plt.show()
X = map_feature(X[:, 0:1], X[:, 1:])
m, n = X.shape
initial_theta = np.ones((n, 1))
lambda_coef = 0.1
theta = minimize(cost_function, initial_theta, method='BFGS', jac=grad_function, options={'disp': False},
args=(X, y, lambda_coef)).x
u = np.linspace(-1, 1.5, 50)
v = np.linspace(-1, 1.5, 50)
X1, X2 = np.meshgrid(u, v)
X1, X2 = X1.reshape(-1, 1), X2.reshape(-1, 1)
temp_X = map_feature(X1, X2)
z = np.dot(temp_X, theta).reshape(len(u), len(v))
plt.plot(x1[f_y == 0], x2[f_y == 0], 'yo')
plt.plot(x1[f_y == 1], x2[f_y == 1], 'bx')
CS = plt.contour(u, v, z)
plt.clabel(CS, inline=1, fontsize=10)
plt.show()
for lambda_coef in (0.03, 0.3, 0.1, 1, 3, 10):
initial_theta = np.ones((n, 1))
theta = minimize(cost_function, initial_theta, method='BFGS', jac=grad_function, options={'disp': False},
args=(X, y, lambda_coef)).x
print 'lambda = {}, Train Accuracy: {}'.format(lambda_coef, lr_accuracy(X, y, theta))
f_y = y.ravel()
plt.plot(x1[f_y == 0], x2[f_y == 0], 'yo')
plt.plot(x1[f_y == 1], x2[f_y == 1], 'bx')
plt.show()
X = add_zero_feature(X)
m, n = X.shape
initial_theta = np.ones((n, 1))
theta = minimize(cost_function,
initial_theta,
method='BFGS',
jac=grad_function,
options={
'disp': False
},
args=(X, y)).x
print theta
print cost_function(theta, X, y)
x1_boundery = np.array([np.min(x1) - 2, np.max(x1) + 2])
x2_boundery = (-1 / theta[2]) * (theta[1] * x1_boundery + theta[0])
plt.plot(x1[f_y == 0], x2[f_y == 0], 'yo')
plt.plot(x1[f_y == 1], x2[f_y == 1], 'bx')
plt.plot(x1_boundery, x2_boundery)
plt.show()
print 'Train Accuracy: {}'.format(lr_accuracy(X, y, theta))
X, y = load_data('ex2data1.txt')
x1, x2 = X.T
f_y = y.ravel()
plt.plot(x1[f_y==0], x2[f_y==0], 'yo')
plt.plot(x1[f_y==1], x2[f_y==1], 'bx')
plt.show()
X = add_zero_feature(X)
m, n = X.shape
initial_theta = np.ones((n, 1))
theta = minimize(cost_function, initial_theta, method='BFGS', jac=grad_function, options={'disp': False},
args=(X, y)).x
print theta
print cost_function(theta, X, y)
x1_boundery = np.array([np.min(x1)-2, np.max(x1)+2])
x2_boundery = (-1/theta[2])*(theta[1]*x1_boundery + theta[0])
plt.plot(x1[f_y==0], x2[f_y==0], 'yo')
plt.plot(x1[f_y==1], x2[f_y==1], 'bx')
plt.plot(x1_boundery, x2_boundery)
plt.show()
print 'Train Accuracy: {}'.format(lr_accuracy(X, y, theta))
