def test_gaussian_kernel_same_state(self):
"""Tests if the gaussian kernel is 1 for two equal states.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
s = np.array([[1, 2, 3]])
assert_equal(1, crkr._gaussian_kernel(s, s))
def test_gaussian_kernel_same_state(self):
"""Tests if the gaussian kernel is 1 for two equal states.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
s = np.array([[1, 2, 3]])
assert_equal(1, crkr._gaussian_kernel(s, s))
def test_delta_estimate(self):
"""Test if a 2-dimensional numpy array is returned.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[1, 2, 5]])
delta_estimate = crkr.delta_estimate(new_s)
assert_equal(delta_estimate.shape, (1, self.S_2x3.shape[1]))
def test_delta_estimate(self):
"""Test if a 2-dimensional numpy array is returned.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[1, 2, 5]])
delta_estimate = crkr.delta_estimate(new_s)
assert_equal(delta_estimate.shape, (1, self.S_2x3.shape[1]))
def test_gaussian_kernel(self):
"""Tests if the gaussian kernel is correctly computed.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3,
self.ridge_factor_05, self.sigma_05, self.a_1)
s1 = np.array([[1, 2, 3]])
s2 = np.array([[4, 5, 6]])
expected_gk = np.exp(-(self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05 ** 2))))
assert_equal(expected_gk, crkr._gaussian_kernel(s1, s2))
def test_gaussian_kernel(self):
"""Tests if the gaussian kernel is correctly computed.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3, self.ridge_factor_05,
self.sigma_05, self.a_1)
s1 = np.array([[1, 2, 3]])
s2 = np.array([[4, 5, 6]])
expected_gk = np.exp(-(self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05**2))))
assert_equal(expected_gk, crkr._gaussian_kernel(s1, s2))
def test_compute_k(self):
"""Tests if k is correctly computed.
"""
S = self.S_2x3
new_s = np.array([[0, 1, 2]])
exponent = (-self.a_1 * np.power(npla.norm(new_s - S, axis=1), 2) /
(2 * (self.sigma_05 ** 2)))
expected_k = np.exp(exponent)
expected_k = np.array([expected_k]).T
crkr = CrKr(S, self.C_2x2, self.D_2x3,
self.ridge_factor_05, self.sigma_05, self.a_1)
result_k = crkr._compute_k(new_s)
assert_equal(expected_k.shape, result_k.shape)
assert_true(np.allclose(expected_k, result_k))
def test_compute_k(self):
"""Tests if k is correctly computed.
"""
S = self.S_2x3
new_s = np.array([[0, 1, 2]])
exponent = (-self.a_1 * np.power(npla.norm(new_s - S, axis=1), 2) /
(2 * (self.sigma_05**2)))
expected_k = np.exp(exponent)
expected_k = np.array([expected_k]).T
crkr = CrKr(S, self.C_2x2, self.D_2x3, self.ridge_factor_05,
self.sigma_05, self.a_1)
result_k = crkr._compute_k(new_s)
assert_equal(expected_k.shape, result_k.shape)
assert_true(np.allclose(expected_k, result_k))
def test_delta_estimate_diff_shape_state(self):
"""Tests if an exception is raised, for wrongly-shaped state
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[1, 2, 3, 4]])
assert_raises(ValueError, crkr.delta_estimate, new_s)
def test_compute_K(self):
"""Tests if K is correctly computed.
"""
S = self.S_2x3
expected_K = np.zeros((S.shape[0], S.shape[0]))
for i in range(0, S.shape[0]):
for j in range(0, S.shape[0]):
s1 = np.array([S[i, :]])
s2 = np.array([S[j, :]])
exponent = (-self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05**2)))
expected_K[i, j] = np.exp(exponent)
crkr = CrKr(S, self.C_2x2, self.D_2x3, self.ridge_factor_05,
self.sigma_05, self.a_1)
assert_true(np.allclose(expected_K, crkr._compute_K()))
def test_compute_K(self):
"""Tests if K is correctly computed.
