def test_numeric_grad_2(self, err):
def J(x):
return sum(1 / x)
theta = array([[5], [8], [20]])
assert_allclose(array([[-0.04], [-0.015625], [-0.0025]]),
numerical_grad(J, theta, err),
rtol=0, atol=0.001, equal_nan=False)
def test_numeric_grad_1(self, err):
def J(x):
return sum(3 * (x ** 2))
theta = array([[0], [4], [10]])
assert_allclose(array([[0], [24], [60]]),
numerical_grad(J, theta, err),
rtol=0, atol=0.001, equal_nan=False)
def test_numeric_grad_5(self, err):
def J(x):
return sum(7 * x)
theta = array([[-10], [-1], [0], [1], [10]])
assert_allclose(array([[7], [7], [7], [7], [7]]),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_numeric_grad_4(self, err):
def J(x):
return sum(exp(x))
theta = array([[-10], [-1], [0], [1], [10]])
assert_allclose(array([[exp(-10)], [exp(-1)], [exp(0)], [exp(1)],
[exp(10)]]),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_numeric_grad_3(self, err):
def J(x):
return sum(cos(x))
theta = array([[radians(30)], [radians(45)], [radians(60)],
[radians(90)]])
assert_allclose(array([[-sin(radians(30))], [-sin(radians(45))],
[-sin(radians(60))], [-sin(radians(90))]]),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_grad_data1_6(self, data1, err):
y = data1[:, -1:]
X = data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[3.46], [-2.76]])
def J(theta):
return cost_func(X, y, theta)
assert_allclose(grad(X, y, theta),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001)
def test_reg_grad_data1_6(self, data1, err):
y = data1[:, -1:]
X = data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-12.4], [23.56]])
_lambda = 943
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001)
def test_reg_grad_data1_4(self, data1, err):
y = data1[:, -1:]
X = data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -8.4 * ones((n + 1, 1), dtype=float64)
_lambda = 0.762
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001)
def test_grad_data4_6(self, data4, err):
Y = data4[:, -1:]
X = data4[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -0.145 * ones((n + 1, 1), dtype=float64)
def J(theta):
return cost_func(X, Y, theta)
assert_allclose(grad(X, Y, theta),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_grad_data3_6(self, data3, err):
Y = data3[:, -1:]
X = data3[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-20], [0.164], [-0.23]])
def J(theta):
return cost_func(X, Y, theta)
assert_allclose(grad(X, Y, theta),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_grad_data4_5(self, data4, err):
Y = data4[:, -1:]
X = data4[:, :-1]
m, _ = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-0.198], [0.25], [-0.234], [-0.793], [0.123], [-0.378],
[0.423], [-0.678], [-0.3]])
def J(theta):
return cost_func(X, Y, theta)
assert_allclose(grad(X, Y, theta),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
def test_reg_grad_data4_6(self, data4, err):
y = data4[:, -1:]
X = data4[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -0.238 * ones((n + 1, 1), dtype=float64)
_lambda = 975032
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, err),
rtol=0,
atol=0.001,
equal_nan=False)
