n", "\n", + "#generate the data\n", + "a=np.random.randint(1,20)\n", + "b=np.random.randint(1,20)\n", + "c=np.random.randint(1,20)\n", + "d=np.random.randint(1,20)\n", "\n", + "x1=np.random.random_integers(-20,20,n)\n", + "y1=np.random.random_integers(-20,20,n)\n", + "z1=-(a*x1+b*y1+d)/c\n", "\n", - "\n" + "#generate the noise\n", + "noise=np.random.random_sample([n])*np.random.random_integers(-30,30,n)\n", + "\n", + "#the normal unit vector is [a,b,c]/magnitude\n", + "magnitude=np.sqrt(np.square(a)+np.square(b)+np.square(c))\n", + "normal_vec=np.array([a,b,c]/magnitude)\n", + "\n", + "#add the noise orthogonally\n", + "x=x1+noise*normal_vec[0]\n", + "y=y1+noise*normal_vec[1]\n", + "z=z1+noise*normal_vec[2]\n", + "threeD_obsmatrix=np.array([x,y,z])" ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to visualize the data, we must plot it.\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "\n", + "#plot the noisy data generated by distorting a plane\n", + "ax.scatter(x, y, z,marker='o', color='g')\n", + "\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])\n", + "pyplot.title('Two dimensional subspace with noise')\n", + "xx, yy = np.meshgrid(range(-30,30), range(-30,30))\n", + "zz=-(a * xx + b * yy + d) / c\n", + "ax.set_xlim(-30,30)\n", + "ax.set_ylim(-30,30)\n", + "ax.set_zlim(-30,30)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 2: Subtract the mean." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Shogun's PCA preprocessor also centralizes the data by subtracting the mean from it.\n", + "train_features = RealFeatures(threeD_obsmatrix)\n", + "preprocessor = PCA()\n", + "\n", + "#If we set the target dimension to 2, Shogun would automagically preserve the required 2 eigenvectors(out of 3) according to their\n", + "#eigenvalues.\n", + "preprocessor.set_target_dim(2)\n", + "preprocessor.init(train_features)\n", + "\n", + "#get the mean for the respective dimensions.\n", + "mean_datapoints=preprocessor.get_mean()\n", + "mean_x=mean_datapoints[0]\n", + "mean_y=mean_datapoints[1]\n", + "mean_z=mean_datapoints[2]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 3 & Step 4: Calculate the eigenvectors of the covariance matrix" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#get the required eigenvectors corresponding to top 2 eigenvalues.\n", + "E = preprocessor.get_transformation_matrix()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Steps 5: Choosing components and forming a feature vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we performed PCA for a target $\\dim = 2$ for the $3 \\dim$ data, we are directly given \n", + "the two required eigenvectors in $\\mathbf{E}$" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", - "\n", - "#we generate the height of the previous data.\n", - "zs=randrange(n, -5, +5)\n", - "mzs=mean(zs)\n", - "zs = zs - np.tile(mzs,[n,1]).T[0]\n", - "threeD_obsmatrix=array([xs, ys, zs])" + "#E is automagically filled by setting target dimension = M. This is different from the 2d data example \n", + "#where we implemented this step manually." ], "language": "python", "metadata": {}, "outputs": [] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#for plotting the 3d data, we import Axes3D from matplotlib module\n", - "from mpl_toolkits.mplot3d import Axes3D" - ], - "language": "python", + "cell_type": "heading", + "level": 4, "metadata": {}, - "outputs": [] + "source": [ + "Step 6: Deriving the new data set\n" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#plot the 3d data\n", - "fig = pyplot.figure()\n", - "ax=fig.add_subplot(111, projection='3d')\n", - "ax.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax.set_xlabel('x label')\n", - "ax.set_ylabel('y label')\n", - "ax.set_zlabel('z label')\n", - "legend([p2],[\"3d data\"])" + "#This can be performed by shogun's PCA preprocessor as follows:\n", + "yn=preprocessor.apply_to_feature_matrix(train_features)" ], "language": "python", "metadata": {}, "outputs": [] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#again applying the previous methadology, we proceed by applying the pca\n", - "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" - ], - "language": "python", + "cell_type": "heading", + "level": 4, "metadata": {}, - "outputs": [] + "source": [ + "Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", - "#are to be taken according to their eigenvalues.\n", - "\n", - "#1st eigenvactor\n", - "eig1=eig[:,0]\n", - "\n", - "#2nd eigenvactor\n", - "eig2=eig[:,1]\n", - "\n", - "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", - "\n", - "#1st set of weights\n", - "y1=y[0,:]\n", - "\n", - "#2nd set of weights\n", - "y2=y[1,:]\n" - ], - "language": "python", + "cell_type": "markdown", "metadata": {}, - "outputs": [] + "source": [ + "The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets visualise the output:\n", - "fig=pyplot.figure()\n", - "ax1=fig.add_subplot(111, projection='3d')\n", - "legend([p3],[\"2d projected data points\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " \n" + "new_data=np.dot(E,yn)\n", + "\n", + "x_new=new_data[0,:]+np.tile(mean_x,[n,1]).T[0]\n", + "y_new=new_data[1,:]+np.tile(mean_y,[n,1]).T[0]\n", + "z_new=new_data[2,:]+np.tile(mean_z,[n,1]).T[0]" ], "language": "python", "metadata": {}, @@ -284,160 +768,249 @@ "cell_type": "code", "collapsed": false, "input": [ - "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "#all the above points lie on the same plane. To make it more clear we will plot the projection also.\n", + "\n", "fig=pyplot.figure()\n", - "ax2=fig.add_subplot(111, projection='3d')\n", - "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax2.set_xlabel('x label')\n", - "ax2.set_ylabel('y label')\n", - "ax2.set_zlabel('z label')\n", + "pyplot.title('PCA Projection of 3D data into 2D subspace')\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(x, y, z,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", "\n", - "\n" + "for i in range(100):\n", + " ax.scatter(x_new[i], y_new[i], z_new[i],marker='o', color='b')\n", + " ax.plot([x[i],x_new[i]],[y[i],y_new[i]],[z[i],z_new[i]],color='r') \n", + "ax.set_xlim(-30,30)\n", + "ax.set_ylim(-30,30)\n", + "ax.set_zlim(-30,30)" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "PCA Performance" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Enough of the toy data! Lets work on something real.\n", + "Performance of PCA depends on the algorithm used according to the situation in hand.\n", + "Our PCA preprocessor class provides 3 method options to compute the transformation matrix:\n", "\n", - "Here we will show the implementation of PCA for data compression and basic face recognition.\n", - "\n" + "* $\\text{EVD}$ : Eigen Value Decomposition of Covariance Matrix $(\\mathbf{XX^T}).$\n", + "The covariance matrix $\\mathbf{XX^T}$ is first formed internally and then\n", + "its eigenvectors and eigenvalues are computed using QR decomposition of the matrix.\n", + "The time complexity of this method is $\\mathcal{O}(D^3)$ and should be used when $\\text{N > D.}$\n", + "\n", + "\n", + "* $\\text{SVD}$ : Singular Value Decomposition of feature matrix $\\mathbf{X}$.\n", + "The transpose of feature matrix, $\\mathbf{X^T}$, is decomposed using SVD. $\\mathbf{X^T = UDV^T}.$\n", + "The matrix V in this decomposition contains the required eigenvectors and\n", + "the diagonal entries of the diagonal matrix D correspond to the non-negative\n", + "eigenvalues.The time complexity of this method is $\\mathcal{O}(DN^2)$ and should be used when $\\text{N < D.}$\n", + "\n", + "\n", + "* $\\text{AUTO}$ : This mode automagically chooses one of the above modes for the user based on whether $\\text{N>D}$ (chooses $\\text{EVD}$) or $\\text{ND)}$ but for the next example $\\text{(N Date: Tue, 18 Mar 2014 00:04:16 +0530 Subject: [PATCH 09/13] removed the .ipynb_checkpoints --- .../pca_notebook-checkpoint.ipynb | 587 ------------------ 1 file changed, 587 deletions(-) delete mode 100644 doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb diff --git a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb deleted file mode 100644 index 99ea0bc1cd6..00000000000 --- a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb +++ /dev/null @@ -1,587 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Principal Component Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's already make it working!!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets generate the toy data.\n", - "import numpy as np\n", - "from modshogun import *\n", - "\n", - "def randrange(n, vmin, vmax):\n", - " return (vmax-vmin)*np.random.rand(n)+vmin\n", - "\n", - "# number of data points:\n", - "n=100\n", - "\n", - "#generate random 2d data\n", - "xs = randrange(n,-20,+20)\n", - "ys = randrange(n,-20,+20)\n", - "\n", - "#subtract the mean from this\n", - "mxs=mean(xs)\n", - "xs = xs - np.tile(mxs,[n,1]).T[0]\n", - "mys=mean(ys)\n", - "ys = ys - np.tile(mys,[n,1]).T[0]\n", - "\n", - "#form the data matrix\n", - "twoD_obsmatrix=np.array([xs, ys])\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets visualise the given data.\n", - "from matplotlib import pyplot\n", - "pyplot.ion() #to set the matplotlib's interactive mode on!\n", - "figure, axis = pyplot.subplots(1,1)\n", - "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", - "\n", - "def apply_pca_to_data(target_dims, data_matrix):\n", - " train_features = RealFeatures(data_matrix)\n", - " submean = PruneVarSubMean(False)\n", - " submean.init(train_features)\n", - " submean.apply_to_feature_matrix(train_features)\n", - " preprocessor = PCA()\n", - " preprocessor.set_target_dim(target_dims)\n", - " preprocessor.init(train_features)\n", - " eigenvectors = preprocessor.get_transformation_matrix()\n", - " preprocessor.apply_to_feature_matrix(train_features)\n", - " return train_features,eigenvectors\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#apply pca\n", - "#the target_dims is made 1 for this 2d data.\n", - "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", - "eig1=eig\n", - "\n", - "#hence we need to take only that set of weights which corresponds to eig1.\n", - "y1=y[0,:]\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# y is just the weights that each eigenvector carries. i.e\n", - "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", - "# dimension reduction).\n", - "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", - "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", - "\n", - "\n", - "\n", - "#we visualise this:\n", - "figure, axis = pyplot.subplots(1,1)\n", - "pyplot.xlim(-20, 20)\n", - "pyplot.ylim(-20, 20)\n", - "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", - "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", - "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", - "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", - "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", - "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", - "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", - "\n", - "\n", - "#we are plotting the projection here:\n", - "for i in range(n):\n", - " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", - "\n", - "\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", - "\n", - "#we generate the height of the previous data.\n", - "zs=randrange(n, -5, +5)\n", - "mzs=mean(zs)\n", - "zs = zs - np.tile(mzs,[n,1]).T[0]\n", - "threeD_obsmatrix=array([xs, ys, zs])" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#for plotting the 3d data, we import Axes3D from matplotlib module\n", - "from mpl_toolkits.mplot3d import Axes3D" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#plot the 3d data\n", - "fig = pyplot.figure()\n", - "ax=fig.add_subplot(111, projection='3d')\n", - "ax.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax.set_xlabel('x label')\n", - "ax.set_ylabel('y label')\n", - "ax.set_zlabel('z label')\n", - "legend([p2],[\"3d data\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#again applying the previous methadology, we proceed by applying the pca\n", - "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", - "#are to be taken according to their eigenvalues.\n", - "\n", - "#1st eigenvactor\n", - "eig1=eig[:,0]\n", - "\n", - "#2nd eigenvactor\n", - "eig2=eig[:,1]\n", - "\n", - "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", - "\n", - "#1st set of weights\n", - "y1=y[0,:]\n", - "\n", - "#2nd set of weights\n", - "y2=y[1,:]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets visualise the output:\n", - "fig=pyplot.figure()\n", - "ax1=fig.add_subplot(111, projection='3d')\n", - "legend([p3],[\"2d projected data points\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " \n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", - "fig=pyplot.figure()\n", - "ax2=fig.add_subplot(111, projection='3d')\n", - "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax2.set_xlabel('x label')\n", - "ax2.set_ylabel('y label')\n", - "ax2.set_zlabel('z label')\n", - "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enough of the toy data! Lets work on something real.\n", - "\n", - "Here we will show the implementation of PCA for data compression and basic face recognition.\n", - "\n" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Eigenfaces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", - "\n", - "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", - "\n", - "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "\n", - "#lets start with data preparation.\n", - "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", - "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", - "\n", - "def get_imlist(path):\n", - " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", - " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", - "\n", - "#set path to the required folder.\n", - "path='../../../data/att_dataset/training/'\n", - "\n", - "#set no. of rows that the images will be resized.\n", - "k1=100\n", - "#set no. of columns that the images will be resized.\n", - "k2=100\n", - "\n", - "filenames = get_imlist(path)\n", - "filenames = np.array(filenames)\n", - "# n is total number of images that has to be analysed.\n", - "n=len(filenames)\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# we will be using this often to visualise the images out there.\n", - "def showfig(image):\n", - " imgplot=plt.imshow(image, cmap='gray')\n", - " imgplot.axes.get_xaxis().set_visible(False)\n", - " imgplot.axes.get_yaxis().set_visible(False)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import Image\n", - "from scipy import misc\n", - "\n", - "# to get a hang of the data, lets see some part of the dataset images.\n", - "fig = plt.figure()\n", - "\n", - "for i in range(49):\n", - " fig.add_subplot(7,7,i)\n", - " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", - " train_img=misc.imresize(train_img, [k1,k2])\n", - " showfig(train_img)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#read each and every image. flatten them to make row vectors and stack them together \n", - "#to form the observation matrix obs_matrix.\n", - "\n", - "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", - "train_img=misc.imresize(train_img, [k1,k2])\n", - "train_img=np.array(train_img, dtype='double')\n", - "train_img=train_img.flatten()\n", - "\n", - "for i in range(1,n):\n", - " temp=np.array(Image.open(filenames[i]).convert('L')) \n", - " temp=misc.imresize(temp, [k1,k2])\n", - " temp=np.array(temp, dtype='double')\n", - " temp=temp.flatten()\n", - " train_img=np.vstack([train_img,temp])\n", - " \n", - "obs_matrix=train_img.T\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Again applying the PCA on the data, the same way we were doing for the last \n", - "# two times.\n", - "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", - "\n", - "y,eig= apply_pca_to_data(100,obs_matrix)\n", - "res=y.get_feature_matrix()\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets see how these eigenfaces/eigenvectors look like:\n", - "fig1 = plt.figure()\n", - "plt.title('top 20 eigenfaces')\n", - "\n", - "\n", - "for i in range(20):\n", - " a = fig1.add_subplot(5,4,i+1)\n", - " eigen_faces=eig[:,i].reshape([k1,k2])\n", - " showfig(eigen_faces)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", - "# which when added provides us with a reconstruction of the original image.\n", - "\n", - "reconstructed_vector=np.mat(eig)*np.mat(res)\n", - "\n", - "#plot the reconstructed images to visualise it. \n", - "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", - "fig2=plt.figure()\n", - "for i in range(1,50):\n", - " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", - " fig2.add_subplot(7,7,i)\n", - " showfig(reconstructed_image)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Face Recognition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets use this technique of eigenfaces to perform some basic face recognition.\n", - "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#get the test image in the list test_file.\n", - "test_files= get_imlist('../../../data/att_dataset/testing/')\n", - "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", - "\n", - "#we plot the test image , for which we have to identify a good match from the training images we already have\n", - "fig = plt.figure()\n", - "t_img=plt.imshow(test_img, cmap='gray')\n", - "plt.title('the test image for which a match is to be identified')\n", - "t_img.axes.get_xaxis().set_visible(False)\n", - "t_img.axes.get_yaxis().set_visible(False)\n", - "\n", - "# we flatten out or test image just the way we have done for the other images\n", - "test_image=misc.imresize(test_img, [k1,k2])\n", - "test_image=test_image.flatten()\n", - "test_image=np.array(test_image, dtype='double')\n", - "test_image=np.mat(test_image).transpose()\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we have to project our training images as well as the test image on the PCA subspace." