def test_multiply_scalar(array, val, data_type, val_type):
np_a = numpy.array(array, dtype=data_type)
dpnp_a = dpnp.array(array, dtype=data_type)
val_ = val_type(val)
result = dpnp.multiply(np_a, val_)
expected = numpy.multiply(dpnp_a, val_)
numpy.testing.assert_array_equal(result, expected)
result = dpnp.multiply(val_, np_a)
expected = numpy.multiply(val_, dpnp_a)
numpy.testing.assert_array_equal(result, expected)
def test_multiply_scalar(array, val, data_type, val_type):
a = numpy.array(array, dtype=data_type)
ia = inp.array(a)
val_ = val_type(val)
result = inp.multiply(a, val_)
expected = numpy.multiply(ia, val_)
numpy.testing.assert_array_equal(result, expected)
result = inp.multiply(val_, a)
expected = numpy.multiply(val_, ia)
numpy.testing.assert_array_equal(result, expected)
def test_multiply_complex(data):
a = numpy.array(data)
ia = inp.array(data)
result = inp.multiply(ia, ia)
expected = numpy.multiply(a, a)
numpy.testing.assert_array_equal(result, expected)
result = inp.multiply(ia, 0.5j)
expected = numpy.multiply(a, 0.5j)
numpy.testing.assert_array_equal(result, expected)
result = inp.multiply(0.5j, ia)
expected = numpy.multiply(0.5j, a)
numpy.testing.assert_array_equal(result, expected)
def test_multiply_complex(data):
np_a = numpy.array(data)
dpnp_a = dpnp.array(data)
result = dpnp.multiply(dpnp_a, dpnp_a)
expected = numpy.multiply(np_a, np_a)
numpy.testing.assert_array_equal(result, expected)
result = dpnp.multiply(dpnp_a, 0.5j)
expected = numpy.multiply(np_a, 0.5j)
numpy.testing.assert_array_equal(result, expected)
result = dpnp.multiply(0.5j, dpnp_a)
expected = numpy.multiply(0.5j, np_a)
numpy.testing.assert_array_equal(result, expected)
def test_multiply(array, val, data_type, val_type):
a = numpy.array(array, dtype=data_type)
ia = inp.array(a)
val_ = val_type(val)
result = inp.multiply(ia, val_)
expected = numpy.multiply(ia, val_)
numpy.testing.assert_array_equal(expected, result)
def __rmul__(self, other):
return dpnp.multiply(other, self)
def __mul__(self, other):
return dpnp.multiply(self, other)