Merge pull request #2227 from IntelPython/update-tests-part-2

This is part 2 of a series of PRs in which the tests are refactored. In
this PR, `test_linalg.py`, `test_product.py`, `test_statistics.py`,
`test_fft.py`, and `test_sort.py` are updated.
Part 1 was #2210.

The main change is to use a common function
`generate_random_numpy_array` from `dpnp/tests/helper.py` for generating
input array for different tests.

Author: vtavana

dpnp/tests/helper.py

@@ -162,7 +162,7 @@ def get_all_dtypes(
 
 
 def generate_random_numpy_array(
-    shape, dtype=None, hermitian=False, seed_value=None
+    shape, dtype=None, hermitian=False, seed_value=None, low=-10, high=10
 ):
     """
     Generate a random numpy array with the specified shape and dtype.
@@ -177,13 +177,19 @@ def generate_random_numpy_array(
     dtype : str or dtype, optional
         Desired data-type for the output array.
         If not specified, data type will be determined by numpy.
-        Default : None
+        Default : ``None``
     hermitian : bool, optional
         If True, generates a Hermitian (symmetric if `dtype` is real) matrix.
-        Default : False
+        Default : ``False``
     seed_value : int, optional
         The seed value to initialize the random number generator.
-        Default : None
+        Default : ``None``
+    low : {int, float}, optional
+        Lower boundary of the generated samples from a uniform distribution.
+        Default : ``-10``.
+    high : {int, float}, optional
+        Upper boundary of the generated samples from a uniform distribution.
+        Default : ``10``.
 
     Returns
     -------
@@ -197,13 +203,17 @@ def generate_random_numpy_array(
 
     """
 
+    if seed_value is None:
+        seed_value = 42
     numpy.random.seed(seed_value)
 
-    a = numpy.random.randn(*shape).astype(dtype)
+    # dtype=int is needed for 0d arrays
+    size = numpy.prod(shape, dtype=int)
+    a = numpy.random.uniform(low, high, size).astype(dtype)
     if numpy.issubdtype(a.dtype, numpy.complexfloating):
-        numpy.random.seed(seed_value)
-        a += 1j * numpy.random.randn(*shape)
+        a += 1j * numpy.random.uniform(low, high, size)
 
+    a = a.reshape(shape)
     if hermitian and a.size > 0:
         if a.ndim > 2:
             orig_shape = a.shape