draft of log-normal function working

partly addresses #1376

pints/_log_likelihoods.py

@@ -842,8 +842,7 @@ def __init__(self, problem):
  # Pre-calculate parts
  self._logn = 0.5 * self._nt * np.log(2 * np.pi)
  vals = np.asarray(self._values)
- print(not np.any(vals))
- if not np.any(vals):
+ if np.any(vals <= 0):
  raise ValueError('All data points must exceed zero.')
  self._log_values = np.log(self._values)
 
@@ -852,13 +851,21 @@ def __call__(self, x):
  if any(sigma < 0):
  return -np.inf
  soln = self._problem.evaluate(x[:-self._no])
+ if np.any(soln < 0):
+ return -np.inf
  error = np.log(self._values) - np.log(soln)
  return np.sum(- self._logn - self._nt * np.log(sigma)
  - np.sum(self._log_values, axis=0)
  - np.sum(error**2, axis=0) / (2 * sigma**2))
 
  def evaluateS1(self, x):
  """ See :meth:`LogPDF.evaluateS1()`. """
+ # Calculate log-likelihood and when log-prob is infinite, gradients are
+ # not defined
+ L = self.__call__(x)
+ if L == -np.inf:
+ return L, np.tile(np.nan, self._n_parameters)
+
  sigma = np.asarray(x[-self._no:])
 
  # Evaluate, and get residuals
@@ -870,12 +877,6 @@ def evaluateS1(self, x):
  # Note: Must be (np.log(data) - simulation), sign now matters!
  r = np.log(self._values) - np.log(y)
 
- # Calculate log-likelihood and when log-prob is infinite, gradients are
- # not defined
- L = self.__call__(x)
- if L == -np.inf:
- return L, np.tile(np.nan, self._n_parameters)
-
  # Calculate derivatives in the model parameters
  dL = np.sum(
  (sigma**(-2.0) * np.sum((r.T * dy.T / y.T).T, axis=0).T).T, axis=0)

pints/tests/test_log_likelihoods.py

@@ -1429,8 +1429,10 @@ def setUpClass(cls):
  def test_bad_constructor(self):
  # tests that bad data types result in error
  data = [0, 4, 5.5, 7.2]
- self.assertRaises(ValueError, pints.SingleOutputProblem,
- self.model_single, self.times, data)
+ problem = pints.SingleOutputProblem(
+ self.model_single, self.times, data)
+ self.assertRaises(ValueError, pints.LogNormalLogLikelihood,
+ problem)
 
  def test_call(self):
  # test calls of log-likelihood
@@ -1482,6 +1484,12 @@ def test_evaluateS1(self):
  self.assertEqual(y, -np.inf)
  self.assertTrue(np.array_equal(dL, np.tile(np.nan, 2), equal_nan=True))
 
+ mu = -1
+ sigma = 1
+ y, dL = self.log_likelihood.evaluateS1([mu, sigma])
+ self.assertEqual(y, -np.inf)
+ self.assertTrue(np.array_equal(dL, np.tile(np.nan, 2), equal_nan=True))
+
  # two dim output problem
  mu1 = 1.5
  mu2 = 3.4 / 2