def test_cost_5():
list_x = []
list_theta = []
list_y = []
x = np.matrix(list_x)
theta = np.matrix(list_theta)
y = np.matrix(list_y)
j = linear_regression_cost(x, y, theta)
assert j == float('inf')
def test_cost_3():
list_x = [[1, 1], [1, 2], [1, 3]]
list_theta = [[0], [1]]
list_y = [[1], [2]]
x = np.matrix(list_x)
theta = np.matrix(list_theta)
y = np.matrix(list_y)
j = linear_regression_cost(x, y, theta)
assert j == float('inf')
def test_cost_1():
list_x = [[1, 1], [1, 2], [1, 3]]
list_theta = [[0], [0]]
list_y = [[1], [2], [3]]
x = np.matrix(list_x)
theta = np.matrix(list_theta)
y = np.matrix(list_y)
j = linear_regression_cost(x, y, theta)
assert j == 2.3333333333333335
def gradient_descent_base(x, y, theta, alpha, num_iters, want_history=True):
m = len(y)
cost_history = None
if want_history:
cost_history = np.zeros(num_iters)
for idx in range(num_iters):
temp = x.T * (x * theta - y);
theta = theta - alpha / m * temp;
if want_history:
cost_history[idx] = pyml.linear_regression_cost(x, y, theta)
return (theta, cost_history)
def gradient_descent_base(x, y, theta, alpha, num_iters, want_history=True):
m = len(y)
cost_history = None
if want_history:
cost_history = np.zeros(num_iters)
for idx in range(num_iters):
temp = x.T * (x * theta - y)
theta = theta - alpha / m * temp
if want_history:
cost_history[idx] = pyml.linear_regression_cost(x, y, theta)
return (theta, cost_history)
