def test_line_model_residuals():
model = LineModel()
model.params = (0, 0)
assert_equal(abs(model.residuals(np.array([[0, 0]]))), 0)
assert_equal(abs(model.residuals(np.array([[0, 10]]))), 0)
assert_equal(abs(model.residuals(np.array([[10, 0]]))), 10)
model.params = (5, np.pi / 4)
assert_equal(abs(model.residuals(np.array([[0, 0]]))), 5)
assert_almost_equal(abs(model.residuals(np.array([[np.sqrt(50), 0]]))), 0)
def test_line_model_residuals():
model = LineModel()
model.params = (0, 0)
assert_equal(abs(model.residuals(np.array([[0, 0]]))), 0)
assert_equal(abs(model.residuals(np.array([[0, 10]]))), 0)
assert_equal(abs(model.residuals(np.array([[10, 0]]))), 10)
model.params = (5, np.pi / 4)
assert_equal(abs(model.residuals(np.array([[0, 0]]))), 5)
assert_almost_equal(abs(model.residuals(np.array([[np.sqrt(50), 0]]))), 0)
def test_line_model_residuals():
model = LineModel()
model.params = (0, 0)
assert_equal(model.residuals(np.array([[0, 0]])), 0)
assert_equal(model.residuals(np.array([[0, 10]])), 10)
assert_equal(model.residuals(np.array([[10, 0]])), 0)
model.params = (1, 0)
assert_equal(model.residuals(np.array([[0, 0]])), 0)
assert_almost_equal(model.residuals(np.array([[1, 0]])), np.sqrt(2) / 2.)
def test_deprecated_params_attribute():
model = LineModel()
model.params = (10, 1)
x = np.arange(-10, 10)
y = model.predict_y(x)
with expected_warnings(['`_params`']):
assert_equal(model.params, model._params)
def test_deprecated_params_attribute():
model = LineModel()
model.params = (10, 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_estimate():
# generate original data without noise
model0 = LineModel()
model0.params = (10, 1)
x0 = np.arange(-100, 100)
y0 = model0.predict_y(x0)
data0 = np.column_stack([x0, y0])
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = LineModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params, model_est.params, 1)
def test_line_model_estimate():
# generate original data without noise
model0 = LineModel()
model0.params = (10, 1)
x0 = np.arange(-100, 100)
y0 = model0.predict_y(x0)
data0 = np.column_stack([x0, y0])
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = LineModel()
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
assert_almost_equal(model0.params, model_est.params, 1)
def test_line_model_estimate():
# generate original data without noise
model0 = LineModel()
model0.params = (0, 1)
x0 = np.arange(-100, 100)
y0 = model0.predict_y(x0)
data0 = np.column_stack([x0, y0])
data = data0 + (np.random.random(data0.shape) - 0.5)
# estimate parameters of noisy data
model_est = LineModel()
model_est.estimate(data)
assert_almost_equal(model_est.residuals(data0), np.zeros(len(data)), 1)
# test whether estimated parameters almost equal original parameters
random_state = np.random.RandomState(1234)
x = random_state.rand(100, 2)
assert_almost_equal(model0.predict_y(x), model_est.predict_y(x), 1)
def test_deprecated_params_attribute():
model = LineModel()
model.params = (10, 1)
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_equal(model.params, model._params)
def test_line_model_predict():
model = LineModel()
model.params = (10, 1)
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
def test_deprecated_params_attribute():
model = LineModel()
model.params = (10, 1)
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_equal(model.params, model._params)
def test_line_model_predict():
model = LineModel()
model.params = (10, 1)
x = np.arange(-10, 10)
y = model.predict_y(x)
assert_almost_equal(x, model.predict_x(y))
