def test_cost_func_data1_3(self, data1):
y = data1[:, -1:]
X = data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-1], [2]])
assert_allclose([[54.242]], cost_func(X, y, theta), rtol=0, atol=0.001)
def test_cost_func_data1_2(self, data1):
y = data1[:, -1:]
X = data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[10.266]], cost_func(X, y, theta), rtol=0, atol=0.001)
def test_cost_func_data2_1(self, data2):
y = data2[:, -1:]
X = data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[65591548106.457]],
cost_func(X, y, theta),
rtol=0,
atol=0.001)
def test_cost_func_data2_3(self, data2):
y = data2[:, -1:]
X = data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-25.3], [32], [7.8]])
assert_allclose([[43502644952.311]],
cost_func(X, y, theta),
rtol=0,
atol=0.001)
def J(theta):
return cost_func(X, y, theta)