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#projecting our training images into pca subspace\n", - "train_proj=np.dot(eig.T , obs_matrix)\n", - "\n", - "#projecting our test image into pca subspace\n", - "test_proj=np.dot(eig.T , test_image)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", - "#between test image and all the training images.\n", - "#The training image which produces the least distance measure with our test image \n", - "#is said to be the best match.\n", - "testfeat = RealFeatures(np.array(test_proj))\n", - "workfeat = RealFeatures(np.array(train_proj))\n", - "RaRb=EuclideanDistance(testfeat, workfeat)\n", - "\n", - "d=np.empty([n,1])\n", - "for i in range(n):\n", - " d[i]= RaRb.distance(0,i)\n", - " \n", - "\n", - "iden=np.array(Image.open(filenames[d.argmin()]))\n", - "i_img=plt.imshow(iden, cmap='gray')\n", - "plt.title('identified image from our set of training images')\n", - "i_img.axes.get_xaxis().set_visible(False)\n", - "i_img.axes.get_yaxis().set_visible(False)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file From 0eac536af84f4ceea53f84a9a235054e7708c4c8 Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Sat, 8 Mar 2014 12:22:37 +0530 Subject: [PATCH 10/13] Added Documentation regarding issue #1878 Added 'pca_notebook.ipynb' named python notebook in doc/ipython-notebooks/pca Implemented PCA on toy data for 2d to 1d and 3d to 2d projection. Implemented Eigenfaces for data compression and face recognition using att_face dataset. --- doc/ipython-notebooks/pca/pca_notebook.ipynb | 719 +++++++++++++++++++ 1 file changed, 719 insertions(+) create mode 100644 doc/ipython-notebooks/pca/pca_notebook.ipynb diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb new file mode 100644 index 00000000000..48cad6cedb9 --- /dev/null +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -0,0 +1,719 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:f5a08ebf445e92d5b42170c036c6da61b1e4d555d91b29483e006d7e34834339" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's already make it working!!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets generate the toy data.\n", + "import numpy as np\n", + "\n", + "\n", + "def randrange(n, vmin, vmax):\n", + " return (vmax-vmin)*np.random.rand(n)+vmin\n", + "\n", + "# number of data points:\n", + "n=100\n", + "\n", + "#generate random 2d data\n", + "xs = randrange(n,-20,+20)\n", + "ys = randrange(n,-20,+20)\n", + "\n", + "#subtract the mean from this\n", + "mxs=mean(xs)\n", + "xs = xs - np.tile(mxs,[n,1]).T[0]\n", + "mys=mean(ys)\n", + "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "\n", + "#form the data matrix\n", + "twoD_obsmatrix=np.array([xs, ys])\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the given data.\n", + "from matplotlib import pyplot\n", + "pyplot.ion() #to set the matplotlib's interactive mode on!\n", + "figure, axis = pyplot.subplots(1,1)\n", + "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "\n", + "def apply_pca_to_data(target_dims, data_matrix):\n", + " #from modshogun import RealFeatures\n", + " #from modshogun import PruneVarSubMean\n", + " train_features = RealFeatures(data_matrix)\n", + " submean = PruneVarSubMean(False)\n", + " submean.init(train_features)\n", + " submean.apply_to_feature_matrix(train_features)\n", + " preprocessor = PCA()\n", + " preprocessor.set_target_dim(target_dims)\n", + " preprocessor.init(train_features)\n", + " eigenvectors = preprocessor.get_transformation_matrix()\n", + " preprocessor.apply_to_feature_matrix(train_features)\n", + " return train_features,eigenvectors\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#apply pca\n", + "#the target_dims is made 1 for this 2d data.\n", + "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", + "eig1=eig\n", + "\n", + "#hence we need to take only that set of weights which corresponds to eig1.\n", + "y1=y[0,:]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# y is just the weights that each eigenvector carries. i.e\n", + "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", + "# dimension reduction).\n", + "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", + "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "\n", + "\n", + "\n", + "#we visualise this:\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-20, 20)\n", + "pyplot.ylim(-20, 20)\n", + "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", + "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", + "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", + "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", + "\n", + "\n", + "#we are plotting the projection here:\n", + "for i in range(n):\n", + " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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g4kRg7Vpda8KoRygdold3BWb9roeBxn1fGodod3d3rFq1Cu3atYOVlRX8/f1R\nVFTEn9+0aROaN28OGxsbDBs2DGnlwqPr6enh22+/RfPmzdGyZUvExsbCxcUFK1asgJ2dHZycnHDg\nwAEcPXoULVq0gI2NDZYvX87nP3fuHLp16wapVAonJyfMnDkTJSUlgvT28fFBWFgYXnvtNVhaWmL4\n8OF8PJjExETo6elhy5YtcHNzQ79+/UBEWLp0Kdzd3WFvb4+goCA8fvxYJb1CoQAA5OTkYOLEiXBy\ncoKLiwsWLlzIn1PWiaenJywsLODl5YULFy4gMDAQycnJGDJkCMzNzbFy5cpKclNTUzF06FDY2Nig\nefPm+O6773iZERER8PPzQ1BQECwsLNC6dWv8+++/NVfE6NHA9euC6oxRjyBGg6BObtXdu0RSKdGj\nR+KXxWgwrItaR/2C+1HKK82Jvv9ea3LVtWkARCJ+NHmO3N3d6bXXXqO0tDTKysqiVq1a0fr164mI\nKDo6mmQyGV24cIGKiopo5syZ1KtXLz6vRCIhX19fys7OpsLCQjp9+jQZGBjQkiVLqLS0lDZt2kQ2\nNjY0ZswYysvLo6tXr5KxsTElJiYSEdG///5Lf//9N5WVlVFiYiK1atWK1qxZoyL/9u3bavX29vYm\nZ2dnunr1KuXn59PIkSNp3LhxRESUkJBAEomEgoKCqKCggJ48eUKbN28mDw8PSkhIoLy8PBoxYgQF\nBgaqpC8rKyMiouHDh1NISAgVFBTQw4cPqUuXLrRhwwYiItq9ezc5OzvTP//8Q0RE8fHxlJSUxNdl\ndHQ0r2NFuT179qTp06dTUVERXbx4kWxtbenUqVNERBQeHk6NGzemY8eOkUKhoLCwMOratav6tlOR\nTz6pm76ToTXY3Wog1NmDFRBAtGpV3ZTFaFgcOED06qtECoVWxDUEo2Tnzp388QcffEAhISFERDRh\nwgSaN28efy4vL48MDQ35l7BEIqHTp0/z50+fPk3GxsakeFp3jx8/JolEQufOnePTdOrUiQ4cOKBW\nl9WrV9Nbb73FH1dnlPj4+FBYWBh/fO3aNWrUqBEpFAreGEhISODP9+nTh9atW8cf37x5kwwNDams\nrEzFeLh//z4ZGRnRkydP+LS7du2i3r17ExGRr68vrV27Vq1O1RklycnJpK+vT3l5efz5sLAwCg4O\nJiLOKOnfvz9/TmnAVUTtvc3IYEZJA4NN3zBUmT2bm8Kpx/PgDB0xeDCQnQ388YeuNakzHBwc+P+N\njY2Rn58x1jl4AAAgAElEQVQPAEhLS4Obmxt/ztTUFDY2NkhJSeG/c3V1VZFlY2PDO3YaP93Xxd7e\nXq38uLg4DB48GI6OjrC0tMSHH36IzMxMwXqXL1sul6OkpAQZGRlqz1e8FrlcjtLSUjx48EBFZlJS\nEkpKSuDo6AipVAqpVIqQkBCkp6cDAO7du4dmzZoJ1lFJamoqrK2tYWpqqqJD+bosX08mJiYoLCxU\nmTaqEhsbjfVh6BZmlDBUefVVwMUF2L9f15ow6hv6+lxgKuY8CCcnJyQmJvLH+fn5yMzMhHO50Oa1\nWVkybdo0eHp6Ij4+Hjk5Ofjkk0+EvYSfkpycrPK/oaEhZDKZWt0qXktycjIMDAxUDAGAM2SMjIyQ\nmZmJ7OxsZGdnIycnB//99x9/Pj4+Xq0+1dWFk5MTsrKykJeXp6KDi4uLsItlvFAwo4RRmTlz2IuH\noZ7gYCAmBkhI0LUmOoGeriAJCAhAZGQkLl26hKKiIixYsABdu3aFXC7XSjl5eXkwNzeHiYkJbty4\ngXXr1mmk444dO3D9+nUUFBTg448/xqhRo6o0DAICArB69WokJiYiLy8PCxYsgL+/P/T0VF8Pjo6O\n8PX1xZw5c5CbmwuFQoHbt2/jzJkzAIBJkyZh5cqVOH/+PIgI8fHxvHFkb2+P27dvqy3f1dUV3bt3\nR1hYGIqKinD58mVs2bIF48aNE3zNjBcHZpQwKjNsGHD/PvDXX7rWhFHfMDMDJkwQbZWW1NwcEkC0\nj9Tc/Ll1U8Y6AYC+fftiyZIlGDlyJJycnJCQkIAffvhBJa26/NUdl2flypXYtWsXLCwsMGXKFPj7\n+6ukry6vRCJBYGAggoOD4ejoiOLiYqwtd78q5p0wYQICAwPRq1cvNG3aFCYmJvjqq6/Uyt62bRuK\ni4vh6ekJa2trjBo1Cvfv3wcAvP322/jwww8xZswYWFhYYMSIEfyqn7CwMCxduhRSqRRffPFFJT2+\n//57JCYmwsnJCSNGjMDixYvRp08fPp0mdcdo2LCIrg2EOl/nv2YN8OefwI8/1l2ZjIbB3btA+/bc\naEktgpGxiK7i0Lt3bwQGBmLChAm1lnXnzh20bNlS8HJkXVJVe2LtrGHBRkoY6pkwATh5Eig3N81g\nAABcXQFfX2DzZl1rwqgCbb2Er1y5And3d63IYjCEwIwShnosLDj/gSqGcRkvObNnA19+yVZp1VO0\nMb3xxRdfYOrUqSpB3RgMsWHTNw0EnQxBJiYCnTpxf2sxF89Qj5g78NYJr7/OGSdvv/1c2dmwOkOb\nsOmbFwM2UlLHTJgwAfb29mjTpg3/XUREBFxcXNChQwd06NABx48f16GG5XB3B/r04XYPZmiduAdx\niG0Sy3+OPjqK9dvWa0X2lHlT4BPsw3+CZgdpRa4KbJUWg8HQMswoqWPeeeedSkaHRCLBnDlzcOHC\nBVy4cAEDBw7UkXZqUA7Tl5XpWpMXEqNS4MtjgL5Cuzvwimnw8AwfDqSmAn//rV25DAbjpYUZJXVM\nz549IZVKK31fb4cXu3UDbG2BQ4d0rckLSZEB0DkVGHZDnB14290HXHK0a/Dw6OsD773HRksYDIbW\nMNC1AgyOr776Ctu2bUPnzp2xatUqWFlZVUoTERHB/+/j4wMfHx/xFSu/Bfjw4eKXV48Q2+fj2Q68\nGfggxgBGfv21vgPv6CuASQmwtJX2DR4A3M7SS5Zwq7S0FDiMwagNMTExiImJ0bUajOeEObrqgMTE\nRAwZMoQPz/zw4UPY2toCABYuXIi0tDRsrrDcUqfOWqWlQNOmXOj5Tp10o4MO8An2QWyTWP5YlijD\nkt5LtGo4rN+2Hvuj92Dv0YswP3IM6NJFK3KDZgfh6KOjaGSdgStfS/B+0Ah8t3GvVmRXYs4cwMAA\n+PxzjbIxB0SGNmGOri8GbPqmHmBnZ8dHLZw0aRLOnTuna5VUMTAAZs58aYfpHXMB58fiTIGEjA/B\nL1HRMJ+/QKv1G7U6Ckt6L4FnVj9kdOqM71p01ZrsSrz3HrBlC1Bu75KXET09Pdy5c0dQ2oiICAQG\nBoqsEYPR8GBGST0gLS2N/3///v0qK3PqDZMnA0ePAuV27nxZmHgeiIgRx+eDZ9Ik4JdfuGipWiJk\nfAh+jfwVzdd+y8WbESumiLs74OOjlVVaFlYWvIEuxsfCSlgE2uLiYkycOBHu7u6wsLDQ+qo4TeKI\nBAcHY+HChVorm8GozzCfkjomICAAsbGxyMjIgKurKxYtWoSYmBhcvHgREokETZo0wYYNG3StZmWs\nrIBx44BvvgGWLdO1NnWC0udjfecM3PpSgn/a99S6zwePpSUwfjxnPGg4DVIjnTsDbm7ATz8Bfn7a\nla1k9mwgKAh4913OAfY5yc3JBSK0p1Yl+RG5gtKVlpZCLpfjzJkzkMvlOHLkCPz8/PDff//Bzc1N\nPAUZjJccNlJSx3z//fdITU1FcXEx7t69iwkTJmDbtm24fPkyLl26hAMHDlTaMrzeMGsWsGkTkJ+v\na03qBOUUSPv0fkjt/jrWu7UXt8D33uNCt4sxDaJ0VhaL7t0BGxvg8GHxyqhDTExMEB4ezu/6O2jQ\nIDRp0gTnz5/n06xYsQJOTk5wcXHBli1bqpWXkJAAb29vWFhYwNfXFxkZGSrnR40aBUdHR1hZWcHb\n2xvXrl0DAGzcuBG7du3C559/DnNzcwwbNgwAsHz5cnh4eMDCwgJeXl44cOCANi+fwdAZzChhCKdZ\nMy6K57ZtutakzlBOgXh+swH49lugsFC8wpo2Bby9ga1btS976FDg4UNuk0UxUK7SeroD7IvGgwcP\nEBcXBy8vLwDA8ePHsWrVKpw8eRJxcXE4efJktfnHjBmDV199FZmZmVi4cCGioqJUpnAGDRqE+Ph4\npKeno2PHjhg7diwAYMqUKRg7dizmzZuH3Nxc/PzzzwAADw8P/Pbbb3j8+DHCw8Mxbtw4frdeBqMh\nw4wShmbMmcPtIKxQ6FqTusXTk9sZ9/vvxS1nzhxxgtXp63MjXWKOlowcCdy5A5QbTXgRKCkpwdix\nYxEcHIwWLVoAAHbv3o0JEybA09MTJiYmWLRoUZX5k5OT8c8//2DJkiUwNDREz549MWTIEJUVIcHB\nwTA1NYWhoSHCw8Nx6dIl5OY+m2qquHrk7bffhoODAwDAz88PzZs3r38O8gzGc8CMEoZm9OwJmJlx\nTq8vG8opEDGXF77+Oue/I8Y0yDvvANHRQFKS9mUDgKHhC7dKS6FQIDAwEI0bN8bXX3/Nf5+WlgZX\nV1f+WF5NjJbU1FRIpVIYGxvz35X3SykrK8P8+fPh4eEBS0tLNGnSBAAqTfGUZ9u2bejQoQOkUimk\nUimuXLmCzMzM57pGBqM+wYwShmaUD6b2suHry41gnDolXhli1q+5OWeYiLnz8+TJwJEjL8QqLSLC\nxIkTkZ6ejn379kG/nAOvo6MjkpOT+ePy/1fE0dER2dnZKCgo4L9LSkrip2927dqFgwcPIjo6Gjk5\nOUhISODLByqv1ElKSsKUKVPwzTffICsrC9nZ2WjdujWLxQFwm4cyGjTMKGFojp8fcOMGcOmSrjWp\nW+rKb2LUKOD2beDCBe3LnjmTW7qbK2wVisZIpcDYsdwqrQbOtGnTcOPGDRw8eBBGRkYq5/z8/LB1\n61Zcv34dBQUF1U7fuLm5oXPnzggPD0dJSQl+++03HC43EpaXlwcjIyNYW1sjPz8fCxYsUMlvb2+v\nEv8kPz8fEokEMpkMCoUCkZGRuHLlipauuoEjpsHNqBuI0SCod7dq2TKi4GBda1H3FBQQ2dkRXb8u\nbjnLlxMFBooj28+PaM0acWQTEd26RSSTEeXnV5tMXZs2tzQnAKJ9zC3NBV1CYmIiSSQSMjY2JjMz\nM/6za9cuPs3y5cvJwcGBnJ2dacuWLaSnp0e3b99WK+/OnTvUs2dPMjMzo/79+9PMmTMp8On9zcvL\no2HDhpG5uTm5u7vTtm3bVGTdunWL2rdvT1ZWVvTWW28REdGHH35I1tbWJJPJaM6cOeTj40ObN28W\ndG0vKgCIrK2JcnIqf89oMLAw8w2EehcqOSuLW41z/Trw1OHupeHjj4H0dGDdOvHKyM7mVuNcvQo4\nOWlX9l9/AWPGALdu1SqmSLUMHw4MHAiEVB3Xpd61aUaDRiKRgPz8uE1EQ0NVv2ftrMHApm8Yz4e1\nNeDvzy2Tfdl4913ghx8AMR0LpVLOcBBjGqRrV8DeHjh4UPuylcye/XKu0mLoltmzgbVrtb96jVFn\nMKOE8fyEhgIbNgBPnuhak7rFwYEbCRA78u6sWcDGjUA5B0mtIbZvTK9egIkJcOyYeGUwGBVRGtxP\n47kwGh5s+qaBUHEIcsq8KYh7EMcfu0ndELU6qu4VGzyYe0FPmiQoeb3Ru7ZcugS8+SaQkAA0aiRe\nOUOHAoMGAVOnalduaSk3/bZvHxeGXgy2bweiooAqAouxYXWGNuHb05493GjJ2bOq3zMaBGykpIES\n9yAOsU1iccY9Fr+5xeLoo6NYv2193SuiYewOpd7Kj870ri3t2gGvvALs3i1uOcr61fY0iIEBF9Ze\nzKXdo0dzPkeXL4tXBoNRkbfe4ja2/N//dK0J4zlgRkkDZ+0xYPK/QIZ7Bvad3lf3CvTpw73gTpwQ\nnEVfAcz8G5CQDvXWBnPmcFMgYv4K8/EBGjcGtLhDLc+kSdz0yr172pcNcCNI06dzviUMRl1hYPDC\nBfF7mWBGSQNntxcQ+hdgm2CDkb1H1r0CzxHsq0wCBF4CBscBskSZbvTWBm+8wfl7nDkjXhkSCWf8\niNHBWloCgYFAuUilWmfqVGD/foDty8KoC65e5f5OmsQZ8mIZ3AzRYEZJA8VN6gZZogxn5UAh9PF+\nhhdCxle9/FJUAgI4Hwtlh1ANblI3yJJkWN0N+OC0Ifqb9ded3rVFT0/8/WQAbpXT1avAf/9pX/as\nWcB334m387ONDTeNI+byaQZDiXJUztISGD9eXIObIQrM0bWBoM5Za/229dh3eh/mG7mg761kbl8T\nXbF4MTePu2lTjUnXb1uPA9F7sPfwBZidjAY6dKgDBUUiPx9wd+d23/XwEK+cpUu5ze62bNG+7BEj\ngH79uKXOYnDjBrf7cWIiUG7/F+aAyNAmEokEZGUFxMUBtrbc89KlCySZmaydNSCYUdJAqLYDLy4G\nmjThNslr165uFVPy8CHQsuWzDkEIn33GjQBs2yaubmKzYAEXtl3MENcZGUDz5twL3t5eu7LPngUm\nTuRk64k0eDpoEOeAWG6V1otmlOjp6SE+Ph5NmzYVtZzk5GR4eXnh8ePHlfbFqY9y6wqJRAKaOBFw\ncwMWLuS+HDECkv37X6h29qLDpm9eBBo1AmbM0Klj15RVH+GwbWNs9u0Cn2AfBM0OEpBpCnDoEJCW\nJr6CYjJ9OrBzJ/DokXhlyGTcnkMCpkGmzJsCn2Af/lPjvejRA7Cw4DbSEwtlMLUaXg4WFtaQSCSi\nfSwsrAWr/PXXX6Nz585o3Lgx3nnnndrWgNaQy+XIzc2tteHg7u6OU+U2l9SWXJ0yezYX0LGo6Nkx\no0HBjJIXhalTuYBBOnIojHsQh3n97+PN+ET86Spwqe+LsnmbszMXs0TA1FWtCA3ljJLCwmqTabzs\nWkxnWiV9+3KjML/+Wm2y3NxsiLj1zVP5wnB2dsbChQsxYcIEwXm0QWlpaZ2U86KNVAEAvLyAtm2B\n77/njnv00K0+DI1hRkkdM2HCBNjb26NNmzb8d1lZWejfvz9atGgBX19fPHqeX9z1IOz7NTsgwQr4\nZbsGS31nzeIioxYUaP4Lvz4xezY3fVNSIl4ZrVoBHTtyozICsCwEjEsE3otRo7ipt4sXtaCoGp5j\nlZaueeuttzBs2DDY2NioPb9ixQo4OTnBxcUFW2rw9fHx8UFYWBhee+01WFpaYvjw4cjO5gykxMRE\n6OnpYcuWLXBzc0O/fv1ARFi6dCnc3d1hb2+PoKAgPH78WCW94mnsmpycHEycOJHXZeHChfw5ANi0\naRM8PT1hYWEBLy8vXLhwAYGBgUhOTsaQIUNgbm6OlStXVpKbmpqKoUOHwsbGBs2bN8d3333Hy4yI\niICfnx+CgoJgYWGB1q1b499//33+ytYm5WMnNeRRn5cUZpTUMe+88w6OV4g5sXz5cvTv3x9xcXHo\n27cvli9f/nzC60HY9896AK/fBV69ZCVsqW/z5twGWtu3q/zC/13ewAKrderE+fXsEznminJEQ8Av\n3K+PApPOC1x2bWgo/hRgQABw4QJw7Zp4ZYiAutGE48ePY9WqVTh58iTi4uJwsoqoteXZvn07IiMj\nkZaWBgMDA7z33nsq58+cOYMbN27g+PHjiIyMRFRUFGJiYnDnzh3k5eVhxowZauUGBwejUaNGuH37\nNi5cuIATJ07wBsSePXuwaNEibN++HY8fP8bBgwdhY2OD7du3Qy6X4/Dhw8jNzcX//d//VZLr7+8P\nuVyOtLQ07N27FwsWLMDp06f584cOHUJAQABycnIwdOjQKvWrcwYM4CIWl9OV0YCouw2JGUoSEhKo\ndevW/HHLli3p/v37RESUlpZGLVu2rJRH8K0aNIho40at6KkJ40PHkyxYRggHZRhJ6FwTZ+GZT50i\neuUV8hnfixABajUddNEehHBQv+B+4imtbfbvJ+rShUihEK8MhYKodWuiEyeqTKK8F10ngu6Y69GY\nd0cLk52ZSWRlRZSaqiVl1RARQTR5MhGpb9MAiLO4xPpo3uV99NFHFBwcrPLdO++8Q2FhYfxxXFwc\nSSQSun37tloZPj4+KumvXbtGjRo1IoVCQQkJCSSRSCghIYE/36dPH1q3bh1/fPPmTTI0NKSysjI+\nfVlZGd2/f5+MjIzoyZMnfNpdu3ZR7969iYjI19eX1q5dq1Ynd3d3io6O5o/Ly01OTiZ9fX3Ky8vj\nz4eFhfH1EB4eTv379+fPXb16lYyNjdWWU1eo3NuNG7m+sOL3jHoPGympBzx48AD2T1dU2Nvb48GD\nB2rTRURE8J+YmBj1wgQ6FGqbqNVRWNJ7Cfol9UPc8OF49e4DIFvg/P3TqKWvpnDpr8sAkgB+f1g0\nrMBqQ4Zwq2T+/FO8MiQSbkSsmhEN5b0wK+sHEzs37Ow3Wphsa2tuZ2IxpwCnTeP2JsnIEK8MLUNq\nnqW0tDS4urryx3K5vEY5FdOXlJQgo1w9lD+flpYGNzc3lfSlpaWV+oakpCSUlJTA0dERUqkUUqkU\nISEhSE9PBwDcu3cPzZo1E3CVqqSmpsLa2hqmpqYqOqSkpPDH9uVWgZmYmKCwsFBl2khXxMTEICIp\nCRGnTyNi5kxdq8PQEANdK8BQRblKQB0RERE1Cygf9n3AAO0qVwMh40O4QGhPngAHpMCyZcCKFTVn\nfOpvMH7hfES6ypDhnoHvPMyw4IYJ2jWkwGr6+s8Mhu7dxStn7FhuGfL165yfiRr4e7F7N6fPW28J\nkz1rFuccuGCBSkwRrWFnB4wcCaxvINNygNrn0dHREcnJyfxx+f+romJ6Q0NDyGQy5D8NXFe+HCcn\nJyQmJqqkNzAwgL29vYocV1dXGBkZITMzE3pqlnO7uroiPj5e8HWVLz8rKwt5eXkwMzPjdXBxcanx\nOnWNj48PfHx8uH4lMxOLdK0QQyPYSEk9wN7eHvefrppJS0uDnZ3d8wurDw6FxsZc+PJ164Q7fvr7\no3UJ8I1HCPol9kPb4GVoVwLgyhVRVdU677zDzWUnJIhXRuPG3IiDkD1lRowAkpIAoU6ILVoAr73G\n7fArFqGhDWLFVVlZGQoLC1FaWoqysjIUFRWhrKwMAODn54etW7fi+vXrKCgowKJF1b/6iAg7duzg\n03/88ccYNWpUlYZBQEAAVq9ejcTEROTl5WHBggXw9/evZHg4OjrC19cXc+bMQW5uLhQKBW7fvo0z\nT7c+mDRpElauXInz58+DiBAfH88bNfb29rh9+7ba8l1dXdG9e3eEhYWhqKgIly9fxpYtWzBu3DiN\n6lCnTJ/+bBUOo+Gg4+mjl5KKPiXvv/8+LV++nIiIPv30U5o3b16lPBrdqsJCIgcHoitXaq3rc5OW\nRmRgQLRhg/A8S5YQTZigejxxovZ1E5v/+z+i2bPFLeP+fc7/Iz295rQrVhCNHStcdnQ0UatW4vrG\n9O+vtk2bm0vFWw8MkLm5VLCK4eHhJJFIVD6LFi3izy9fvpwcHBzI2dmZtmzZQnp6ejX6lHTp0oUs\nLCxo6NChlJmZSURcf6Cnp0dlZWV8eoVCQYsXLyZXV1eytbWlwMBAevToEZ9e6ftBRJSTk0PTpk0j\nFxcXsrS0pA4dOtCPP/7Iy1q/fj21bNmSzMzMqE2bNnTx4kUiIvr5559JLpeTlZUVrVq1qpIe9+7d\no8GDB5O1tTU1a9aMNpR7liMiIigwMJA/VncNdY3aPjIoiPmUNDDY3apj/P39ydHRkQwNDcnFxYW2\nbNlCmZmZ1LdvX2revDn179+fsrOzK+XT+MFatIho0iQtaf2c9OlD5Ows/OWWns69aJ86/fLHDx6I\np6MYJCURWVsT5eSIW8477xAtXVpzuuxsIqmU6N49YXIVCqJ27YiOHaudftVx9OhL9bLw8fGhzZs3\na0XW7du3ycDAQCuyXiTUtqeLF1+qdvYiwMLMNxA0DnT0PGHftc3580CXLsDJk5wzqxCmTgUcHQGl\n/8yUKYCLC/Dxx9VmmzJvCuIexPHHblI3RK2Oej69tYG/PzcNImZEyf/+4/yGEhIAI6Pq086aBZiY\nAJ9+Kkx2VBSwaxfwyy+111MdCgUk+vovXvCuKujduzfGjRuHiRMn1lrWwYMHMXfuXNy6dUsLmr04\nVNVHvpBB4l5gmE/Ji4qdHfD227p1KOzYEWjWDPjgA+F5QkM5nZVRS0NDudUg2o5iKjazZwNr1wJP\nfRBEoU0bLoLljz/WnPa99zTbDdjfH7h8WTyfHrH22KnHaCN8+xdffIGpU6c+fywjBqOe8/L1DC8T\nyhe6ch8IXbB0KTdicueOsPStWnG7Bu/axR17enLHAh3WVh8HBt7SIKKsWLz2Gjfic+CAuOWUj15Z\nHc2acatqhG5+aGTEOQoKcaZl1Mjp06e1Eq5+zpw5SEtLw8iRDWipPIOhAWz6poHw3EOQAwZwkTSD\ng7WukyAUCm4zub59ufgUQvj1Vy5y6eXL3GqiEyeA//s/4NKlKsNG+wT7ILZJLAIvAYGXgKEdG8Ml\n0wXOcmcAOprO2buXe6n/9pt4ZSgUnOG2fn3NU2RnznC79ArdDTgjA/kuzhg7rBMeGTcCoN16ZMPq\nDG3Cpm9eDNhIyYuOBmHJRUFPD5g7l9ssMCdHWJ5+/bi/ytDd/ftzL99yO5pWxE3qBlmiDD+0Btqk\n6aHLNRPEe8frdjpn+HDg3j3g3DnxytDT40bEvvii5rQ9e3K7AR89Kky2TIZTchu0Tf+z/kyLMRiM\nFxpmlLzo+Prqfh+I0FDu5fnZZ8LSV4xaqjyu5sWrjGLqfbcfkt4cjHnF3C97PQVgXaCj6RwDA86X\nQ+yYMePHc1Fka3J8fI4YNns9nfHu/wCTYg0292MwGIznhE3fNBBqNQS5aRM3UnH4sHaV0oTgYOCn\nn4CsLO5lXROFhYC7O2dMtWrFRYl1dwdiY4FXXqk+b0YGcp0d4fFeKd68BQyJA6a+JsOS3ku4KKd1\nSU4Ot1HfpUtAuTDiWufDD7myvv66+nTFxUDTplxbaN++RrE+wT6Y93ssCMA5F+Abd+3Vo7W1Nb9T\nLoNRW6RSKbKysip9z6ZvGhZspORlYNw4bgrh5k3d6fDJJ5xhsXOnsPSNGwMhIcCXX3LHxsaqx9Uh\nk+Gflk0w96QJdnsB3nckGFvWte4NEgCwtASCgmo2FmrL9Olc3db0km/USCMHVjepG7Y0sUCzbGDa\nXxK82biP1uoxKysLxMVKAvn4gHbufHb8on4KC0EODqArV0CvvQZq316z/J99Bho3TlBa7yBvIAL4\n6RVg2iBAFizD8ZlTQF27PkuXmwsyNQW98Uad14VSP72PAXkoMNQfuC6zACkUz9KdOweSy0ElJTXK\nU2eQMBogxGgQ1PpWLVxING2a4OSTP5hM3kHe/Gd86PjalU9E1LMnkZub8PQVo5YqjzMyas577Rrl\nW5jTG4G96d/BA4jee++5VNYKd+4Q2dgQ5eaKW05gINHTyMDVkpnJBVNLSxMkdt3Wb+mOlSk9aOJG\npKUAYJU4eJCoU6cqA+2J0h51xeLFXGDDc+eIJBKixEThebOyuGdAQCA85W7RPd8BxVvo0Zjp/kSl\npURNmhD98cezhLNmETVuzLXTOsQ7yJsQAer5DuiaDGQfaE2P7GyJfvtNNWGPHkS7dz93Oew117Bg\nd6uBUOsHKy1N+AudnnUYCOc+smAZrYtaV3PG6vjrLyJ9/cqdTnVUjFoaHEz0ySfC8r7xBtF333Ed\nuFRK9DRMt04YMYLoq6/ELeP8eSIXF6Li4prTTpvGGapC2bSJqEsXotatxQk/X1ZG1Lw50Zkzak/z\n7fHpRyvtUVc8fMg9iw8fckb64MGa5Z8xgygsTFDSdVHrqF9QX3roLucMPyKiNWuIRo16ligpiTNK\n3n1XMz1qidJoQjjosrU+LR/izT0jI0eqJty3j6hr1+cuhxklDQt2txoIWnmwgoKIli0TlFT5Eohq\nBxocwL0I+gX3q70OTZsSdesmPP3ly0SOjkRFRdzxpUtETk7PjqvjxAkiLy/uJTpmDNHKlc+nszY4\ne5bIw4N7+YqJtzfRrl01p7txg8jWlqigQJjcggIuffPmXL2KwddfE731ltpTyvZo/3+gFjO02B51\nxaRJ3FYQW7dyhvqTJ8Lz3rpFJJMR5ecLz7N9O1Hv3tz/jx9z2yCUH6EZMoTIxET8rREqsC5qHfUL\n7kfRk4OI+vfnRhNtbFRHbZSjO3/++VxlMKOkYcF8Sl4mZs/mfBuKiwVnOe4BzP4LkCXKMLK3FgI2\nLagHSsUAACAASURBVF7M+bcI2OodQOWopW3bco6vu3fXnLdfP27FycmTzyKslpY+v+614fXXAalU\nfGfj2bO5VUpUg2Nfy5ZcgLcdO4TJNTbmdiZ2chJvNVFwMBdLpYqdawFgYDyw9pgW26OuCA3ldtEe\nPRowNQXCwoTn9fAAuncXHggPAPz8OJ+yixcBc3Ourr/66tn5Dz/knpVNm4TL1AIh40Pwa+Sv6PP1\nRi56cEICMGEC96wq0dfntknQ5c7njLpD11YRQxhau1V9+hDt2FFjMuXQqsFCUIqJHs0fPVA75ZeW\nEllaciMXQjlyhKh9+2fTBocPE3XoIGwaYfNmooFPde/Zk6jc7ql1zq5d3EiGmJSWciMyZ8/WnDY6\nmsjTU/h0zP373L2ztSW6dq12elbFvHlq/X+U7bHRR6D7xhJ6P+ANccqvSwYMIIqMJHr/fSIzM82m\nxWJiiFq00GzkbdkybrSUiBslsbbmRk2UtGnD3dvSUuEytYlyl/DkZG66tfyojbrRHYGw11zDgt2t\nBoLWHqxDh4g6dhTUASqHVv8cNZxovBYdC8PDiQwNhTt+lpURtWxJdPq06