def test_linear_regression_qr(self):
X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T
y = numpy.array([1, 1.3, 3.9])
b1 = linear_regression(X, y)
b3 = linear_regression(X, y, algo="gram")
b2 = linear_regression(X, y, algo="qr")
self.assertEqualArray(b1, b3)
self.assertEqualArray(b1, b2)
def test_dim_lin_reg(self):
X = rnd.randn(100, 7)
eps = rnd.randn(100, 1) / 3
y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps
y = y.ravel()
b1 = linear_regression(X, y)
b3 = linear_regression(X, y, algo="gram")
b2 = linear_regression(X, y, algo="qr")
self.assertEqualArray(b1.ravel(), b3.ravel())
self.assertEqualArray(b1.ravel(), b2.ravel())
def test_streaming_linear_regression(self):
X0 = numpy.array([[1, 0.5, 10., 5., -2.], [0, 0.4, 20, 4., 2.],
[0, 0.4, 19, 4.2, 2.], [0, 0.7, 18, 4.1, 1.4]],
dtype=float).T
y0 = numpy.array([1., 2.1, 3.1, 3.9, 5.])
for dim in (2, 3, 4):
X = X0[:, :dim]
y = y0
algo1 = []
for i in range(dim, X.shape[0] + 1):
bk = linear_regression(X[:i], y[:i])
algo1.append(bk)
algo2 = []
self.assertRaise(
lambda: list(streaming_linear_regression(X.T, y)), # pylint: disable=W0640
RuntimeError)
for i, bk in enumerate(streaming_linear_regression(X, y)):
algo2.append(bk.copy())
self.assertNotEmpty(bk)
self.assertEqualArray(algo1[i], algo2[i])
self.assertEqual(len(algo1), len(algo2))
def test_lineear_regression(self):
X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T
y = numpy.array([1, 1.3, 3.9])
b1 = linear_regression(X, y)
b2 = linear_regression(X, y, algo="gram")
self.assertEqualArray(b1, b2)
