Add standard_exp and standard_t

Author: Smit-create

aesara/tensor/random/basic.py

@@ -778,6 +778,9 @@ def __call__(self, scale=1.0, size=None, **kwargs):
 
 
 exponential = ExponentialRV()
+standard_exponential = get_partial_wrapper(
+    exponential, "standard_exponential", scale=1.0
+)
 
 
 class WeibullRV(RandomVariable):
@@ -1525,6 +1528,7 @@ def rng_fn_scipy(cls, rng, df, loc, scale, size):
 
 
 t = StudentTRV()
+standard_t = get_partial_wrapper(t, "standard_t", loc=0.0, scale=1.0)
 
 
 class BernoulliRV(ScipyRandomVariable):
@@ -2229,8 +2233,10 @@ def __call__(self, x, **kwargs):
     "triangular",
     "uniform",
     "standard_cauchy",
+    "standard_exponential",
     "standard_gamma",
     "standard_normal",
+    "standard_t",
     "negative_binomial",
     "gengamma",
     "t",

tests/tensor/random/test_basic.py

@@ -51,8 +51,10 @@
     random,
     rayleigh,
     standard_cauchy,
+    standard_exponential,
     standard_gamma,
     standard_normal,
+    standard_t,
     t,
     triangular,
     truncexpon,
@@ -449,6 +451,18 @@ def test_exponential_default_args():
     compare_sample_values(exponential)
 
 
+@pytest.mark.parametrize(
+    "size",
+    [
+        (10_000,),
+        None,
+        (100, 100),
+    ],
+)
+def test_std_exponential_args(size):
+    compare_sample_values(standard_exponential, size=size)
+
+
 def test_rayleigh_default_args():
     compare_sample_values(rayleigh)
 
@@ -1031,6 +1045,34 @@ def test_t_samples(df, loc, scale, size):
     )
 
 
+@pytest.mark.parametrize(
+    "df, size",
+    [
+        (
+            np.array(2, dtype=config.floatX),
+            None,
+        ),
+        (
+            np.array(2.30, dtype=config.floatX),
+            [2, 3],
+        ),
+        (
+            np.full((1, 2), 5, dtype=config.floatX),
+            None,
+        ),
+    ],
+)
+def test_standard_t_samples(df, size):
+    compare_sample_values(
+        standard_t,
+        df,
+        size=size,
+        test_fn=lambda df, size=None, random_state=None, **kwargs: t.rng_fn(
+            random_state, df, 0.0, 1.0, size
+        ),
+    )
+
+
 @pytest.mark.parametrize(
     "p, size",
     [

tests/tensor/random/test_utils.py

@@ -111,6 +111,8 @@ def test_basics(self, rng_ctor):
         assert hasattr(random, "standard_normal")
         assert hasattr(random, "standard_cauchy")
         assert hasattr(random, "standard_gamma")
+        assert hasattr(random, "standard_exponential")
+        assert hasattr(random, "standard_t")
 
         with pytest.raises(AttributeError):
             np_random = RandomStream(namespace=np, rng_ctor=rng_ctor)