nFMTM15nzwhsrfnXqI/\n/VSruelaU1zM+XycPy9uOV99xfmw1IRCQdS2LdHx48JlBwdzhu3Uqc+vX3XcvVul/4+yPf49YgjR\nxInilF8BUR1sjx/n6v/JEyIDA85vRygKBWeYHz4sPE9mJufLkprKHY8axfmXKNm9m8jcnGjvXuEy\ntUn5XcL9/Ym++EL1/Jw5RHPnaiyWGSUNC3a3Gghae7DKyrhfWLGxwvNU7Mxqy+PHRI0accaJUNav\nJxo69NnxunVEw4YJyxseTjRlCvcLsGlT1ZUHdc1nn3GrZMRE3bx8VURGEvn6Cpd96RJn5JVfFaVt\nAgKq9/9ROoo+eCBO+eUQ1cFWoeBGqk6eJBo+nMjVVbP827YR9e2rWZ6QEKKPPuL+/+MPzldDOTJS\nUsKNlLRtq5lMbTJlCve8/v03kbs7p5OShITKozsCYEZJw4LdrQaCVh+sb7/lOkFNePfdZ52ZNvD3\n56YChA4V5+dzzn1xcarHt27VnLf80uKKKw/qGuWSzpQUcct5/32i0NCa0xUWEjk4EF25Ilj0NTcn\numBnSZs6uIuzPPd//yOSy1VfSBVROoqKjNIoWdsF5PWuCA62GzcSDRrErRCTSDRbmVZUxDmBX7ok\nPE9FB+euXbkRRCUrVnAOr3//LVymNrl2jTN6nzwh6t6daM8e1fOjRhF9+aVGIplR0rBgd6uBoNUH\nKy+Pe6HHxwvPc/OmZqs1aiIhgRuyFrJSRMmCBUTTpz87DgvjlkcKYcIEbs66FnPTWmP6dO5axEQ5\nLy9kGbQyboZA5vVtTVdkoBQzUKOPRFqe26NH9f4/V648e3mJiNIo+bAPaFNHEa61oIDIzo4zFjp2\n5GK1aMInn3BTapowaBBnDBFxddyjx7Nzjx4RGRurjkrWNQMHcr5ge/dyhkl5Ko7uCIAZJQ0Ldrca\nCFp/sObPJ5o5U7M8gwcTbdigPR26duWmU4SSksKNMmRlPTuWSomys2vOq1xaXFjIzUs/x9y0plTp\njxAXp/mSzufB359o1aqa05WPmyEAn/G96LoMdMEe9EtTLo6N1pfnColNMWAA0ZYt2i23AkoHW5sP\nQNmNJDR1gvoly7VCGdjw7FlutESTUbSMDO7eCQyER0TcdFGrVtz0UUkJNyr1v/89Oz9tGhe3JDlZ\nuExtcuIEFw+npISbwqk4avPaa6qjOzXAjJKGBbtbDQStP1jKgGJCXuhKoqOfdWba4OxZLkbDuXPC\n8wQGcn4ZSsaNI/r8c2F5+/cniopSv/JABKr1Rxg6lPOTEZO//+aCc1U3DaJEg+kQ7yBvmjoY9Icz\n6Ik+aPAAC+2PlKiLPFqR48e5FSNiBHMrh9LB9qpPD6KICO0XUD6woYuLMCfl8kydSvTxx8LTKx2c\njx3jjlesUF0Nd/s2Z5QImf4TA4WCM0pOnOCcXf39+VOTJ39KEa1G0kVLOXl7h9P48YtrFMeMkoYF\nu1sNBFEerLFjuQ5JKAoFUbt2zzozbSCXE/XqJTx9xail//7LOQgKiWJ69Cinv0JReeWBCHgHeZMk\nHNQ/8Glk3PL+CKdPcyuIxA6mpm5eXh1XrnC+JYWFNSYdHzqeXMfZ0ENjUIaRhK472mpBUTXU5P9T\n3lG0Lrh6VbwpI2Vgww0buGlNAfeB5/p1bgpIk6nV8g7O2dncD5S7d5+dHzCA8y0Re2uEqvjuO7rs\n2pI87IZRlqQReZmPJHv7YLK3H0H6KKFEyKkT/kcy2R5at6769s2MkoYFu1v1CDc3N2rTpg21b9+e\nXn31VZVz1T1Yz71s8Z9/anYorMjWrdyIg7bYsoXrhAXs5cFTMWppr15E339fc76yMqJXXiE6dYqL\nDlnD3HRtl4MqjZI4a9DrEyqMlCgUXOyVI0c0kqkxe/cKj6CrjJshgHVR62hnG3e618KDW0mliX+S\nUIT4/ygdReuKgQPFmTK6eJGLVFxYyMUs+eADzfK/+aZmS4orOji/9x4XI0bJ2bNEpqYaO5Vqi2nv\nLKb7aESvYBitwmz6DO8TQKSnt4WAPTQXK2gHxhBA1K9f9Q74zChpWLC7VY9wd3enzMxMteeqe7Bq\ntWyxZ0+iH34QrqSyM/vvP+F5qqOkhIuNoAzqJISffyZ69dVnw/YHDnD7slQYxldrVKxfz4XUJqq8\n8qACtV0OqvRHePdN0CF5IwqYEaCaICqKqJ/IodJLS7l5+b/+qjmtptMh9+5x0w7GxuItc64pNoXS\nUfT6dXHKr8gvv4g3ZdSnDxcOftYsIgsLzcr49ddnWyoIZdGiZw7O8fHcMvK8PO5YoeAMeEdH8Ufz\n1ODtHU7haEfr0Yma4SZlwJpMkUtcqOKPyBLZdA2vkLPNTjZS8oLBwszXM6im8ODV0DQLmPoPkOGe\ngX2n9wnLNHu2ZuGbjYyA6dOBNWueT8mKGBhw8n74ASgoEJZn8GAgOxv4449nx5mZwJ9/qiSLexCH\n2Cax/Ofoo6P4Tq8I+OsvIC7uWUj2GnB+DLS9r2G9AohaHYUlvZcgWeqDvlmG2DV7qWoCf3/g6lXg\nv/8Ey9QYfX3gvfeE3WNfX6CsDDh1SphsZ2eu7jt25ML+P3pUO13VMXMmEBkJ5OaqP29sDEydCnz5\npfbLVkf//oBCAURHa1+28llctgzIzxe+BQAA9O0L6OkBv/4qPM+0acDevcDDh0CzZkCvXkBUFHdO\nIgEWLgTy8sTfGqEK1qEl/HAV32MMFNBDe1wEsAWmpqXIgRW8bRahl28cQkLe1ol+DHFgRkk94v/Z\nO/M4G+v3/79mxszYGUZDFNllSypLctBYImQnzNhGdjOKZBtSJi2UFKVFqGgopEWbfIqk0iIpEi1U\n9l1h5vX745r3zH3OuZf3PXNGzu97no+HR80597nv+5z73Oe+7ut6Xa8rLCwM8fHxuOGGG7DIZAbF\n9OnTs/99/PHHfs+fjgLSPgCq/1hKfy5Ip07AoUN+F3Rbhg0DVq2SH7NAMHGi/NDrBjrh4TILQwUU\nDrMxoi4Cxf6VoGLF5jeBoUPlIta1q8zg+fJL2801/gN4+q3czVsZljAMby7bgEIjRnrP8wCAqCgJ\nyPJ7psfgwXKxcpo3FBbmPkhNSZF5JZmZ/u8vEFSqJBfcF16wXmbECAlqjxwJ/PZ9yc1npEv79hIE\nfPkl0K6dBAVu9is5WSvIzqZMGaB7d2DhQvk7JUXOwcxM+btHDyAyEpgxQ3+dAeQgCqMtEvAYmqEk\njuEAPseNN36KRx9tiPj4qbj/gXC88sp0v9d9/PHHXr+VIYKM/zpVEyKHA1mOqQcPHmT9+vX5P8MY\nd7tDlT0CfDr4ctVoLm/s0pHxiSfcG4olJQW2E6F7dxHb6aaKfV1LTVxMVfnlwVvAtGaG8suBA1J2\nOHJEnEMt5vCozzViGvhr0XBO6eHC+dQXK/t0o7V2fpKSQt5zj/NyRt8MXZo3F31MyZLu9Em66HhT\nDBigPQE7z5w7l38lI2VsuHevtAe76UxTIxV27NB/jVHgnJkpPilr1+Y8/+CDInj1GY2QlJRGjyc1\n+59OF4wbEhLuZ3T0IAJLGYYVPIIovl+yYq7WFbrMBReho3WZMn36dD5qsNp2OrFU2+KKB6ZIHfjf\nf/U3pgSFe/fqvybQnQg//yztwTqdIooJE7zbFsePl4tvFiqouGasdIkMGNo9Z9n+/cmHHpIgwbfz\nwID6XDf16ebVmpgrrOzTlbW2C1yLcJVFt043xdSpYkeuyxtviJ4hKkpr2GOucPKmUEJRN9/7vDBt\nWv7M/1HGhrt3S6eY21lN06fLDYMb2rTJETgvW0a2bJn91NiEafwnvAA/iq3lFXx4PKlZ+g75p9MF\n45YFC9JZs2Z/1qyZxC0de8u8LB0zQB9CQUlwETpalwlnzpzhySzfjNOnT7Np06Zcv3599vOuTqxb\nb5W5GG64+24RFboh0J0IDRvKXB5dfvtNLrTqh0r9bZguqoKKPQ2vI+fPz3mtsbXYt/PAjOPHZd15\nMZTautW82+mHH+TO20WAlysRbrdu5Lx5ziv/808J1A4f1tuZixfJKlUkm1Glit5r3LJ8uYiy7VBC\n0UuBGl2g+xm5QRkbfvihZEvczPj5+29XRngkswXOSUNm8eqyCfwjrDCbFe3IuLiuLFSoFZ9FA55F\nBMthHKOjB3HBgvTsoKQYTjACF7S6YPLE6dMS9E6c6PqloaAkuAgdrcuEX375hfXr12f9+vVZu3Zt\nzvJJRbs6sd56SyaIulHiK0Mx47hwJwLdifDhh5It+fpr/df4upb26kXOneu/3CefkFWrepeHWrQg\nX37Zv/PAiuRkycbkhZtvlmmsvtx2G/ncc9qr8SR6iFQwtQVY7D7NmSybNknQoGPR7bYcMm+etB5H\nR8t2Ao2Z86gvLiZgB4SBA6W8EWiMxoblysl32oBjlmzQIBkdoEnSkFn8pXAZdizShkAq70VrLkYC\ngXQC/VgVu3gWBfkQxhOQ7IUKSt7CbeyJ5fmSKfEjMVE69VyWCENBSXAROlpBgqsTKyNDjLk2bHC3\nkZ49zS/oVmRmStr+/ffdbcdufeXLyx2vLr6upVu2SAus74U3M5O84QZpJ1asWSOPZWaSXbqQTz1l\nv61ffpHgJS+GUqtWmfuGvPeeq5ZOlSlZURsc004zU5KZKa3Tb7zhvAG35ZBTpySoLVXKK/0fUHyd\nR33JzQTsvKBGF+RHyUgZG86bJ2ULw4U4O0uWapEl277dywjPSf/h8aRyCJ7lm+hAgIzBCzyKwiyL\nAwQSCJAfoBX/whUMQwZr1kxiQsL9jI1N5x14nV8UqMY+fVID/xn48vvv4mm0eLGrl4WCkuAidLSC\nBNcn1oIF7odq2RiKWd6dLVokxk2BQjlaupnlcfPN3lqUpk3FNMyXl1+W7IgiI0OyJ598Qv7vf2S1\nas5C265dySef1N83X5R9+mefeT9utNbWQOllGg0B9xYN550jejm/iBSTOV0HXbflkPHjpcQSGelO\nn6SLmfOoL08/TXbuHPhtW5GbUqkOX36Z41RcuLDofLJQQcm7VcD6w/yzZElJadwaU5mzanSmx5PK\nuLiutvoPjyeVBXGWf6MMq+NHVsIvPIEo3onRDAvrR4AsjUMshNOMilqc/doFC9LZ5tZJPB4bZz8O\nIJDccotkzFwQCkqCi9DRChJcn1hnzuQI5tzQpInczfvgpWFINdydBdq86vx5cZIcOlT/Nb7TRNPT\nJVAxW3f58t7lofnzJUti1nlgxqefSiDjYkqpH3PnSlbKl+efF52OJkov82eVa0yPmSnnz4uW5quv\nnJddt85dOURNJi5YkBw8WO81bhk92l7/YxSKXgpyUyrVRRkbDh8un2sW6ly8Nx5cXKoowwq1Z6HC\nfViiRCLj4royLm4A2+FtfoN6BDJZoMDirFIMGYMjfvoPVYrpgRWsjh8JpPMjXM2EAs1ZqVJXFi26\nlABZtOhS84xIbrr3csuWLVLi/eQT7ZeEgpLgInS0goRcnViTJpGjRrl7zWuveY8yz0L9EJaaAG69\nEiww1XB3Nm2au24NJ8aNkwub7iwPX9fSCxekpOM7XZSUjpsEQw1etRLv2ePXeWBKZqa4ya5erbdv\nZpw4IWWOX3/1fly1dP7wg7v1+Y6fd+Lhh2WQoROqDOimHNKrlwSI0dH5M/BQR/9z333uJ2DnFvUZ\nffxx4Nf9+uvSdXTqFBkeTi5fzqSkNMZV6MWwQs0YE96ORxHJshhH4P6sTEg6w8O7MgwZ3IFabIkP\ns11QY3CEh1CalUst9sqUqFKMCjyKF7+Nw8u34N8Vq5CZmVywIJ3x8VOsNSO56d7LC5Ur649OYCgo\nCTZCRytIyNWJtX+/KPGPHtV/jbqg+/gjGDMlH1UChzYvmlPHDnQnwpEjUsJxMyxwzhxvQeBjj5m3\n8B49Kvua5QlDUlqLx44VbYBvJsWMV16R+Tt5Ydw4c9+Q1FR3WSJSTwRqRJVBdOYNKd8MXbZskUxM\nZCSZlqb/OjfccYe9/ic3E7DzQm5KpTYoDUiRgm35M4qwMZrxHcRxNwoxIqIXgW4EJLvxFIZzJiaz\nOBbzOszmRMwi0JsAOQAvMAWPMTp6MYsUmUiAfC36Fi67zn92lV/gYSxt6pCb7r3csmyZ/D7YzUQy\nEApKgovQ0QoScn1i9etHzp7t7jWPPiqeGgaMBm19WxXjz1eU8k5ZDxgQ2E6ETp0kDa+bFvfNPti1\n8I4YQU6enPO30dgsLc15Do+bEogVyjfEN5ugArxDh9ytz8YEzpRRoySj4ERuyiFNm5L16sn7y0uZ\ny4qNG531P337SkboUpDbUqkBoxi1RInErMxHKkfjCb6G7qyM3cwAWBePEFjKwujDSXiANbCDFxDB\nQXiWW1GO36M8W4c3zi65xMams0+f1OygI33iQ6JV0elgmT9fNFQ6qO69/MiO+XLxIlmihN9vlBWh\noCS4CB2tICHXJ9ZXX+X4cehiYSimNAwLXnzK/y7q228Da171449SOzZ2yziRkuLdspucbD5t9aef\nyDJlvMtDytjsyBH/TIoZs2frlUDs6N7dfArroEHkzJnu1uVgAufH7t1yIT1zxnlZt+WQ9HQJSqKj\nuaBTqzxNWjYlM1O0Lnb6HyUUzQ+HWTM0S6VWnTC+ZmSiAUnktdjO4yjOzniD21GbG3A1ATIM3bkD\ntdgCH/FZDOYsdOYuxPDhsNr86spq9iUX3SGcxtKmDj17ko8/rrdsXpk2TXxLNIKgUFASXISOVpCQ\npxPL45GuCzeMHWsvKDS7iwp0J0L9+tImq4uva6ldC2/HjtLpozAamw0fTk5xMIJSZaD9+/X3z5dN\nm6Q+7ptNUK2mWS2d2uiYwBnp3FlKD064LYeoEmDFitxXvHCeJi1boqP/ad7c3QTsvLB/v3xGFqVS\nFYxIFiQ1WwOiOmFUUNIOb7MKdhMgi6A7jyCGy9Gdn+Em1sQcXomFBJYS6MwkJHANOrIyHuVBRHJi\nVA3+Uv9GCbjtxgQorYoOqrSpg033XsA5eVKCEg0n5FBQElyEjlaQkKcTa/VqEWe66RDYs8fek8Ps\nLmrdusB2Irz7rmRLtm/Xf0337t6upd26mbfwfvSRjGY3lgCaNROhr1kmxYyRI+UO2QQtG/jMTLk4\nmPmGxMeTL71kv31fdE3gFB9/LL4eOvOG3JZDHnuMbNqU/4aH8YYkcGw78MYkTZM3HXT0P2+8Ib4s\nl8BMLSkpjeuvqMcF18RbeoGoLEgYMhiFV9gTydmdMOr56ZjGpzGMYWFLCQzgE2jDhzCefyCGdbIe\nL1WqLePjpzChx908HFGQbSt35i/1bxJdVenSkrGxE55fvCjBsE4br+qo0rV3b9xYvxMsj2ypVYUn\nIyPYsn9z2yxcKCgJLkJHK0jI04mlbMA//dTd67p08bZm98X3LirQnQiZmWIC5aJN1s+1VLXw+l54\nMzMlE/P22zmPrVqVM2vk9tu9Mylm7NplWQLRtoG3sk9/+20Zcufigpo0IYkbr47lnEZV9UolmZkS\nRK5b57xyt+WQrHLSycgIvlsFHH2bGL0FLFNCiuOsnf5HXXzzwWE2KSmNZct2Z4kSiSxRIpGRkd3Z\nAMP4GyqwAM6beoEo8eljSGEELnAvSjC+RBoXLEjP7oC5An/xWFgR3tqgD+Pjp7BznY48FBbNucWr\ncFnUNezTx8dmffJk0Ugpnc348TL7xkl4/sQTEsDrYDWzyQy3nWB5oGe3RjwfDvbuan+OhYKS4CJ0\ntIKEPJ9Y8+ZJ1sANn3xiLyg0m3y7YEFgzavmzROlva7w09e1VLXwmmlTFi8mWxs6EYzGZh9+SNaq\n5RwUdOpkWgIxum4WnmSTIbhwQS72X37p/XhGhmRyPvrI4Q17b7PZQPCn0mBYqmYAsHSplN10cFsO\nSU7mziuv4L/hYPVR4JHoMI5O6Kj/eid09D9uLr4ukCDDVweylBtwLXvhVUsvkPL4nUcQw+JYzHuj\nWvHTinWyl1E6kJ1NWniLxu+4Q7JUZtOkDxzImVN0/fUyiyomRgIJuzEBbtp4rWY2maE6wdxMN84l\nnkQPN1UAd8fkBP9m51goKAkuwhHi/wYDBwIffwzs3av/mptvBkqUAN56y/z5ChWAdu2A557LeSwh\nAdi0Cfj55zztbjZDhwJRUcD06XrLh4UBKSnA3Lnef8+Z479s797A99/LPwCIiADGjJHXtmwJREYC\n69fbby8lBXj8cSAz0/TpQV8DC9cBsfti0a1lN/8FChQARo/O2V9FeDiQnOz/uAOfXg2cjAL6fwsc\nrnQYqzassn9Bz57Azp3Ad985r1x9jqTezowZg5rnLiA8vAAeeb88fmt5K+bFVtN7rQ6lSgF9+gBP\nP229zMCBwIYNwL59gduugRgcxVbcgKrYBaAfHkJ9lMRxxMauRLdu9bOXq1gxArGxK7EfFfBBgZpI\nLvooqj+UgJtP7gd+/x0AMGxYd7z//kzUXPA48NRTwPnz8uKUFGDRIjlWCxZ470C5ckDHjnIOjhsH\nLFsGtG0LXHklMH9+zjp8KVYMGDQIePJJ5zd5443AVVcBb7zhvGyBAjnn0CVgQmug4gmg0lGbcyxE\ncPFfR0Uh9AjIoRo/XjpS3OBrze6L2V3Uffe5N22zY9QoslAhfeHn+fOSfVAtu6qFd9s2/2VnzpRu\nF4VqLd63TzIpbdrYbyszU8osb73l9bBqoS4xETwaFcYRA2yyR1a+IWfOiLblp5/s9yELlZ2Z3RQ8\nEQXGJpbWK5U8+KC0dDuRm3JI165SEitYkNyxw/3QRyd+/NFZ/3PPPaYeGkp8qsowcXEDTPUgZqhM\nSRgyeAiluApdCLxIID27DdcXlQlZeW9azjkzdqx5h9itt+ZY/KtuowULzKdJf/216GtOn5b/Ll0q\nQuMWLezHBPz6q9bxSJqQxCktruX2MsX1yoJuO8FySUJyAqMbRfOzYmD36hG84uYrTPctdJkLLkJH\nK0gIyImlRGtuLgpm1uy++E6+1ejW0BKCKg4dkhKOUcDqhK9r6ezZZP/+5usuWdJ7PPy4cWIG9c8/\nomlxEtouWSLCVB9UC/V38S2cPUFGjzYfy640AxqoQChqCnghDFzQSrPD4vBh+Qx05g3Nm+euHPLp\np3KBjIoS8avboY86dOhAPvus6VNJSWlsWKY9DyOSxdCcQA+GhXVnZGR3FizYKqvskpotQAUytSbe\nJiTcz+joQQTSOQFp/BcRvLXBnfbOp0bUOWMlKPcVjS9dKrOI2rc3nybdsqXcQCidzc03SyeW05gA\njePhSfQwfBq4JwZsNESzLOjUvRcgqrWrxh49wP9dba0rCQUlwUXoaAUJATuxevcWlb4bfK3ZfTGb\nfNuvn223hrqrD7OadOpLu3Ziwa4r/PTNPhw9Kn+btfAmJZHTp+f8bTQ2u/9+5zku//4rLbzffmv+\nvI4nyM8/yzK+nTMHDkjAcOSI/T5koQKhXxrUcze4bNgw8X5wQnVd6VqKK01PnTqS0di0KfBtox98\nYKn/URmNV1GbY9HWRwPyIoGJbIgkjsSTfA3deRve8tODWLFgQTpr1uzPBtUH8EKBSG+PHCeM50zX\nrn6C8qHjh/DX4oU4pm19ehI9HDi6r/gALVxoPk167VqZeK0CzOeek/U7TU3WaONV5+rYduCL12l2\nUDl17wUIT6KHEdPAfSXAhkPN9y0UlAQXIU3J/zVSUoAnngAuXtR/zdChwJtvAn/+af58587AX38B\nW7Z4b2fePODCBev1Evj0BaDWIQ39wyOPAIcPO2s8FCVLAn37Sm0eAGJigDvvzPnbSHKy1Or/+Uf+\nrlQJaNUKePFFYNgwYNUq4OBB621FRQEjR4q2xIyqVUWfs2SJ9TqqVAGaNfNfRmkGFi2yfq2BYQnD\n8P6L7+Oa1W+KVsF4TOxITgYWLgTOnbNfrmhR0WnoaBGAHE1PgQLAyZPyOcbFAWvW6L1eg6HLt+Ln\n345iQv3+aNFiOhITZ/otMx83Yjw2Ywwex1Y0xGxMADAAwD4cRmHMQCreQxukYK6fHsSKYcO6Y+fO\nJdj204sokNBfvkN233cjxnPGRJf008HdmNX8HFr++i02XrMRb55aj8+b3gh89pnojT74wHt9HToA\nJ06IPqhPH9GO/fmnbMdO39G4MVC2rNbxeLYhMLK9pnajcmWgeXPgpZcc15tXMsKB+z3AladCupL/\nL/ivo6IQegT0UDVtKq6bbvC1ZvfFbPJt8+aWpm3q7mtaC/CZhpop4dq1xSlUF98Mxa5dcrdulrFo\n106m9Co2b84xNktKImfMsN+WKgP5dkcodDxBrOzTt21z78pLitblppv0l2/fnly0yHk5TS1CNqoE\nWKGCZExWrDCf4pxLPJ5UJuJFvos2XoZk6jkglVfgbp5DBF9AIneiGk+jMItgAaOi7iRwP1/DlB0k\nAgAAIABJREFUTUzGYzwQXooTbsvFcMk//pASo87np1DnTGamZDkMHWKeRA8LTQYPFgarjJEMQJc+\nHvmOzZlj3iavJl4rnc3DD0vHnZMF/muv2R4P44iJ2AGx7DNKz96d//uf8ziAPKKzb6HLXHAROlpB\nQkBPrPR0CUzc4GQoZjb5dvVqS/Mq9WNSZjx4LCqMSYM1ZmysXStmanZulb74upZ26iQpcF/Wryfr\n1vXe10aNxP1yxw4pHfkKDH256y5rh0kdTxAlaHzzTf/nWrQQzYAbNm4kw8L0tCIk+f775qUBM3r1\ncqcNmT1bLnxRUTI4MIBtox5PKqPwDw+gLGtju1f5JUf7MYjPoxpnoxOPoQTPogCnx9TNFp9Oi+/H\n/YVK8vOOvciBA3O7IxJ45WZek4+gXAXto24DbxloCNqHDRN9ktk06dOncwwNO3SQduhSpcSh2G5M\ngMUQTiPZIybceMyoYMtuHEAAcNq3UFASXISOVpAQ0BPrwgWyUiWZ6OoGX2t2X3wn3yrTNotuDfVj\n8kPzpnqzXjIzJTDq6MLrwjdDsWGDv5OrWnft2nJhVhiNzdq2FQ8IO3buNO+OUCxZ4uwJogSNvqxZ\nIz/wbt1Jr7xSX5iamSmB2fr1zstu2eJOG6Js+YsVkwumYehjUlIaCxVqx4iIXoyI6MXIyO6sXFnf\nU0d5gNTFt4zCP35CVaX98JRqyQNh0Xw94koeLFxMLuzqe6DcdV96yT7jZcfWrZItcWMeqM4Znw4x\nywzAjz/Kd2zyZPNp0vfeKyJTpbNJSZFAxmlMgMkQzoDg1L13CQgFJcFF6GgFCQE/sebMkbtdN5hZ\nsxsxm3yrY9q2fbt0uei0/D72mPzwawo//TIUFi28JEUceNttOX8rI6gvvjDPpJhx223m3RGkCGKv\nvNJaEEty2N2DeLBwFAd1bOjdleR2lLziqafIyEj90s8LL+g76DZpom0pnpSUxucLV+FGxPIfhLE6\n2vIICrBBbA/GxQ3wMSLLJPCiv3upBcoN1TgV15L4eBH0xsVJm/nq1TnPqSDULuPlRNWq7kpmxnPm\noYe8OsQsMwAdOsh5YDZNWhkaHjsmpc7Fi2X9PXuSjzxivR+qjddsqnZe0Oney2dCQUlwETpalxHv\nvPMOa9SowapVq/Khhx7yei7gJ9aJE/IjZCy3OGFmze6L7+Rb1a3xyy/2627dWn5AnTh3TjwvUlL0\n9pn0dy21aOHluXNyF2pMiz/yCHnnnTmZlA8+sN+WUwnEwRPEk+jhxFtzuhy8tDZuRskrMjPJIkUs\nZ/T4ce6cXLB37HBe1kGLQHoPoquKLjyEQjyHaD6OMXwc7fkQOjMiYhiBVA7GszyAskzGYwTIUqV6\n6+0zaT8V18hbb0mQ2rixXNSvuy7nOeWuu3KlXrnOjFdekaD555/1X6POGZVNcppQ/cEH5LXXSpnJ\nLMOobOFffFF8drp3l84gpzEBVp4peSUtzb57L58JBSXBRehoXSZcvHiRVapU4d69e3n+/HnWr1+f\nPxgujvlyYiUnu2tjJCW9bbRm98Vs8u348c5BxNtvS8CjU54YOpQsXFj/7t83Q6H+/u47/2WnTZM7\nZYWxtXjRIhGD2uFUAnHwBPEkehhzL3gyCkxu49Pi6HaUvGLUKLJECf3lp08Xca8Ddw1+gH9Gl+DQ\nBkMsTceMg+gAcjVu4GeoytMozJrYwd9QnBGYSiCVPbCCh1CKf6EMgRe0MyWuUPOZpk2T0mLBgt6m\neo88IsMH7TJedly8KJ+1r+jbDuM54yQoJ+U7Vq+eaKPMpkkrzc7p05KBXLpU1t+smf2YgPxq4z1y\nRM4hp2ArnwgFJcFF6GhdJmzevJlt27bN/jstLY1paWnZf+fLifXLL+5/hJQnh5WhmNnk299+c+7W\nyMiQGrjOrJe//pK7UTPBqhW+GYoHHvB2cjWu2zctrozNzp6VTMrOnfbbev55+xLIXXdZeoIogePy\n2uLKWq6/jyurm1HyijNnRCC8bJne8n//bV4ayMKY/UhBG76Mun5dL9nvx5NKIJMfoQWvxj7ejE/4\nIJpyDpJZBEtYBEtYoMAAhoX1ZwSW8w+U4wWEMbFME9NtB4SFC8nbb+eRYkV4LjyM/7uqdE6pTAWh\nr76qL/r1ZcYMKZnpTtY1njNKUG7naUPmZEFatzafJq0mXiufnZtuEr1JIwdDPachnLlFJ9jKJ0JB\nSXAROlqXCenp6RwyZEj230uXLuUog1U7AKampmb/27BhQ2A23K2bO6dU0t+a3Rezybe9ejmbtj3z\njEzn1SE+XrIduhcN3wyFXQvvwIEStCiMxmbTptmPhSdzSiC+3REKG0GsEjhWHwX+Gw4ubOmjT3A7\nSl5x220iTNWgRo1efCmyGmdGX8cSJRL9RKfG7EdxHOcRFGUFLDA1HVPLPoK7+QjuJrCUQHrWf1MZ\nFbWYffqkcsGCdMbFdeZ9kbV5KjJaAoL8Isu+f1mdq7ijNHguAix7t6FUNnq0XMDr1iXfe8/9+k+e\nlC4jNxdh4znTsaNzwK3chhcsMJ8mrczZDh6U7/kzz0iLfuXK0u5uxSefmE/VzitO3XsBZMOGDV6/\nlaGgJLgIHa3LhJUrVzoGJfnCp59KGtuNw6aZNbsRs8m3W7ZIx4/dds6e1Z/18s03cvf/4Yf6++3r\nWmolaPzuO8kG/ftvzmN33CGiUZVJsRsLT0oJxKw7QmFlF84cgePf11SUz9H3gtOnjwgd3bBnj7QH\nq3lAJqgMSEREL9bGdh5AWUbhH4aFLfUqpajsx3MYxFI4zEdwN/uhk2mmRIlQr8Y+HkZRXlmsFePi\nejEurj1r1kzy14AcO0YWLSrH9vvv3b1HN0yaxDXVyvJoQQlKHmliKJWpIPTpp72Fz27o25csXlxv\nsi7pfc44CcoVM2ZIFsRsmrRx4vWQIfK9v+oq6fSx68Yy8UwJGLffbt+9l0+EgpLgInS0LhM+++wz\nr/LNrFmzvMSu+XZiKRtwYxeCDkOHeluz+6Jq80aaNhURoR0uZr2wRg2yYUO9Zcmcdkp1t7Zzp7Wg\n8dZbRRCrMBqbDRjgPVreDIcSiJYnyHvvyR23b1bMzSj5LGrU6MXt4SX5WUQZ0+wHacyAyH/fw628\nF7P8RKdquRcwgBMxi8BrLFJkomXXixKh/nx9E28RtBWjR4s4N7cBgQ779/NkVAEuaAh+VwY8Fg1e\n1c9QKrvjDtlXu4yXHb/+KiVGHfG24uGH5ZzREZSTOVmQRx81b5N//HHRtnz/vWRVZs2Sv0uVsh8T\nkF9tvB99JCXafDRTMyMUlAQXoaN1mXDhwgVWrlyZe/fu5b///ntphK6KV18V4yc3/PADjxcpxNb9\nbjEfqmc2+TY93dnJ88ABeZ1Oy+/KlXJHbedW6Yuva6mVoFF1aaigITNTAqA335QszZVXemdSzBg0\nyNp/RccTJDNT2ikbN/Z/zncIog1JSWmMiOjFW9GPGQBL416/7AeZkwFZirqMw59MwaM8ipIMx0te\ny6rsRz18w/1hMaxwRWe9IXSbN2t5m0xK6sWzEeG8EAZ26tXUeSptLvmkVmU+UacgDxUG11eI5KIW\nN+Y8qYLQ1FT7jJcdN98s2UFdjOeMk6BcMWQIOWWKeYbx5Mkcc7a2bSXTFxMj78dkanI2dlO184Ju\nsBVgQkFJcBE6WpcRb7/9NqtXr84qVapw1qxZXs/l64mlfoRsUvtmbCkfw4GdJe1tOlTPd/Ktco78\n/HP7FSckSBuhExkZ8qPrpk32gw+8MxRWGQvVpWHMUixbJtNYSfLWW/lchxb2k45VGcjKf8VJEEtK\nujsy0j/wytIMqJKL+ufUAXMAcVyGZgTS/Vpu1XILcAOnozuBTH6BShxsIjpV2Y/fa9SR7g5dGjcW\nl1wbPIkevlFDNDWP36Q5giA3bNvGU6Vi+OnVZfhtm1beJRPlrrt0qX3Gy45NmyRbYmEeaIo6Z5Sg\n3KxDzIjKgtx7r3mGUZmzvfuuBMGjR8tyTsJzH8+UgLF4sV6wFUBCQUlwETpaQUK+n1izZ8tkXxfc\n3bouv70CXNgQjJ5iMqHTbPLtY4/JpGI7vv5aMgQ6Lb+zZrnvdKhXLydDoTIWZoLGBQvEll7x7785\nRlDr1vHHUkWJVJugjLTujiD1PEH++UdKGT5um0MHP8hfwgqxEVoR6EWgO4FuNh0w5DA8zZXowkMo\nTWCwX6ZEZUBqYCf/QmEWLdCeTzbpaJ/KX7dOLt66guMVK/xF0D54Ej1sNhA8Gg2eLgBGTtWYSptb\nPB7RW1StKoJR4128cte1y3g5UamSuzk/xnPGSVCuaNNGLP/NMozKnO3ECfE2WbJE1t+1q/2YAF3P\nFLcoga5V914+EApKgovQ0QoS8v3EUj9CxnKLA56E5tx+Bfj5leCAzhYX5TvuEMGg4vhx+ZF0co7U\nnfVy5gwZHS13irq88IKks0kmTUjiQ02r87PypfyzHWfO+A8zmzWLTEwkMzL4a/FCbD4ALDwJLDPe\nYqS7k/+KgydIUlIaHwmrynMIY0l0ZljYrSxatBU9nlSORVsuR8+sLEgGy+AJAhMtO2BuwFbuw9Us\niEUsVcr8Iq8yIPtqN5CylnLktErlq4zSxo2W78ELo0uuBZ5ED5EKrqsKng8DxzcqnC+ZkqQJSbyv\nVW3+ULoofyxdlO80qOVtqqf8bNLT7TNedixeLNkSNyaFnTvLOeMkKFe8+64E2v37S4bDlx49RB+z\naJG4wXbuLN5BTqW0/GrjnTlTBLqXiFBQElyEjlaQcElOrFGjZNiXJgnJCRzTtCi3XAl+HxPBPiNN\nMiBmk2+Tk52dI9eu1Z/1MnCgdGzoCj8NGQpPoofRU8A/i4I1R5oEVpMmyeeiOHIk+w7yscbV+EZN\ncHxr8IXrLIKyjAzz7giFgyDW40llGfzNfxDF6Zia1U57D4sWbc9imMjDiOEO1OSr6MUdqEWgq2UH\nDED+D9U4PPZG0215YSxrpaXZp/KfflqCT12US64Fxrkv75eP5NHCBXPnF+KAJ9HDsFRwVylwugf8\nJK4AT5cs4V0yUUGoXcbLjgsXZN6Pr+jbjo0bc+Y1OQnKSflsrr1W2ojNpkmridenTonQe8kSWX/j\nxvZjAnQ9U9yiG2wFiFBQElyEjlaQcElOrN27JTPg4kfo2UXzeDQ6ksfiyngPs1OYTL6dOLQXj0cX\nYLs7m5lrMUj5Qa5WTcafO7F/vwhen39ee785YwaZlJRtVvZ2FXBJPZNsx/79PB0dxQ69m2ZrRz6o\nV52cMoVDRvbhoegwNkySScfDBlpcmBcuzO6O8NWAVKvWg+vKNuBzlVqaakJUlmMV7uBJFOE+XM0C\nuI9AJwKp7ITVXIvbeRqF+B3Ks38Z84BDZUDeHXqPuXDWF6MQ1xCImXL6tH9GyQ4zEbTv/ma1Rb/6\n4FQ5tjpDAl2ijv3I9uDKWuDvxcHVNSt4l0zUe3/5ZXM/EB0mT5YuKl2TQuM588MPepb3zz4rWRCr\nDKMyZ5s6VVrhr79etCtOpSUdz5TckJTkHGwFiFBQElyEjlaQcMlOrE6dREvhhtRUMWaysmD3mXzr\nSfRwZS25GFhqMUhxluzSRW8fmjeXsoAuWRmKTr2aENPB5xqAZwuA1XuX8tuX9ZWv4PjWOdqRRl1K\n8myxouTZs/zq9rZ8o2YFft/qFlOX1qSkNF5Ttj8PhhXk9UXvYGRkGwL3ZwtPIyNHsDa2cz/KmU64\nVUFJDezkIgzkv4jgWLRkqVKtGB6eQEC6YM4ikq9FlhV9gR1G/wonjEJcp1T+ffeJiFKXMWO8RdB2\n1K7tPaMmQKigpMgkKT/e37Awdzdq6G+qN3y4dLjYZbzsOH5cdE8zZui/xnjOtGvnHHArj5+nnzbP\nMCpztj//lPe3YIGItitWlBZzK3Q9U9yyY0fu5wu5JBSUBBehoxUk5OXESpqQZN8lYuTjj0Uj4OZH\nSBmKlSljbsGuavPffENSLgZNB4E/lwTv6GWhxSDlDlx31svWrXJH7WaK7uDBXNnkOsYOiGXV0eDZ\nCDD9xjp+iw25/Xr+VhyMvQe8O2sezWcVYuXu9I8/yJgYPtB5BI9EFmHrZpO8Mh4qqJiJyZyPEVnl\nl4ksiGUsipPZniDr0Zp98LKfK2pCwv0EFmUFMelcgYr8G5FkZma2C2rhwndwT0ycfP5xcc4iQuVf\n4YTRmdYplZ/1OfDYMef1kiLoLF3aWwRtxdq1cmx37dJbtybGMlHsgFgOGdJVvsf9+nmb6v34o7z3\nJ5809wPRoUcPWbemSaFxWvS41vX4W2xJ5yzN1KliEFitmv95kKXlmZlwB9+uEsdF11XiwcJRfLtB\nLT8RtRf52cbbrp3ou/KZUFASXISOVpCQlxNL3RHadokoMjPFn2PdOncbGThQ7ryMw+yMqNo8c4SM\nX5YDT0WC1/b0z05kc++9+rNeqlbVK00otm8ny5blM8/NY/yAeP5Rs5oMU/MRNHoSPdxQCbyzq6T4\nW3Qqya4xN3BneAlGRfbisrCKHI8qXIf2HJwVQKiMhwpK6mMbT6IoY3CEwBTOxU2cjmmMjBxBgCyL\nAyyA86bdMwsWpLNYsTsYHt6K7et2JsPD/S8Sa9fKkMLevZ1FhMq/Yt8+58/I6Ex7++32qfy+fUUv\nootyyXUiM1PKQ507669bE1Umyv7+DR8uF3bfu/gOHSQoKVMmd8HRzz+L4PXVV7UW95oWnQruKBnB\ntRPG2L9IZUFmzzZvk3/kEb5X+QrWGwb+UQyc0hJ8rXIUzxUpbC881/VMccv69VIizAe9kJFQUBJc\nhI5WkBCooKTERJvMhGLJEnE0dcN334mIzsqCXdXm//wz+w61dzfwQOFwrrrBZs7J77/rz3p59VW5\no7Zzq/SlTZsc182PPpILu48LZ7U6bdm1ZH1+Hl6UE8NrcUnE1YwI78lvUI9t8Q6vx5f8FaXYBpO4\nHbUJZGZnPFRQ0hBf8AhiWBnzCNzDGnicf4eVYPWKnbNFqLGx6ZauqF7ceCNZxyejk5EhHSK1a+uJ\nCMeNI+++23lbRiGuUyr/yy/FylxXcPy///mLoK149FG5qB89qrfu3KIyQm3bepdMPvhAxKSTJ5Mj\nR+Zu3TfdJIGzBmpa9J9Fpbw0qBO4pXxp5xcmJkogaZZhPHaMJ6IK8Mpx4AfXgHd1AI8WBNdVv9Je\neK7rmeKSpPFD+EvJwhzXup5zBjcPhIKS4CIcIf7PUPsgsO0Z4IpfSqNby27WC/bqBfzwA/Ddd/or\nr1sXqFdP/vvMM/7PlyoF9O4NPP00Xpr7Ema2nIljRVqheGRRdN1zADh3zny9FSoA7doBzz3nvA89\negDFigH33ae/3ykpwNy5AImawxZi77lw7BkwEgUieqFQoXZo0WI6Th4qgtXHv0KpzEh8nfkYbs84\niTKZxFyk4H5MxU7Uwl7UQQx+wBkUQS3sRGzsSnTrVh8VK0YgOvolfIUbsANxaFTgddx44xFcFX8Y\n52pXwU9TO2LmTCA+fipmzgReeWW68z7PmSPHZ9eunMfCw4FJk4A9e4CWLYGFC+3XMXo08OKLwKlT\n9stdcQXQtausr0ULIDoaWL/efNmGDYFKlYBVq5zfAwA0awYULw689ZbzsiNHAgUKAA8+qLfu3FK9\nOtCoEVCrlnzOpDzeqpVsv3Zt4OWXgaNH3a979mxg3z7giy+0Fj9WCKgyBjgTBawvURp1T50Hdu60\nf1FKCvDss8DAgcC8eV5PDU2bgFdiwjFqKzC3CXDLb8DqigVx9dXVgOefB06fNl9nVBQwYgTw+ONa\n+63LroO78WDzs2i17ztsvGYj3j7+NhYucfjehvj/n/86KgqhR14OlbF2vrVMAc65rZnzix58UEoy\nbnjrLbmTtrJg9509Q0qquXx5+0FdX3yhP+tl+nQRFZ48qbXLNar35M7wEry9SBsCPZiAF3kKhenB\nhiz9RzoLFFhMIJ0jcRvT0Y1PYTjvRz1G4R/+iTiOwhOshZm8AsMZjossWnSpV8ZjwYJ01qyZxFHl\nPfyzcvWcjevMv7Hiqqv8Z8OcPi1Ga61a6YkIlX+FE0ZnWidHzjfekE4PXYwuuU4MHSrvT8dULy98\n+KF8j+vU8TbVe/FFyaxZ+YE4kZkpLbsaIx189S59RvURnYtVedRIq1Yy+dsnw+hJ9LByY/BgNFj4\nPjCsK9jNU0fOyU6dRFhuRT608XoSPSw4GfyrCFhjlEYGN5eELnPBRehoBQl5PbFU7Xz9iCFkM42g\n5PDh7HKLNspI6/rrvYfZGenQQQSiiqNHxWOkWjX7i3OzZnqzXk6dkvbLqVO1drlEiUQOwbNci9sJ\npDIS//IYivMjeFgEp7gUdRiOiwSmsAju40xMZjn8wXJoT2ApPdjAnSjHMHRhkSITGRfXx3oOjG/n\ni878Gyuee05KVb5lreRkef8ej7OIUHMWDckcnw7lyGmVyr94UTwxNm/WehteLrlO/PWXlHDye9Ks\ncv1NTvYO/NR7T0839wPRYdEieQ8aJoV+epe//pJAw2lC9ZtvyjnYu7eUvbJQZdzXy4PDqoLomRUE\ntG8v5ZuqVe1LaTqeKS5Q+zO1JXjX7fk3TiAUlAQXoaMVJATsxFKOmnZtgIq77jJtc7VlwQK5UzYO\nszOiavPG50aMkLu1d96xXm/WrBcnkpLSuLJgZR5HJGOK92dcXFfTeTCKEiUSWRBneRzF2DnLHfU+\nPMCv0ICReIWbUJ29CoxikSITCdzPsLCl2fqPq6/uwFIxvfhjoRhOuq633lA6386XF15wnn9jxsWL\nEsz5tuH+8Yc43HbpoicibNTIcRYNSW9nWif78yeeILt3d16nIi0tWwTtyK23ShCTz+JILl4s2/Kd\nEjxjhgzBa9GCfOUV9+s9f16Om459vBkDBzpPqM7IEHO0Z57xyjCqIODqZLDkvYYgQJ2TDRuSa9ZY\nr1fXM0WT7GxQqiEblA+EgpLgInS0goSAnliPPmrfBqjYuVOCBTc/QsqavXJlaS/2Rd2FvvtuzmO7\ndonrpZ24VtNfw+NJZQX8xguI4J1YSiCdRYpMtAwYSpRIJEA+jwFcjRoElma13y4lkMpBxcbxx9ir\nss3H+vQRG3ev9S1Z4m1PbodxciupN//GinvuEWGub6ajUyd5vGZNueDYofwrnDA60zql8tV71BUc\nO5mzGfn6a8kQGQcl5gcqKzJ8uPeU4IMHZV+XLNF3HPZl/HgJHHXaoX1RpTSnCdVPPSXdTYYMo2lJ\niMw5J++9137OEannmeICv2xQPhAKSoKL0NEKEgJ6Yh0/Lmng3393XrZ9eykVuGHSJPlxs2rhVLV5\nIx06kMWLW3psJCWlcV6VtvywTG2WLdudcXEDTCfjqm6XT9GUP+OarABjit88GEXlyt0YFraUxXGc\nR1CE1QrexFKlevOGG+5kfPwULpy/3NlgSnUnfPutzYdiQE1uVTjMv7Hk1CkpBfi21X75JVmokFi5\nWxnaKTRm0WRjcKZ1dOS85x55n7oogzIdatSQgCC/uf9++Qx9xwAMGSL6jqpV3fniKI4eFd2TziRs\nM+LjrcujCuWy+9RTXhlGyyDghRfknHQqpb33nmht8jtTFUBCQUlwETpaQULAT6wxY/SG2OVGjLl/\nv/h9lC5tbj2u7kK//z7nsQ0b5Ec0K63ta8ceF9eVRXGSh1GKVyE52xHV6AlC5gQljbGZ36EOC2GZ\nbaaEJPv0mchSpXrz7eo3mLdGPvaYc2bpgQf0hcFqcqsS4zrMv7GlUycJiHypX18etzK0M+Iwiyab\nM2dkfT/9ZOvImTQhiT26NcoaJXCzXqunasU1iqCtWLlSsiU6pnp5QWVF7rxTjq/i++/l+/v44+Z+\nIDp06SLfgdw4pb71lnV51MjEiTK3ScfBV2XsUlLIBJvjlZnpLwC+zAkFJcFF6GgFCQE/sfbskaDB\naR5HbsWY/ftLV4VxmJ0RVZvPokb1nvw+rCRPoQCrFOvJggXbegUeqgPmUYzjw2hKgHwZfdgAX3m5\noCYk3M/o6MXZDqjR0QP1vD9I689EZZbsDKZUScNoT26Hb+fL4MGi1XDLL7+ImdqHH3o//vrrOWZq\nw4bZr0NjFk02kyeLBoi0TOUr7cLy2uCYdi4EjLff7i2CtiIjQy7obnQruSUpSd6v75Tgtm1FP6Xr\nOOzLTz9JYGU3EM8KJSg3K48aGD/8Tp6IKsCFDSrxw0plnIPDGTMkIHEqpT33nH/n12VMKCgJLkJH\nK0gAEPiUaZcu9m2ACuMMlCwcreu3bZMf8izrcWPmo1y5gby2TAeeLFCQnZrcw4SE+1miRCL7YQl/\nR3lOw3QCLzAcyzkQzzMMGdllmKuxj3PRiAA5HrO5BP38XFBVC27Nmv31xKdGunY1/0zGjnWebOxG\nGKwmtyo9yPbt/hc+XRo08J8Nc/Gi6IHq17c2tDMyerTeLJoDB2R9R45IoGqSyldBSaMh4O5S4kiq\n1er54YdkrVp63/NZs6QEcuKE87J5YccOyYq0auU9JfjddyVYnzBB33HYlwYNJLjIDQsWSJbMBk+i\nh0vrgZNagYcLgfW7x9gHhypjN3Cg/Zyjc+fku2UUAF/GhIKS4CJ0tIIEALkbBmaHrqOmiRjTybo+\nKSmNm6LKcFPEFZxSsCEjI3t5ZT6AdD6LWzkVMxgbm86CBTswEv/yL5ThIZRmFP4hMJlbcQM7Yg2j\noxdndcCQ0dGDGBW1mCVxlMfCinB4ZxfaBSc++cS8NVIns+RWGOzb+dK6tZ+brBYffSTZEt879jlz\nyIIF5eLl1LHx889SPtMRXyqfjsxMKe35pPKN342rUlxkSpTg0q4LS3HmjIhFJ01yXjavtG0r7cHG\nKcGZmdKx8uqr+o7DvqxfL5qgbdtcv3RkykAeLRjJO7vcZOmG6kn08Pqh4K8lwMcagw831QgOBw+W\nIMuplKbrmXIZEApKgouQo2swMXduYNen66hZsCAwfLipo2OHXUDFlRVw+OPamHTvelzO3OB9AAAg\nAElEQVR55SCULdsNr732I2afr4PiGcTQf/4GL1QDAETiPCrgdwDd8TiqYgSexonDnXDhQhQuIAo9\nkY7uWInzWIaoqH8xFymYEDkJXbvuxaOPNkR8/FQ8/vhteOKJIrghfg7+aH4Lnq5dMHCfyc03AzEx\nwLp13o9Xrgx4PMBLL1m/tmZNcTV9+WW9bSk3WcW4cdnusq5o2RKIi5P1GRj927f458J5bP3ifzj0\n4AwMGtPPeh1Vqsj3we79Gfd7/nzg4kX/9wCgYkxFxO6LBQCcOxaL1kVbY1jCMOf1hoXlfAZOFC4M\n9OkDPPmk7Ed+kpICfPAB8M8/wMaNOfuanAwsWyaOw88/7369rVsDZcoAEya4fun3R3/B0zdeQOP9\nW23dULddCbxcF3ilLjD4qzD0btzBfsXJycCKFcCNNwJLl1ovN3y4LHf4sOt9DxHClv86KgqhB4Ac\nkWEg0XXU9BFjehI9RMNr+FDxqzg3orJfFiQMKxiOC9yFstyGSuyFbgTI7niNG+DJdkuthR2MjU1n\nbGzrbA+QsLClrFixKxcsSGfbVvfxVMlS1h0Bu3bZT6/NDa+8Yt4aaZVFMeJGGKw6X778Uv7OzJTy\nRW4yYk89JXfdhkyOJ9HDJ28Ez0WAG68Gh91S1D5jsXGj/iyaFi3Il1+2TOXnutVTiaCdJh2TIqiO\niJBurvxEZUWSk72nBJ89K+89PV3fcdgXddzcmBRSjm25u8EjhcRzxMwN1bcF+LOqV+k5+LZpIyJ4\nuzlHpJR5jALgy5TQZS64CB2ty4DU1FSWL1+e1113Ha+77jq+Y5K+BuAtMgwUmo6aSUlpXFe2AZ+r\n1JIeTyor12rDyLLxLI/feQQFWRzHeQ32sDk+JkCuRnXego3si6WciBZsiiEElrIAzvM3lGKjyE5+\nQ+hUF0yfPj7ahocesu8I6NTJfnqtW86fF8dO37R6Zqa0otoZTLkVBj/yiEzXVTzzjPeFT5cLF0TY\nahiy50n08KoUCUpW1AK/KgfGJ9p4wWRmioHWm286b2/NmhyfjmnTApvKv/9+50nHiubNpWU7v1m0\nSMo4sbHeU4KnThUh8c036zkO+/Lvv3LcXH5+qkR23V1gWKp1icwrOPTVMVnxzjtSRqtXz38atRFd\nz5T/mFBQElyEjtZlwPTp0/nYY4/ZLgPAW2Rog6MI1bhsUhqfueZWvhNXnx5PKqtV6+HViqs8QDye\nVNbGdh5AWUbhH8bGpvOKuL5ZXTB1OATPsjk+5k7UYBhWcBiG8HXckWVCls6oqMW84YZExsdP4eYu\n/fjTTc39TcisOHrUviNgwwYRDOamvdKKhx4S7YQvL7/sbDBlIgy2xLfz5ezZ3GfERo8Wt9Csi466\ncC2uDx4uCO4uHs7V96XYr2PZMhF1OpGRkePT8ddfuW9pNkO14urMWdm6VbIln34amG1bobIiw4Z5\nTwn+80/Z18WLtRyHTRk7VrQ/Ou3QWVgaoTnRqJHMJ7JDZezuvdd+zhGp55nyHxMKSoKL0NG6DJg+\nfTofNcyoMCP7xEpIcBwGZidCNfP/iMERHkVJlsUBRkaOMPUAUf4f69GaXbCKABkTk0CALIZJDMMK\nApn8Clezc4F4FsZpHkIx1i/ajDVrJnkHHyrI2L9f/0MaMcK6IyAzU0SIb72lvz4njh6VYME3ELLK\nohhRwmDd7oQxY7w7X6ZMyV1G7PhxuUAvWkTS+8L1fvlIflK9orWhncLNLJr583N8OgKdyk9KkhZV\nHapUyX1A4IapUyVQjYmR74ciMVHeu44fiBmHDkkJx+HGxJdclch0HXyfeUaM98qVs55zRMo5ZxQA\nX4aEgpLgInS0LgOmT5/OihUrsl69ehw0aBCPHTvmtwwApqamMvWuu5havDg3vP++5fqU3iPmqqaM\nvaoJUcnDclVkCJ8KLnz9P2Ty7RQC8nwXrOLDuCfbA0S9rjiOE8hkbGw6GzUaxNjYdALMHkb3wYBR\n/L1mXcbHT+FXbe/wn82iGDXKXeeEjblW0oQkPtisBr8oV9IxM+SKkSPNAyGrLIqR6dO97cnt8O18\nOXBALnwOGTFT2reXoCkLdeF6c/wYufu1MrQzMmuW3iyaU6dyfDoCncq3MWfz45VXJBjbty8w27ZC\nZUV69vS+MfjmG5mM/eij3nON3NChg3y/8/vi7qtjskJl7MaOtZ/TozxT8tv23wUbNmyQ38qsf6Gg\nJLgIHa1LRHx8POvUqeP3b82aNfz777+ZmZnJzMxMTp48mYNMfgS8TiwlMrTAk+ghKnmYhns5J8v9\ntGjRJV4Zj0I4Qw82ZPt/VMNP3IobGFlgOAHySvzBI4hh5VKLuWBBOhMS7s8OQIw6EDUTJjsTYrRc\n/+OPbJ8SP3bvlguxG4Fqx46mE2I9iR5GTgX3FwPrDA/gtFErEa1TOYl079J6xx3k00/n/K2REbPc\n5/Bwf/tz5cTZq5e1oZ3iyBE5bjriS6NPx623BjaV37at86RjUspVJUroudLmlQEDJFj1nRLcqpUY\nvxnnGrlhxw7JlqxdG7h9tcJXx2TFlCmSAXMqpWl4pvyXhIKS4CJ0tC4z9u7dyzp16vg9rk6spKQ0\n3le7N3cWu5Ke5tNMJ+AmJCcwsmw8K+A3HkEMi+GEX8ajDP7mUZRk+ah52f4fZUqvYKVKXbODjxXR\nzbnsuhxFv18AYsWDD+ZYrvftKz+CZnTqJD9ounz0kWlHgCpXTWoFPtfAvBMh13TsaC6itSsnKQYN\n0ndp9e18+fprKaMYL3y61KljPhvm+eel08oqUDQyfLiUK5z47bccn4516wKbylcGZTrrmz6djIpy\ndijOKyor4vF4Twl+803y+uv95xq5oW5dOXb5ja6Dr9KwJSTYzzlS4weMAuDLiFBQElyEjtZlwAHD\nHfecOXPYx2TOijqxPJ5UhiGDu1CVN+MTPzdTRbWaSQTIV9GLvfBq9nLGjMeS6Fv5Wt0WfsGG+jv9\nvtn+d4Q6HD4sP2Z//ilp4quuMm+XdCtQzcwUh1KfjgAVlJSeAL5aB4xNLB24qaMbNpi3Rv70k3Om\nR5U0dFxazTpfWra0zYhZ8s47ki3xtcVXWpfbbycffth+HT/+qD+Lpk8f0UMEOpWvWnFtSpXZnDol\nQUlqamC2bUerVjIjxjglOCODrF5dNBvGuUZuWLdOsiXGmVD5ha6Db2KivFenUtqkSd4C4MuIUFAS\nXISO1mVA//79WbduXdarV4+dO3fmXybzU4xBCUCOwHyuRFevuS9GVPChOmWM819U0LF86hzxhLC7\naDZvLq6VbjFart9yi/xY+5KZKVbbbgSqL73k1xGQ604EHZSI1qw10iqLYqR1a297cjt8O1/WrvW+\n8OmSmSkXkW7d/J9LTZXxAldd5RxsduigN4tm69Ycn45Ap/IXLXKedKy4804p4zi1vOaVdevke+s7\nJfjpp0VI3L27nh+IL5mZ0uGj+37zgq6Dr8oMtWljOucom/375UbEKAC+TAgFJcFF6GgFCb5BSWGc\n5iY0YfnSL1uWU7TKLW3a2JtPvfEGedNN7i+MynL97FmxUm/UyHy5JUtEi6CL0qz4mGvl2qxLh5de\nktZHXyzKSV68/bZkd3Q+P9X58s038ndGhpR0fPUhOsydK3fdvpmcv/7i6egoflemGGc0r2UvDHYz\ni0b5dJw54+/lkRdUK67TpGNStBwREd5llfxAZYTGjvWeEnz6tLz3FSv0/EDMmDNHjtvBg4HbXyt8\ndUxWtGwp7cFOpbT+/cnZswO3fwEiFJQEF6GjFSSoE8tKcJprlFGS1Y/NxYvScrlpk/t1t28vd7oX\nL8qP9ObN/ssYhbG6zJypb64VCNQ++rZG2mVRFBkZErjourT6dr7Mny+ZDbecPy/eF/fd5/fUuqpl\nuawuuKW8DMuzFAa7mUWzcmVOW26gU/nKoEyHpk3l+5qPJE1I4mONq3FT+VI8Hl2A4wYajs/EiSIk\n1vEDMePcOTluVl1rgUTXwXftWiktOpXStm3LXbk3nwkFJcFF6GgFCcYTS1twqoOq23/4ofUy8+aZ\nlwKcMFquP/GE9aj5Bx7IEcbqcOiQvrlWoHjgAfPWSKssihE3Lq0+nS8jkwfweHQB9u5qPXjNkmHD\nyOLF/S46Azo15IGi4K5SYM2RDsLgF1+UbJoTFy/m+HQEOpWvWnGdJh2TEjxHRJCffx6YbZvgSfSw\n8CTwYGFwYUNwwbUFc4I61XH2/PNS+swNw4eThQrlbmK0G3QdfFXGbsIE8rbb7Jf1FQBfBoSCkuAi\ndLSChHw9sZ59VsSPVig/il9+cbdeo+X6yZMiANy71385FWSYaGnMSJqQxDXVy/GF+hUD60tih9U+\nWmVRjLh1aTV0vngSPUxrBs5tbD6N2ZajR+UC7aNp8SR6+F5lcEhHjXWqWTQ64su5c3N8Ovr1C2wq\nf8AA50nHiooV9QzCcokSVj94C/j8dTJ/ptOdBpffvn3JtDTR7Tj5gZgwbmRfXggL49ybqub/91vX\nwfepp0QrZDLnyIvVq8kbb7yszNRCQUlwEZoSHALo1w/4/HPgp5/Mny9aFBg4UCayuiEsLGeKbLFi\n1uuIjQV69QKeflprtbv+3oV7W/+J2/b8ii1XWU9IDSixsUDPnsCCBd6PR0UBI0aYTlDOplAhYOhQ\n4Ikn9LY1dizwzDPAuXMAgPk3AQnfAsX/AQ5XOoxVG1bprScmRiYIT5vm9XDFmIp4vnJxjPwCiN1b\n2n6Kb3S08/tTDBok03R/+02O+5NPAhcu6O2rE8nJwFNPAefPOy87YwaweTNw4EBgtm3BUzcC1Y4C\nH5eNwr0snfNESop8l0eOzNVk769O/4EPKxO9fvgZGyvl8/e7Rw8577/91n65xERg0yY5B+y+x7ff\nDhw7Jp9/iBC5IBSUhJCL5l132f/YjB4tY+1PnnS37j59gK+/Bn74QdaxeLH5OpKTgYULZTy8Bj+W\nAb4uC/T63uWFOi9Y7eOwYcDrrwN//2392pEjgVdeAY4edd5OjRoyOn7ZMgDA/uJASlsgKgOI3ReL\nbi276e/zE08Av/8uQWcWL819CS36pqHUucJ4vlx/vPLkK/brGDYMWLkSOHjQfrnixeXi9eSTwPXX\nA1WqyOsCQf36QM2awIoVzsv27QsULgxMmhSYbftQMaYiYvfF4kBxoGurWHx9Qws03fwlcPGiLNCw\nIVCpElCmDPD228D+/a63cU9rIPYs0GZPPn+/o6L0gqciRYAhQ+S7v2IFcPiw+XIRERJUz5kT+H0N\n8X+CUFASQhg5Eli+3PqiefXVQOvWwPPPu1tvwYLA8OFyp12xIhAfD7zwgv9yNWvKj/nLL2uvekgn\nYEWdXFyoc0utWnKxfcXnIm6VRTFSrhzQqROwaJHetsaNAx5/HBVLXo3YfbFYch2AQ7H2WQ0zrr1W\ngpyUFK+Hhw0Ygatnz0Gn7392XkeZMkD37hKQOXDf2T9x4snHcVvfZpgcfhi/pIwW0+BAMG6cXDyd\n1leggATAr74KnD0bmG0beGnuS5jZcibi98VjZsuZmPnaeqBCBWD1au99feYZyULOn+96G9+XBVbX\nBFI+uwTf77vuAtauBf76y365UaOAVauA9u3lvVkxYACwcSOwd29AdzPE/xH+6/pRCD0uyaFKTJRa\nuBVbtpCVKrlvdTRarn/2mQgizdZhFMbakK++JE689564bvru4w8/OBtMff21fneC6nx59928tzuv\nWSNmar62+MqJU0fr8v33zp42FL3Fa9eCo28Dw6eBe4qF8/XJuXQ49UW14n78sfOyJ06QkZH6OpS8\nYuw+InM6zl57Tc8PxID6fhecDB4sGMZxfTvkww77oOvge+ed4lrrNOdo/HgyOTlw+5cHQpe54CJ0\ntIKES3JiKaMkux+bpk3lB9gtgwfnWK43aUKuWuW/jFEY60C++pLYoWbIvPee/3Pt2tkbTJGOc4u8\n0O18cSIzUy6MJk7BnDxZfyJx27b2njaUoKTJYPDnGAlKRt0G/q/iFe732QplUKZD9+7SCaPrGJwX\nLl6UgH3LlpzHVMdZ5856fiAG1Pf7y063ScCQ3/z4Y46vkB1ffCFGeS1b2s85+u03EbYfPx7Y/cwF\noaAkuAgdrSDhkp1YLVuKIt+K9HQJTNyyfXvOnfZrr4nZlhnPPy8X98uZ554zb4187z3nTM+aNfou\nrW46X5yYPVsyB76ZHDXfRGciscYsGk+ih0gFl9cGK40FK/YtzbNFi7jv3LJCGZQ5TTomZXpxRIR8\nZy8Fc+fKwEPFyZPStbZ8uVjQ5yY4+vPP3E+Mdouug2+zZuLH0qCB/fe4d28ZP/AfEwpKgouQpiSE\nN+PGiUjNqm5/xx0i3Nu61d1669QB6tYV3UqXLsAffwBffOG/3J13ijB25073+36p6NsX2LbNfx/j\n46Xj6IMPrF97++3A8ePSyeCEm84XJ5KTgfBw4KGHvB8vVw7o2BF49lnndbRpA2RkAB99ZLlIxZiK\niP01Fr17AKdPxKJpTBsUGj4CmDcvj28giyJFgKQkvU6mypVFAzR5cmC27cSgQcD770v3EZDTcbZl\ni3Swvf22+3WWLStaJJ3jk1eydEyOmp1x44CPPxa9zsaN1sulpMhxVwLgECF0+K+johB6XLJDpQaL\nbdxovcxjj8ldkFuMluuPPmpeTiBlIunQoe7XfylJTTXfR6ssipH5873tye04eFAyGXm0HU+akMS3\nqpblyagIehKae3tfbNumP5F40SK5o7bBr7RmnCQcCJRBmY4524YNki3Zti0w23YiJcV7SvCvv0oZ\n49ln9fxAzNApqwYCg47JFqWXGT/eec5R06aXLlNlQegyF1yEjlaQcElPrKeekrkYVhw/Lj+0vlNo\nncjIkFkqH30k64iJMV+HURh7ufLXX+b7eO6cs8GUMqPbs0dvW0OGkDNm5H5fKWWVUuPBC2Fgz+4m\nhmm6Whc1i+bHH93tQKBT+X376pmzZWaKuNjNfKW8sHevnBunTuU81rMn+cgjEliouUZuadXKvqwa\nKHR1TI8/LoG105yjlStzV+4NIKGgJLgIHa0g4ZKeWKpu//PP1sskJ8udklueeSbHPXbsWLGuNmPQ\noBxh7OXKoEFiP++LVRbFyIQJ8v510Ox8sUO5kH5YCdwVY2Itv2aNWI7raF3czKJRfP65OK1euODu\ndVZ8+aV+J9Ozz8qQuyzr/nyne3cZzaBQXWsPPCDOtLnhzTf1j09e0NUxKYfmkSPt5xyZCYAvMaGg\nJLgIHa0g4ZKfWBMnkmPGWD//yy9yt2+8I9TBaLm+Z4/1Or77TtoO83v+R16w2kerLIqR3393V9LQ\n6HyxQwUlVUfLzJamd5T0zpRkZJBVq5L/+5/zytzMojES6FR+8+bkq686L3f+PFmkCJmUFLht27Fp\nk/+U4CZN5PiVLJm74EiVVXWOT165/37Jzjlx993kXXc5zzmaM8dbAHyJCQUlwUVI6BrCnFGjgKVL\nRZRpxjXXAB6POLS6wWi5Xrky0Ly5OMX6UreuiGN1HDz/K+rWBWrX9t/HuDgR89oZTFWoALRrBzz3\nnN62lF1/Lo3IlAvpz6WBF6oXxv27S3ubsIWHixhWxxa9bFkRPLsVX6r3EChSUuxF2YrISDHwW7JE\n2zE4TzRpIoZ6b76Z81hKihzr3r21xyl4ER5+6ZxSlYPvoUP2y40eDaSnA23b2psCDh4MvPdejgA4\nRAg7/uuoKIQe/8mhuvNOqYVb8emncnft1kztwIGcNsdPPpF1mLVLvv02ed11l9VwLz/eest8H3Uy\nPVu3iueDTklDTXP+4INc76oSoL745MPmmRyldbEr2ylyI768cCGwqXwluPz0U+dljx6Vlmi773Mg\nefVVrynBd90zmH8WieZUTy0eKRjJwaPudL9OnbJqoNDVMfXoIcJep1KarwD4EhK6zAUXoaMVJPwn\nJ5YySrK6aGZmykTQ1avdrzshQdxjMzPFt2PNGv9lMjLImjVFGHu5ovZxwwb/5+Lj/Sb0+tGsmfi2\n6KDR+aKN0czOyIQJ9mU7I61akUuXuttuoFP58+aR3brpLdu5s1zUL0WQe/6815RgT6KHKW3Bl+uC\nb1YHxzYtmjvjP6eyaqDQ1TFt3iwOzc2bk6+8Yr2cmQD4EhEKSoKL0NEKEgC473gIBLfcIuZPJiRN\nSOKMW2rx67gS7kesf/11Thvqyy9L94cZCxeSHTvmYscvIQsXmrdGWmVRjKxa5W1PbkduO1/M2L7d\nPJPjRuvy5pvk9de7u8ifOCHr//VXd/trhcru6Jiz7dol7cFmAXB+8PDD0iVECUqKTwSPFAJ7dQO/\nLwPGJ+aiI0i1Qx87FuCdNUFXx9S4sQRLN95o/13o1s1bAHyJCAUlwUVIUxJM6BhGBRobHcCuv3dh\nZoudKH3uBE4WdDli/brrgOrVpSbdowewezfwzTf+y/XvL+ZTu3fn4U3kM/37y6h2331s1w44d87e\nYKpzZxmEtmWL83Z0pjnrUqeO/Fu+3PtxN1qX9u2B06eBTz7R366aJJyLIXWmFC0qBmVPPum8bLVq\nogO6777AbNuJpCSvKcEnCwItBgDp1wK8GIGxZa51v87y5YHbbtPXIuUFXR1TSgrw6acyzHPzZuvl\nxo2T725GRmD3M8T/V4SCkktIeno6ateujYiICGzbts3rubS0NFSrVg01a9bEe++9Z76CV18Fjhy5\nBHtqoFMnEbx99pnp0xcjgCcbyTRT1yPWlVBRTXU1C34KF84Rxl6uWO2jjng0IgIYM0ZfADpiROC+\nB1YXHV0nTjfiWCNjxsi06dOn3b3OitGjRXB98qTzsg8/DPz0E/D994HZth0lS4r771NPZQuNt8cB\npX6PxafX34Dbt+/K3XpTUiQIy2+n1DZtZBs2Dr4AgK5dRcTatav9d6FJE6B0aW8BcIgQvvzXqZr/\nS+zcuZM//fQTW7Rowa+++ir78R07drB+/fo8f/489+7dyypVqjDDR/gJQDwOZs261Lstg8V69PB7\nWLWZlrwXnNncxJDLiYwMslo1aXM8elTEl76TbEl381n+K/bvN3cZPXPG2WDqxAmpt+uWNAL1PTCa\n2fly883kihXO63Azi8ZIoFP5vXqJXsWJzEwpW7VtG7ht27F7d/aUYC+n27zONbrlFr3jk1d0dUyP\nPCImcU6lNB8B8KUgdJkLLkJH6z/ANyiZNWsWH3rooey/27Zty88++8zrNQAund20L8ooae9er4fV\niHVMl4CkzygL23g75s8nu3SR/x8xQqbWmtG/P2n4jC5L+vc3dxmdNMneYIqUcfC63QnffCN6nEB8\nD4xmdkZWrRKtgA733UeOHu1uu59+Slap4r5zywplUKbTyTR/vpipXSrHYKspwTNm6PmBmPH66/rH\nJy9o6JiSJiSxQ5+beSKqAN+oXo7vNKhlvb7z56VTx/D7l9+EgpLgInS0/gN8g5JRo0ZxmcFCevDg\nwVy5cqXXawAwNTWVqddcw9QuXbjBrNsjP7n7brlw+uA358Qtp0/ntKH+9JMYq5mNT9+2Td/B87/C\nah/373c2mFLdCSdP6m0rULbjRjM7IxcvSleFT3BsSm7El3np3LKiSRM9c7Z//yULF3YOFAPFxx+b\nTwlW4xRyM9dItUNv3hyYfbTDwcFXZUwfbwTOvwE8GhXG5xbaZK0efpjs1y8fdlTYsGGD/FZm/QsF\nJcFF6GgFmPj4eNapU8fv39q1a7OX0QlKVq1a5bXe7BMrNx0PgWDfPncXTTfce29Om2PHjnL3bkaL\nFvZth5cDH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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", + "\n", + "#we generate the height of the previous data.\n", + "zs=randrange(n, -5, +5)\n", + "mzs=mean(zs)\n", + "zs = zs - np.tile(mzs,[n,1]).T[0]\n", + "threeD_obsmatrix=array([xs, ys, zs])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#for plotting the 3d data, we import Axes3D from matplotlib module\n", + "from mpl_toolkits.mplot3d import Axes3D" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the 3d data\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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jCTnB0xufZkrlFKZUTUm7ndTwNDUWOBKJ9BPbbMears7tfcfdxw2rb2Bt+1qq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FQAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#again applying the previous methadology, we proceed by applying the pca\n", + "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", + "#are to be taken according to their eigenvalues.\n", + "\n", + "#1st eigenvactor\n", + "eig1=eig[:,0]\n", + "\n", + "#2nd eigenvactor\n", + "eig2=eig[:,1]\n", + "\n", + "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", + "\n", + "#1st set of weights\n", + "y1=y[0,:]\n", + "\n", + "#2nd set of weights\n", + "y2=y[1,:]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the output:\n", + "fig=pyplot.figure()\n", + "ax1=fig.add_subplot(111, projection='3d')\n", + "legend([p3],[\"2d projected data points\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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OtwQhMUekXtzROmOpF9Gsym+mWiCrra01fT5GCJG2/KEPC9X1Gj2GEVBFByCT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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "fig=pyplot.figure()\n", + "ax2=fig.add_subplot(111, projection='3d')\n", + "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax2.set_xlabel('x label')\n", + "ax2.set_ylabel('y label')\n", + "ax2.set_zlabel('z label')\n", + "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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tDlWW2IMPJbiHckd51pZKRIkw691ZfLXvK2Qpvg+/Ou5XnNk3N6Ec1GUQz49/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Ah6b+v1epODXVCQhZbWXkQs1tMajUYwtbUwKpUwmkmYrtCoIZAvdbWVlZVISEhw++86deqE0tJS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\n990n/FwqleLdd9/FvffeC57nM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TiAAAII4BYiQLoBBBBAALcQAdINIIAAAriFCJBuAAEE\nEMAtRIB0AwgggABuIQKkG0AAAQRwCxEg3QACCCCAW4j/Bw+B04wzDaeaAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough of the toy data! Lets work on something real.\n", + "\n", + "Here we will show the implementation of PCA for data compression and basic face recognition.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Eigenfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", + "\n", + "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", + "\n", + "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "\n", + "#lets start with data preparation.\n", + "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", + "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", + "\n", + "def get_imlist(path):\n", + " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", + " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", + "\n", + "#set path to the required folder.\n", + "path='../../gsoc/att_faces/att_aligned/'\n", + "\n", + "#set no. of rows that the images will be resized.\n", + "k1=100\n", + "#set no. of columns that the images will be resized.\n", + "k2=100\n", + "\n", + "filenames = get_imlist(path)\n", + "filenames = np.array(filenames)\n", + "# n is total number of images that has to be analysed.\n", + "n=len(filenames)\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# we will be using this often to visualise the images out there.\n", + "def showfig(image):\n", + " imgplot=plt.imshow(image, cmap='gray')\n", + " imgplot.axes.get_xaxis().set_visible(False)\n", + " imgplot.