Assignment 6

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diff --git a/Assignment 6/ex62.py b/Assignment 6/ex62.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan  8 17:43:24 2016
+
+@author: camil
+"""
+
+import numpy as np
+import scipy.io as sciio
+from sklearn import cross_validation
+
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+import neurolab as nl
+
+def load_data():
+    data = sciio.loadmat('./data/xor.mat')
+    X, y = data['X'], data['y']
+    return X, y
+
+def plot_boundaries(nw, data=None):
+    plt.figure(1)
+    plt.hold(True)    
+    delta = 0.05; levels = 100
+    a = np.arange(-1,2,delta)
+    b = np.arange(-1,2,delta)
+    A, B = np.meshgrid(a, b)
+    values = np.zeros(A.shape)
+    
+    for i in range(len(a)):
+        for j in range(len(b)):
+            values[i,j] = nw.sim( np.mat([a[i],b[j]]) )[0,0]
+    plt.contour(A, B, values, levels=[.5], colors=['k'], linestyles='dashed')
+    plt.contourf(A, B, values, cmap=cm.RdBu,
+                 levels=np.linspace(values.min(),values.max(),levels))
+                 
+    if data != None:
+        plt.scatter(data[0][:,0], data[0][:,1], c=data[1], cmap=cm.RdBu, s=40)
+
+# 6.2.1
+def ex621(X, y):
+    colors = ['green' if x == 0 else 'red' for x in y]
+    plt.scatter(X[:,0], X[:,1], c=colors, s=40, alpha=0.6)
+    plt.ylim([-1,2])
+    plt.xlim([-1,2])
+    plt.show()
+
+# 6.2.2
+def run_exercise(X, y, hidden_units=1, plot=True):
+    cv = cross_validation.KFold(len(X), 10)
+    errors = []
+    c = 0
+    
+    if plot:
+        plt.figure(figsize=(15,6))
+        
+    for train, test in cv:
+        c += 1
+        X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
+        
+        shape = [hidden_units, 1] if hidden_units > 0 else [1]
+        net = nl.net.newff([[0,1], [0,1]], shape, 
+                           np.repeat(nl.trans.TanSig(), len(shape)))
+        net.train(X_train, y_train, show=501)
+    
+        if plot:
+            plt.subplot(2, 5, c)
+            plot_boundaries(net, data=(X_train, y_train))
+    
+        predictions = [1 if p > 0.5 else 0 for p in net.sim(X_test)]
+        correct = len([i for i, p in zip(y_test, predictions) if i == p])
+        error = 1 - correct / float(len(X_test))
+        errors.append(error)
+        
+    if plot:
+        plt.suptitle('XOR with ' + str(hidden_units) + ' hidden unit' +\
+                    ('s' if hidden_units != 1 else ''),
+                     fontsize=18)
+        plt.show()
+    
+    return np.mean(errors), np.std(errors)
+    
+def main():
+    X, y = load_data()
+    
+    ex621(X, y)
+    
+    unit_counts = range(11)
+    means, stds = [], []
+    for i in unit_counts:
+        mean, std = run_exercise(X, y, hidden_units=i, plot=True)
+        means.append(mean)
+        stds.append(std)
+        
+    means, stds = np.array(means), np.array(stds)
+    plt.plot(unit_counts, means, c='red')
+    plt.fill_between(unit_counts, means - stds, means + stds, alpha=0.1, color="r")
+    
+if __name__ == '__main__':
+    main()