merged some changes

mini_proj/Load_Images.py

@@ -56,11 +56,9 @@ def gen_data(w_path, n_w_path):
         print("ERROR: Data may not be completely saved")
 
 
-def __main__():
+if __name__ == "__main__":
     # Paths to the Waldo images
     waldo_path = 'waldo_data/64/waldo'
     n_waldo_path = 'waldo_data/64/notwaldo'
 
     gen_data(waldo_path, n_waldo_path)
-
-__main__()

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

@@ -8,11 +8,12 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 from keras.layers import Dense, Dropout, Activation, Flatten, Input
 from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
 from keras.models import Model 
-from keras.layers.normalization import BatchNormalization
-from keras.utils import np_utils
+
+from sklearn import svm, tree, naive_bayes, ensemble
+from _image_classifier import ImageClassifier
+
 from keras.optimizers import Adadelta
 from keras.callbacks import ModelCheckpoint
-
 from keras import backend as K
 K.set_image_dim_ordering('th')
 np.random.seed(7)
@@ -58,6 +59,10 @@ 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 = 20             # an epoch is one forward pass and back propogation of all training data 

wk11/week11.tex

@@ -47,10 +47,9 @@ f(1,0,0) & = & 1\\
 \text{otherwise }f(\_,\_,\_) & = & 0
 \end{eqnarray*}
 
-\begin{figure}[H]
-	\includegraphics[scale=0.55]{plots}
+\begin{figure}[ht]
+	\includegraphics[scale=0.6]{plots}
 	\centering
-	\captionsetup{width=0.80\textwidth}
 	\caption{Plots of the execution of the cellular automata with the different
     updating methods. From top-left to top-right: Synchronous, Random
     Independent, Random Order. From bottom-left to bottom-right: Clocked,
@@ -131,10 +130,9 @@ simulate the system at varying densities between 0\% and 20\% and use
 the graphs showing the energy released from the system over time to
 gauge how where the runaway reaction occurs.
 
-\begin{figure}[H]
+\begin{figure}[ht]
 	\includegraphics[scale=0.70]{plots2}
 	\centering
-	\captionsetup{width=0.80\textwidth}
 	\caption{Plots of energy released over time. Each plot corresponds a
     different density: 0\%, 5\%, 8\%, 10\%, 11\%, 12\%, 13\%, 15\%, 17\% and 20\%}
 	\label{fig:plot2}