Merge pull request #978 from pints-team/i975-multivariate-gaussian-se…

…nsitivities Multivariate Gaussian sensitivities
pints-team · Oct 1, 2019 · d2c6b48 · d2c6b48
2 parents 84d67ae + 94c429c
commit d2c6b48
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Showing 2 changed files with 39 additions and 9 deletions.
diff --git a/pints/_log_priors.py b/pints/_log_priors.py
@@ -660,12 +660,17 @@ def __init__(self, mean, cov):
         self._mean = mean
         self._cov = cov
         self._n_parameters = mean.shape[0]
+        self._cov_inverse = np.linalg.inv(self._cov)
 
     def __call__(self, x):
         return np.log(
             scipy.stats.multivariate_normal.pdf(
                 x, mean=self._mean, cov=self._cov))
 
+    def evaluateS1(self, x):
+        """ See :meth:`LogPDF.evaluateS1()`. """
+        return self(x), -np.matmul(self._cov_inverse, x - self._mean)
+
     def mean(self):
         """ See :meth:`LogPrior.mean()`. """
         return self._mean

diff --git a/pints/tests/test_log_priors.py b/pints/tests/test_log_priors.py
@@ -590,20 +590,15 @@ def test_log_normal_prior_sampling(self):
         self.assertTrue(0.9 * analyt_mean < sample_mean < 1.1 * analyt_mean)
 
     def test_multivariate_normal_prior(self):
-        # 1d test
-        mean = 0
-        covariance = 1
 
         # Input must be a matrix
         self.assertRaises(
-            ValueError, pints.MultivariateGaussianLogPrior, mean, covariance)
-        covariance = [1]
+            ValueError, pints.MultivariateGaussianLogPrior, 0, 1)
         self.assertRaises(
-            ValueError, pints.MultivariateGaussianLogPrior, mean, covariance)
+            ValueError, pints.MultivariateGaussianLogPrior, 0, [1])
 
-        # Basic test
-        covariance = [[1]]
-        p = pints.MultivariateGaussianLogPrior(mean, covariance)
+        # 1d test
+        p = pints.MultivariateGaussianLogPrior(0, [[1]])
         self.assertEqual(p([0]), -0.5 * np.log(2 * np.pi))
 
         # 5d tests
@@ -623,6 +618,36 @@ def test_multivariate_normal_prior(self):
             ValueError, pints.MultivariateGaussianLogPrior, [1, 2],
             [[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 
+        # Test sensitivities
+        mean = [1, 3]
+        covariance = [[2, 0.5], [0.5, 2]]
+        p = pints.MultivariateGaussianLogPrior(mean, covariance)
+        y, dy = p.evaluateS1([4, 5])
+        self.assertEqual(len(dy), 2)
+        self.assertAlmostEqual(y, -5.165421653067172, places=6)
+        dy_test = [-float(4 / 3), -float(2 / 3)]
+        self.assertAlmostEqual(dy[0], dy_test[0], places=6)
+        self.assertAlmostEqual(dy[1], dy_test[1], places=6)
+
+        mean = [-5.5, 6.7, 3.2]
+        covariance = [[3.4, -0.5, -0.7], [-0.5, 2.7, 1.4], [-0.7, 1.4, 5]]
+        p = pints.MultivariateGaussianLogPrior(mean, covariance)
+        y, dy = p.evaluateS1([4.4, 3.5, -3])
+        self.assertEqual(len(dy), 3)
+        self.assertAlmostEqual(y, -20.855279298674258, places=6)
+        dy_test = [-2.709773397444412, 0.27739553170576203, 0.7829609754801692]
+        self.assertAlmostEqual(dy[0], dy_test[0], places=6)
+        self.assertAlmostEqual(dy[1], dy_test[1], places=6)
+        self.assertAlmostEqual(dy[2], dy_test[2], places=6)
+
+        # 1d sensitivity test
+        p = pints.MultivariateGaussianLogPrior(0, [[1]])
+        x = [0]
+        y, dy = p.evaluateS1(x)
+        self.assertEqual(y, p(x))
+        self.assertTrue(len(dy), 1)
+        self.assertEqual(dy[0], 0)
+
     def test_multivariate_normal_sampling(self):
         d = 1
         mean = 2