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fix the issue 3428 #3426

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fix the issue 3428 #3426

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22 changes: 12 additions & 10 deletions pymc3/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -571,15 +571,17 @@ def __init__(self, mu=0, sigma=None, tau=None, lower=None, upper=None,
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
self.sigma = self.sd = tt.as_tensor_variable(sigma)
self.tau = tt.as_tensor_variable(tau)
self.lower = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
self.upper = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
self.lower_check = tt.as_tensor_variable(floatX(lower)) if lower is not None else lower
self.upper_check = tt.as_tensor_variable(floatX(upper)) if upper is not None else upper
self.lower = tt.as_tensor_variable(floatX(lower)) if lower is not None else tt.as_tensor_variable(floatX(-np.inf))
self.upper = tt.as_tensor_variable(floatX(upper)) if upper is not None else tt.as_tensor_variable(floatX(np.inf))
self.mu = tt.as_tensor_variable(floatX(mu))

if self.lower is None and self.upper is None:
if self.lower_check is None and self.upper_check is None:
self._defaultval = mu
elif self.lower is None and self.upper is not None:
elif self.lower_check is None and self.upper_check is not None:
self._defaultval = self.upper - 1.
elif self.lower is not None and self.upper is None:
elif self.lower_check is not None and self.upper_check is None:
self._defaultval = self.lower + 1.
else:
self._defaultval = (self.lower + self.upper) / 2
Expand Down Expand Up @@ -639,19 +641,19 @@ def logp(self, value):
logp = Normal.dist(mu=mu, sigma=sigma).logp(value) - norm

bounds = [sigma > 0]
if self.lower is not None:
if self.lower_check is not None:
bounds.append(value >= self.lower)
if self.upper is not None:
if self.upper_check is not None:
bounds.append(value <= self.upper)
return bound(logp, *bounds)

def _normalization(self):
mu, sigma = self.mu, self.sigma

if self.lower is None and self.upper is None:
if self.lower_check is None and self.upper_check is None:
return 0.

if self.lower is not None and self.upper is not None:
if self.lower_check is not None and self.upper_check is not None:
lcdf_a = normal_lcdf(mu, sigma, self.lower)
lcdf_b = normal_lcdf(mu, sigma, self.upper)
lsf_a = normal_lccdf(mu, sigma, self.lower)
Expand All @@ -663,7 +665,7 @@ def _normalization(self):
logdiffexp(lcdf_b, lcdf_a),
)

if self.lower is not None:
if self.lower_check is not None:
return normal_lccdf(mu, sigma, self.lower)
else:
return normal_lcdf(mu, sigma, self.upper)
Expand Down
20 changes: 20 additions & 0 deletions pymc3/tests/test_distributions_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,6 +259,14 @@ class TestTruncatedNormal(BaseTestCases.BaseTestCase):
distribution = pm.TruncatedNormal
params = {'mu': 0., 'tau': 1., 'lower':-0.5, 'upper':0.5}

class TestTruncatedNormalLower(BaseTestCases.BaseTestCase):
distribution = pm.TruncatedNormal
params = {'mu': 0., 'tau': 1., 'lower':-0.5}

class TestTruncatedNormalUpper(BaseTestCases.BaseTestCase):
distribution = pm.TruncatedNormal
params = {'mu': 0., 'tau': 1., 'upper':0.5}

class TestSkewNormal(BaseTestCases.BaseTestCase):
distribution = pm.SkewNormal
params = {'mu': 0., 'sigma': 1., 'alpha': 5.}
Expand Down Expand Up @@ -475,6 +483,18 @@ def ref_rand(size, mu, sigma, lower, upper):
pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'lower':-Rplusbig, 'upper':Rplusbig},
ref_rand=ref_rand)

def test_truncated_normal_lower(self):
def ref_rand(size, mu, sigma, lower):
return st.truncnorm.rvs((lower-mu)/sigma, np.inf, size=size, loc=mu, scale=sigma)
pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'lower':-Rplusbig},
ref_rand=ref_rand)

def test_truncated_normal_upper(self):
def ref_rand(size, mu, sigma, upper):
return st.truncnorm.rvs(-np.inf, (upper-mu)/sigma, size=size, loc=mu, scale=sigma)
pymc3_random(pm.TruncatedNormal, {'mu': R, 'sigma': Rplusbig, 'upper':Rplusbig},
ref_rand=ref_rand)

def test_skew_normal(self):
def ref_rand(size, alpha, mu, sigma):
return st.skewnorm.rvs(size=size, a=alpha, loc=mu, scale=sigma)
Expand Down