def test_gradientdescent_regparam():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X, y, gradient(),
reg_param=0.01)
target = [0.47, 0.84]
np.testing.assert_array_almost_equal(weights, target, 1)
def test_gradientdescent_initialweights():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X, y, gradient(),
initial_weights=np.array([0.2, 0.2]))
target = [0.47, 0.84]
np.testing.assert_array_almost_equal(weights, target, 1)
def test_gradientdescent_initialweights():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X,
y,
gradient(),
initial_weights=np.array(
[0.2, 0.2]))
target = [0.47, 0.84]
np.testing.assert_array_almost_equal(weights, target, 1)
def test_gradientdescent_regparam():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X, y, gradient(), reg_param=0.01)
target = [0.47, 0.84]
np.testing.assert_array_almost_equal(weights, target, 1)
def test_gradientdescent_lowiterations():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X, y, gradient(), iterations=2)
target = [0.1, 0.4]
np.testing.assert_array_almost_equal(weights, target, 1)
def test_gradientdescent_lowiterations():
x, y = data.continuous_data_complicated()
X = np.column_stack((np.ones(np.shape(x)[0]), x))
weights = descentmethods.gradientdescent(X, y, gradient(), iterations=2)
target = [0.1, 0.4]
np.testing.assert_array_almost_equal(weights, target, 1)