def shape(self, newshape):
"""Set new lengths of axes. A tuple of numbers represents size of each dimention.
It involves reshaping without copy. If the array cannot be reshaped without copy,
it raises an exception.
.. seealso: :attr:`numpy.ndarray.shape`
"""
dpnp.reshape(self, newshape)
def test_multi_dot(type):
n = 16
a = inp.reshape(inp.arange(n, dtype=type), (4, 4))
b = inp.reshape(inp.arange(n, dtype=type), (4, 4))
c = inp.reshape(inp.arange(n, dtype=type), (4, 4))
d = inp.reshape(inp.arange(n, dtype=type), (4, 4))
a1 = numpy.arange(n, dtype=type).reshape((4, 4))
b1 = numpy.arange(n, dtype=type).reshape((4, 4))
c1 = numpy.arange(n, dtype=type).reshape((4, 4))
d1 = numpy.arange(n, dtype=type).reshape((4, 4))
result = inp.linalg.multi_dot([a, b, c, d])
expected = numpy.linalg.multi_dot([a1, b1, c1, d1])
numpy.testing.assert_array_equal(expected, result)
def reshape(self, d0, *dn, order=b'C'):
"""
Returns an array containing the same data with a new shape.
Refer to `dpnp.reshape` for full documentation.
.. seealso::
:meth:`numpy.ndarray.reshape`
Notes
-----
Unlike the free function `dpnp.reshape`, this method on `ndarray` allows
the elements of the shape parameter to be passed in as separate arguments.
For example, ``a.reshape(10, 11)`` is equivalent to
``a.reshape((10, 11))``.
"""
if dn:
if not isinstance(d0, int):
msg_tmpl = "'{}' object cannot be interpreted as an integer"
raise TypeError(msg_tmpl.format(type(d0).__name__))
shape = [d0, *dn]
else:
shape = d0
shape_tup = dpnp.dpnp_utils._object_to_tuple(shape)
return dpnp.reshape(self, shape_tup)
def test_matmul2(self):
array_data1 = [1., 2., 3., 4., 5., 6.]
array_data2 = [1., 2., 3., 4., 5., 6., 7., 8.]
# DPNP
array1 = inp.reshape(inp.array(array_data1, dtype=inp.float64), (3, 2))
array2 = inp.reshape(inp.array(array_data2, dtype=inp.float64), (2, 4))
result = inp.matmul(array1, array2)
# print(result)
# original
array_1 = numpy.array(array_data1, dtype=numpy.float64).reshape((3, 2))
array_2 = numpy.array(array_data2, dtype=numpy.float64).reshape((2, 4))
expected = numpy.matmul(array_1, array_2)
# print(expected)
numpy.testing.assert_array_equal(expected, result)
def test_strides_true_devide(dtype, shape):
a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
b = a.T + 1
dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape)
dpb = dpa.T + 1
result = dpnp.fmod(dpa, dpb)
expected = numpy.fmod(a, b)
numpy.testing.assert_allclose(result, expected)
def test_strides_copysign(dtype, shape):
a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
b = -a.T
dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape)
dpb = dpnp.negative(dpa.T)
result = dpnp.copysign(dpa, dpb)
expected = numpy.copysign(a, b)
numpy.testing.assert_allclose(result, expected)
def test_strides_tan(dtype, shape):
a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
b = a[::2]
dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape)
dpb = dpa[::2]
result = dpnp.tan(dpb)
expected = numpy.tan(b)
numpy.testing.assert_allclose(result, expected, rtol=1e-06)
def test_concatenate(self):
# Test concatenate function
# One sequence returns unmodified (but as array)
r4 = list(range(4))
numpy.testing.assert_array_equal(dpnp.concatenate((r4, )), r4)
