cleanup python modular graphical examples

examples/undocumented/python_modular/graphical/interactive_clustering_demo.py

@@ -11,10 +11,9 @@
 from PyQt4.QtGui import *
 
 import matplotlib
-from matplotlib import mpl
 from matplotlib.colorbar import make_axes, Colorbar
 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
+from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
 from modshogun import *

examples/undocumented/python_modular/graphical/interactive_gp_demo.py

@@ -24,10 +24,9 @@
 from numpy import *
 
 import matplotlib
-from matplotlib import mpl
 from matplotlib.colorbar import make_axes, Colorbar
 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
+from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
 from modshogun import *
@@ -409,4 +408,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

examples/undocumented/python_modular/graphical/interactive_kmm_demo.py

@@ -20,15 +20,13 @@
 from PyQt4.QtGui import *
 
 import matplotlib
-from matplotlib import mpl
 from matplotlib.colorbar import make_axes, Colorbar
 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
+from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
 from modshogun import *
 from modshogun import KernelMeanMatching
-from modshogun import *
 from modshogun import Math
 import util
 

examples/undocumented/python_modular/graphical/interactive_svm_demo.py

@@ -11,14 +11,11 @@
 from PyQt4.QtGui import *
 
 import matplotlib
-from matplotlib import mpl
 from matplotlib.colorbar import make_axes, Colorbar
 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
+from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
-from modshogun import *
-from modshogun import *
 from modshogun import *
 import util
 
@@ -132,7 +129,7 @@ def train_svm(self):
         svm.set_epsilon(1e-2)
 
         x, y, z = util.compute_output_plot_isolines(svm, gk, train)
-        plt=self.axes.pcolor(x, y, z, shading='interp')
+        plt=self.axes.pcolor(x, y, z)
         CS=self.axes.contour(x, y, z, [-1,0,1], linewidths=1, colors='black', hold=True)
         #CS=self.axes.contour(x, y, z, linewidths=1, colors='black', hold=True)
         #CS=self.axes.contour(x, y, z, 5, linewidths=1, colors='black', hold=True)
@@ -142,7 +139,7 @@ def train_svm(self):
         self.axes.set_ylim((-5,5))
 
         cmap = matplotlib.cm.jet
-        norm = mpl.colors.Normalize(numpy.min(z), numpy.max(z))
+        norm = matplotlib.colors.Normalize(numpy.min(z), numpy.max(z))
         print CS.get_clim()
         if not self.cax:
             self.cax, kw = make_axes(self.axes)
@@ -152,7 +149,7 @@ def train_svm(self):
 # standalone colorbar.  There are many more kwargs, but the
 # following gives a basic continuous colorbar with ticks
 # and labels.
-        cb1 = mpl.colorbar.ColorbarBase(self.cax, cmap=cmap,
+        cb1 = matplotlib.colorbar.ColorbarBase(self.cax, cmap=cmap,
                                            norm=norm)
         self.canvas.draw()
 

examples/undocumented/python_modular/graphical/interactive_svr_demo.py

@@ -11,14 +11,11 @@
 from PyQt4.QtGui import *
 
 import matplotlib
-from matplotlib import mpl
 from matplotlib.colorbar import make_axes, Colorbar
 from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
-from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
+from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
-from modshogun import *
-from modshogun import *
 from modshogun import *
 
 class Form(QMainWindow):

examples/undocumented/python_modular/graphical/kernel_ridge_regression.py

@@ -2,8 +2,6 @@
 from numpy import array,meshgrid,reshape,linspace,min,max
 from numpy import concatenate,transpose,ravel
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('KernelRidgeRegression')
@@ -18,10 +16,10 @@
 neg=util.get_realdata(False)
 plot(neg[0,:], neg[1,:], "b.")
 
-# train svm
+# train krr
 labels = util.get_labels(type='regression')
 train = util.get_realfeatures(pos, neg)
-gk=GaussianKernel(train, train, width)
+gk = GaussianKernel(train, train, width)
 krr = KernelRidgeRegression()
 krr.set_labels(labels)
 krr.set_kernel(gk)
@@ -31,9 +29,8 @@
 # compute output plot iso-lines
 x, y, z=util.compute_output_plot_isolines(krr, gk, train, regression=True)
 
-pcolor(x, y, z, shading='interp')
+pcolor(x, y, z)
 contour(x, y, z, linewidths=1, colors='black', hold=True)
 
 connect('key_press_event', util.quit)
 show()
-

examples/undocumented/python_modular/graphical/kernel_ridge_regression_sinc.py

@@ -1,7 +1,5 @@
 from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend,connect
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('KernelRidgeRegression on Sine')

examples/undocumented/python_modular/graphical/lda.py

@@ -1,6 +1,5 @@
 from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
 from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('LDA')
@@ -25,10 +24,9 @@
 # compute output plot iso-lines
 x, y, z=util.compute_output_plot_isolines(lda)
 
-c=pcolor(x, y, z, shading='interp')
+c=pcolor(x, y, z)
 contour(x, y, z, linewidths=1, colors='black', hold=True)
 colorbar(c)
 
 connect('key_press_event', util.quit)
 show()
-

examples/undocumented/python_modular/graphical/mclda.py

@@ -53,7 +53,7 @@
 
 z = dense_labels.reshape((size, size))
 
