Added traditional classifiers:

svm, decision tree, gaussian naive bayes, random forest.

mini_proj/_image_classifier.py

@@ -0,0 +1,50 @@
+class ImageClassifier:
+    """Class to create an ImageClassifier from a regular classifier with 5
+    methods that are common amongst classifiers.
+    """
+
+    def __init__(self, clf, *args, **kwargs):
+        self.clf = clf(*args, **kwargs)
+
+    def fit(self, X, *args, **kwargs):
+        X = X.reshape((len(X), -1))
+        return self.clf.fit(X, *args, **kwargs)
+
+    def predict(self, X, *args, **kwargs):
+        X = X.reshape((len(X), -1))
+        return self.clf.predict(X, *args, **kwargs)
+
+    def score(self, X, *args, **kwargs):
+        X = X.reshape((len(X), -1))
+        return self.clf.score(X, *args, **kwargs)
+
+    def get_params(self, *args, **kwargs):
+        return self.clf.get_params(*args, **kwargs)
+
+    def set_params(self, **params):
+        return self.set_params(**params)
+
+if __name__ == '__main__':
+    
+    # Import datasets, classifiers and performance metrics
+    from sklearn import datasets, svm, metrics
+
+    # The digits dataset
+    digits = datasets.load_digits()
+
+    n_samples = len(digits.images)
+    data = digits.images
+
+    # Create a classifier: a support vector classifier
+    classifier = ImageClassifier(svm.SVC, gamma=0.001)
+
+    # We learn the digits on the first half of the digits
+    classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])
+
+    # Now predict the value of the digit on the second half:
+    expected = digits.target[n_samples // 2:]
+    predicted = classifier.predict(data[n_samples // 2:])
+
+    print("Classification report for classifier %s:\n%s\n"
+          % (classifier, metrics.classification_report(expected, predicted)))
+    print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))

mini_proj/waldo_model.py

@@ -1,6 +1,8 @@
 import numpy as np
 import sys
 import time as t
+from sklearn import svm, tree, naive_bayes, ensemble
+from _image_classifier import ImageClassifier
 '''
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten, Reshape, Merge, Permute
@@ -43,7 +45,7 @@ def FCN():
     # up4x = UpSampling2D(size=(4, 4))(conv4x)
     # model = Model(input=inputs, output=up4x)
     # # Optimizer uses recommended Adadelta values
-    # model.compile(optimizer=Adadelta(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])  
+    # model.compile(optimizer=Adadelta(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])
     return model
 
 
@@ -55,9 +57,13 @@ lbl_test = np.load('Waldo_test_lbl.npy')
 
 ## Define model
 model = FCN()
+svm_iclf = ImageClassifier(svm.SVC)
+tree_iclf = ImageClassifier(tree.DecisionTreeClassifier)
+naive_bayes_iclf = ImageClassifier(naive_bayes.GaussianNBd)
+ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
 
 ## Define training parameters
-epochs = 40             # an epoch is one forward pass and back propogation of all training data 
+epochs = 40             # an epoch is one forward pass and back propogation of all training data
 batch_size = 5
 #lrate = 0.01
 #decay = lrate/epochs
@@ -125,4 +131,3 @@ pred_lbl = model.predict(im_test, verbose=1, batch_size=batch_size)
 end = t.time()
 print("Images generated in {} seconds".format(end - start))
 np.save('Test/predicted_results.npy', pred_lbl)
-