SVM 3D class notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90561b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08bd017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17d2a58c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26ec4cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9972e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm,datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0c53e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85ec7f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efc9ff4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       [5.8, 2.7, 4.1, 1. ],\n",
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      "       [7.7, 3. , 6.1, 2.3],\n",
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      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'frame': None, 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "print(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c4acec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16662f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2973c428",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X[np.logical_or(Y==0,Y==1)]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6caf754",
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Y[np.logical_or(Y==0,Y==1)]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d4b7e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = svm.SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "040e05b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4f34910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac5c2c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def F1(x,y):\n",
    "    return(-model.intercept_[0]-model.coef_[0][0]*x-model.coef_[0][1]*y/model.coef_[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8744151",
   "metadata": {},
   "outputs": [],
   "source": [
    "F = lambda x,y:(-model.intercept_[0]-model.coef_[0][0]*x-model.coef_[0][1]*y/model.coef_[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "313bd4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.        , -5.87878788, -5.75757576, -5.63636364, -5.51515152,\n",
       "       -5.39393939, -5.27272727, -5.15151515, -5.03030303, -4.90909091,\n",
       "       -4.78787879, -4.66666667, -4.54545455, -4.42424242, -4.3030303 ,\n",
       "       -4.18181818, -4.06060606, -3.93939394, -3.81818182, -3.6969697 ,\n",
       "       -3.57575758, -3.45454545, -3.33333333, -3.21212121, -3.09090909,\n",
       "       -2.96969697, -2.84848485, -2.72727273, -2.60606061, -2.48484848,\n",
       "       -2.36363636, -2.24242424, -2.12121212, -2.        , -1.87878788,\n",
       "       -1.75757576, -1.63636364, -1.51515152, -1.39393939, -1.27272727,\n",
       "       -1.15151515, -1.03030303, -0.90909091, -0.78787879, -0.66666667,\n",
       "       -0.54545455, -0.42424242, -0.3030303 , -0.18181818, -0.06060606,\n",
       "        0.06060606,  0.18181818,  0.3030303 ,  0.42424242,  0.54545455,\n",
       "        0.66666667,  0.78787879,  0.90909091,  1.03030303,  1.15151515,\n",
       "        1.27272727,  1.39393939,  1.51515152,  1.63636364,  1.75757576,\n",
       "        1.87878788,  2.        ,  2.12121212,  2.24242424,  2.36363636,\n",
       "        2.48484848,  2.60606061,  2.72727273,  2.84848485,  2.96969697,\n",
       "        3.09090909,  3.21212121,  3.33333333,  3.45454545,  3.57575758,\n",
       "        3.6969697 ,  3.81818182,  3.93939394,  4.06060606,  4.18181818,\n",
       "        4.3030303 ,  4.42424242,  4.54545455,  4.66666667,  4.78787879,\n",
       "        4.90909091,  5.03030303,  5.15151515,  5.27272727,  5.39393939,\n",
       "        5.51515152,  5.63636364,  5.75757576,  5.87878788,  6.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = np.linspace(-6,6,100)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7db0a086",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = np.meshgrid(temp,temp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3d9d93c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ],\n",
       "       [-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ],\n",
       "       [-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ],\n",
       "       ...,\n",
       "       [-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ],\n",
       "       [-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ],\n",
       "       [-6.        , -5.87878788, -5.75757576, ...,  5.75757576,\n",
       "         5.87878788,  6.        ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b6e2d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.        , -6.        , -6.        , ..., -6.        ,\n",
       "        -6.        , -6.        ],\n",
       "       [-5.87878788, -5.87878788, -5.87878788, ..., -5.87878788,\n",
       "        -5.87878788, -5.87878788],\n",
       "       [-5.75757576, -5.75757576, -5.75757576, ..., -5.75757576,\n",
       "        -5.75757576, -5.75757576],\n",
       "       ...,\n",
       "       [ 5.75757576,  5.75757576,  5.75757576, ...,  5.75757576,\n",
       "         5.75757576,  5.75757576],\n",
       "       [ 5.87878788,  5.87878788,  5.87878788, ...,  5.87878788,\n",
       "         5.87878788,  5.87878788],\n",
       "       [ 6.        ,  6.        ,  6.        , ...,  6.        ,\n",
       "         6.        ,  6.        ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4cc70b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x , y = np.meshgrid([1,2],[3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "562db4e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5490ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3],\n",
       "       [4, 4]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6520fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x , y = np.meshgrid([1,2,3],[4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "298bfe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2547ac58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4],\n",
       "       [5, 5, 5],\n",
       "       [6, 6, 6]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c544742",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x , y = np.meshgrid([1,2,3],[4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d6cf729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06e789df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4],\n",
       "       [5, 5, 5],\n",
       "       [6, 6, 6],\n",
       "       [7, 7, 7]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "48fcc09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111,projection='3d')\n",
    "ax.plot3D( X[Y==0 , 0] , X[Y==0 , 1], X[Y==0 , 2], 'ob' )\n",
    "ax.plot3D( X[Y==1 , 0] , X[Y==1 , 1], X[Y==1 , 2], 'sr' )\n",
    "ax.plot_surface( x,y, F1(x,y) )  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a86f8f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}