# Any sequence
numpy.testing.assert_array_equal(dpnp.concatenate((tuple(r4), )), r4)
numpy.testing.assert_array_equal(dpnp.concatenate((dpnp.array(r4), )),
r4)
# 1D default concatenation
r3 = list(range(3))
numpy.testing.assert_array_equal(dpnp.concatenate((r4, r3)), r4 + r3)
# Mixed sequence types
numpy.testing.assert_array_equal(dpnp.concatenate((tuple(r4), r3)),
r4 + r3)
numpy.testing.assert_array_equal(
dpnp.concatenate((dpnp.array(r4), r3)), r4 + r3)
# Explicit axis specification
numpy.testing.assert_array_equal(dpnp.concatenate((r4, r3), 0),
r4 + r3)
# Including negative
numpy.testing.assert_array_equal(dpnp.concatenate((r4, r3), -1),
r4 + r3)
# 2D
a23 = dpnp.array([[10, 11, 12], [13, 14, 15]])
a13 = dpnp.array([[0, 1, 2]])
res = dpnp.array([[10, 11, 12], [13, 14, 15], [0, 1, 2]])
numpy.testing.assert_array_equal(dpnp.concatenate((a23, a13)), res)
numpy.testing.assert_array_equal(dpnp.concatenate((a23, a13), 0), res)
numpy.testing.assert_array_equal(dpnp.concatenate((a23.T, a13.T), 1),
res.T)
numpy.testing.assert_array_equal(dpnp.concatenate((a23.T, a13.T), -1),
res.T)
# Arrays much match shape
numpy.testing.assert_raises(ValueError, dpnp.concatenate,
(a23.T, a13.T), 0)
# 3D
res = dpnp.reshape(dpnp.arange(2 * 3 * 7), (2, 3, 7))
a0 = res[..., :4]
a1 = res[..., 4:6]
a2 = res[..., 6:]
numpy.testing.assert_array_equal(dpnp.concatenate((a0, a1, a2), 2),
res)
numpy.testing.assert_array_equal(dpnp.concatenate((a0, a1, a2), -1),
res)
numpy.testing.assert_array_equal(
dpnp.concatenate((a0.T, a1.T, a2.T), 0), res.T)
out = dpnp.copy(res)
rout = dpnp.concatenate((a0, a1, a2), 2, out=out)
numpy.testing.assert_(out is rout)
numpy.testing.assert_equal(res, rout)
def test_matmul(self):
array_data = [1., 2., 3., 4.]
size = 2
# DPNP
array1 = inp.reshape(inp.array(array_data, dtype=inp.float64),
(size, size))
array2 = inp.reshape(inp.array(array_data, dtype=inp.float64),
(size, size))
result = inp.matmul(array1, array2)
# print(result)
# original
array_1 = numpy.array(array_data, dtype=numpy.float64).reshape(
(size, size))
array_2 = numpy.array(array_data, dtype=numpy.float64).reshape(
(size, size))
expected = numpy.matmul(array_1, array_2)
# print(expected)
# passed
numpy.testing.assert_array_equal(expected, result)
def test_strides_reciprocal(dtype, shape):
start, stop = 1, numpy.prod(shape) + 1
a = numpy.arange(start, stop, dtype=dtype).reshape(shape)
b = a[::2]
dpa = dpnp.reshape(dpnp.arange(start, stop, dtype=dtype), shape)
dpb = dpa[::2]
result = dpnp.reciprocal(dpb)
expected = numpy.reciprocal(b)
numpy.testing.assert_allclose(result, expected, rtol=1e-06)
def test_strides_erf(dtype, shape):
a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
b = a[::2]
dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape)
dpb = dpa[::2]
result = dpnp.erf(dpb)
expected = numpy.empty_like(b)
for idx, val in enumerate(b):
expected[idx] = math.erf(val)
numpy.testing.assert_allclose(result, expected)
def test_strides_1arg(func_name, dtype, shape):
a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
b = a[::2]
dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape)
dpb = dpa[::2]
dpnp_func = _getattr(dpnp, func_name)
result = dpnp_func(dpb)
numpy_func = _getattr(numpy, func_name)
expected = numpy_func(b)
numpy.testing.assert_allclose(result, expected)