Implement log CDF for Triangular distribution

Author: domenzain

pymc3/distributions/continuous.py

@@ -1414,3 +1414,21 @@ def logp(self, value):
                          tt.switch(tt.eq(value, c), tt.log(2 / (upper - lower)),
                          tt.switch(alltrue_elemwise([c < value, value <= upper]),
                          tt.log(2 * (upper - value) / ((upper - lower) * (upper - c))),np.inf)))
+
+    def logcdf(self, value):
+        l = self.lower
+        u = self.upper
+        c = self.c
+        return tt.switch(
+            tt.le(value, l),
+            -np.inf,
+            tt.switch(
+                tt.le(value, c),
+                tt.log(((value - l) ** 2) / ((u - l) * (c - l))),
+                tt.switch(
+                    tt.lt(value, u),
+                    tt.log1p(-((u - value) ** 2) / ((u - l) * (u - c))),
+                    0
+                )
+            )
+        )

pymc3/tests/test_distributions.py

@@ -373,6 +373,8 @@ def test_triangular(self):
         self.pymc3_matches_scipy(
             Triangular, Runif, {'lower': -Rplusunif, 'c': Runif, 'upper': Rplusunif},
             lambda value, c, lower, upper: sp.triang.logpdf(value, c-lower, lower, upper-lower))
+        self.check_logcdf(Triangular, Runif, {'lower': -Rplusunif, 'c': Runif, 'upper': Rplusunif},
+                          lambda value, c, lower, upper: sp.triang.logcdf(value, c-lower, lower, upper-lower))
 
     def test_bound_normal(self):
         PositiveNormal = Bound(Normal, lower=0.)