def test_kernel_X_Y_one_point_different(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[1]], dtype=numpy.bool8)
Y = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X, Y)
self.assertEqual(K[0, 0], tanh(gamma))
def test_kernel_X_alone_dimension(self):
k = HypercubeKernel(1.)
n = 3
d = 2
X = zeros((n, d), dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K.shape, (n, n))
def test_kernel_X_Y_one_point_different(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[1]], dtype=numpy.bool8)
Y = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X, Y)
self.assertEqual(K[0, 0], tanh(gamma))
def test_kernel_X_alone_type(self):
k = HypercubeKernel(1.)
n = 3
d = 2
X = zeros((n, d), dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(type(K), numpy.ndarray)
def test_kernel_X_alone_dimension(self):
k = HypercubeKernel(1.)
n = 3
d = 2
X = zeros((n, d), dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K.shape, (n, n))
def test_kernel_X_alone_type(self):
k = HypercubeKernel(1.)
n = 3
d = 2
X = zeros((n, d), dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(type(K), numpy.ndarray)
def test_kernel_X_Y_dimension(self):
k = HypercubeKernel(1.)
n_X = 3
n_Y = 4
d = 2
X = zeros((n_X, d), dtype=numpy.bool8)
Y = zeros((n_Y, d), dtype=numpy.bool8)
K = k.kernel(X, Y)
self.assertEqual(K.shape, (n_X, n_Y))
def test_kernel_X_Y_dimension(self):
k = HypercubeKernel(1.)
n_X = 3
n_Y = 4
d = 2
X = zeros((n_X, d), dtype=numpy.bool8)
Y = zeros((n_Y, d), dtype=numpy.bool8)
K = k.kernel(X, Y)
self.assertEqual(K.shape, (n_X, n_Y))
def test_kernel_X_two_points_fixed(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[1, 0], [1, 1]], dtype=numpy.bool8)
K = zeros((2, 2))
for i in range(2):
for j in range(2):
dist = sum(X[i] != X[j])
K[i, j] = tanh(gamma)**dist
self.assertAlmostEqual(norm(K - k.kernel(X)), 0)
def test_kernel_X_two_points_fixed(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[1, 0], [1, 1]], dtype=numpy.bool8)
K = zeros((2, 2))
for i in range(2):
for j in range(2):
dist = sum(X[i] != X[j])
K[i, j] = tanh(gamma) ** dist
self.assertAlmostEqual(norm(K - k.kernel(X)), 0)
def test_kernel_X_many_points_random(self):
gamma = .2
n_X = 4
d = 5
num_runs = 100
k = HypercubeKernel(gamma)
for _ in range(num_runs):
X = randint(0, 2, (n_X, d)).astype(numpy.bool8)
K = zeros((n_X, n_X))
for i in range(n_X):
for j in range(n_X):
dist = sum(X[i] != X[j])
K[i, j] = tanh(gamma)**dist
self.assertAlmostEqual(norm(K - k.kernel(X)), 0)
def test_kernel_X_many_points_random(self):
gamma = .2
n_X = 4
d = 5
num_runs = 100
k = HypercubeKernel(gamma)
for _ in range(num_runs):
X = randint(0, 2, (n_X, d)).astype(numpy.bool8)
K = zeros((n_X, n_X))
for i in range(n_X):
for j in range(n_X):
dist = sum(X[i] != X[j])
K[i, j] = tanh(gamma) ** dist
self.assertAlmostEqual(norm(K - k.kernel(X)), 0)
def test_kernel_X_one_point_zero(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K[0, 0], 1.)
def test_kernel_X_alone_dtype(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K.dtype, numpy.float)
def test_kernel_X_one_point_zero(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K[0, 0], 1.)
def test_kernel_X_alone_dtype(self):
gamma = .2
k = HypercubeKernel(gamma)
X = asarray([[0]], dtype=numpy.bool8)
K = k.kernel(X)
self.assertEqual(K.dtype, numpy.float)
