p = sigmoid(np.dot(X, theta0))
print p.shape
print p
p = np.dot(X, all_theta.T)
return np.array(map(max_index, p))
def ex3():
input_layer_size = 400
num_labels = 10
vars = io.loadmat(os.path.join(ex3path, 'ex3data1.mat'))
_X, y = np.array(vars['X'] * 128 + 128,
dtype=np.ubyte), vars['y'].reshape(vars['y'].size)
size = (math.sqrt(_X.shape[1]), math.sqrt(_X.shape[1]))
#FIXME: image loading seems not fully correct
#img = make_grid(np.random.permutation(_X)[:400,:], size, 20, 20)
#img.show()
print 'Training One-vs-All Logistic Regression'
lambda_ = 1.
all_theta = one_vs_all(_X, y, num_labels, lambda_)
p = predict_one_vs_all(all_theta, _X)
print 'Training Set Accuracy:', (p == y).mean() * 100
if __name__ == '__main__':
ex3path = os.path.join(ml_class_path(), 'ex3')
ex3()
pl.show()
print 'Theta computed from gradient descent:', theta
a = (1650 - mu[0]) / sigma[0]
b = (3 - mu[1]) / sigma[1]
price = np.matrix([1, a, b]) * theta
print 'Predicted price of a 1650 sq-ft, 3 br house',\
'(using gradient descent):', price
# Normal Equations
data = load_txt(os.path.join(ex1path, 'ex1data2.txt'))
_X = data[:, :2]
y = data[:, 2]
X = np.hstack((np.ones((m, 1)), _X))
theta = normal_eqn(X, y)
print 'Theta computed from the normal equations:', theta
price = np.matrix('1 1650 3') * theta
print 'Predicted price of a 1650 sq-ft, 3 br house',\
'(using normal equations):', price
if __name__ == '__main__':
ex1path = os.path.join(ml_class_path(), 'ex1')
ex1_multi()
theta0 = all_theta[0]
p = sigmoid(np.dot(X, theta0))
print p.shape
print p
p = np.dot(X, all_theta.T)
return np.array(map(max_index, p))
def ex3():
input_layer_size = 400
num_labels = 10
vars = io.loadmat(os.path.join(ex3path, 'ex3data1.mat'))
_X, y = np.array(vars['X']*128+128, dtype=np.ubyte), vars['y'].reshape(vars['y'].size)
size = (math.sqrt(_X.shape[1]), math.sqrt(_X.shape[1]))
#FIXME: image loading seems not fully correct
#img = make_grid(np.random.permutation(_X)[:400,:], size, 20, 20)
#img.show()
print 'Training One-vs-All Logistic Regression'
lambda_ = 1.
all_theta = one_vs_all(_X, y, num_labels, lambda_)
p = predict_one_vs_all(all_theta, _X)
print 'Training Set Accuracy:', (p == y).mean() * 100
if __name__ == '__main__':
ex3path = os.path.join(ml_class_path(), 'ex3')
ex3()
def costf(theta):
return logistic_cost_function(theta, X, y, lambda_)
def difff(theta):
return logistic_grad_function(theta, X, y, lambda_)
maxiter = 50
theta, allvec = opt.fmin_ncg(costf,
initial_theta,
difff,
retall=1,
maxiter=maxiter,
callback=step())
# theta, allvec = opt.fmin_bfgs(costf, initial_theta, difff, retall=1, maxiter=maxiter, callback=step())
print 'optimal cost:', costf(theta)
Jhist = [costf(t) for t in allvec]
pl.figure()
pl.plot(Jhist)
plot_decision_boundary(theta, X, y)
# Compute accuracy on our training set
h = np.dot(X, theta)
print 'Train Accuracy:', ((h > 0) == y).mean() * 100
if __name__ == '__main__':
ex2path = os.path.join(ml_class_path(), 'ex2')
ex2_reg()
pl.show()
