def test_tile():
# Currently arrayfire is missing support for int64
b = numpy.array([0, 1, 2], dtype=numpy.float32)
a = afnumpy.array(b)
iassert(afnumpy.tile(a, 2), numpy.tile(b, 2))
iassert(afnumpy.tile(a, (2, 2)), numpy.tile(b, (2, 2)))
iassert(afnumpy.tile(a, (2, 1, 2)), numpy.tile(b, (2, 1, 2)))
def test_tile():
# Currently arrayfire is missing support for int64
b = numpy.array([0, 1, 2], dtype=numpy.float32)
a = afnumpy.array(b)
iassert(afnumpy.tile(a, 2), numpy.tile(b, 2))
iassert(afnumpy.tile(a, (2,2)), numpy.tile(b, (2,2)))
iassert(afnumpy.tile(a, (2,1,2)), numpy.tile(b, (2,1,2)))
def broadcast_arrays(*args, **kwargs):
subok = kwargs.pop('subok', False)
if kwargs:
raise TypeError('broadcast_arrays() got an unexpected keyword '
'argument {}'.format(kwargs.pop()))
args = [afnumpy.array(_m, copy=False, subok=subok) for _m in args]
shapes = [x.shape for x in args]
if len(set(shapes)) == 1:
# Common case where nothing needs to be broadcasted.
return args
shapes = [list(s) for s in shapes]
strides = [list(x.strides) for x in args]
nds = [len(s) for s in shapes]
biggest = max(nds)
# Go through each array and prepend dimensions of length 1 to each of
# the shapes in order to make the number of dimensions equal.
for i in range(len(args)):
diff = biggest - nds[i]
if diff > 0:
shapes[i] = [1] * diff + shapes[i]
strides[i] = [0] * diff + strides[i]
# Chech each dimension for compatibility. A dimension length of 1 is
# accepted as compatible with any other length.
common_shape = []
for axis in range(biggest):
lengths = [s[axis] for s in shapes]
unique = set(lengths + [1])
if len(unique) > 2:
# There must be at least two non-1 lengths for this axis.
raise ValueError("shape mismatch: two or more arrays have "
"incompatible dimensions on axis %r." % (axis,))
elif len(unique) == 2:
# There is exactly one non-1 length. The common shape will take
# this value.
unique.remove(1)
new_length = unique.pop()
common_shape.append(new_length)
# For each array, if this axis is being broadcasted from a
# length of 1, then set its stride to 0 so that it repeats its
# data.
for i in range(len(args)):
if shapes[i][axis] == 1:
shapes[i][axis] = new_length
strides[i][axis] = 0
else:
# Every array has a length of 1 on this axis. Strides can be
# left alone as nothing is broadcasted.
common_shape.append(1)
# Construct the new arrays.
broadcasted = []
for (x, sh) in zip(args, shapes):
x_sh = x.shape + (1,)*(len(sh)-x.ndim)
reps = numpy.array(sh)/numpy.array(x_sh)
if(numpy.prod(reps) > 1):
broadcasted.append(afnumpy.tile(x, reps))
else:
if(x.shape != tuple(sh)):
x = x.reshape(sh)
broadcasted.append(x)
return broadcasted
def broadcast_arrays(*args, **kwargs):
subok = kwargs.pop('subok', False)
if kwargs:
raise TypeError('broadcast_arrays() got an unexpected keyword '
'argument {}'.format(kwargs.pop()))
args = [afnumpy.array(_m, copy=False, subok=subok) for _m in args]
shapes = [x.shape for x in args]
if len(set(shapes)) == 1:
# Common case where nothing needs to be broadcasted.
return args
shapes = [list(s) for s in shapes]
strides = [list(x.strides) for x in args]
nds = [len(s) for s in shapes]
biggest = max(nds)
# Go through each array and prepend dimensions of length 1 to each of
# the shapes in order to make the number of dimensions equal.
for i in range(len(args)):
diff = biggest - nds[i]
if diff > 0:
shapes[i] = [1] * diff + shapes[i]
strides[i] = [0] * diff + strides[i]
# Chech each dimension for compatibility. A dimension length of 1 is
# accepted as compatible with any other length.
common_shape = []
for axis in range(biggest):
lengths = [s[axis] for s in shapes]
unique = set(lengths + [1])
if len(unique) > 2:
# There must be at least two non-1 lengths for this axis.
raise ValueError("shape mismatch: two or more arrays have "
"incompatible dimensions on axis %r." % (axis, ))
elif len(unique) == 2:
# There is exactly one non-1 length. The common shape will take
# this value.
unique.remove(1)
new_length = unique.pop()
common_shape.append(new_length)
# For each array, if this axis is being broadcasted from a
# length of 1, then set its stride to 0 so that it repeats its
# data.
for i in range(len(args)):
if shapes[i][axis] == 1:
shapes[i][axis] = new_length
strides[i][axis] = 0
else:
# Every array has a length of 1 on this axis. Strides can be
# left alone as nothing is broadcasted.
common_shape.append(1)
# Construct the new arrays.
broadcasted = []
for (x, sh) in zip(args, shapes):
x_sh = x.shape + (1, ) * (len(sh) - x.ndim)
reps = numpy.array(sh) / numpy.array(x_sh)
if (numpy.prod(reps) > 1):
broadcasted.append(afnumpy.tile(x, reps))
else:
if (x.shape != tuple(sh)):
x = x.reshape(sh)
broadcasted.append(x)
return broadcasted