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import Image\n", + "from scipy import misc\n", + "\n", + "# to get a hang of the data, lets see some part of the dataset images.\n", + "fig = plt.figure()\n", + "\n", + "for i in range(49):\n", + " fig.add_subplot(7,7,i)\n", + " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", + " train_img=misc.imresize(train_img, [k1,k2])\n", + " showfig(train_img)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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Pw+12Y+vWrXzQCLc0mUx8SRMJe2M7JxVU4/E4otEozGYzHA4H8ygByN5wUSwW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\n7YXBYOBsjbLMTosK6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D5yuRwf4M8++yzK5TJGRkawurqKer2Ol156CSaTCcFgEAcOHEAk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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#read each and every image. flatten them to make row vectors and stack them together \n", + "#to form the observation matrix obs_matrix.\n", + "from modshogun import PCA\n", + "\n", + "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", + "train_img=misc.imresize(train_img, [k1,k2])\n", + "train_img=np.array(train_img, dtype='double')\n", + "train_img=train_img.flatten()\n", + "\n", + "for i in range(1,n):\n", + " temp=np.array(Image.open(filenames[i]).convert('L')) \n", + " temp=misc.imresize(temp, [k1,k2])\n", + " temp=np.array(temp, dtype='double')\n", + " temp=temp.flatten()\n", + " train_img=np.vstack([train_img,temp])\n", + " \n", + "obs_matrix=train_img.T\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Again applying the PCA on the data, the same way we were doing for the last \n", + "# two times.\n", + "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", + "\n", + "y,eig= apply_pca_to_data(100,obs_matrix)\n", + "res=y.get_feature_matrix()\n", + "#print res.shape" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets see how these eigenfaces/eigenvectors look like:\n", + "fig1 = plt.figure()\n", + "plt.title('top 20 eigenfaces')\n", + "\n", + "\n", + "for i in range(20):\n", + " a = fig1.add_subplot(5,4,i+1)\n", + " eigen_faces=eig[:,i].reshape([k1,k2])\n", + " showfig(eigen_faces)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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PLRuPx9rY2AiUA/JptZlMxtaJwMOnNJIh+fomYMlLS0tTjTf+9CaCGyCZXq9n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4AAAg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\nanBaGbNH86Oqqiq0t7djx44dc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+ "text": [ + "" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", + "# which when added provides us with a reconstruction of the original image.\n", + "\n", + "reconstructed_vector=np.mat(eig)*np.mat(res)\n", + "\n", + "#plot the reconstructed images to visualise it. \n", + "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", + "fig2=plt.figure()\n", + "for i in range(1,50):\n", + " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", + " fig2.add_subplot(7,7,i)\n", + " showfig(reconstructed_image)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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W3i/a4XBgbW0Nzz//vPTgGwwGkZKjI2daeOzYMSSTSUl5ecgPtgXS+QKP09Ev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\nPeMkdho7pKWVaFF7g1ABCelccc8N/4C/xB/gCgmBBBcgBKVVa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+ "text": [ + "" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Face Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets use this technique of eigenfaces to perform some basic face recognition.\n", + "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from modshogun import RealFeatures\n", + "from modshogun import EuclideanDistance\n", + "\n", + "#get the test image in the list test_file.\n", + "test_files= get_imlist('../../gsoc/att_faces/')\n", + "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", + "\n", + "\n", + "#we plot the test image , for which we have to identify a good match from the training images we already have\n", + "fig = plt.figure()\n", + "t_img=plt.imshow(test_img, cmap='gray')\n", + "plt.title('the test image for which a match is to be identified')\n", + "t_img.axes.get_xaxis().set_visible(False)\n", + "t_img.axes.get_yaxis().set_visible(False)\n", + "\n", + "\n", + "# we flatten out or test image just the way we have done for the other images\n", + "test_image=misc.imresize(test_img, [k1,k2])\n", + "test_image=test_image.flatten()\n", + "test_image=np.array(test_image, dtype='double')\n", + "test_image=np.mat(test_image).transpose()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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rRSDXZKVdTMyHThIxUdVJQKdJrP+VD0HKwYhAMz8/H4PBoKWKZ2aBiLYNkdey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+ "text": [ + "" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have to project our training images as well as the test image on the PCA subspace." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#projecting our training images into pca subspace\n", + "train_proj=np.dot(eig.T , obs_matrix)\n", + "\n", + "#projecting our test image into pca subspace\n", + "test_proj=np.dot(eig.T , test_image)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", + "#between test image and all the training images.\n", + "#The training image which produces the least distance measure with our test image \n", + "#is said to be the best match.\n", + "testfeat = RealFeatures(np.array(test_proj))\n", + "workfeat = RealFeatures(np.array(train_proj))\n", + "RaRb=EuclideanDistance(testfeat, workfeat)\n", + "\n", + "d=np.empty([n,1])\n", + "for i in range(n):\n", + " d[i]= RaRb.distance(0,i)\n", + " \n", + "\n", + "iden=np.array(Image.open(filenames[d.argmin()]))\n", + "i_img=plt.imshow(iden, cmap='gray')\n", + "plt.title('identified image from our set of training images')\n", + "i_img.axes.get_xaxis().set_visible(False)\n", + "i_img.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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p7nkIAd1VARDDJBTn5bFjdIl8Nws2eAGOOqcvXlb5m7QhCc50Db0OMsBjOEGW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+ "text": [ + "" + ] + } + ], + "prompt_number": 27 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file From c97ff6fb7888679660250a5ce2af47d7cb635db4 Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Sat, 8 Mar 2014 20:46:58 +0530 Subject: [PATCH 11/13] cleared the output & added nbviewer link --- .../pca_notebook-checkpoint.ipynb | 587 ++++++++++++++++++ doc/ipython-notebooks/pca/pca_notebook.ipynb | 204 ++---- 2 files changed, 623 insertions(+), 168 deletions(-) create mode 100644 doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb diff --git a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb new file mode 100644 index 00000000000..99ea0bc1cd6 --- /dev/null +++ b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb @@ -0,0 +1,587 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's already make it working!!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets generate the toy data.\n", + "import numpy as np\n", + "from modshogun import *\n", + "\n", + "def randrange(n, vmin, vmax):\n", + " return (vmax-vmin)*np.random.rand(n)+vmin\n", + "\n", + "# number of data points:\n", + "n=100\n", + "\n", + "#generate random 2d data\n", + "xs = randrange(n,-20,+20)\n", + "ys = randrange(n,-20,+20)\n", + "\n", + "#subtract the mean from this\n", + "mxs=mean(xs)\n", + "xs = xs - np.tile(mxs,[n,1]).T[0]\n", + "mys=mean(ys)\n", + "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "\n", + "#form the data matrix\n", + "twoD_obsmatrix=np.array([xs, ys])\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the given data.\n", + "from matplotlib import pyplot\n", + "pyplot.ion() #to set the matplotlib's interactive mode on!\n", + "figure, axis = pyplot.subplots(1,1)\n", + "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "\n", + "def apply_pca_to_data(target_dims, data_matrix):\n", + " train_features = RealFeatures(data_matrix)\n", + " submean = PruneVarSubMean(False)\n", + " submean.init(train_features)\n", + " submean.apply_to_feature_matrix(train_features)\n", + " preprocessor = PCA()\n", + " preprocessor.set_target_dim(target_dims)\n", + " preprocessor.init(train_features)\n", + " eigenvectors = preprocessor.get_transformation_matrix()\n", + " preprocessor.apply_to_feature_matrix(train_features)\n", + " return train_features,eigenvectors\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#apply pca\n", + "#the target_dims is made 1 for this 2d data.\n", + "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", + "eig1=eig\n", + "\n", + "#hence we need to take only that set of weights which corresponds to eig1.\n", + "y1=y[0,:]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# y is just the weights that each eigenvector carries. i.e\n", + "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", + "# dimension reduction).\n", + "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", + "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "\n", + "\n", + "\n", + "#we visualise this:\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-20, 20)\n", + "pyplot.ylim(-20, 20)\n", + "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", + "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", + "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", + "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", + "\n", + "\n", + "#we are plotting the projection here:\n", + "for i in range(n):\n", + " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", + "\n", + "#we generate the height of the previous data.\n", + "zs=randrange(n, -5, +5)\n", + "mzs=mean(zs)\n", + "zs = zs - np.tile(mzs,[n,1]).T[0]\n", + "threeD_obsmatrix=array([xs, ys, zs])" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#for plotting the 3d data, we import Axes3D from matplotlib module\n", + "from mpl_toolkits.mplot3d import Axes3D" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the 3d data\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#again applying the previous methadology, we proceed by applying the pca\n", + "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", + "#are to be taken according to their eigenvalues.\n", + "\n", + "#1st eigenvactor\n", + "eig1=eig[:,0]\n", + "\n", + "#2nd eigenvactor\n", + "eig2=eig[:,1]\n", + "\n", + "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", + "\n", + "#1st set of weights\n", + "y1=y[0,:]\n", + "\n", + "#2nd set of weights\n", + "y2=y[1,:]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets visualise the output:\n", + "fig=pyplot.figure()\n", + "ax1=fig.add_subplot(111, projection='3d')\n", + "legend([p3],[\"2d projected data points\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " \n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "fig=pyplot.figure()\n", + "ax2=fig.add_subplot(111, projection='3d')\n", + "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", + "ax2.set_xlabel('x label')\n", + "ax2.set_ylabel('y label')\n", + "ax2.set_zlabel('z label')\n", + "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", + "for i in range(100):\n", + " points=y1[i]*eig1+y2[i]*eig2\n", + " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", + " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough of the toy data! Lets work on something real.\n", + "\n", + "Here we will show the implementation of PCA for data compression and basic face recognition.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Eigenfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", + "\n", + "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", + "\n", + "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import os\n", + "\n", + "#lets start with data preparation.\n", + "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", + "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", + "\n", + "def get_imlist(path):\n", + " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", + " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", + "\n", + "#set path to the required folder.\n", + "path='../../../data/att_dataset/training/'\n", + "\n", + "#set no. of rows that the images will be resized.\n", + "k1=100\n", + "#set no. of columns that the images will be resized.\n", + "k2=100\n", + "\n", + "filenames = get_imlist(path)\n", + "filenames = np.array(filenames)\n", + "# n is total number of images that has to be analysed.\n", + "n=len(filenames)\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# we will be using this often to visualise the images out there.\n", + "def showfig(image):\n", + " imgplot=plt.imshow(image, cmap='gray')\n", + " imgplot.axes.get_xaxis().set_visible(False)\n", + " imgplot.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import Image\n", + "from scipy import misc\n", + "\n", + "# to get a hang of the data, lets see some part of the dataset images.\n", + "fig = plt.figure()\n", + "\n", + "for i in range(49):\n", + " fig.add_subplot(7,7,i)\n", + " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", + " train_img=misc.imresize(train_img, [k1,k2])\n", + " showfig(train_img)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#read each and every image. flatten them to make row vectors and stack them together \n", + "#to form the observation matrix obs_matrix.\n", + "\n", + "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", + "train_img=misc.imresize(train_img, [k1,k2])\n", + "train_img=np.array(train_img, dtype='double')\n", + "train_img=train_img.flatten()\n", + "\n", + "for i in range(1,n):\n", + " temp=np.array(Image.open(filenames[i]).convert('L')) \n", + " temp=misc.imresize(temp, [k1,k2])\n", + " temp=np.array(temp, dtype='double')\n", + " temp=temp.flatten()\n", + " train_img=np.vstack([train_img,temp])\n", + " \n", + "obs_matrix=train_img.T\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Again applying the PCA on the data, the same way we were doing for the last \n", + "# two times.\n", + "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", + "\n", + "y,eig= apply_pca_to_data(100,obs_matrix)\n", + "res=y.get_feature_matrix()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#lets see how these eigenfaces/eigenvectors look like:\n", + "fig1 = plt.figure()\n", + "plt.title('top 20 eigenfaces')\n", + "\n", + "\n", + "for i in range(20):\n", + " a = fig1.add_subplot(5,4,i+1)\n", + " eigen_faces=eig[:,i].reshape([k1,k2])\n", + " showfig(eigen_faces)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", + "# which when added provides us with a reconstruction of the original image.\n", + "\n", + "reconstructed_vector=np.mat(eig)*np.mat(res)\n", + "\n", + "#plot the reconstructed images to visualise it. \n", + "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", + "fig2=plt.figure()\n", + "for i in range(1,50):\n", + " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", + " fig2.add_subplot(7,7,i)\n", + " showfig(reconstructed_image)\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Face Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets use this technique of eigenfaces to perform some basic face recognition.\n", + "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#get the test image in the list test_file.\n", + "test_files= get_imlist('../../../data/att_dataset/testing/')\n", + "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", + "\n", + "#we plot the test image , for which we have to identify a good match from the training images we already have\n", + "fig = plt.figure()\n", + "t_img=plt.imshow(test_img, cmap='gray')\n", + "plt.title('the test image for which a match is to be identified')\n", + "t_img.axes.get_xaxis().set_visible(False)\n", + "t_img.axes.get_yaxis().set_visible(False)\n", + "\n", + "# we flatten out or test image just the way we have done for the other images\n", + "test_image=misc.imresize(test_img, [k1,k2])\n", + "test_image=test_image.flatten()\n", + "test_image=np.array(test_image, dtype='double')\n", + "test_image=np.mat(test_image).transpose()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have to project our training images as well as the test image on the PCA subspace." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#projecting our training images into pca subspace\n", + "train_proj=np.dot(eig.T , obs_matrix)\n", + "\n", + "#projecting our test image into pca subspace\n", + "test_proj=np.dot(eig.T , test_image)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", + "#between test image and all the training images.\n", + "#The training image which produces the least distance measure with our test image \n", + "#is said to be the best match.\n", + "testfeat = RealFeatures(np.array(test_proj))\n", + "workfeat = RealFeatures(np.array(train_proj))\n", + "RaRb=EuclideanDistance(testfeat, workfeat)\n", + "\n", + "d=np.empty([n,1])\n", + "for i in range(n):\n", + " d[i]= RaRb.distance(0,i)\n", + " \n", + "\n", + "iden=np.array(Image.open(filenames[d.argmin()]))\n", + "i_img=plt.imshow(iden, cmap='gray')\n", + "plt.title('identified image from our set of training images')\n", + "i_img.axes.get_xaxis().set_visible(False)\n", + "i_img.axes.get_yaxis().set_visible(False)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb index 48cad6cedb9..99ea0bc1cd6 100644 --- a/doc/ipython-notebooks/pca/pca_notebook.ipynb +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:f5a08ebf445e92d5b42170c036c6da61b1e4d555d91b29483e006d7e34834339" + "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" }, "nbformat": 3, "nbformat_minor": 0, @@ -16,6 +16,13 @@ "Principal Component Analysis" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +57,7 @@ "input": [ "#lets generate the toy data.