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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()
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