Implemented normalisation for weights with sum larger than one. Also …

src/qp/mixmod_pdf.py

@@ -49,7 +49,7 @@ def __init__(self, means, stds, weights, *args, **kwargs):
         stds:  array_like
             The standard deviations of the Gaussians
         weights : array_like
-            The weights to attach to the Gaussians
+            The weights to attach to the Gaussians. Weights should sum up to one. If not, the weights are interpreted as relative weights.
         """
         self._scipy_version_warning()
         self._means = reshape_to_pdf_size(means, -1)
@@ -58,6 +58,9 @@ def __init__(self, means, stds, weights, *args, **kwargs):
         kwargs['shape'] = means.shape[:-1]
         self._ncomps = means.shape[-1]
         super().__init__(*args, **kwargs)
+        if np.any(self._weights<0):
+            raise ValueError('All weights need to be larger than zero')
+        self._weights = self._weights/self._weights.sum(axis=1)[:,None]
         self._addobjdata('weights', self._weights)
         self._addobjdata('stds', self._stds)
         self._addobjdata('means', self._means)

tests/qp/test_ensemble.py

@@ -213,5 +213,13 @@ def test_iterator(self):
             test_vals = ens_i.pdf(test_grid)
             assert np.allclose(check_vals, test_vals)
 
+    def test_mixmod_with_negative_weights(self):
+        """Verify that an exception is raised when setting up a mixture model with negative weights"""
+        means = np.array([0.5,1.1, 2.9])
+        sigmas = np.array([0.15,0.13,0.14])
+        weights = np.array([1,0.5,-0.25])
+        with self.assertRaises(ValueError):
+            _ = qp.mixmod(weights=weights, means=means, stds=sigmas)
+
 if __name__ == '__main__':
     unittest.main()