def test_set_resps(self):
data = np.array([[1, 3], [1, 6]])
mean1 = np.array([1, 5])
C_det1 = 0.75
C_inv1 = np.array([[4, -2], [-2, 4]]) / 3
mean2 = np.array([2, 2])
C_det2 = 1
C_inv2 = np.array([[1, 0], [0, 1]])
mixture = PCAMixture(data, 2, 2)
model1 = PCAModel(mixture, 2)
model2 = PCAModel(mixture, 2)
mixture.models = [model1, model2]
model1.mean = mean1
model1.C_det = C_det1
model1.C_inv = C_inv1
model2.mean = mean2
model2.C_det = C_det2
model2.C_inv = C_inv2
mixture.set_resps()
resps1 = mixture.resps[0]
resps2 = mixture.resps[1]
self.assertAlmostEqual(resps1[0], 0.1790458078)
self.assertAlmostEqual(resps1[1], 0.99965691)
self.assertAlmostEqual(resps2[0], 0.8209541918)
self.assertAlmostEqual(resps2[1], 3.43090081e-04)
def test_set_mixing_coefficients(self):
data = np.array([[1, 3], [1, 6]])
mean1 = np.array([1, 5])
C_det1 = 0.75
C_inv1 = np.array([[4, -2], [-2, 4]]) / 3
mean2 = np.array([2, 2])
C_det2 = 1
C_inv2 = np.array([[1, 0], [0, 1]])
mixture = PCAMixture(data, 2, 2)
model1 = PCAModel(mixture, 2)
model2 = PCAModel(mixture, 2)
mixture.models = [model1, model2]
model1.mean = mean1
model1.C_det = C_det1
model1.C_inv = C_inv1
model2.mean = mean2
model2.C_det = C_det2
model2.C_inv = C_inv2
mixture.set_resps()
mixture.set_mixing_coefficients()
mixing_coeffs = mixture.mixing_coeffs
self.assertAlmostEqual(mixing_coeffs[0], 0.5893513589)
self.assertAlmostEqual(mixing_coeffs[1], 0.41064864094)
def test_get_model_resps(self):
# Test 1
# ----------------------------------
data = np.array([[1, 3], [1, 6]])
mixture = PCAMixture(data, 1, 2)
model = PCAModel(mixture, 2)
mean = np.array([1, 5])
C_det = 0.75
C_inv = np.array([[4, -2], [-2, 4]]) / 3
mixture = PCAMixture(data, 1, 2)
model = PCAModel(mixture, 2)
mixture.models = [model]
model.mean = mean
model.C_det = C_det
model.C_inv = C_inv
resps = mixture.get_model_resps(model)
res = np.array_equal(resps, np.array([1, 1]))
self.assertTrue(res)
# Test 2
# ---------------------------------
data = np.array([[1, 3], [1, 6]])
mean1 = np.array([1, 5])
C_det1 = 0.75
C_inv1 = np.array([[4, -2], [-2, 4]]) / 3
mean2 = np.array([2, 2])
C_det2 = 1
C_inv2 = np.array([[1, 0], [0, 1]])
mixture = PCAMixture(data, 2, 2)
model1 = PCAModel(mixture, 2)
model2 = PCAModel(mixture, 2)
mixture.models = [model1, model2]
model1.mean = mean1
model1.C_det = C_det1
model1.C_inv = C_inv1
model2.mean = mean2
model2.C_det = C_det2
model2.C_inv = C_inv2
resps1 = mixture.get_model_resps(model1)
self.assertAlmostEqual(resps1[0], 0.1790458078)
self.assertAlmostEqual(resps1[1], 0.99965691)
resps2 = mixture.get_model_resps(model2)
self.assertAlmostEqual(resps2[0], 0.8209541918)
self.assertAlmostEqual(resps2[1], 3.43090081e-04)
def test_calc_prob_data(self):
data = np.array([[1, 3], [1, 6]])
mean = np.array([1, 5])
C_det = 0.75
C_inv = np.array([[4, -2], [-2, 4]]) / 3
mixture = PCAMixture(data, 1, 2)
model = PCAModel(mixture, 2)
model.mean = mean
model.C_det = C_det
model.C_inv = C_inv
prob = model.calc_prob_data(data[0])
correct_prob = 0.0127694115
self.assertAlmostEqual(prob, correct_prob)
def test_calc_prob_data(self):
data = np.array([[1, 3], [1, 6]])
mean = np.array([1, 5])
C_det = 0.75
C_inv = np.array([[4, -2], [-2, 4]]) / 3
mixture = PCAMixture(data, 1, 2)
model = PCAModel(mixture, 2)
mixture.models = [model]
model.mean = mean
model.C_det = C_det
model.C_inv = C_inv
prob = mixture.calc_prob_data(data[0])
real_prob = 0.0127694115
self.assertAlmostEqual(prob, real_prob)
data = np.array([[1, 3], [1, 6]])
mean1 = np.array([1, 5])
C_det1 = 0.75
C_inv1 = np.array([[4, -2], [-2, 4]]) / 3
mean2 = np.array([2, 2])
C_det2 = 1
C_inv2 = np.array([[1, 0], [0, 1]])
mixture = PCAMixture(data, 2, 2)
model1 = PCAModel(mixture, 2)
model2 = PCAModel(mixture, 2)
mixture.models = [model1, model2]
model1.mean = mean1
model1.C_det = C_det1
model1.C_inv = C_inv1
model2.mean = mean2
model2.C_det = C_det2
model2.C_inv = C_inv2
prob = mixture.calc_prob_data(data[0])
real_prob = 0.0356596215
self.assertAlmostEqual(real_prob, prob)