Add MALA methods

pints/functionaltests/mala_mcmc.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+#
+# This file is part of PINTS (https://github.com/pints-team/pints/) which is
+# released under the BSD 3-clause license. See accompanying LICENSE.md for
+# copyright notice and full license details.
+#
+
+from __future__ import division
+
+import pints
+
+from ._problems import (RunMcmcMethodOnTwoDimGaussian,
+                        RunMcmcMethodOnBanana,
+                        RunMcmcMethodOnHighDimensionalGaussian,
+                        RunMcmcMethodOnCorrelatedGaussian,
+                        RunMcmcMethodOnAnnulus,
+                        RunMcmcMethodOnMultimodalGaussian,
+                        RunMcmcMethodOnCone)
+
+
+def test_mala_mcmc_on_two_dim_gaussian(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 1000
+    problem = RunMcmcMethodOnTwoDimGaussian(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=200,
+        method_hyper_parameters=[[1.0, 1.0]]
+    )
+
+    return {
+        'kld': problem.estimate_kld(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_banana(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnBanana(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[0.8] * 2]
+    )
+
+    return {
+        'kld': problem.estimate_kld(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_high_dim_gaussian(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnHighDimensionalGaussian(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[1.2] * 20]
+    )
+
+    return {
+        'kld': problem.estimate_kld(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_correlated_gaussian(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnCorrelatedGaussian(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[1.0] * 6]
+    )
+
+    return {
+        'kld': problem.estimate_kld(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_annulus(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnAnnulus(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[1.2] * 2]
+    )
+
+    return {
+        'distance': problem.estimate_distance(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_multimodal_gaussian(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnMultimodalGaussian(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[2.0] * 2]
+    )
+
+    return {
+        'kld': problem.estimate_kld(),
+        'mean-ess': problem.estimate_mean_ess()
+    }
+
+
+def test_mala_mcmc_on_cone(n_iterations=None):
+    if n_iterations is None:
+        n_iterations = 2000
+    problem = RunMcmcMethodOnCone(
+        method=pints.MALAMCMC,
+        n_chains=4,
+        n_iterations=n_iterations,
+        n_warmup=500,
+        method_hyper_parameters=[[1.0, 1.0]]
+    )
+
+    return {
+        'distance': problem.estimate_distance(),
+        'mean-ess': problem.estimate_mean_ess()
+    }