I612 toy inputs issue

pints/tests/test_toy_gaussian_logpdf.py

@@ -46,6 +46,11 @@ def test_gaussian_logpdf(self):
         self.assertRaises(
             ValueError, pints.toy.GaussianLogPDF, [1, 2, 3], [1, 2, 3, 4])
 
+        # Bad calls to function
+        f = pints.toy.GaussianLogPDF([1, 2, 3], [1, 1, 1])
+        self.assertRaises(ValueError, f.__call__, [1, 2])
+        self.assertRaises(ValueError, f.__call__, [1, 2, 3, 4, 5])
+
     def test_sampling_and_kl_divergence(self):
         # Test GaussianLogPDF.kl_divergence() and .sample().
 

pints/tests/test_toy_high_dimensional_gaussian_logpdf.py

@@ -106,6 +106,11 @@ def test_high_dimensional_log_pdf(self):
         self.assertRaises(
             ValueError, pints.toy.HighDimensionalGaussianLogPDF, 11, -0.11)
 
+        # Bad calls to function
+        f = pints.toy.HighDimensionalGaussianLogPDF(3)
+        self.assertRaises(ValueError, f.__call__, [1, 2])
+        self.assertRaises(ValueError, f.__call__, [1, 2, 3, 4, 5])
+
     def test_sensitivities(self):
         # tests that sensitivities are correct
 

pints/tests/test_toy_multimodal_gaussian_logpdf.py

@@ -65,6 +65,11 @@ def test_basic(self):
             [1, 1], [1.5, 1.5], [3, 0], [0, 3.5]
         ])
 
+        # Bad calls to function
+        f = pints.toy.MultimodalGaussianLogPDF([[1, 1, 1]])
+        self.assertRaises(ValueError, f.__call__, [1, 2])
+        self.assertRaises(ValueError, f.__call__, [1, 2, 3, 4, 5])
+
     def test_bad_constructors(self):
         # Tests bad Constructors.
 

pints/tests/test_toy_parabolic_error.py

@@ -49,6 +49,11 @@ def test_parabolic_error(self):
         self.assertAlmostEqual(dfx[1], 0)
         self.assertAlmostEqual(dfx[2], -0.4)
 
+        # Bad calls to function
+        f = pints.toy.ParabolicError([1, 1, 1])
+        self.assertRaises(ValueError, f.__call__, [1, 2])
+        self.assertRaises(ValueError, f.__call__, [1, 2, 3, 4, 5])
+
 
 if __name__ == '__main__':
     unittest.main()

pints/tests/test_toy_twisted_gaussian_logpdf.py

@@ -28,6 +28,11 @@ def test_twisted_gaussian_logpdf(self):
         self.assertRaises(ValueError, pints.toy.TwistedGaussianLogPDF, 1)
         self.assertRaises(ValueError, pints.toy.TwistedGaussianLogPDF, b=-1)
 
+        # Bad calls to function
+        f = pints.toy.TwistedGaussianLogPDF(3)
+        self.assertRaises(ValueError, f.__call__, [1, 2])
+        self.assertRaises(ValueError, f.__call__, [1, 2, 3, 4, 5])
+
     def test_sampling_and_kl_divergence(self):
         # Test TwistedGaussianLogPDF.kl_divergence() and .sample().
 

pints/toy/_gaussian.py

@@ -9,6 +9,7 @@
 import scipy.stats
 
 from . import ToyLogPDF
+from .. import vector
 
 
 class GaussianLogPDF(ToyLogPDF):
@@ -58,7 +59,8 @@ def __init__(self, mean=[0, 0], sigma=[1, 1]):
         self._sigma_inv = np.linalg.inv(self._sigma)
 
     def __call__(self, x):
-        return self._phi.logpdf(x)
+        y = vector(x).reshape(self._n_parameters)
+        return self._phi.logpdf(y)
 
     def distance(self, samples):
         """

pints/toy/_high_dimensional_gaussian.py

@@ -9,6 +9,7 @@
 import scipy.stats
 
 from . import ToyLogPDF
+from .. import vector
 
 
 class HighDimensionalGaussianLogPDF(ToyLogPDF):
@@ -61,7 +62,8 @@ def __init__(self, dimension=20, rho=0.5):
         self._var = scipy.stats.multivariate_normal(self._mean, self._cov)
 
     def __call__(self, x):
-        return self._var.logpdf(x)
+        y = vector(x).reshape(self._n_parameters)
+        return self._var.logpdf(y)
 
     def distance(self, samples):
         """

pints/toy/_multimodal_gaussian.py

@@ -98,7 +98,8 @@ def __init__(self, modes=None, covariances=None):
             np.linalg.inv(self._covs[i]) for i, mode in enumerate(self._modes)]
 
     def __call__(self, x):
-        f = np.sum([var.pdf(x) for var in self._vars])
+        y = pints.vector(x).reshape(self._n_parameters)
+        f = np.sum([var.pdf(y) for var in self._vars])
         return -np.inf if f == 0 else np.log(f)
 
     def distance(self, samples):

pints/toy/_parabola.py

@@ -29,7 +29,8 @@ def __init__(self, c=[0, 0]):
         self._n = len(self._c)
 
     def __call__(self, x):
-        return np.sum((self._c - x)**2)
+        y = pints.vector(x).reshape(self._n)
+        return np.sum((self._c - y)**2)
 
     def evaluateS1(self, x):
         """ See :meth:`pints.ErrorMeasure.evaluateS1()`. """

pints/toy/_twisted_gaussian_banana.py

@@ -66,6 +66,7 @@ def __call__(self, x):
         y = np.array(x, copy=True, dtype='float')
         y[0] = float(y[0]) / np.sqrt(self._V)
         y[1] += self._b * ((x[0] ** 2) - self._V)
+        y = y.reshape(self._n_parameters)
         return self._phi.logpdf(y)
 
     def distance(self, samples):