def load_features(thetapath=None):
global visibleSize, hiddenSize, outputSize
levels = [visibleSize, hiddenSize, outputSize]
X = load_data()
X, zca_white, avg = zca_whitening(X)
print X.shape, zca_white.shape, avg.shape
init_theta = initialization(levels)
theta = train(init_theta, X, X, levels, thetapath)
WB = vec2mat(theta, levels)
print 'W shape:', WB[0][0].shape
return WB, zca_white, avg
return theta
def load_features(thetapath=None):
global visibleSize, hiddenSize, outputSize
levels = [visibleSize, hiddenSize, outputSize]
X = load_data()
X, zca_white, avg = zca_whitening(X)
print X.shape, zca_white.shape, avg.shape
init_theta = initialization(levels)
theta = train(init_theta, X, X, levels, thetapath)
WB = vec2mat(theta, levels)
print 'W shape:', WB[0][0].shape
return WB, zca_white, avg
if __name__ == '__main__':
levels = [visibleSize, hiddenSize, outputSize]
X = load_data()
print X.shape
X, zca_white, _ = zca_whitening(X)
print X.shape
init_theta = initialization(levels)
theta = train(init_theta, X, X, levels)
if theta is not None:
WB = vec2mat(theta, levels)
print 'W shape:', WB[0][0].shape
v = WB[0][0].dot(zca_white)
print v.shape
sio.savemat('W', {'W': v})
