astrophot.fit.func package

Submodules

astrophot.fit.func.lm module

astrophot.fit.func.lm.damp_hessian(hess, L)

astrophot.fit.func.lm.gradient(J, W, D, M)

astrophot.fit.func.lm.gradient_poisson(J, D, M)

astrophot.fit.func.lm.hessian(J, W)

astrophot.fit.func.lm.hessian_poisson(J, D, M)

astrophot.fit.func.lm.lm_step(x, data, model, weight, jacobian, L=1.0, Lup=9.0, Ldn=11.0, tolerance=0.0001, likelihood='gaussian')

astrophot.fit.func.lm.nll(D, M, W)

Negative log-likelihood for Gaussian noise. D: data M: model prediction W: weights

astrophot.fit.func.lm.nll_poisson(D, M)

Negative log-likelihood for Poisson noise. D: data M: model prediction

astrophot.fit.func.lm.solve(hess, grad, L)

astrophot.fit.func.mala module

astrophot.fit.func.mala.mala(initial_state, log_prob, log_prob_grad, num_samples, epsilon, mass_matrix, progress=True, desc='MALA')

astrophot.fit.func.slalom module

astrophot.fit.func.slalom.slalom_step(f, g, x0, m, S, N=10, up=1.3, down=0.5)

Module contents

astrophot.fit.func.gradient(J, W, D, M)

astrophot.fit.func.gradient_poisson(J, D, M)

astrophot.fit.func.hessian(J, W)

astrophot.fit.func.hessian_poisson(J, D, M)

astrophot.fit.func.lm_step(x, data, model, weight, jacobian, L=1.0, Lup=9.0, Ldn=11.0, tolerance=0.0001, likelihood='gaussian')

astrophot.fit.func.mala(initial_state, log_prob, log_prob_grad, num_samples, epsilon, mass_matrix, progress=True, desc='MALA')

astrophot.fit.func.slalom_step(f, g, x0, m, S, N=10, up=1.3, down=0.5)