Resolve "Add multivariate gaussian to core likelihood"

bilby/core/likelihood.py

@@ -3,6 +3,7 @@ import copy
 
 import numpy as np
 from scipy.special import gammaln
+from scipy.stats import multivariate_normal
 
 from .utils import infer_parameters_from_function
 
@@ -401,6 +402,69 @@ class StudentTLikelihood(Analytical1DLikelihood):
         self._nu = nu
 
 
+class AnalyticalMultidimensionalCovariantGaussian(Likelihood):
+    """
+        A multivariate Gaussian likelihood
+        with known analytic solution.
+
+        Parameters
+        ----------
+        mean: array_like
+            Array with the mean values of distribution
+        cov: array_like
+            The ndim*ndim covariance matrix
+        """
+
+    def __init__(self, mean, cov):
+        self.cov = np.atleast_2d(cov)
+        self.mean = np.atleast_1d(mean)
+        self.sigma = np.sqrt(np.diag(self.cov))
+        self.pdf = multivariate_normal(mean=self.mean, cov=self.cov)
+        parameters = {"x{0}".format(i): 0 for i in range(self.dim)}
+        super(AnalyticalMultidimensionalCovariantGaussian, self).__init__(parameters=parameters)
+
+    @property
+    def dim(self):
+        return len(self.cov[0])
+
+    def log_likelihood(self):
+        x = np.array([self.parameters["x{0}".format(i)] for i in range(self.dim)])
+        return self.pdf.logpdf(x)
+
+
+class AnalyticalMultidimensionalBimodalCovariantGaussian(Likelihood):
+    """
+        A multivariate Gaussian likelihood
+        with known analytic solution.
+
+        Parameters
+        ----------
+        mean_1: array_like
+            Array with the mean value of the first mode
+        mean_2: array_like
+            Array with the mean value of the second mode
+        cov: array_like
+        """
+
+    def __init__(self, mean_1, mean_2, cov):
+        self.cov = np.atleast_2d(cov)
+        self.sigma = np.sqrt(np.diag(self.cov))
+        self.mean_1 = np.atleast_1d(mean_1)
+        self.mean_2 = np.atleast_1d(mean_2)
+        self.pdf_1 = multivariate_normal(mean=self.mean_1, cov=self.cov)
+        self.pdf_2 = multivariate_normal(mean=self.mean_2, cov=self.cov)
+        parameters = {"x{0}".format(i): 0 for i in range(self.dim)}
+        super(AnalyticalMultidimensionalBimodalCovariantGaussian, self).__init__(parameters=parameters)
+
+    @property
+    def dim(self):
+        return len(self.cov[0])
+
+    def log_likelihood(self):
+        x = np.array([self.parameters["x{0}".format(i)] for i in range(self.dim)])
+        return -np.log(2) + np.logaddexp(self.pdf_1.logpdf(x), self.pdf_2.logpdf(x))
+
+
 class JointLikelihood(Likelihood):
     def __init__(self, *likelihoods):
         """

examples/core_examples/15d_gaussian.py

-from scipy.stats import multivariate_normal
-
 import bilby
 import numpy as np
 
+from bilby.core.likelihood import AnalyticalMultidimensionalCovariantGaussian, \
+    AnalyticalMultidimensionalBimodalCovariantGaussian
+
 logger = bilby.core.utils.logger
 
 cov = [
@@ -58,68 +59,6 @@ cov = [
 dim = 15
 mean = np.zeros(dim)
 
-
-class AnalyticalMultidimensionalCovariantGaussian(bilby.Likelihood):
-    """
-        A multivariate Gaussian likelihood
-        with known analytic solution.
-
-        Parameters
-        ----------
-        mean: array_like
-            Array with the mean value of distribution
-        cov: array_like
-            The ndim*ndim covariance matrix
-        """
-
-    def __init__(self, mean, cov):
-        super(AnalyticalMultidimensionalCovariantGaussian, self).__init__(parameters=dict())
-        self.cov = np.array(cov)
-        self.mean = np.array(mean)
-        self.sigma = np.sqrt(np.diag(self.cov))
-        self.pdf = multivariate_normal(mean=self.mean, cov=self.cov)
-
-    @property
-    def dim(self):
-        return len(self.cov[0])
-
-    def log_likelihood(self):
-        x = np.array([self.parameters["x{0}".format(i)] for i in range(self.dim)])
-        return self.pdf.logpdf(x)
-
-
-class AnalyticalMultidimensionalBimodalCovariantGaussian(bilby.Likelihood):
-    """
-        A multivariate Gaussian likelihood
-        with known analytic solution.
-
-        Parameters
-        ----------
-        mean_1: array_like
-            Array with the mean value of the first mode
-        mean_2: array_like
-            Array with the mean value of the second mode
-        cov: array_like
-        """
-
-    def __init__(self, mean_1, mean_2, cov):
-        super(AnalyticalMultidimensionalBimodalCovariantGaussian, self).__init__(parameters=dict())
-        self.cov = np.array(cov)
-        self.mean_1 = np.array(mean_1)
-        self.mean_2 = np.array(mean_2)
-        self.sigma = np.sqrt(np.diag(self.cov))
-        self.pdf_1 = multivariate_normal(mean=self.mean_1, cov=self.cov)
-        self.pdf_2 = multivariate_normal(mean=self.mean_2, cov=self.cov)
-
-    @property
-    def dim(self):
-        return len(self.cov[0])
-
-    def log_likelihood(self):
-        x = np.array([self.parameters["x{0}".format(i)] for i in range(self.dim)])
-        return -np.log(2) + np.logaddexp(self.pdf_1.logpdf(x), self.pdf_2.logpdf(x))
-
-
 label = "multidim_gaussian_unimodal"
 outdir = "outdir"
 