-pcolor(x, y, z, shading = 'interp')
+pcolor(x, y, z)
 contour(x, y, z, linewidths = 1, colors = 'black', hold = True)
 
 axis([x1_min, x1_max, x2_min, x2_max])

examples/undocumented/python_modular/graphical/multiple_smvs.py

@@ -5,8 +5,6 @@
 from numpy import concatenate
 from numpy.random import randn
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('Multiple SVMS')
@@ -40,7 +38,7 @@
 	x, y, z=util.compute_output_plot_isolines(
 		currentSVM, kernelList[i], trainfeatList[i])
 	subplot(num_svms/2, 2, i+1)
-	pcolor(x, y, z, shading='interp')
+	pcolor(x, y, z)
 	contour(x, y, z, linewidths=1, colors='black', hold=True)
 	scatter(traindatList[i][0,:],traindatList[i][1,:], s=20, marker='o', c=trainlabsList[i], hold=True)
 	axis('tight')

examples/undocumented/python_modular/graphical/qda.py

@@ -53,7 +53,7 @@
 
 z = dense_labels.reshape((size, size))
 
-pcolor(x, y, z, shading = 'interp')
+pcolor(x, y, z)
 contour(x, y, z, linewidths = 1, colors = 'black', hold = True)
 
 axis([x1_min, x1_max, x2_min, x2_max])

examples/undocumented/python_modular/graphical/regression_gaussian_process_demo.py

@@ -7,9 +7,6 @@
 from numpy import *
 from numpy.random import randn
 from modshogun import *
-from modshogun import *
-from modshogun import *
-from modshogun import *
 import pylab as PL
 import matplotlib
 import logging as LG
@@ -18,8 +15,6 @@
 from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
 from modshogun import ParameterCombination
 
-
-
 def plot_training_data(x, y,
                        shift=None,
                        replicate_indices=None,

examples/undocumented/python_modular/graphical/so_multiclass_BMRM.py

@@ -3,10 +3,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from modshogun	import RealFeatures
-from modshogun	import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM
-from modshogun	import BMRM, PPBMRM, P3BMRM
-from modshogun	import StructuredAccuracy
+from modshogun import RealFeatures
+from modshogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM
+from modshogun import BMRM, PPBMRM, P3BMRM
+from modshogun import StructuredAccuracy
 
 def fill_data(cnt, minv, maxv):
 	x1 = np.linspace(minv, maxv, cnt)
@@ -96,7 +96,7 @@ def get_so_labels(out):
 z = Z.reshape(cnt, cnt)
 
 plt.subplot(223)
-plt.pcolor(x, y, z, shading='interp')
+plt.pcolor(x, y, z)
 plt.contour(x, y, z, linewidths=1, colors='black', hold=True)
 plt.plot(X[:,0], X[:,1], 'yo')
 plt.axis('tight')

examples/undocumented/python_modular/graphical/so_multiclass_director_BMRM.py

@@ -3,10 +3,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from modshogun	import RealFeatures
-from modshogun	import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM, DirectorStructuredModel
-from modshogun	import BMRM, PPBMRM, P3BMRM, ResultSet, RealVector
-from modshogun	import StructuredAccuracy
+from modshogun import RealFeatures
+from modshogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM, DirectorStructuredModel
+from modshogun import BMRM, PPBMRM, P3BMRM, ResultSet, RealVector
+from modshogun import StructuredAccuracy
 
 class MulticlassStructuredModel(DirectorStructuredModel):
 	def __init__(self,features,labels):
@@ -142,7 +142,7 @@ def get_so_labels(out):
 z = Z.reshape(cnt, cnt)
 
 plt.subplot(223)
-plt.pcolor(x, y, z, shading='interp')
+plt.pcolor(x, y, z)
 plt.contour(x, y, z, linewidths=1, colors='black', hold=True)
 plt.plot(X[:,0], X[:,1], 'yo')
 plt.axis('tight')

examples/undocumented/python_modular/graphical/svm.py

@@ -1,8 +1,6 @@
 from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect,axis
 from numpy.random import randn
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('SVM')
@@ -26,10 +24,9 @@
 svm.train()
 
 x, y, z=util.compute_output_plot_isolines(svm, gk, train)
-pcolor(x, y, z, shading='interp')
+pcolor(x, y, z)
 contour(x, y, z, linewidths=1, colors='black', hold=True)
 axis('tight')
 
 connect('key_press_event', util.quit)
 show()
-

examples/undocumented/python_modular/graphical/svmlin.py

@@ -1,7 +1,5 @@
 from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,axis, connect
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('SVM Linear 1')
@@ -33,7 +31,7 @@
 x, y, z=util.compute_output_plot_isolines(svm, None, None, True, pos, neg)
 x, y, zlight=util.compute_output_plot_isolines(svmlight, lk, dense, False, pos, neg)
 
-c=pcolor(x, y, z, shading='interp')
+c=pcolor(x, y, z)
 contour(x, y, z, linewidths=1, colors='black', hold=True)
 colorbar(c)
 scatter(pos[0,:], pos[1,:],s=20, c='r')
@@ -43,7 +41,7 @@
 
 figure()
 util.set_title('SVM Linear 2')
-c=pcolor(x, y, zlight, shading='interp')
+c=pcolor(x, y, zlight)
 contour(x, y, zlight, linewidths=1, colors='black', hold=True)
 colorbar(c)
 
@@ -53,4 +51,3 @@
 connect('key_press_event', util.quit)
 
 show()
-

examples/undocumented/python_modular/graphical/svr_sinc.py

@@ -1,7 +1,5 @@
 from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend, connect
 from modshogun import *
-from modshogun import *
-from modshogun import *
 import util
 
 util.set_title('SVR on Sinus')