Added Laplace and Wald RV (#321)

aesara/tensor/random/basic.py

@@ -258,6 +258,20 @@ class InvGammaRV(RandomVariable):
 invgamma = InvGammaRV()
 
 
+class WaldRV(RandomVariable):
+    name = "wald"
+    ndim_supp = 0
+    ndims_params = [0, 0]
+    dtype = "floatX"
+    _print_name_ = ("Wald", "\\operatorname{Wald}")
+
+    def __call__(self, mean=1.0, scale=1.0, size=None, **kwargs):
+        return super().__call__(mean, scale, size=size, **kwargs)
+
+
+wald = WaldRV()
+
+
 class TruncExponentialRV(RandomVariable):
     name = "truncexpon"
     ndim_supp = 0
@@ -290,6 +304,17 @@ class BernoulliRV(RandomVariable):
 bernoulli = BernoulliRV()
 
 
+class LaplaceRV(RandomVariable):
+    name = "laplace"
+    ndim_supp = 0
+    ndims_params = [0, 0]
+    dtype = "floatX"
+    _print_name = ("Laplace", "\\operatorname{Laplace}")
+
+
+laplace = LaplaceRV()
+
+
 class BinomialRV(RandomVariable):
     name = "binomial"
     ndim_supp = 0

tests/tensor/random/test_basic.py

@@ -25,6 +25,7 @@ from aesara.tensor.random.basic import (
     halfcauchy,
     halfnormal,
     invgamma,
+    laplace,
     multinomial,
     multivariate_normal,
     nbinom,
@@ -35,6 +36,7 @@ from aesara.tensor.random.basic import (
     randint,
     truncexpon,
     uniform,
+    wald,
 )
 from aesara.tensor.type import iscalar, scalar, tensor
 
@@ -464,6 +466,14 @@ def test_invgamma_samples():
     )
 
 
+def test_wald_samples():
+    test_mean = np.array(10, dtype=config.floatX)
+    test_scale = np.array(1, dtype=config.floatX)
+
+    rv_numpy_tester(wald, test_mean, test_scale)
+    rv_numpy_tester(wald, test_mean, test_scale, size=[2, 3])
+
+
 def test_truncexpon_samples():
     test_b = np.array(5, dtype=config.floatX)
     test_loc = np.array(0, dtype=config.floatX)
@@ -498,6 +508,14 @@ def test_bernoulli_samples():
     )
 
 
+def test_laplace_samples():
+    test_loc = np.array(10, dtype=config.floatX)
+    test_scale = np.array(5, dtype=config.floatX)
+
+    rv_numpy_tester(laplace, test_loc, test_scale)
+    rv_numpy_tester(laplace, test_loc, test_scale, size=[2, 3])
+
+
 def test_binomial_samples():
     test_M = np.array(10, dtype="int64")
     test_p = np.array(0.5, dtype=config.floatX)