add random_sample and aliases

CHANGELOG.md

@@ -46,6 +46,7 @@
 - [#652](https://github.com/helmholtz-analytics/heat/pull/652) Feature: benchmark scripts and jobscript generation
 - [#653](https://github.com/helmholtz-analytics/heat/pull/653) Printing above threshold gathers the data without a buffer now
 - [#653](https://github.com/helmholtz-analytics/heat/pull/653) Bugfixes: Update unittests argmax & argmin + force index order in mpi_argmax & mpi_argmin. Add device parameter for tensor creation in dndarray.get_halo().
+- [#664](https://github.com/helmholtz-analytics/heat/pull/664) New feature / enhancement: `random.random_sample`, `random.random`, `random.sample`, `random.ranf`, `random.random_integer`
 
 # v0.4.0
 

heat/core/random.py

@@ -7,6 +7,7 @@
 from . import dndarray
 from . import stride_tricks
 from . import types
+from typing import Type, List, Optional, Tuple
 
 
 # introduce the global random state variables, will be correctly initialized at the end of file
@@ -374,6 +375,10 @@ def randint(low, high=None, size=None, dtype=None, split=None, device=None, comm
     return dndarray.DNDarray(values, shape, dtype, split, device, comm)
 
 
+# alias
+random_integer = randint
+
+
 def randn(*args, dtype=types.float32, split=None, device=None, comm=None):
     """
     Returns a tensor filled with random numbers from a standard normal distribution with zero mean and variance of one.
@@ -422,6 +427,43 @@ def randn(*args, dtype=types.float32, split=None, device=None, comm=None):
     return normal_tensor
 
 
+def random(
+    shape: Optional[Tuple[int]] = None,
+    dtype=types.float32,
+    split: Optional[int] = None,
+    device: Optional[str] = None,
+    comm=None,
+):
+    """
+    Random values in a given shape.
+    Create a :class:`~heat.core.dndarray.DNDarray`  of the given shape and populate it with random samples from a
+    uniform distribution over [0, 1).
+    Parameters
+    ----------
+    shape : Tuple[int]
+        The shape of the returned array, should all be positive. If no argument is given a single random sample is
+        generated.
+    dtype: Type[datatype], optional
+        The datatype of the returned values. Has to be one of
+        [:class:`~heat.core.types.float32, :class:`~heat.core.types.float64`].
+    split: int, optional
+        The axis along which the array is split and distributed, defaults to no distribution.
+    device : str, optional
+        Specifies the :class:`~heat.core.devices.Device`  the array shall be allocated on, defaults to globally
+        set default device.
+    comm: Communication, optional
+        Handle to the nodes holding distributed parts or copies of this array.
+    """
+    if not shape:
+        shape = (1,)
+    shape = stride_tricks.sanitize_shape(shape)
+    return rand(*shape, dtype=dtype, split=split, device=device, comm=comm)
+
+
+# aliases
+random_sample = ranf = sample = random
+
+
 def seed(seed=None):
     """
     Seed the generator.

heat/core/tests/test_random.py

@@ -245,6 +245,13 @@ def test_randint(self):
         self.assertTrue(4900 < median < 5100)
         self.assertTrue(std < 2900)
 
+        # test aliases
+        ht.random.seed(234)
+        a = ht.random.randint(10, 50)
+        ht.random.seed(234)
+        b = ht.random.random_integer(10, 50)
+        self.assertTrue(ht.equal(a, b))
+
     def test_randn(self):
         # Test that the random values have the correct distribution
         ht.random.seed(54321)
@@ -310,6 +317,29 @@ def test_randn(self):
         self.assertFalse(np.allclose(a, c))
         self.assertFalse(np.allclose(b, c))
 
+    def test_random(self):
+        # short test
+        # compare random and aliases with rand
+        ht.random.seed(534)
+        a = ht.random.rand(6, 2, 3)
+        ht.random.seed(534)
+        b = ht.random.random((6, 2, 3))
+        ht.random.seed(534)
+        c = ht.random.random_sample((6, 2, 3))
+        ht.random.seed(534)
+        d = ht.random.ranf((6, 2, 3))
+        ht.random.seed(534)
+        e = ht.random.sample((6, 2, 3))
+
+        self.assertTrue(ht.equal(a, b))
+        self.assertTrue(ht.equal(a, c))
+        self.assertTrue(ht.equal(a, d))
+        self.assertTrue(ht.equal(a, e))
+
+        # empty input
+        a = ht.random.random()
+        self.assertEqual(a.shape, (1,))
+
     def test_set_state(self):
         ht.random.set_state(("Threefry", 12345, 0xFFF))
         self.assertEqual(ht.random.get_state(), ("Threefry", 12345, 0xFFF, 0, 0.0))