diff --git a/3_more_examples.py b/3_more_examples.py index a843ed24914191f643cbeaed5074afccf707cb57..28c865779b678258f0bc2d43ca79428083b658c3 100644 --- a/3_more_examples.py +++ b/3_more_examples.py @@ -20,9 +20,9 @@ import numpy as np import helpers as h import nifty5 as ift -seeds = [123, 42, 42] -name = ['bernoulli', 'gauss', 'poisson'] -for mode in [0, 1, 2]: +seeds = [123, 42] +name = ['bernoulli', 'poisson'] +for mode in [0, 1]: np.random.seed(seeds[mode]) position_space = ift.RGSpace([256, 256]) @@ -50,12 +50,7 @@ for mode in [0, 1, 2]: if mode == 0: signal = correlated_field.sigmoid() R = h.checkerboard_response(position_space) - elif mode == 1: - signal = correlated_field.exp() - R = h.radial_tomography_response(position_space, lines_of_sight=256) - N = ift.ScalingOperator(5., R.target) - dct['N'] = N - elif mode == 2: + else: signal = correlated_field.exp() R = h.exposure_response(position_space) h.plot_prior_samples_2d(5, signal, R, correlated_field, A, name[mode], @@ -65,10 +60,7 @@ for mode in [0, 1, 2]: signal_response = signal_response.clip(1e-5, 1 - 1e-5) data, ground_truth = h.generate_bernoulli_data(signal_response) likelihood = ift.BernoulliEnergy(data) @ signal_response - elif mode == 1: - data, ground_truth = h.generate_gaussian_data(signal_response, N) - likelihood = ift.GaussianEnergy(data, N) @ signal_response - elif mode == 2: + else: data, ground_truth = h.generate_poisson_data(signal_response) likelihood = ift.PoissonianEnergy(data) @ signal_response @@ -80,7 +72,7 @@ for mode in [0, 1, 2]: H = ift.StandardHamiltonian(likelihood, ic_sampling) initial_mean = ift.MultiField.full(H.domain, 0.) mean = initial_mean - N_samples = 5 if mode in [0, 2] else 10 + N_samples = 5 for _ in range(5): # Draw new samples and minimize KL KL = ift.MetricGaussianKL(mean, H, N_samples)