Merge pull request #19 from arm61/variable_mode

Variable mode

docs/source/max_likelihood.ipynb

@@ -168,6 +168,7 @@
    "metadata": {},
    "source": [
     "We can see that the variables have changed to be close to the values used in the data synthesis. \n",
+    "Note, that here `variable_medians` are in fact the variable values that maximise the likelihood.\n",
     "\n",
     "Let's inspect the model visually. \n",
     "This can be achieved easily with the `plotting` module in `uravu`."

uravu/distribution.py

@@ -109,12 +109,16 @@ def negative_pdf(self, x):
     @property
     def dist_max(self):
         """
-        Get the value that maximises the distribution.
+        Get the value that maximises the distribution. If no :py:attr:`kde` has been created (for example if the distribution has fewer than 8 values) the median is returned. 
         
         Returns
             :py:attr:`float`: Most likely value.
         """
-        return minimize(self.negative_pdf, x0=[self.n]).x
+        try: 
+            return minimize(self.negative_pdf, x0=[self.n]).x
+        except AttributeError:
+            return self.n
+
 
     @property
     def min(self):

uravu/relationship.py

@@ -114,6 +114,19 @@ def variable_medians(self):
             medians[i] = var.n
         return medians
 
+    @property
+    def variable_modes(self):
+        """
+        The mode values for each of the variables.
+
+        Returns:
+            :py:attr:`array_like`: Variable modes.
+        """
+        modes = np.zeros((len(self.variables)))
+        for i, var in enumerate(self.variables):
+            modes[i] = var.dist_max
+        return modes
+
     @property
     def mcmc_done(self):
         """

uravu/tests/test_relationship.py

@@ -104,6 +104,17 @@ def test_variable_medians(self):
         r.max_likelihood('diff_evo')
         assert_equal(np.allclose(r.variable_medians, [1, 0], atol=1.5), True)
 
+    def test_variable_modes(self):
+        r = Relationship(utils.straight_line, TEST_X, TEST_Y, bounds=((0, 10), (-1, 1)))
+        r.max_likelihood('diff_evo')
+        r.mcmc(n_burn=10, n_samples=10, progress=False, walkers=5)
+        assert_equal(np.allclose(r.variable_modes, [1, 0], atol=1.5), True)
+
+    def test_variable_modes_no_sampling(self):
+        r = Relationship(utils.straight_line, TEST_X, TEST_Y, bounds=((0, 10), (-1, 1)))
+        r.max_likelihood('diff_evo')
+        assert_equal(np.allclose(r.variable_modes, [1, 0], atol=1.5), True)
+
     def test_mcmc_done(self):
         r = Relationship(utils.straight_line, TEST_X, TEST_Y, bounds=((0, 10), (-1, 1)))
         r.mcmc(n_burn=10, n_samples=10, progress=False, walkers=5)