def test_line_model_residuals():
model = LineModelND()
model.params = (np.array([0, 0]), np.array([0, 1]))
assert_equal(model.residuals(np.array([[0, 0]])), 0)
assert_equal(model.residuals(np.array([[0, 10]])), 0)
assert_equal(model.residuals(np.array([[10, 0]])), 10)
model.params = (np.array([-2, 0]), np.array([1, 1]) / np.sqrt(2))
assert_equal(model.residuals(np.array([[0, 0]])), np.sqrt(2))
assert_almost_equal(model.residuals(np.array([[-4, 0]])), np.sqrt(2))
def test_line_modelND_estimate():
# generate original data without noise
model0 = LineModelND()
model0.params = (np.array([0,0,0], dtype='float'),
np.array([1,1,1], dtype='float')/np.sqrt(3))
# we scale the unit vector with a factor 10 when generating points on the
# line in order to compensate for the scale of the random noise
data0 = (model0.params[0] +
10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1])
# add gaussian noise to data
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = LineModelND()
model_est.estimate(data)
# test whether estimated parameters are correct
# we use the following geometric property: two aligned vectors have
# a cross-product equal to zero
# test if direction vectors are aligned
assert_almost_equal(np.linalg.norm(np.cross(model0.params[1],
model_est.params[1])), 0, 1)
# test if origins are aligned with the direction
a = model_est.params[0] - model0.params[0]
if np.linalg.norm(a) > 0:
a /= np.linalg.norm(a)
assert_almost_equal(np.linalg.norm(np.cross(model0.params[1], a)), 0, 1)
def test_deprecated_params_attribute():
model = LineModelND()
model.params = ((0, 0), (1, 1))
x = np.arange(-10, 10)
y = model.predict_y(x)
with expected_warnings(['`_params`']):
assert_equal(model.params, model._params)
def test_line_model_nd_residuals():
model = LineModelND()
model.params = (np.array([0, 0, 0]), np.array([0, 0, 1]))
assert_equal(abs(model.residuals(np.array([[0, 0, 0]]))), 0)
assert_equal(abs(model.residuals(np.array([[0, 0, 1]]))), 0)
assert_equal(abs(model.residuals(np.array([[10, 0, 0]]))), 10)
# test params argument in model.rediduals
data = np.array([[10, 0, 0]])
params = (np.array([0, 0, 0]), np.array([2, 0, 0]))
assert_equal(abs(model.residuals(data, params=params)), 30)
def test_line_model_estimate():
# generate original data without noise
model0 = LineModelND()
model0.params = ((0, 0), (1, 1))
x0 = np.arange(-100, 100)
y0 = model0.predict_y(x0)
data = np.column_stack([x0, y0])
# estimate parameters of noisy data
model_est = LineModelND()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
x = np.random.rand(100, 2)
assert_almost_equal(model0.predict(x), model_est.predict(x), 1)
def test_line_modelND_residuals():
model = LineModelND()
model.params = (np.array([0, 0, 0]), np.array([0, 0, 1]))
assert_equal(abs(model.residuals(np.array([[0, 0, 0]]))), 0)
assert_equal(abs(model.residuals(np.array([[0, 0, 1]]))), 0)
assert_equal(abs(model.residuals(np.array([[10, 0, 0]]))), 10)
def test_line_modelND_predict():
model = LineModelND()
model.params = (np.array([0, 0]), np.array([0.2, 0.98]))
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
def test_line_model_predict():
model = LineModelND()
model.params = ((0, 0), (1, 1))
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
def test_line_model_predict():
model = LineModelND()
model.params = ((0, 0), (1, 1))
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
def test_line_model_nd_predict():
model = LineModelND()
model.params = (np.array([0, 0]), np.array([0.2, 0.8]))
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
def test_line_model_nd_residuals():
model = LineModelND()
model.params = (np.array([0, 0, 0]), np.array([0, 0, 1]))
assert_equal(abs(model.residuals(np.array([[0, 0, 0]]))), 0)
assert_equal(abs(model.residuals(np.array([[0, 0, 1]]))), 0)
assert_equal(abs(model.residuals(np.array([[10, 0, 0]]))), 10)