\n", "import numpy as np\n", - "\n", + "from modshogun import *\n", "\n", "def randrange(n, vmin, vmax):\n", " return (vmax-vmin)*np.random.rand(n)+vmin\n", @@ -73,8 +80,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 1 + "outputs": [] }, { "cell_type": "code", @@ -88,25 +94,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 2 + "outputs": [] }, { "cell_type": "code", @@ -115,8 +103,6 @@ "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", "\n", "def apply_pca_to_data(target_dims, data_matrix):\n", - " #from modshogun import RealFeatures\n", - " #from modshogun import PruneVarSubMean\n", " train_features = RealFeatures(data_matrix)\n", " submean = PruneVarSubMean(False)\n", " submean.init(train_features)\n", @@ -131,8 +117,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 3 + "outputs": [] }, { "cell_type": "code", @@ -144,8 +129,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 5 + "outputs": [] }, { "cell_type": "code", @@ -160,8 +144,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 6 + "outputs": [] }, { "cell_type": "code", @@ -198,17 +181,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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g4kRg7Vpda8KoRygdold3BWb9roeBxn1fGodod3d3rFq1Cu3atYOVlRX8/f1R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\nFvZth5cDH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jCTnB0xufZkrlFKZUTUm7ndTwNDUWOBKJ9BPbbMears7tfcfdxw2rb2Bt+1qq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OtwQhMUekXtzROmOpF9Gsym+mWiCrra01fT5GCJG2/KEPC9X1Gj2GEVBFByCT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tDlWW2IMPJbiHckd51pZKRIkw691ZfLXvK2Qpvg+/Ou5XnNk3N6Ec1GUQz49/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Ah6b+v1epODXVCQhZbWXkQs1tMajUYwtbUwKpUwmkmYrtCoIZAvdbWVlZVISEhw++86deqE0tJS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\n4ULs2bPHawP4AALwBoGcbgABBBDALUSgD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- "text": [ - "" - ] - } - ], - "prompt_number": 14 + "outputs": [] }, { "cell_type": "markdown", @@ -419,7 +350,7 @@ " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", "\n", "#set path to the required folder.\n", - "path='../../gsoc/att_faces/att_aligned/'\n", + "path='../../../data/att_dataset/training/'\n", "\n", "#set no. of rows that the images will be resized.\n", "k1=100\n", @@ -435,8 +366,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 15 + "outputs": [] }, { "cell_type": "code", @@ -450,8 +380,7 @@ ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 16 + "outputs": [] }, { "cell_type": "code", @@ -472,17 +401,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Pw+12Y+vWrXzQCLc0mUx8SRMJe2M7JxVU4/E4otEozGYzHA4H8ygByN5wUSwW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\n7YXBYOBsjbLMTosK6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D5yuRwf4M8++yzK5TJGRkawurqKer2Ol156CSaTCcFgEAcOHEAk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- "text": [ - "" - ] - } - ], - "prompt_number": 17 + "outputs": [] }, { "cell_type": "code", @@ -490,7 +409,6 @@ "input": [ "#read each and every image. flatten them to make row vectors and stack them together \n", "#to form the observation matrix obs_matrix.\n", - "from modshogun import PCA\n", "\n", "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", "train_img=misc.imresize(train_img, [k1,k2])\n", @@ -504,13 +422,11 @@ " temp=temp.flatten()\n", " train_img=np.vstack([train_img,temp])\n", " \n", - "obs_matrix=train_img.T\n", - "\n" + "obs_matrix=train_img.T\n" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 18 + "outputs": [] }, { "cell_type": "code", @@ -521,13 +437,11 @@ "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", "\n", "y,eig= apply_pca_to_data(100,obs_matrix)\n", - "res=y.get_feature_matrix()\n", - "#print res.shape" + "res=y.get_feature_matrix()\n" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 19 + "outputs": [] }, { "cell_type": "code", @@ -546,17 +460,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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PLRuPx9rY2AiUA/JptZlMxtaJwMOnNJIh+fomYMlLS0tTjTf+9CaCGyCZXq9n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4AAAg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\nanBaGbNH86Oqqiq0t7djx44dc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W3i/a4XBgbW0Nzz//vPTgGwwGkZKjI2daeOzYMSSTSUl5ecgPtgXS+QKP09Ev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\nHWiahvF4LImGeuTzrlYkQv8TQXECrd3Y4rIToTyWFxcHXzQCWVpaEoyU9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- "text": [ - "" - ] - } - ], - "prompt_number": 21 + "outputs": [] }, { "cell_type": "heading", @@ -617,14 +511,10 @@ "cell_type": "code", "collapsed": false, "input": [ - "from modshogun import RealFeatures\n", - "from modshogun import EuclideanDistance\n", - "\n", "#get the test image in the list test_file.\n", - "test_files= get_imlist('../../gsoc/att_faces/')\n", + "test_files= get_imlist('../../../data/att_dataset/testing/')\n", "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", "\n", - "\n", "#we plot the test image , for which we have to identify a good match from the training images we already have\n", "fig = plt.figure()\n", "t_img=plt.imshow(test_img, cmap='gray')\n", @@ -632,7 +522,6 @@ "t_img.axes.get_xaxis().set_visible(False)\n", "t_img.axes.get_yaxis().set_visible(False)\n", "\n", - "\n", "# we flatten out or test image just the way we have done for the other images\n", "test_image=misc.imresize(test_img, [k1,k2])\n", "test_image=test_image.flatten()\n", @@ -641,17 +530,7 @@ ], "language": "python", "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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rRSDXZKVdTMyHThIxUdVJQKdJrP+VD0HKwYhAMz8/H4PBoKWKZ2aBiLYNkdey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p7nkIAd1VARDDJBTn5bFjdIl8Nws2eAGOOqcvXlb5m7QhCc50Db0OMsBjOEGW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- "text": [ - "" - ] - } - ], - "prompt_number": 27 + "outputs": [] } ], "metadata": {} From 71a2a398f9d4ec11fe7108bd0d903f01ca238db3 Mon Sep 17 00:00:00 2001 From: Abhijeet Date: Mon, 17 Mar 2014 23:46:52 +0530 Subject: [PATCH 12/13] pca ipython notebook revised --- data | 2 +- doc/ipython-notebooks/pca/pca_notebook.ipynb | 1164 ++++++++++++++---- 2 files changed, 921 insertions(+), 245 deletions(-) diff --git a/data b/data index 6615cf00763..464bbd7c277 160000 --- a/data +++ b/data @@ -1 +1 @@ -Subproject commit 6615cf007634595d459853bf4dc6f1a227d2450c +Subproject commit 464bbd7c2776b50e1271a4330cae8814faa340c3 diff --git a/doc/ipython-notebooks/pca/pca_notebook.ipynb b/doc/ipython-notebooks/pca/pca_notebook.ipynb index 99ea0bc1cd6..e9aafb4bb87 100644 --- a/doc/ipython-notebooks/pca/pca_notebook.ipynb +++ b/doc/ipython-notebooks/pca/pca_notebook.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" + "signature": "sha256:a4af83329559fc62ad25f232e7026f28d4a0477cbf33bc4b68427c32ed11eff4" }, "nbformat": 3, "nbformat_minor": 0, @@ -10,73 +10,235 @@ "cells": [ { "cell_type": "heading", - "level": 3, + "level": 1, "metadata": {}, "source": [ - "Principal Component Analysis" + "Principal Component Analysis in Shogun" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "By Abhijeet Kislay (GitHub ID: kislayabhi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" + "This notebook is about finding Principal Components of data. It's dimensional reduction capabilities are further utilised to show it's application in data compression, image processing and face recognition. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# import all shogun classes\n", + "from modshogun import *" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Some Formal Background (Skip if you just want code examples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." + "PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.\n", + "\n", + "In machine learning problems data is often high dimensional - images, bag-of-word descriptions etc. In such cases we cannot expect the training data to densely populate the space, meaning that there will be large parts in which little is known about the data. Hence it is expected that only a small number of directions are relevant for describing the data to a reasonable accuracy.\n", + "\n", + "Whilst the data vectors may be very high dimensional, they will therefore typically lie closer to a much lower dimensional 'manifold'.\n", + "Here we concentrate on linear dimensional reduction techniques. In this approach a high dimensional datapoint $\\mathbf{x}$ is 'projected down' to a lower dimensional vector $\\mathbf{y}$ by:\n", + "$$\\mathbf{y}=\\mathbf{F}\\mathbf{x}+\\text{const}.$$\n", + "where the matrix $\\mathbf{F}\\in\\mathbb{R}^{\\text{M}\\times \\text{D}}$, with $\\text{M}<\\text{D}$. Here $\\text{M}=\\dim(\\mathbf{y})$ and $\\text{D}=\\dim(\\mathbf{x})$.\n", + "\n", + "From the above scenario, we infer that\n", + "\n", + "* The number of principal components to use is $\\text{M}$.\n", + "* The dimension of each data point is $\\text{D}$.\n", + "* The number of data points is $\\text{N}$.\n", + "\n", + "We express the approximation for datapoint $\\mathbf{x}^n$ as:$$\\mathbf{x}^n \\approx \\mathbf{c} + \\sum\\limits_{i=1}^{\\text{M}}y_i^n \\mathbf{b}^i \\equiv \\tilde{\\mathbf{x}}^n.$$\n", + "* Here the vector $\\mathbf{c}$ is a constant and defines a point in the hyperplane.\n", + "* The $\\mathbf{b}^i$ define vectors in the hyperplane (also known as 'principal component coefficients' or 'loadings').\n", + "* The $y_i^n$ are the low dimensional co-ordinates of the data.\n", + "\n", + "Hence our motive is to find the reconstruction $\\tilde{\\mathbf{x}}^n$ given the lower dimensional representation $\\mathbf{y}^n$(which has components $y_i^n,i = 1,...,\\text{M})$. For a data space of dimension $\\dim(\\mathbf{x})=\\text{D}$, we hope to accurately describe the data using only a small number$(\\text{M}\\ll \\text{D})$ of coordinates of $\\mathbf{y}$.\n", + "To determine the best lower dimensional representation it is convenient to use the square distance error between $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:$$\\text{E}(\\mathbf{B},\\mathbf{Y},\\mathbf{c})=\\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}[x_i^n - \\tilde{x}_i^n]^2.$$\n", + "* Here the best basis vectors are defined as $\\mathbf{B} = [\\mathbf{b}^1,...,\\mathbf{b}^\\text{M}]$ (defining $[\\text{B}]_{i,j} = b_i^j$).\n", + "* Corresponding low dimensional coordinates are defined as $\\mathbf{Y} = [\\mathbf{y}^1,...,\\mathbf{y}^\\text{N}].$\n", + "* Also, $x_i^n$ and $\\tilde{x}_i^n$ represents the coordinates of the data points for the original and the reconstructed data respectively.\n", + "* The optimal bias $\\mathbf{c}$ is given by the mean of the data $\\sum_n\\mathbf{x}^n/\\text{N}$.\n", + "\n", + "Therefore, for simplification purposes we centre our data, so as to set $\\mathbf{c}$ to zero. Now we concentrate on finding the optimal basis $\\mathbf{B}$( which has the components $\\mathbf{b}^i, i=1,...,\\text{M} $).\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Deriving the optimal linear reconstruction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" + "To find the best basis vectors $\\mathbf{B}$ and corresponding low dimensional coordinates $\\mathbf{Y}$, we may minimize the sum of squared differences between each vector $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:\n", + "\n", + "$\\text{E}(\\mathbf{B},\\mathbf{Y}) = \\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}\\left[x_i^n - \\sum\\limits_{j=1}^{\\text{M}}y_j^nb_i^j\\right]^2 = \\text{trace} \\left( (\\mathbf{X}-\\mathbf{B}\\mathbf{Y})^T(\\mathbf{X}-\\mathbf{B}\\mathbf{Y}) \\right)$\n", + "\n", + "where $\\mathbf{X} = [\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n", + "Considering the above equation under the orthonormality constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$ ( i.e the basis vectors are mutually orthogonal and of unit length ), we differentiate it w.r.t $y_k^n$. The squared error $\\text{E}(\\mathbf{B},\\mathbf{Y})$ therefore has zero derivative when: \n", + "\n", + "$y_k^n = \\sum_i b_i^kx_i^n$\n", + "\n", + "By substituting this solution in the above equation, the objective becomes\n", + "\n", + "$\\text{E}(\\mathbf{B}) = (\\text{N}-1)\\left[\\text{trace}(\\mathbf{S}) - \\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)\\right],$\n", + "\n", + "where $\\mathbf{S}$ is the sample covariance matrix of the data.\n", + "To minimise equation under the constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$, we use a set of Lagrange Multipliers $\\mathbf{L}$, so that the objective is to minimize: \n", + "\n", + "$-\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)+\\text{trace}\\left(\\mathbf{L}\\left(\\mathbf{B}^T\\mathbf{B} - \\mathbf{I}\\right)\\right).$\n", + "\n", + "Since the constraint is symmetric, we can assume that $\\mathbf{L}$ is also symmetric. Differentiating with respect to $\\mathbf{B}$ and equating to zero we obtain that at the optimum \n", + "\n", + "$\\mathbf{S}\\mathbf{B} = \\mathbf{B}\\mathbf{L}$.\n", + "\n", + "This is a form of eigen-equation so that a solution is given by taking $\\mathbf{L}$ to be diagonal and $\\mathbf{B}$ as the matrix whose columns are the corresponding eigenvectors of $\\mathbf{S}$. In this case,\n", + "\n", + "$\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right) =\\text{trace}(\\mathbf{L}),$\n", + "\n", + "which is the sum of the eigenvalues corresponding to the eigenvectors forming $\\mathbf{B}$. Since we wish to minimise $\\text{E}(\\mathbf{B})$, we take the eigenvectors with the largest corresponding eigenvalues.\n", + "Whilst the solution to this eigen-problem is unique, this only serves to define the solution subspace since one may rotate and scale $\\mathbf{B}$ and $\\mathbf{Y}$ such that the value of the squared loss is exactly the same. The justification for choosing the non-rotated eigen solution is given by the additional requirement that the principal components corresponds to directions of maximal variance." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Maximum variance criterion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." + "We aim to find that single direction $\\mathbf{b}$ such that, when the data is projected onto this direction, the variance of this projection is maximal amongst all possible such projections.\n", + "The projection of a datapoint onto a direction $\\mathbf{b}$ is $\\mathbf{b}^T\\mathbf{x}^n$ for a unit length vector $\\mathbf{b}$. Hence the sum of squared projections is: $$\\sum\\limits_{n}\\left(\\mathbf{b}^T\\mathbf{x}^n\\right)^2 = \\mathbf{b}^T\\left[\\sum\\limits_{n}\\mathbf{x}^n(\\mathbf{x}^n)^T\\right]\\mathbf{b} = (\\text{N}-1)\\mathbf{b}^T\\mathbf{S}\\mathbf{b} = \\lambda(\\text{N} - 1)$$ \n", + "which ignoring constants, is simply the negative of the equation for a single retained eigenvector $\\mathbf{b}$(with $\\mathbf{S}\\mathbf{b} = \\lambda\\mathbf{b}$). Hence the optimal single $\\text{b}$ which maximises the projection variance is given by the eigenvector corresponding to the largest eigenvalues of $\\mathbf{S}.$ The second largest eigenvector corresponds to the next orthogonal optimal direction and so on. This explains why, despite the squared loss equation being invariant with respect to arbitrary rotation of the basis vectors, the ones given by the eigen-decomposition have the additional property that they correspond to directions of maximal variance. These maximal variance directions found by PCA are called the $\\text{principal} $ $\\text{directions}.$\n", + "\n", + "There are two eigenvalue methods through which shogun can perform PCA namely\n", + "* Eigenvalue Decomposition Method.\n", + "* Singular Value Decomposition.\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "EGD vs SVD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The EGD viewpoint requires that one compute the eigenvalues and eigenvectors of the covariance matrix, which is the product of $\\mathbf{X}\\mathbf{X}^\\text{T}$, where $\\mathbf{X}$ is the data matrix. Since the covariance matrix is symmetric, the matrix is diagonalizable, and the eigenvectors can be normalized such that they are orthonormal:\n", + "\n", + "$\\mathbf{S}=\\frac{1}{\\text{N}-1}\\mathbf{X}\\mathbf{X}^\\text{T},$\n", + "\n", + "where the $\\text{D}\\times\\text{N}$ matrix $\\mathbf{X}$ contains all the data vectors: $\\mathbf{X}=[\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n", + "Writing the $\\text{D}\\times\\text{N}$ matrix of eigenvectors as $\\mathbf{E}$ and the eigenvalues as an $\\text{N}\\times\\text{N}$ diagonal matrix $\\mathbf{\\Lambda}$, the eigen-decomposition of the covariance $\\mathbf{S}$ is\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{X}^\\text{T}\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\tilde{\\mathbf{E}}=\\tilde{\\mathbf{E}}\\mathbf{\\Lambda},$\n", + "\n", + "where we defined $\\tilde{\\mathbf{E}}=\\mathbf{X}^\\text{T}\\mathbf{E}$. The final expression above represents the eigenvector equation for $\\mathbf{X}^\\text{T}\\mathbf{X}.$ This is a matrix of dimensions $\\text{N}\\times\\text{N}$ so that calculating the eigen-decomposition takes $\\mathcal{O}(\\text{N}^3)$ operations, compared with $\\mathcal{O}(\\text{D}^3)$ operations in the original high-dimensional space. We then can therefore calculate the eigenvectors $\\tilde{\\mathbf{E}}$ and eigenvalues $\\mathbf{\\Lambda}$ of this matrix more easily. Once found, we use the fact that the eigenvalues of $\\mathbf{S}$ are given by the diagonal entries of $\\mathbf{\\Lambda}$ and the eigenvectors by\n", + "\n", + "$\\mathbf{E}=\\mathbf{X}\\tilde{\\mathbf{E}}\\mathbf{\\Lambda}^{-1}$\n", + "\n", + "\n", + "\n", + "\n", + "* On the other hand, applying SVD to the data matrix $\\mathbf{X}$ follows like:\n", + "\n", + "$\\mathbf{X}=\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}$\n", + "\n", + "where $\\mathbf{U}^\\text{T}\\mathbf{U}=\\mathbf{I}_\\text{D}$ and $\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}_\\text{N}$ and $\\mathbf{\\Sigma}$ is a diagonal matrix of the (positive) singular values. We assume that the decomposition has ordered the singular values so that the upper left diagonal element of $\\mathbf{\\Sigma}$ contains the largest singular value.