flow_CPCA

This file implements information geometry methods working on normalizing flows.

This file needs work and is not complete.

tensiometer.synthetic_probability.flow_CPCA.solve_KL_ode(flow, prior_flow, y0, n, length=1.5, side='both', integrator_options=None, num_points=100, **kwargs)

Solve eigenvalue problem in abstract space side = ‘+’, ‘-’, ‘both’ length = 1.5 num_points = 100 n=0

tensiometer.synthetic_probability.flow_CPCA.solve_eigenvalue_ode_abs(self, y0, n, length=1.5, side='both', integrator_options=None, num_points=100, **kwargs)

Solve eigenvalue problem in abstract space side = ‘+’, ‘-’, ‘both’

tensiometer.synthetic_probability.flow_CPCA.solve_eigenvalue_ode_par(self, y0, n, **kwargs)

Solve eigenvalue ODE in parameter space

tensiometer.synthetic_probability.flow_CPCA.tf_CPC_decomposition(matrix_a, matrix_b)

Covariant Principal Components decomposition impolemented in tensorflow.

Args:

matrix_a (tf.Tensor): Input matrix A. matrix_b (tf.Tensor): Input matrix B.

Returns:

tf.Tensor: Eigenvalues of A_prime. tf.Tensor: Eigenvectors of A_prime.