"""
S = self.S_2x3
expected_K = np.zeros((S.shape[0], S.shape[0]))
for i in range(0, S.shape[0]):
for j in range(0, S.shape[0]):
s1 = np.array([S[i, :]])
s2 = np.array([S[j, :]])
exponent = (-self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05 ** 2)))
expected_K[i, j] = np.exp(exponent)
crkr = CrKr(S, self.C_2x2, self.D_2x3,
self.ridge_factor_05, self.sigma_05, self.a_1)
assert_true(np.allclose(expected_K, crkr._compute_K()))
def test_update_matrices(self):
"""Tests if the matrices are correctly updated.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[7, 8, 9]])
new_reward = 20
new_d = np.array([[13, 14, 15]])
expected_S = np.vstack((self.S_2x3, new_s))
expected_C_diag = np.append(np.diagonal(self.C_2x2), 1.0 / new_reward)
expected_C = np.diag(expected_C_diag)
expected_D = np.vstack((self.D_2x3, new_d))
crkr.update_matrices(new_s, new_reward, new_d)
assert_true(np.allclose(expected_S, crkr.S))
assert_true(np.allclose(expected_C, crkr.C))
assert_true(np.allclose(expected_D, crkr.D))
def test_update_matrices(self):
"""Tests if the matrices are correctly updated.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[7, 8, 9]])
new_reward = 20
new_d = np.array([[13, 14, 15]])
expected_S = np.vstack((self.S_2x3, new_s))
expected_C_diag = np.append(np.diagonal(self.C_2x2), 1.0 / new_reward)
expected_C = np.diag(expected_C_diag)
expected_D = np.vstack((self.D_2x3, new_d))
crkr.update_matrices(new_s, new_reward, new_d)
assert_true(np.allclose(expected_S, crkr.S))
assert_true(np.allclose(expected_C, crkr.C))
assert_true(np.allclose(expected_D, crkr.D))
def test_delta_mean(self):
"""Tests if delta mean is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[0, 0, 0]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dm = np.dot(k.T,
np.dot(np.linalg.inv(K + ridge_factor * C), D))
assert_true(np.allclose(expected_dm, crkr._delta_mean(k, K)))
def test_delta_mean(self):
"""Tests if delta mean is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[0, 0, 0]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dm = np.dot(k.T, np.dot(np.linalg.inv(K + ridge_factor * C),
D))
assert_true(np.allclose(expected_dm, crkr._delta_mean(k, K)))
def test_delta_variance(self):
"""Tests if delta variance is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[1, 1, 1]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dv = (a + ridge_factor -
np.dot(k.T, np.dot(npla.inv(K + ridge_factor * C), k)))
assert_true(np.allclose(expected_dv, crkr._delta_variance(k, K)))
def test_update_matrices_diff_shape_state(self):
"""Tests if an exception is raises, for wrongly-shaped state.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[7, 8, 9, 10]])
new_reward = 20
new_d = np.array([[13, 14, 15]])
assert_raises(ValueError, crkr.update_matrices, new_s, new_reward,
new_d)
def test_init_default(self):
"""Tests if default variables exists and their data types.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
ridge_factor = crkr.ridge_factor
sigma = crkr.sigma
a = crkr.a
assert_true(isinstance(ridge_factor, numbers.Number))
assert_true(isinstance(sigma, numbers.Number))
assert_true(isinstance(a, numbers.Number))
def test_update_matrices_diff_shape_delta(self):
"""Tests if an exception is raises, for reward of 0.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3)
new_s = np.array([[7, 8, 9]])
new_reward = 0.2
new_d = np.array([[13, 14, 15, 16]])
assert_raises(ValueError, crkr.update_matrices, new_s, new_reward,
new_d)
def test_delta_variance(self):
"""Tests if delta variance is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[1, 1, 1]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dv = (a +
ridge_factor -
np.dot(k.T, np.dot(npla.inv(K + ridge_factor * C), k)))
assert_true(np.allclose(expected_dv, crkr._delta_variance(k, K)))
def test_init_custom(self):
"""Tests if custom variables are assigned.
"""
crkr = CrKr(self.S_2x3, self.C_2x2, self.D_2x3, self.ridge_factor_05,
self.sigma_05, self.a_1)
assert_true(np.array_equal(self.S_2x3, crkr.S))
assert_true(np.array_equal(self.C_2x2, crkr.C))
assert_true(np.array_equal(self.D_2x3, crkr.D))
assert_equal(self.ridge_factor_05, crkr.ridge_factor)
assert_equal(self.sigma_05, crkr.sigma)
assert_equal(self.a_1, crkr.a)