\n", + "\n", + "Attempting to construct the covariance matrix $(\\mathbf{X}\\mathbf{X}^\\text{T})$from this decomposition gives:\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)^\\text{T}$\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{V}\\mathbf{\\Sigma}\\mathbf{U}^\\text{T}\\right)$\n", + "\n", + "and since $\\mathbf{V}$ is an orthogonal matrix $\\left(\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}\\right),$\n", + "\n", + "$\\mathbf{X}\\mathbf{X}^\\text{T}=\\left(\\mathbf{U}\\mathbf{\\Sigma}^\\mathbf{2}\\mathbf{U}^\\text{T}\\right)$\n", + "\n", + "Since it is in the form of an eigen-decomposition, the PCA solution given by performing the SVD decomposition of $\\mathbf{X}$, for which the eigenvectors are then given by $\\mathbf{U}$, and corresponding eigenvalues by the square of the singular values.\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "PCA on 2D data" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 1: Get some data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's already make it working!!" + "We will generate the toy data by adding orthogonal noise to a set of points lying on an arbitrary 2d line. We expect PCA to recover this line, which is a one-dimensional linear sub-space." ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets generate the toy data.\n", - "import numpy as np\n", - "from modshogun import *\n", - "\n", - "def randrange(n, vmin, vmax):\n", - " return (vmax-vmin)*np.random.rand(n)+vmin\n", - "\n", - "# number of data points:\n", + "#number of data points.\n", "n=100\n", "\n", - "#generate random 2d data\n", - "xs = randrange(n,-20,+20)\n", - "ys = randrange(n,-20,+20)\n", + "#generate a random 2d line(y1 = mx1 + c)\n", + "m=np.random.randint(1,10)\n", + "c=np.random.randint(1,10)\n", + "x1=np.random.random_integers(-20,20,n)\n", + "y1=m*x1+c\n", + "\n", + "#generate the noise.\n", + "noise=np.random.random_sample([n])*np.random.random_integers(-35,35,n)\n", "\n", - "#subtract the mean from this\n", - "mxs=mean(xs)\n", - "xs = xs - np.tile(mxs,[n,1]).T[0]\n", - "mys=mean(ys)\n", - "ys = ys - np.tile(mys,[n,1]).T[0]\n", + "#make the noise orthogonal to the line y=mx+c and add it.\n", + "x=x1 + noise*m/np.sqrt(1+np.square(m))\n", + "y=y1 + noise/np.sqrt(1+np.square(m))\n", "\n", - "#form the data matrix\n", - "twoD_obsmatrix=np.array([xs, ys])\n" + "twoD_obsmatrix=np.array([x,y])" ], "language": "python", "metadata": {}, @@ -86,195 +248,517 @@ "cell_type": "code", "collapsed": false, "input": [ - "#lets visualise the given data.\n", + "#to visualise the data we must plot it.\n", "from matplotlib import pyplot\n", - "pyplot.ion() #to set the matplotlib's interactive mode on!\n", - "figure, axis = pyplot.subplots(1,1)\n", - "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" + "pylab.rcParams['figure.figsize'] = 7, 7 \n", + "figure,axis=pyplot.subplots(1,1)\n", + "pyplot.xlim(-50,50)\n", + "pyplot.ylim(-50,50)\n", + "axis.plot(twoD_obsmatrix[0,:],twoD_obsmatrix[1,:],'o',color='green',markersize=6)\n", + "\n", + "#the line from which we generated the data is plotted in red\n", + "axis.plot(x1[:],y1[:],linewidth=0.3,color='red')\n", + "pyplot.title('One-Dimensional sub-space with noise')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 2: Subtract the mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For PCA to work properly, we must subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. So, all the $x$ values have $\\bar{x}$ subtracted, and all the $y$ values have $\\bar{y}$ subtracted from them, where:$$\\bar{\\mathbf{x}} = \\frac{\\sum\\limits_{i=1}^{n}x_i}{n}$$ $\\bar{\\mathbf{x}}$ denotes the mean of the $x_i^{'s}$" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", + "#Preprocessor PCA performs principial component analysis on input\n", + "#feature vectors/matrices.\n", + "train_features = RealFeatures(twoD_obsmatrix)\n", + "preprocessor = PCA()\n", "\n", - "def apply_pca_to_data(target_dims, data_matrix):\n", - " train_features = RealFeatures(data_matrix)\n", - " submean = PruneVarSubMean(False)\n", - " submean.init(train_features)\n", - " submean.apply_to_feature_matrix(train_features)\n", - " preprocessor = PCA()\n", - " preprocessor.set_target_dim(target_dims)\n", - " preprocessor.init(train_features)\n", - " eigenvectors = preprocessor.get_transformation_matrix()\n", - " preprocessor.apply_to_feature_matrix(train_features)\n", - " return train_features,eigenvectors\n", - "\n" + "#since we are projecting down the 2d data, the target dim is 1. But here the exhaustive method is detailed by\n", + "#setting the target dimension to 2 to visualize both the eigen vectors.\n", + "#However, in future examples we will get rid of this step by implementing it directly.\n", + "preprocessor.set_target_dim(2)\n", + "\n", + "#When the init method in PCA is called with proper\n", + "#feature matrix X (with say N number of vectors and D feature dimension), a\n", + "#transformation matrix is computed and stored internally.It inherenty also\n", + "#centralizes the data by subtracting the mean from it.\n", + "preprocessor.init(train_features)\n", + "\n", + "#get the mean for the respective dimensions.\n", + "mean_datapoints=preprocessor.get_mean()\n", + "mean_x=mean_datapoints[0]\n", + "mean_y=mean_datapoints[1]" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 3: Calculate the covariance matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand the relationship between 2 dimension we define $\\text{covariance}$. It is a measure to find out how much the dimensions vary from the mean $with$ $respect$ $to$ $each$ $other.$$$cov(X,Y)=\\frac{\\sum\\limits_{i=1}^{n}(X_i-\\bar{X})(Y_i-\\bar{Y})}{n-1}$$\n", + "A useful way to get all the possible covariance values between all the different dimensions is to calculate them all and put them in a matrix.\n", + "\n", + "Example: For a 3d dataset with usual dimensions of $x,y$ and $z$, the covariance matrix has 3 rows and 3 columns, and the values are this:\n", + "$$\\mathbf{S} = \\quad\\begin{pmatrix}cov(x,x)&cov(x,y)&cov(x,z)\\\\cov(y,x)&cov(y,y)&cov(y,z)\\\\cov(z,x)&cov(z,y)&cov(z,z)\\end{pmatrix}$$\n", + "\n", + "\n" + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the eigenvectors $e^1,....e^M$ of the covariance matrix $\\mathbf{S}$." + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "#apply pca\n", - "#the target_dims is made 1 for this 2d data.\n", - "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" + "#Step 3 and step 4 are directly implemented by the PCA preprocessor of Shogun toolbar.\n", + "\n", + "#The transformation matrix is essentially a DxM matrix, the columns of which correspond\n", + "#to the eigenvectors of the covariance matrix(XX') having top M eigenvalues.\n", + "E = preprocessor.get_transformation_matrix()\n", + "\n", + "#Get all the eigenvalues returned by PCA.\n", + "eig_value=preprocessor.get_eigenvalues()\n", + "\n", + "e1 = E[:,0]\n", + "e2 = E[:,1]\n", + "eig_value1 = eig_value[0]\n", + "eig_value2 = eig_value[1]" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Steps 5: Choosing components and forming a feature vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets visualize the eigenvectors and decide upon which to choose as the $principle$ $component$ of the data set." + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", - "eig1=eig\n", + "#find out the M eigenvectors corresponding to top M number of eigenvalues and store it in E\n", + "#Here M=1\n", "\n", - "#hence we need to take only that set of weights which corresponds to eig1.\n", - "y1=y[0,:]\n", - "\n" + "#slope of e1 & e2\n", + "m1=e1[1]/e1[0]\n", + "m2=e2[1]/e2[0]\n", + "\n", + "#generate the two lines\n", + "x1=range(-50,50)\n", + "x2=x1\n", + "y1=np.multiply(m1,x1)\n", + "y2=np.multiply(m2,x2)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#plot the data along with those two eigenvectors\n", + "figure, axis = pyplot.subplots(1,1)\n", + "pyplot.xlim(-50, 50)\n", + "pyplot.ylim(-50, 50)\n", + "axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n", + "axis.plot(x1[:], y1[:], linewidth=0.7, color='black')\n", + "axis.plot(x2[:], y2[:], linewidth=0.7, color='blue')\n", + "p1 = Rectangle((0, 0), 1, 1, fc=\"black\")\n", + "p2 = Rectangle((0, 0), 1, 1, fc=\"blue\")\n", + "legend([p1,p2],[\"1st eigenvector\",\"2nd eigenvector\"])\n", + "pyplot.title('Eigenvectors selection')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above figure, the blue line is a good fit of the data. It shows the most significant relationship between the data dimensions.\n", + "It turns out that the eigenvector with the $highest$ eigenvalue is the $principle$ $component$ of the data set.\n", + "Form the matrix $\\mathbf{E}=[\\mathbf{e}^1,...,\\mathbf{e}^M].$\n", + "Here $\\text{M}$ represents the target dimension of our final projection" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# y is just the weights that each eigenvector carries. i.e\n", - "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", - "# dimension reduction).\n", - "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", - "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", + "#The eigenvector corresponding to higher eigenvalue(i.e eig_value2) is choosen(i.e e2).\n", + "#E is the feature vector.\n", + "E=e2" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step6: Deriving the new data set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the final step in PCA. Once we have choosen the components(eigenvectors) that we wish to keep in our data and formed a feature vector, we simply take the vector and multiply it on the left of the original dataset.\n", + "The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by \n", "\n", + "$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$\n", "\n", + "Here the $\\mathbf{E}^T$ is the matrix with the eigenvectors in rows, with the most significant eigenvector at the top. The mean adjusted data, with data items in each column, with each row holding a seperate dimension is multiplied to it." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#This can be performed by shogun's PCA preprocessor as follows:\n", + "#The transformation matrix that we got after init is used to transform all D-dimensional feature\n", + "#matrices (with D feature dimensions) supplied via apply_to_feature_matrix methods.This tranformation\n", + "#outputs the M-Dimensional approximation of all these input vectors and matrices (where M<=min(D,N)).\n", + "yn=preprocessor.apply_to_feature_matrix(train_features)\n", "\n", - "#we visualise this:\n", + "#Since, here we are manually trying to find the eigenvector corresponding to top eigenvalue\n", + "#The 2nd row of yn is choosen as it corresponds to the required eigenvector e2.\n", + "yn1=yn[1,:]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 5 and Step 6 can be applied directly with Shogun's PCA preprocessor(from next example). It has been done manually here to show the exhaustive nature of Principal Component Analysis." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x_new=(yn1 * E[0])+np.tile(mean_x,[n,1]).T[0]\n", + "y_new=(yn1 * E[1])+np.tile(mean_y,[n,1]).T[0]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new data is plotted below" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ "figure, axis = pyplot.subplots(1,1)\n", - "pyplot.xlim(-20, 20)\n", - "pyplot.ylim(-20, 20)\n", - "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", - "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", - "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", + "pyplot.xlim(-50, 50)\n", + "pyplot.ylim(-50, 50)\n", + "\n", + "axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n", + "axis.plot(x_new, y_new, 'o', color='blue', markersize=5, label=\"red\")\n", + "pyplot.title('PCA Projection of 2D data into 1D subspace')\n", + "pyplot.xlabel(\"x axis\")\n", + "pyplot.ylabel(\"y axis\")\n", + "\n", + "#add some legend for information\n", "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", "\n", - "\n", - "#we are plotting the projection here:\n", + "#plot the projections in red:\n", "for i in range(n):\n", - " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", + " axis.plot([x[i],x_new[i]],[y[i],y_new[i]] , color='red')" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "PCA on a 3d data." + ] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step1: Get some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We generate points from a plane and then add random noise orthogonal to it. The general equation of a plane is: $$\\text{a}\\mathbf{x}+\\text{b}\\mathbf{y}+\\text{c}\\mathbf{z}+\\text{d}=0$$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#number of points\n", + "n=100\n", "\n", + "#generate the data\n", + "a=np.random.randint(1,20)\n", + "b=np.random.randint(1,20)\n", + "c=np.random.randint(1,20)\n", + "d=np.random.randint(1,20)\n", "\n", + "x1=np.random.random_integers(-20,20,n)\n", + "y1=np.random.random_integers(-20,20,n)\n", + "z1=-(a*x1+b*y1+d)/c\n", "\n", - "\n" + "#generate the noise\n", + "noise=np.random.random_sample([n])*np.random.random_integers(-30,30,n)\n", + "\n", + "#the normal unit vector is [a,b,c]/magnitude\n", + "magnitude=np.sqrt(np.square(a)+np.square(b)+np.square(c))\n", + "normal_vec=np.array([a,b,c]/magnitude)\n", + "\n", + "#add the noise orthogonally\n", + "x=x1+noise*normal_vec[0]\n", + "y=y1+noise*normal_vec[1]\n", + "z=z1+noise*normal_vec[2]\n", + "threeD_obsmatrix=np.array([x,y,z])" ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#to visualize the data, we must plot it.\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = pyplot.figure()\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "\n", + "#plot the noisy data generated by distorting a plane\n", + "ax.scatter(x, y, z,marker='o', color='g')\n", + "\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", + "legend([p2],[\"3d data\"])\n", + "pyplot.title('Two dimensional subspace with noise')\n", + "xx, yy = np.meshgrid(range(-30,30), range(-30,30))\n", + "zz=-(a * xx + b * yy + d) / c\n", + "ax.set_xlim(-30,30)\n", + "ax.set_ylim(-30,30)\n", + "ax.set_zlim(-30,30)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 2: Subtract the mean." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Shogun's PCA preprocessor also centralizes the data by subtracting the mean from it.\n", + "train_features = RealFeatures(threeD_obsmatrix)\n", + "preprocessor = PCA()\n", + "\n", + "#If we set the target dimension to 2, Shogun would automagically preserve the required 2 eigenvectors(out of 3) according to their\n", + "#eigenvalues.\n", + "preprocessor.set_target_dim(2)\n", + "preprocessor.init(train_features)\n", + "\n", + "#get the mean for the respective dimensions.\n", + "mean_datapoints=preprocessor.get_mean()\n", + "mean_x=mean_datapoints[0]\n", + "mean_y=mean_datapoints[1]\n", + "mean_z=mean_datapoints[2]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Step 3 & Step 4: Calculate the eigenvectors of the covariance matrix" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#get the required eigenvectors corresponding to top 2 eigenvalues.\n", + "E = preprocessor.get_transformation_matrix()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "Steps 5: Choosing components and forming a feature vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we performed PCA for a target $\\dim = 2$ for the $3 \\dim$ data, we are directly given \n", + "the two required eigenvectors in $\\mathbf{E}$" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", - "\n", - "#we generate the height of the previous data.\n", - "zs=randrange(n, -5, +5)\n", - "mzs=mean(zs)\n", - "zs = zs - np.tile(mzs,[n,1]).T[0]\n", - "threeD_obsmatrix=array([xs, ys, zs])" + "#E is automagically filled by setting target dimension = M. This is different from the 2d data example \n", + "#where we implemented this step manually." ], "language": "python", "metadata": {}, "outputs": [] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#for plotting the 3d data, we import Axes3D from matplotlib module\n", - "from mpl_toolkits.mplot3d import Axes3D" - ], - "language": "python", + "cell_type": "heading", + "level": 4, "metadata": {}, - "outputs": [] + "source": [ + "Step 6: Deriving the new data set\n" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#plot the 3d data\n", - "fig = pyplot.figure()\n", - "ax=fig.add_subplot(111, projection='3d')\n", - "ax.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax.set_xlabel('x label')\n", - "ax.set_ylabel('y label')\n", - "ax.set_zlabel('z label')\n", - "legend([p2],[\"3d data\"])" + "#This can be performed by shogun's PCA preprocessor as follows:\n", + "yn=preprocessor.apply_to_feature_matrix(train_features)" ], "language": "python", "metadata": {}, "outputs": [] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#again applying the previous methadology, we proceed by applying the pca\n", - "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" - ], - "language": "python", + "cell_type": "heading", + "level": 4, "metadata": {}, - "outputs": [] + "source": [ + "Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", - "#are to be taken according to their eigenvalues.\n", - "\n", - "#1st eigenvactor\n", - "eig1=eig[:,0]\n", - "\n", - "#2nd eigenvactor\n", - "eig2=eig[:,1]\n", - "\n", - "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", - "\n", - "#1st set of weights\n", - "y1=y[0,:]\n", - "\n", - "#2nd set of weights\n", - "y2=y[1,:]\n" - ], - "language": "python", + "cell_type": "markdown", "metadata": {}, - "outputs": [] + "source": [ + "The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#lets visualise the output:\n", - "fig=pyplot.figure()\n", - "ax1=fig.add_subplot(111, projection='3d')\n", - "legend([p3],[\"2d projected data points\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " \n" + "new_data=np.dot(E,yn)\n", + "\n", + "x_new=new_data[0,:]+np.tile(mean_x,[n,1]).T[0]\n", + "y_new=new_data[1,:]+np.tile(mean_y,[n,1]).T[0]\n", + "z_new=new_data[2,:]+np.tile(mean_z,[n,1]).T[0]" ], "language": "python", "metadata": {}, @@ -284,160 +768,249 @@ "cell_type": "code", "collapsed": false, "input": [ - "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", + "#all the above points lie on the same plane. To make it more clear we will plot the projection also.