From a6e1e5e228158b8b0c08e909916245bb6e5e4fec Mon Sep 17 00:00:00 2001 From: Martin Reinecke Date: Fri, 10 Aug 2018 12:28:57 +0200 Subject: [PATCH] tweaks --- demos/bernoulli_demo.py | 15 +++++---------- test/test_models/test_model_gradients.py | 2 +- 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/demos/bernoulli_demo.py b/demos/bernoulli_demo.py index dbcd16cf..5ae269ea 100644 --- a/demos/bernoulli_demo.py +++ b/demos/bernoulli_demo.py @@ -47,25 +47,20 @@ if __name__ == '__main__': def sqrtpspec(k): return 1. / (20. + k**2) - p_space = ift.PowerSpace(harmonic_space) - pd = ift.PowerDistributor(harmonic_space, p_space) - a = ift.PS_field(p_space, sqrtpspec) - A = pd(a) + A = ift.create_power_operator(harmonic_space, sqrtpspec) # Set up a sky model - sky = HT(ift.makeOp(A)).positive_tanh() + sky = ift.positive_tanh(HT(A)) GR = ift.GeometryRemover(position_space) # Set up instrumental response R = GR # Generate mock data - d_space = R.target[0] p = R(sky) mock_position = ift.from_random('normal', harmonic_space) - pp = p(mock_position) - data = np.random.binomial(1, pp.to_global_data().astype(np.float64)) - data = ift.Field.from_global_data(d_space, data) + data = np.random.binomial(1, p(mock_position).local_data.astype(np.float64)) + data = ift.Field.from_local_data(R.target, data) # Compute likelihood and Hamiltonian position = ift.from_random('normal', harmonic_space) @@ -79,7 +74,7 @@ if __name__ == '__main__': # Minimize the Hamiltonian H = ift.Hamiltonian(likelihood, ic_sampling) H = ift.EnergyAdapter(position, H, ic_cg) - # minimizer = ift.SteepestDescent(ic_newton) + # minimizer = ift.L_BFGS(ic_newton) H, convergence = minimizer(H) reconstruction = sky(H.position) diff --git a/test/test_models/test_model_gradients.py b/test/test_models/test_model_gradients.py index 236ba145..47befaa2 100644 --- a/test/test_models/test_model_gradients.py +++ b/test/test_models/test_model_gradients.py @@ -69,7 +69,7 @@ class Model_Tests(unittest.TestCase): pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos) model = ift.FieldAdapter(dom, "s1").scale(3.) - pos = ift.from_random("normal", dom) + pos = ift.from_random("normal", dom1) ift.extra.check_value_gradient_consistency(model, pos) model = ift.ScalingOperator(2.456, space)( ift.FieldAdapter(dom, "s1")*ift.FieldAdapter(dom, "s2")) -- GitLab