added histograms

complexity.py

@@ -7,7 +7,6 @@
 
 import modules.complexity_estimator as ce
 import modules.util as u
-from modules import mnist
 from modules.oracle import Oracle
 from nd_boundary_plot.plots import nd_boundary_plot
 
@@ -107,6 +106,7 @@ def active(classifiers, datasets, experiments, quota=25, plot_every_n=5):
     plt.tight_layout()
     plt.show()
 
+
 def main():
     # clfs = [SVC(), LinearSVC(), AdaBoostClassifier(), GaussianNB()]
     datasets = []
@@ -121,11 +121,11 @@ def main():
     # experiments.append('moons')
     # datasets.append((u.hastie(1000), u.hastie(1000)))
 
-    # datasets.append((make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=3),
-    #                 make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=3)))
-    # experiments.append('gauus')
-    datasets.append((mnist.load_mnist(), mnist.load_mnist_rotated()))
-    experiments.append('MNIST_vs_MNIST_Rotated')
+    datasets.append((make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=3),
+                     make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=3)))
+    experiments.append('gauus')
+    # datasets.append((mnist.load_mnist(), mnist.load_mnist_rotated()))
+    # experiments.append('MNIST_vs_MNIST_Rotated')
 
     active(classifiers=clfs, datasets=datasets, experiments=experiments)
 

demo.py

@@ -27,6 +27,8 @@ def demo(datasets, dsnames, classifiers, nwindows):
     f1 = figure.number
     figure2 = plt.figure(figsize=(27, 9))
     f2 = figure2.number
+    figure3 = plt.figure(figsize=(27, 9))
+    f3 = figure3.number
 
     i = 1
     j = 1
@@ -110,6 +112,32 @@ def demo(datasets, dsnames, classifiers, nwindows):
             ax.set_title('Avg. Complexity')
         ax.plot(Ks, Es)
         j+=1
+
+
+        # plot data and
+        figure3, a = plt.subplots(nrows=len(datasets), ncols=2,figsize=(27,9))
+        a = a.ravel()
+
+        for idx,ax in enumerate(a):
+            if idx % 2 == 0:
+                ax.set_title(dsnames[ds_cnt])
+                # Plot also the training points
+                ax.scatter(X[:, 0], X[:, 1], c=y)
+                # and seeds
+                ax.scatter(X[estimator.seeds, 0], X[estimator.seeds, 1],
+                           alpha=1.0, facecolors='black')
+                ax.set_xlim(xx.min(), xx.max())
+                ax.set_ylim(yy.min(), yy.max())
+                ax.set_xticks(())
+                ax.set_yticks(())
+            else:
+                ax.hist(Es, 10)
+                ax.set_xlabel('E')
+                ax.set_ylabel('frequency')
+                ax.set_title('Hist. of Entropy')
+        figure3.tight_layout()
+        figure3.savefig(filename=('./vis/' + dsnames[ds_cnt] + 'Histograms.png'))
+
         '''
                 ws = estimator.get_w_complexity()
                 for wi, w in enumerate(ws):
@@ -121,16 +149,18 @@ def demo(datasets, dsnames, classifiers, nwindows):
 
     figure.tight_layout()
     figure2.tight_layout()
+
+
     figure.savefig(filename=('./vis/'+ ''.join(dsnames)+'Classifications.png'))
     figure2.savefig(filename=('./vis/'+''.join(dsnames) + 'Complexities.png'))
+
     plt.show()
 
 def main():
     classifiers = [
         LinearDiscriminantAnalysis(),
         QuadraticDiscriminantAnalysis(),
         KNeighborsClassifier(3),
-        MLPClassifier(alpha=1),
         SVC(gamma=2, C=1),
         LinearSVC(),
         GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),