def test_estimate_gaussian_from_supervectors_with_no_variance_at_all(self):
svectors = [
SuperVector([4, 4, 4]),
SuperVector([4, 4, 4]),
SuperVector([4, 4, 4]),
]
with self.assertRaises(em.NoVarianceException):
em.estimate_n_gaussians(svectors, 1, 20)
def test_estimate_gaussian_from_supervectors_with_same_one_dimension(self):
svectors = [
SuperVector([4, 0, 3]),
SuperVector([4, 2, 4]),
SuperVector([4, 3, 4]),
SuperVector([4, 1, 2]),
SuperVector([4, 5, 1]),
]
with self.assertRaises(em.NoVarianceException):
em.estimate_n_gaussians(svectors, 1, 20)
def test_correct_number_of_estimated_gaussians(self):
svectors = [SuperVector([1, 3, 2, 4]), SuperVector([3, 5, 5, 3])]
gaussians = em.estimate_n_gaussians(svectors, 1, 1)
self.assertEqual(len(gaussians), 1,
"Invalid number of estimated gaussians!")
gaussians = em.estimate_n_gaussians(svectors, 3, 1)
self.assertEqual(len(gaussians), 3,
"Invalid number of estimated gaussians!")
gaussians = em.estimate_n_gaussians(svectors, 18, 1)
self.assertEqual(len(gaussians), 18,
"Invalid number of estimated gaussians!")
def init_from_mfccphrase(cls, mfccphrase, em_gaussians, em_iterations):
vectors = []
for i in range(mfccphrase.count_mfccs()):
vectors.append(SuperVector.init_from_mfcc(mfccphrase.get_mfcc(i)))
gaussians = em.estimate_n_gaussians(vectors, em_gaussians,
em_iterations)
return Command(mfccphrase.get_name(), gaussians)
def test_estimating_two_2d_gaussians(self):
svectors = [
SuperVector([0, 0]),
SuperVector([0, 2]),
SuperVector([2, 0]),
SuperVector([2, 2]),
SuperVector([5, 5]),
SuperVector([5, 4]),
SuperVector([5, 6]),
SuperVector([4, 5]),
SuperVector([6, 5]),
]
gaussians = em.estimate_n_gaussians(svectors, 2, 40)
expected_first_mean = np.array([1, 1], dtype=float)
expected_second_mean = np.array([5, 5], dtype=float)
expected_first_cov = np.array([1, 1], dtype=float)
expected_second_cov = np.array([0.4, 0.4], dtype=float)
expected_gaussians = [
Gaussian(expected_first_mean, expected_first_cov),
Gaussian(expected_second_mean, expected_second_cov)
]
self.assertTrue(are_gaussians_equal(gaussians, expected_gaussians))
def test_estimating_one_gaussian(self):
svectors = [SuperVector([1, 3, 2]), SuperVector([3, 5, 1])]
gaussians = em.estimate_n_gaussians(svectors, 1, 1)
expected_mean = np.array([2, 4, 1.5], dtype=float)
expected_cov = np.array([1, 1, 0.25], dtype=float)
expected_gaussians = [Gaussian(expected_mean, expected_cov)]
self.assertTrue(are_gaussians_equal(gaussians, expected_gaussians))
def test_estimating_three_2d_gaussians(self):
svectors = [
SuperVector([0, 0]),
SuperVector([0, 2]),
SuperVector([2, 0]),
SuperVector([2, 2]),
SuperVector([7, 9]),
SuperVector([9, 7]),
SuperVector([7, 7]),
SuperVector([9, 9]),
SuperVector([5, 5]),
SuperVector([5, 4]),
SuperVector([5, 6]),
SuperVector([4, 5]),
SuperVector([6, 5]),
]
gaussians = em.estimate_n_gaussians(svectors, 3, 13)
expected_first_mean = np.array([1, 1], dtype=float)
expected_second_mean = np.array([8, 8], dtype=float)
expected_third_mean = np.array([5, 5], dtype=float)
expected_first_cov = np.array([1, 1], dtype=float)
expected_second_cov = np.array([1, 1], dtype=float)
expected_third_cov = np.array([0.4, 0.4], dtype=float)
expected_gaussians = [
Gaussian(expected_first_mean, expected_first_cov),
Gaussian(expected_second_mean, expected_second_cov),
Gaussian(expected_third_mean, expected_third_cov)
]
print("expected")
print(expected_gaussians[0].get_mean())
print(expected_gaussians[0].get_covariance())
print("actual")
print(gaussians[0].get_mean())
print(gaussians[0].get_covariance())
self.assertTrue(are_gaussians_equal(gaussians, expected_gaussians))
def extract_gaussians(self, n_gaussians, iterations):
"""
If limit is not none it will set to 0 each value which is lower than limit value.
"""
return em.estimate_n_gaussians(self.__data, n_gaussians, iterations)
def test_estimate_gaussian_from_single_supervector(self):
svectors = [SuperVector([0, 0])]
with self.assertRaises(em.NotEnoughSuperVectorsException):
em.estimate_n_gaussians(svectors, 1, 20)
def test_estimating_from_supervectors_with_different_amount_of_dimensions(
self):
svectors = [SuperVector([0, 0]), SuperVector([0, 2, 4])]
with self.assertRaises(em.DifferentNumberOfDimensionsException):
em.estimate_n_gaussians(svectors, 1, 20)
def test_estimating_from_empty_supervectors(self):
with self.assertRaises(em.NoSuperVectorsException):
em.estimate_n_gaussians([], 1, 20)
