Cleaning up mnist_svm

Author: mnielsen

code/mnist_svm.py

@@ -3,23 +3,13 @@
 ~~~~~~~~~
 
 A classifier program for recognizing handwritten digits from the MNIST
-data set, using an SVM classifier.
-
-The program first trains the classifier, and then applies the
-classifier to the MNIST test data to see how many digits are correctly
-classified.  Finally, the program uses matplotlib to create an image
-of the first 10 digits which are incorrectly classified."""
+data set, using an SVM classifier."""
 
 #### Libraries
 # My libraries
-import mnist_loader # to load the MNIST data.  For details on the
-                    # format the data is loaded in, see the module's
-                    # code
+import mnist_loader 
 
 # Third-party libraries
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
 from sklearn import svm
 
 def svm_baseline():
@@ -32,18 +22,6 @@ def svm_baseline():
     num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
     print "Baseline classifier using an SVM."
     print "%s of %s values correct." % (num_correct, len(test_data[1]))
-    # finally, plot the first ten images where the classifier fails
-    failure_indices = [j for (j, z) in enumerate(zip(predictions, test_data[1]))
-                       if z[0] != z[1]]
-    failed_images = [np.reshape(test_data[0][failure_indices[j]], (-1, 28))
-                     for j in xrange(10)]
-    fig = plt.figure()
-    for j in xrange(1, 11):
-        ax = fig.add_subplot(1, 10, j)
-        ax.matshow(failed_images[j-1], cmap = matplotlib.cm.binary)
-        plt.xticks(np.array([]))
-        plt.yticks(np.array([]))
-    plt.show()
 
 if __name__ == "__main__":
     svm_baseline()