def test_linear_model_project_vector(): data = np.zeros((3, 120)) data[0, 0] = 1 data[1, 1] = 1 data[2, 2] = 1 linear_model = LinearModel(data) sample = np.random.random(120) weights = linear_model.project_vector(sample) assert_allclose(weights, sample[:3])
def test_linear_model_project_vector(): data = np.zeros((3, 120)) data[0, 0] = 1 data[1, 1] = 1 data[2, 2] = 1 linear_model = LinearModel(data) sample = np.random.random(120) weights = linear_model.project(sample) assert_allclose(weights, sample[:3])
def test_linear_model_instance_vector(): data = np.zeros((3, 120)) data[0, 0] = 1 data[1, 1] = 1 data[2, 2] = 1 linear_model = LinearModel(data) weights = np.array([0.263, 7, 41.2]) projected = linear_model.instance_vector(weights) # only the first 3 features are non zero... assert_allclose(projected[:3], weights) # rest should be nil assert_allclose(projected[3:], 0)
def test_linear_model_instance_vector(): data = np.zeros((3, 120)) data[0, 0] = 1 data[1, 1] = 1 data[2, 2] = 1 linear_model = LinearModel(data) weights = np.array([0.263, 7, 41.2]) projected = linear_model.instance(weights) # only the first 3 features are non zero... assert_allclose(projected[:3], weights) # rest should be nil assert_allclose(projected[3:], 0)
def test_pca_orthogonalize_against(): pca_samples = [PointCloud(np.random.randn(10)) for _ in range(10)] pca_model = PCAModel(pca_samples) lm_samples = np.asarray([np.random.randn(10) for _ in range(4)]) lm_model = LinearModel(np.asarray(lm_samples)) # orthogonalize pca_model.orthonormalize_against_inplace(lm_model) # number of active components must remain the same assert_equal(pca_model.n_active_components, 6)
def test_linear_model_creation(): data = np.zeros((3, 120)) LinearModel(data)
def test_linear_model_component(): data = np.random.random((3, 120)) linear_model = LinearModel(data) assert_equal(linear_model.component_vector(2), data[2])
def test_linear_model_basics(): data = np.random.random((3, 120)) linear_model = LinearModel(data) assert (linear_model.n_components == 3) assert (linear_model.n_components == 3) assert (linear_model.n_features == 120)
def test_linear_model_component(): data = np.random.random((3, 120)) linear_model = LinearModel(data) assert_equal(linear_model.component(2), data[2])