\n", + "\n", "fig=pyplot.figure()\n", - "ax2=fig.add_subplot(111, projection='3d')\n", - "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax2.set_xlabel('x label')\n", - "ax2.set_ylabel('y label')\n", - "ax2.set_zlabel('z label')\n", + "pyplot.title('PCA Projection of 3D data into 2D subspace')\n", + "ax=fig.add_subplot(111, projection='3d')\n", + "ax.scatter(x, y, z,marker='o', color='g')\n", + "ax.set_xlabel('x label')\n", + "ax.set_ylabel('y label')\n", + "ax.set_zlabel('z label')\n", "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", "\n", - "\n" + "for i in range(100):\n", + " ax.scatter(x_new[i], y_new[i], z_new[i],marker='o', color='b')\n", + " ax.plot([x[i],x_new[i]],[y[i],y_new[i]],[z[i],z_new[i]],color='r') \n", + "ax.set_xlim(-30,30)\n", + "ax.set_ylim(-30,30)\n", + "ax.set_zlim(-30,30)" ], "language": "python", "metadata": {}, "outputs": [] }, + { + "cell_type": "heading", + "level": 4, + "metadata": {}, + "source": [ + "PCA Performance" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Enough of the toy data! Lets work on something real.\n", + "Performance of PCA depends on the algorithm used according to the situation in hand.\n", + "Our PCA preprocessor class provides 3 method options to compute the transformation matrix:\n", "\n", - "Here we will show the implementation of PCA for data compression and basic face recognition.\n", - "\n" + "* $\\text{EVD}$ : Eigen Value Decomposition of Covariance Matrix $(\\mathbf{XX^T}).$\n", + "The covariance matrix $\\mathbf{XX^T}$ is first formed internally and then\n", + "its eigenvectors and eigenvalues are computed using QR decomposition of the matrix.\n", + "The time complexity of this method is $\\mathcal{O}(D^3)$ and should be used when $\\text{N > D.}$\n", + "\n", + "\n", + "* $\\text{SVD}$ : Singular Value Decomposition of feature matrix $\\mathbf{X}$.\n", + "The transpose of feature matrix, $\\mathbf{X^T}$, is decomposed using SVD. $\\mathbf{X^T = UDV^T}.$\n", + "The matrix V in this decomposition contains the required eigenvectors and\n", + "the diagonal entries of the diagonal matrix D correspond to the non-negative\n", + "eigenvalues.The time complexity of this method is $\\mathcal{O}(DN^2)$ and should be used when $\\text{N < D.}$\n", + "\n", + "\n", + "* $\\text{AUTO}$ : This mode automagically chooses one of the above modes for the user based on whether $\\text{N>D}$ (chooses $\\text{EVD}$) or $\\text{ND)}$ but for the next example $\\text{(N Date: Tue, 18 Mar 2014 00:04:16 +0530 Subject: [PATCH 13/13] removed the .ipynb_checkpoints --- .../pca_notebook-checkpoint.ipynb | 587 ------------------ 1 file changed, 587 deletions(-) delete mode 100644 doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb diff --git a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb b/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb deleted file mode 100644 index 99ea0bc1cd6..00000000000 --- a/doc/ipython-notebooks/pca/.ipynb_checkpoints/pca_notebook-checkpoint.ipynb +++ /dev/null @@ -1,587 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:224105e9fb538267eabc12f3e834d563e5c3d05558c027c295c6b4b14e4e90f4" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Principal Component Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "nbviewer: http://nbviewer.ipython.org/gist/kislayabhi/9431770" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PCA finds a linear projection of high dimensional data into a lower dimensional subspace such that the variance is retained and least square reconstruction error is minimized." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we concentrate on linear dimension reduction techniques. In this approach a high dimensional datapoint $x$ is 'projected down' to a lower dimensional vector $y$ by using $y=F*x+const$ where the non-square matrix $F$ has dimensions $dim(y)*dim(x)$, with $dim(y) < dim(x)$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Effectively, we are trying to discover a low dimensional co-ordinate system in which we can approximately represent the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's already make it working!!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets generate the toy data.\n", - "import numpy as np\n", - "from modshogun import *\n", - "\n", - "def randrange(n, vmin, vmax):\n", - " return (vmax-vmin)*np.random.rand(n)+vmin\n", - "\n", - "# number of data points:\n", - "n=100\n", - "\n", - "#generate random 2d data\n", - "xs = randrange(n,-20,+20)\n", - "ys = randrange(n,-20,+20)\n", - "\n", - "#subtract the mean from this\n", - "mxs=mean(xs)\n", - "xs = xs - np.tile(mxs,[n,1]).T[0]\n", - "mys=mean(ys)\n", - "ys = ys - np.tile(mys,[n,1]).T[0]\n", - "\n", - "#form the data matrix\n", - "twoD_obsmatrix=np.array([xs, ys])\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets visualise the given data.\n", - "from matplotlib import pyplot\n", - "pyplot.ion() #to set the matplotlib's interactive mode on!\n", - "figure, axis = pyplot.subplots(1,1)\n", - "axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# lets get our pca preprocessor ready to cut down this data's dimensions from 2 to 1\n", - "\n", - "def apply_pca_to_data(target_dims, data_matrix):\n", - " train_features = RealFeatures(data_matrix)\n", - " submean = PruneVarSubMean(False)\n", - " submean.init(train_features)\n", - " submean.apply_to_feature_matrix(train_features)\n", - " preprocessor = PCA()\n", - " preprocessor.set_target_dim(target_dims)\n", - " preprocessor.init(train_features)\n", - " eigenvectors = preprocessor.get_transformation_matrix()\n", - " preprocessor.apply_to_feature_matrix(train_features)\n", - " return train_features,eigenvectors\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#apply pca\n", - "#the target_dims is made 1 for this 2d data.\n", - "y,eig = apply_pca_to_data(1, twoD_obsmatrix)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#Automatically we are returned with that eigenvector which corresponds to higher eigenvalue\n", - "eig1=eig\n", - "\n", - "#hence we need to take only that set of weights which corresponds to eig1.\n", - "y1=y[0,:]\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# y is just the weights that each eigenvector carries. i.e\n", - "# here we have only 1 eigenvector at our disposal(we are removing the other one corresponding to lesser eigenvalue to achieve\n", - "# dimension reduction).\n", - "# so for datapoint1 (x,y) in 2d is approximated by y1[0]*eig1 in 1d\n", - "# similarly datapoint2 (x,y) in 2d is approximated by y1[1]*eig1 in 1d ...\n", - "\n", - "\n", - "\n", - "#we visualise this:\n", - "figure, axis = pyplot.subplots(1,1)\n", - "pyplot.xlim(-20, 20)\n", - "pyplot.ylim(-20, 20)\n", - "a1=axis.plot(twoD_obsmatrix[0,:], twoD_obsmatrix[1,:],'o',color='green', markersize=5, label=\"green\")\n", - "a2=axis.plot((y1 * eig1[0]), (y[0,:] * eig1[1]), 'o', color='blue', markersize=5, label=\"red\")\n", - "pyplot.title('the projection of 2d data onto 1d maintaining the best variance between the datapoints!')\n", - "p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n", - "p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n", - "p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n", - "legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"])\n", - "\n", - "\n", - "#we are plotting the projection here:\n", - "for i in range(n):\n", - " axis.plot([xs[i],(y[0,i]*eig1[0])],[ys[i],(y[0,i]*eig1[1])] , color='red')\n", - "\n", - "\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets take one more example to make things more clearer. here we will convert a 3d data to 2d.\n", - "\n", - "#we generate the height of the previous data.\n", - "zs=randrange(n, -5, +5)\n", - "mzs=mean(zs)\n", - "zs = zs - np.tile(mzs,[n,1]).T[0]\n", - "threeD_obsmatrix=array([xs, ys, zs])" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#for plotting the 3d data, we import Axes3D from matplotlib module\n", - "from mpl_toolkits.mplot3d import Axes3D" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#plot the 3d data\n", - "fig = pyplot.figure()\n", - "ax=fig.add_subplot(111, projection='3d')\n", - "ax.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax.set_xlabel('x label')\n", - "ax.set_ylabel('y label')\n", - "ax.set_zlabel('z label')\n", - "legend([p2],[\"3d data\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#again applying the previous methadology, we proceed by applying the pca\n", - "y,eig= apply_pca_to_data(2, threeD_obsmatrix)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are trying to reduce dimensionality to two, hence only two eigenvectors\n", - "#are to be taken according to their eigenvalues.\n", - "\n", - "#1st eigenvactor\n", - "eig1=eig[:,0]\n", - "\n", - "#2nd eigenvactor\n", - "eig2=eig[:,1]\n", - "\n", - "#similarly, we need to have two sets of weights. one corresponding to eig1 and the other corresponding to eig2\n", - "\n", - "#1st set of weights\n", - "y1=y[0,:]\n", - "\n", - "#2nd set of weights\n", - "y2=y[1,:]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets visualise the output:\n", - "fig=pyplot.figure()\n", - "ax1=fig.add_subplot(111, projection='3d')\n", - "legend([p3],[\"2d projected data points\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax1.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " \n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#all the above points lie on the same plane. to make it more clear we will plot the projection also.\n", - "fig=pyplot.figure()\n", - "ax2=fig.add_subplot(111, projection='3d')\n", - "ax2.scatter(xs, ys, zs,marker='o', color='g')\n", - "ax2.set_xlabel('x label')\n", - "ax2.set_ylabel('y label')\n", - "ax2.set_zlabel('z label')\n", - "legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"])\n", - "for i in range(100):\n", - " points=y1[i]*eig1+y2[i]*eig2\n", - " ax2.scatter(points[0], points[1], points[2],marker='o', color='b')\n", - " ax2.plot([xs[i],points[0]],[ys[i],points[1]],[zs[i],points[2]],color='r')\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enough of the toy data! Lets work on something real.\n", - "\n", - "Here we will show the implementation of PCA for data compression and basic face recognition.\n", - "\n" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Eigenfaces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic manner.\n", - "\n", - "Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with $N$ pixels is considered a point(or vector) in N-dimension space.\n", - "\n", - "The eigenfaces may be considered as a set of features which characterize the global variation among face images. Then each face image is approximated using a subset of the eigenfaces, those associated with the largest eigenvalues. These features account for the most variance in the training set." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "\n", - "#lets start with data preparation.\n", - "#Download the att_data set from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n", - "#Then make sure to put all images (in personwise different folders) in a single folder(lots of renaming may be required!!)\n", - "\n", - "def get_imlist(path):\n", - " \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n", - " return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n", - "\n", - "#set path to the required folder.\n", - "path='../../../data/att_dataset/training/'\n", - "\n", - "#set no. of rows that the images will be resized.\n", - "k1=100\n", - "#set no. of columns that the images will be resized.\n", - "k2=100\n", - "\n", - "filenames = get_imlist(path)\n", - "filenames = np.array(filenames)\n", - "# n is total number of images that has to be analysed.\n", - "n=len(filenames)\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# we will be using this often to visualise the images out there.\n", - "def showfig(image):\n", - " imgplot=plt.imshow(image, cmap='gray')\n", - " imgplot.axes.get_xaxis().set_visible(False)\n", - " imgplot.axes.get_yaxis().set_visible(False)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import Image\n", - "from scipy import misc\n", - "\n", - "# to get a hang of the data, lets see some part of the dataset images.\n", - "fig = plt.figure()\n", - "\n", - "for i in range(49):\n", - " fig.add_subplot(7,7,i)\n", - " train_img=np.array(Image.open(filenames[i]).convert('L'))\n", - " train_img=misc.imresize(train_img, [k1,k2])\n", - " showfig(train_img)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#read each and every image. flatten them to make row vectors and stack them together \n", - "#to form the observation matrix obs_matrix.\n", - "\n", - "train_img = np.array(Image.open(filenames[0]).convert('L'))\n", - "train_img=misc.imresize(train_img, [k1,k2])\n", - "train_img=np.array(train_img, dtype='double')\n", - "train_img=train_img.flatten()\n", - "\n", - "for i in range(1,n):\n", - " temp=np.array(Image.open(filenames[i]).convert('L')) \n", - " temp=misc.imresize(temp, [k1,k2])\n", - " temp=np.array(temp, dtype='double')\n", - " temp=temp.flatten()\n", - " train_img=np.vstack([train_img,temp])\n", - " \n", - "obs_matrix=train_img.T\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Again applying the PCA on the data, the same way we were doing for the last \n", - "# two times.\n", - "# here we are setting the target dimension as 100. Hence we are trying to represent n*10000 dim. data to 100*10000 dim.\n", - "\n", - "y,eig= apply_pca_to_data(100,obs_matrix)\n", - "res=y.get_feature_matrix()\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#lets see how these eigenfaces/eigenvectors look like:\n", - "fig1 = plt.figure()\n", - "plt.title('top 20 eigenfaces')\n", - "\n", - "\n", - "for i in range(20):\n", - " a = fig1.add_subplot(5,4,i+1)\n", - " eigen_faces=eig[:,i].reshape([k1,k2])\n", - " showfig(eigen_faces)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#to see the reconstructed image from 100 eigenvectors/eigenfaces, we multiply each eigenfaces with their respective weights,\n", - "# which when added provides us with a reconstruction of the original image.\n", - "\n", - "reconstructed_vector=np.mat(eig)*np.mat(res)\n", - "\n", - "#plot the reconstructed images to visualise it. \n", - "#We are here able to reconstruct all the images by shedding (n-100) * 10000 dimensions!! Thats data compression !!\n", - "fig2=plt.figure()\n", - "for i in range(1,50):\n", - " reconstructed_image = reconstructed_vector[:,i].reshape([k1,k2])\n", - " fig2.add_subplot(7,7,i)\n", - " showfig(reconstructed_image)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Face Recognition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets use this technique of eigenfaces to perform some basic face recognition.\n", - "The eigenfaces span an m-dimensional subspace of the original image space by choosing the subset of eigenvectors $U\u02c6={u1,\u22ef,um}$ associated with the m largest eigenvalues. This results in the so-called face space, whose origin is the average face, and whose axes are the eigenfaces (see Figure 3). To perform face detection or recognition, one may compute the distance within or from the face space.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A new face test_img is projected into the face space by $eig^T * testimg$ , where $eig^T$ is the set of significant eigenvectors. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#get the test image in the list test_file.\n", - "test_files= get_imlist('../../../data/att_dataset/testing/')\n", - "test_img=np.array(Image.open(test_files[0]).convert('L'))\n", - "\n", - "#we plot the test image , for which we have to identify a good match from the training images we already have\n", - "fig = plt.figure()\n", - "t_img=plt.imshow(test_img, cmap='gray')\n", - "plt.title('the test image for which a match is to be identified')\n", - "t_img.axes.get_xaxis().set_visible(False)\n", - "t_img.axes.get_yaxis().set_visible(False)\n", - "\n", - "# we flatten out or test image just the way we have done for the other images\n", - "test_image=misc.imresize(test_img, [k1,k2])\n", - "test_image=test_image.flatten()\n", - "test_image=np.array(test_image, dtype='double')\n", - "test_image=np.mat(test_image).transpose()\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we have to project our training images as well as the test image on the PCA subspace." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#projecting our training images into pca subspace\n", - "train_proj=np.dot(eig.T , obs_matrix)\n", - "\n", - "#projecting our test image into pca subspace\n", - "test_proj=np.dot(eig.T , test_image)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#here we are using the Eucledian Diatance Measure for finding out the similarity\n", - "#between test image and all the training images.\n", - "#The training image which produces the least distance measure with our test image \n", - "#is said to be the best match.\n", - "testfeat = RealFeatures(np.array(test_proj))\n", - "workfeat = RealFeatures(np.array(train_proj))\n", - "RaRb=EuclideanDistance(testfeat, workfeat)\n", - "\n", - "d=np.empty([n,1])\n", - "for i in range(n):\n", - " d[i]= RaRb.distance(0,i)\n", - " \n", - "\n", - "iden=np.array(Image.open(filenames[d.argmin()]))\n", - "i_img=plt.imshow(iden, cmap='gray')\n", - "plt.title('identified image from our set of training images')\n", - "i_img.axes.get_xaxis().set_visible(False)\n", - "i_img.axes.get_yaxis().set_visible(False)" - ], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file