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 = LinearVectorModel(data)
sample = np.random.random(120)
weights = linear_model.project(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 = LinearVectorModel(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 = LinearVectorModel(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_linear_model_instance_vector():
data = np.zeros((3, 120))
data[0, 0] = 1
data[1, 1] = 1
data[2, 2] = 1
linear_model = LinearVectorModel(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 = np.random.randn(10, 10)
pca_model = PCAVectorModel(pca_samples)
lm_samples = np.asarray([np.random.randn(10) for _ in range(4)])
lm_model = LinearVectorModel(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_component():
data = np.random.random((3, 120))
linear_model = LinearVectorModel(data)
assert_equal(linear_model.component(2), data[2])
def test_linear_model_basics():
data = np.random.random((3, 120))
linear_model = LinearVectorModel(data)
assert linear_model.n_components == 3
assert linear_model.n_components == 3
assert linear_model.n_features == 120
def test_linear_model_creation():
data = np.zeros((3, 120))
LinearVectorModel(data)
def test_linear_model_component():
data = np.random.random((3, 120))
linear_model = LinearVectorModel(data)
assert_equal(linear_model.component(2), data[2])
