Lines Matching refs:numpy
23 import numpy
61 self.transition = numpy.random.normal(
64 self.noise_transform = numpy.random.normal(
68 self.observation_model = numpy.random.normal(
156 self._gap_test_template(times=[20, 21, 22], values=numpy.arange(3))
159 self._gap_test_template(times=[2, 60, 61], values=numpy.arange(3))
162 self._gap_test_template(times=[2, 3, 73], values=numpy.arange(3))
166 values=numpy.arange(7))
336 numpy.random.seed(2)
341 numpy.random.seed(4)
435 new_variances = numpy.diag(evaled_new_covariance)
436 original_variances = numpy.diag(evaled_original_covariance)
461 new_variances = numpy.diag(evaled_new_covariance)
462 original_variances = numpy.diag(evaled_original_covariance)
474 self.noise_transform = numpy.random.normal(
475 size=(transition.shape[0], state_noise_dimension)).astype(numpy.float32)
477 self.observation_model = numpy.random.normal(
478 size=(transition.shape[0])).astype(numpy.float32)
499 cycle_transition = numpy.zeros([period - 1, period - 1],
500 dtype=numpy.float32)
502 cycle_transition[1:, :-1] = numpy.identity(period - 2)
505 _adder_transition = numpy.array([[1, 1],
506 [0, 1]], dtype=numpy.float32)
509 numpy.random.seed(8)
546 self.assertAllClose(numpy.zeros([1, 4, 4]), posterior_var,
549 numpy.dot(
550 numpy.linalg.matrix_power(
585 self.assertAllClose(numpy.zeros([1, 4, 4]), posterior_var,
588 numpy.dot(
589 numpy.linalg.matrix_power(
639 values = numpy.reshape([1., 2., 3., 4.],
660 times = numpy.arange(100)
661 values = numpy.arange(100)
706 covariance = numpy.eye(num_features)
712 values = numpy.cumsum(
713 numpy.random.multivariate_normal(
714 mean=numpy.zeros(num_features),
718 times = numpy.arange(dataset_size)