test/likelihood_test.py

@@ -6,7 +6,9 @@ import mock
 import numpy as np
 from bilby.core.likelihood import (
     Likelihood, GaussianLikelihood, PoissonLikelihood, StudentTLikelihood,
-    Analytical1DLikelihood, ExponentialLikelihood, JointLikelihood)
+    Analytical1DLikelihood, ExponentialLikelihood,
+    AnalyticalMultidimensionalCovariantGaussian,
+    AnalyticalMultidimensionalBimodalCovariantGaussian, JointLikelihood)
 
 
 class TestLikelihoodBase(unittest.TestCase):
@@ -490,6 +492,83 @@ class TestExponentialLikelihood(unittest.TestCase):
         self.assertEqual(expected, repr(self.exponential_likelihood))
 
 
+class TestAnalyticalMultidimensionalCovariantGaussian(unittest.TestCase):
+
+    def setUp(self):
+        self.cov = [[1, 0, 0], [0, 4, 0], [0, 0, 9]]
+        self.sigma = [1, 2, 3]
+        self.mean = [10, 11, 12]
+        self.likelihood = AnalyticalMultidimensionalCovariantGaussian(
+            mean=self.mean,
+            cov=self.cov)
+
+    def tearDown(self):
+        del self.cov
+        del self.sigma
+        del self.mean
+        del self.likelihood
+
+    def test_cov(self):
+        self.assertTrue(np.array_equal(self.cov, self.likelihood.cov))
+
+    def test_mean(self):
+        self.assertTrue(np.array_equal(self.mean, self.likelihood.mean))
+
+    def test_sigma(self):
+        self.assertTrue(np.array_equal(self.sigma, self.likelihood.sigma))
+
+    def test_parameters(self):
+        self.assertDictEqual(dict(x0=0, x1=0, x2=0), self.likelihood.parameters)
+
+    def test_dim(self):
+        self.assertEqual(3, self.likelihood.dim)
+
+    def test_log_likelihood(self):
+        likelihood = AnalyticalMultidimensionalCovariantGaussian(mean=[0], cov=[1])
+        self.assertEqual(-np.log(2*np.pi)/2, likelihood.log_likelihood())
+
+
+class TestAnalyticalMultidimensionalBimodalCovariantGaussian(unittest.TestCase):
+
+    def setUp(self):
+        self.cov = [[1, 0, 0], [0, 4, 0], [0, 0, 9]]
+        self.sigma = [1, 2, 3]
+        self.mean_1 = [10, 11, 12]
+        self.mean_2 = [20, 21, 22]
+        self.likelihood = AnalyticalMultidimensionalBimodalCovariantGaussian(
+            mean_1=self.mean_1,
+            mean_2=self.mean_2,
+            cov=self.cov)
+
+    def tearDown(self):
+        del self.cov
+        del self.sigma
+        del self.mean_1
+        del self.mean_2
+        del self.likelihood
+
+    def test_cov(self):
+        self.assertTrue(np.array_equal(self.cov, self.likelihood.cov))
+
+    def test_mean_1(self):
+        self.assertTrue(np.array_equal(self.mean_1, self.likelihood.mean_1))
+
+    def test_mean_2(self):
+        self.assertTrue(np.array_equal(self.mean_2, self.likelihood.mean_2))
+
+    def test_sigma(self):
+        self.assertTrue(np.array_equal(self.sigma, self.likelihood.sigma))
+
+    def test_parameters(self):
+        self.assertDictEqual(dict(x0=0, x1=0, x2=0), self.likelihood.parameters)
+
+    def test_dim(self):
+        self.assertEqual(3, self.likelihood.dim)
+
+    def test_log_likelihood(self):
+        likelihood = AnalyticalMultidimensionalBimodalCovariantGaussian(mean_1=[0], mean_2=[0], cov=[1])
+        self.assertEqual(-np.log(2*np.pi)/2, likelihood.log_likelihood())
+
 class TestJointLikelihood(unittest.TestCase):
 
     def setUp(self):