Address review feedback

- remove upper and lower checks
- pass default value for dist as `testval` to Distribution dist method

pymc3/distributions/continuous.py

@@ -671,25 +671,39 @@ def dist(
         *args,
         **kwargs,
     ):
-        mu, sigma = _truncated_normal_prepare_mu_sigma_params(mu, sigma, tau, sd)
-        lower, lower_check, upper, upper_check = _truncated_normal_prepare_lower_and_upper(
-            lower, upper
-        )
-
-        if lower_check is None and upper_check is None:
-            _defaultval = mu
-        elif lower_check is None and upper_check is not None:
-            _defaultval = upper - 1.0
-        elif lower_check is not None and upper_check is None:
-            _defaultval = lower + 1.0
+        sigma = sd if sd is not None else sigma
+        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
+        sigma = at.as_tensor_variable(sigma)
+        tau = at.as_tensor_variable(tau)
+        mu = at.as_tensor_variable(floatX(mu))
+        assert_negative_support(sigma, "sigma", "TruncatedNormal")
+        assert_negative_support(tau, "tau", "TruncatedNormal")
+
+        if lower is None and upper is None:
+            initval = mu
+        elif lower is None and upper is not None:
+            initval = upper - 1.0
+        elif lower is not None and upper is None:
+            initval = lower + 1.0
         else:
-            _defaultval = (lower + upper) / 2
+            initval = (lower + upper) / 2
 
-        return super().dist([mu, sigma, lower, upper], **kwargs)
+        lower = (
+            at.as_tensor_variable(floatX(lower))
+            if lower is not None
+            else at.as_tensor_variable(-np.inf)
+        )
+        upper = (
+            at.as_tensor_variable(floatX(upper))
+            if upper is not None
+            else at.as_tensor_variable(np.inf)
+        )
+        res = super().dist([mu, sigma, lower, upper], testval=initval, **kwargs)
+        return res
 
     def logp(value, mu, sigma, lower, upper):
         """
-        Calculate log-probability of TruncatedNormal distribution at specified value.
+        Calculate log-probaDirichletMultinomialbility of TruncatedNormal distribution at specified value.
 
         Parameters
         ----------
@@ -701,54 +715,32 @@ def logp(value, mu, sigma, lower, upper):
         -------
         TensorVariable
         """
-        mu, sigma = _truncated_normal_prepare_mu_sigma_params(mu, sigma, tau=None, sd=None)
-        print(lower.eval())
-        lower, lower_check, upper, upper_check = _truncated_normal_prepare_lower_and_upper(
-            lower, upper
-        )
-        print(lower.eval())
-        norm = _truncated_normal_normalization(mu, sigma, lower, upper)
+
+        if lower is not None and upper is not None:
+            lcdf_a = normal_lcdf(mu, sigma, lower)
+            lcdf_b = normal_lcdf(mu, sigma, upper)
+            lsf_a = normal_lccdf(mu, sigma, lower)
+            lsf_b = normal_lccdf(mu, sigma, upper)
+            norm = at.switch(lower > 0, logdiffexp(lsf_a, lsf_b), logdiffexp(lcdf_b, lcdf_a))
+        elif lower is not None:
+            norm = normal_lccdf(mu, sigma, lower)
+        elif upper is not None:
+            norm = normal_lcdf(mu, sigma, upper)
+        else:
+            norm = 0.0
+
         logp = Normal.logp(value, mu=mu, sigma=sigma) - norm
         bounds = [sigma > 0]
-        if lower_check is not None:
+        if lower is not None:
             bounds.append(value >= lower)
-        if upper_check is not None:
+        if upper is not None:
             bounds.append(value <= upper)
-        print([e.eval() for e in bounds])
         return bound(logp, *bounds)
 
     def _distr_parameters_for_repr(self):
         return ["mu", "sigma", "lower", "upper"]
 
 
-def _truncated_normal_prepare_mu_sigma_params(mu, sigma, tau, sd):
-    sigma = sd if sd is not None else sigma
-    tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-    sigma = at.as_tensor_variable(sigma)
-    tau = at.as_tensor_variable(tau)
-    mu = at.as_tensor_variable(floatX(mu))
-    assert_negative_support(sigma, "sigma", "TruncatedNormal")
-    assert_negative_support(tau, "tau", "TruncatedNormal")
-    return mu, sigma
-
-
-def _truncated_normal_normalization(mu, sigma, lower, upper):
-    lower, lower_check, upper, upper_check = _truncated_normal_prepare_lower_and_upper(lower, upper)
-    if lower_check is None and upper_check is None:
-        return 0.0
-
-    if lower_check is not None and upper_check is not None:
-        lcdf_a = normal_lcdf(mu, sigma, lower)
-        lcdf_b = normal_lcdf(mu, sigma, upper)
-        lsf_a = normal_lccdf(mu, sigma, lower)
-        lsf_b = normal_lccdf(mu, sigma, upper)
-        return at.switch(lower > 0, logdiffexp(lsf_a, lsf_b), logdiffexp(lcdf_b, lcdf_a))
-    if lower_check is not None:
-        return normal_lccdf(mu, sigma, lower)
-    else:
-        return normal_lcdf(mu, sigma, upper)
-
-
 def _truncated_normal_prepare_lower_and_upper(lower, upper):
     lower_check = at.as_tensor_variable(floatX(lower)) if lower is not None else lower
     upper_check = at.as_tensor_variable(floatX(upper)) if upper is not None else upper