Merge e2f4fc3 into 8c87f73

pymc3/distributions/__init__.py

@@ -24,6 +24,7 @@
 from .continuous import ExGaussian
 from .continuous import VonMises
 from .continuous import SkewNormal
+from .continuous import Triangular
 
 from .discrete import Binomial
 from .discrete import BetaBinomial

pymc3/distributions/continuous.py

@@ -13,7 +13,7 @@
 import warnings
 
 from . import transforms
-from .dist_math import bound, logpow, gammaln, betaln, std_cdf, i0, i1
+from .dist_math import bound, logpow, gammaln, betaln, std_cdf, i0, i1, alltrue_elemwise
 from .distribution import Continuous, draw_values, generate_samples
 
 __all__ = ['Uniform', 'Flat', 'Normal', 'Beta', 'Exponential', 'Laplace',
@@ -1410,3 +1410,46 @@ def logp(self, value):
             + (-tau * (value - mu)**2
             + tt.log(tau / np.pi / 2.)) / 2.,
             tau > 0, sd > 0)
+
+
+class Triangular(Continuous):
+    """
+    Continuous Triangular log-likelihood
+    Implemented by J. A. Fonseca 22/12/16
+
+    Parameters
+    ----------
+    lower : float
+        Lower limit.
+    c: float
+        mode
+    upper : float
+        Upper limit.
+    """
+
+    def __init__(self, lower=0, upper=1, c=0.5,
+                 *args, **kwargs):
+        super(Triangular, self).__init__(*args, **kwargs)
+
+        self.c = c
+        self.lower = lower
+        self.upper = upper
+        self.mean = c
+        self.median = self.mean
+
+    def random(self, point=None, size=None):
+        c, lower, upper = draw_values([self.c, self.lower, self.upper],
+                                      point=point)
+        return generate_samples(stats.triang.rvs, c=c-lower, loc=lower, scale=upper-lower,
+                                size=size, random_state=None)
+
+    def logp(self, value):
+        c = self.c
+        lower = self.lower
+        upper = self.upper
+        return tt.switch(alltrue_elemwise([lower <= value, value < c]),
+                         tt.log(2 * (value - lower) / ((upper - lower) * (c - lower))),
+                         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)))
+

pymc3/tests/test_distributions.py

@@ -11,7 +11,7 @@
                              BetaBinomial, HalfStudentT, StudentT, Weibull, Pareto, InverseGamma,
                              Gamma, Cauchy, HalfCauchy, Lognormal, Laplace, NegativeBinomial,
                              Geometric, Exponential, ExGaussian, Normal, Flat, LKJCorr, Wald,
-                             ChiSquared, HalfNormal, DiscreteUniform, Bound, Uniform,
+                             ChiSquared, HalfNormal, DiscreteUniform, Bound, Uniform, Triangular,
                              Binomial, Wishart, SkewNormal)
 from ..distributions import continuous, multivariate
 from numpy import array, inf, log, exp
@@ -333,6 +333,14 @@ def test_uniform(self):
             Uniform, Runif, {'lower': -Rplusunif, 'upper': Rplusunif},
             lambda value, lower, upper: sp.uniform.logpdf(value, lower, upper - lower))
 
+    Runif = Domain([-1, -.4, 0, .4, 1])
+    Rdunif = Domain([-10, 0, 10.])
+    Rplusunif = Domain([0, .5, inf])
+    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))
+
     def test_bound_normal(self):
         PositiveNormal = Bound(Normal, lower=0.)
         self.pymc3_matches_scipy(PositiveNormal, Rplus, {'mu': Rplus, 'sd': Rplus},
@@ -462,7 +470,6 @@ def test_student_tpos(self):
     def test_skew_normal(self):
         self.pymc3_matches_scipy(SkewNormal, R, {'mu': R, 'sd': Rplusbig, 'alpha': R},
                                  lambda value, alpha, mu, sd: sp.skewnorm.logpdf(value, alpha, mu, sd))
-
     def test_binomial(self):
         self.pymc3_matches_scipy(Binomial, Nat, {'n': NatSmall, 'p': Unit},
                                  lambda value, n, p: sp.binom.logpmf(value, n, p))

pymc3/tests/test_distributions_random.py

@@ -180,6 +180,10 @@ class TestUniform(BaseTestCases.BaseTestCase):
     distribution = pm.Uniform
     params = {'lower': 0., 'upper': 1.}
 
+class TestTriangular(BaseTestCases.BaseTestCase):
+    distribution = pm.Triangular
+    params = {'c': 0.5,'lower': 0., 'upper': 1.}
+
 
 class TestWald(BaseTestCases.BaseTestCase):
     distribution = pm.Wald