def dot(a, b):
# Arrayfire requires that the types match for dot and matmul
res_dtype = numpy.result_type(a, b)
a = a.astype(res_dtype, copy=False)
b = b.astype(res_dtype, copy=False)
if a.ndim == 1 and b.ndim == 1:
s = arrayfire.dot(a.d_array, b.d_array)
return afnumpy.ndarray((), dtype=a.dtype, af_array=s)[()]
a_shape = a.shape
b_shape = b.shape
if a.ndim == 1:
a = a.reshape((a.shape[0], 1))
if b.ndim == 1:
b = b.reshape((b.shape[0], 1))
if a.ndim == 2 and b.ndim == 2:
# Notice the order of the arguments to matmul. It's not a bug!
s = arrayfire.matmul(b.d_array, a.d_array)
return afnumpy.ndarray(pu.af_shape(s),
dtype=pu.typemap(s.dtype()),
af_array=s)
# Multidimensional dot is done with loops
# Calculate the shape of the result array
a_shape = list(a_shape)
a_shape.pop(-1)
b_shape = list(b_shape)
b_shape.pop(-2)
res_shape = a_shape + b_shape
# Make sure the arrays are at least 3D
if a.ndim < 3:
a = a.reshape((1, ) + a.shape)
if b.ndim < 3:
b = b.reshape((1, ) + b.shape)
# We're going to flatten the regions over which we're going to loop over
# to make our life easier and reduce the amount of indexing code
a = a.reshape((-1, a.shape[-2], a.shape[-1]))
b = b.reshape((-1, b.shape[-2], b.shape[-1]))
# Initialize the output array. The shape matches the reshape a and b.
res = afnumpy.empty((a.shape[0], a.shape[-2], b.shape[0], b.shape[-1]),
dtype=a.dtype)
# Loop through the flattened indices and calculate the matmuls
for i in range(0, a.shape[0]):
for j in range(0, b.shape[0]):
res[i, :, j, :] = afnumpy.dot(a[i], b[j])
# Finally appropriately reshape the result array
return res.reshape(res_shape)
def dot(a, b):
# Arrayfire requires that the types match for dot and matmul
res_dtype = numpy.result_type(a,b)
a = a.astype(res_dtype, copy=False)
b = b.astype(res_dtype, copy=False)
if a.ndim == 1 and b.ndim == 1:
s = arrayfire.dot(a.d_array, b.d_array)
return afnumpy.ndarray((), dtype=a.dtype, af_array=s)[()]
a_shape = a.shape
b_shape = b.shape
if a.ndim == 1:
a = a.reshape((a.shape[0],1))
if b.ndim == 1:
b = b.reshape((b.shape[0],1))
if a.ndim == 2 and b.ndim == 2:
# Notice the order of the arguments to matmul. It's not a bug!
s = arrayfire.matmul(b.d_array, a.d_array)
return afnumpy.ndarray(pu.af_shape(s), dtype=pu.typemap(s.dtype()),
af_array=s)
# Multidimensional dot is done with loops
# Calculate the shape of the result array
a_shape = list(a_shape)
a_shape.pop(-1)
b_shape = list(b_shape)
b_shape.pop(-2)
res_shape = a_shape + b_shape
# Make sure the arrays are at least 3D
if a.ndim < 3:
a = a.reshape((1,)+a.shape)
if b.ndim < 3:
b = b.reshape((1,)+b.shape)
# We're going to flatten the regions over which we're going to loop over
# to make our life easier and reduce the amount of indexing code
a = a.reshape((-1,a.shape[-2],a.shape[-1]))
b = b.reshape((-1,b.shape[-2],b.shape[-1]))
# Initialize the output array. The shape matches the reshape a and b.
res = afnumpy.empty((a.shape[0], a.shape[-2], b.shape[0],
b.shape[-1]), dtype=a.dtype)
# Loop through the flattened indices and calculate the matmuls
for i in range(0,a.shape[0]):
for j in range(0,b.shape[0]):
res[i,:,j,:] = afnumpy.dot(a[i],b[j])
# Finally appropriately reshape the result array
return res.reshape(res_shape)
def test_empty():
a = afnumpy.empty((2,3))
b = numpy.array(a)
a[:] = 1
b[:] = 1
fassert(a,b)
def arrayfire_flip(a):
b = afnumpy.empty(a.shape, a.dtype)
b.d_array = afnumpy.arrayfire.data.flip(a.d_array, dim=0)
b.d_array = afnumpy.arrayfire.data.shift(b.d_array, d0=1)
return b
def afnumpy_flip(a):
b = afnumpy.empty(a.shape, a.dtype)
b[0] = a[0]
b[1 :] = a[-1:0:-1]
return b
def afnumpy_3Dflip(a):
b = afnumpy.empty(a.shape, a.dtype)
b[:, 0, :] = a[:, 0, :]
b[:, 1:, :] = a[:, -1:0:-1, :]
return b
def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):
if axis is not None:
axisa, axisb, axisc = (axis,) * 3
a = asarray(a)
b = asarray(b)
# Move working axis to the end of the shape
a = rollaxis(a, axisa, a.ndim)
b = rollaxis(b, axisb, b.ndim)
msg = ("incompatible dimensions for cross product\n"
"(dimension must be 2 or 3)")
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
raise ValueError(msg)
# Create the output array
shape = broadcast(a[..., 0], b[..., 0]).shape
if a.shape[-1] == 3 or b.shape[-1] == 3:
shape += (3,)
dtype = afnumpy.promote_types(a.dtype, b.dtype)
cp = afnumpy.empty(shape, dtype)
# create local aliases for readability
a0 = a[..., 0]
a1 = a[..., 1]
if a.shape[-1] == 3:
a2 = a[..., 2]
b0 = b[..., 0]
b1 = b[..., 1]
if b.shape[-1] == 3:
b2 = b[..., 2]
if cp.ndim != 0 and cp.shape[-1] == 3:
cp0 = cp[..., 0]
cp1 = cp[..., 1]
cp2 = cp[..., 2]
if a.shape[-1] == 2:
if b.shape[-1] == 2:
# a0 * b1 - a1 * b0
afnumpy.multiply(a0, b1, out=cp)
cp -= a1 * b0
if cp.ndim == 0:
return cp
else:
# This works because we are moving the last axis
return rollaxis(cp, -1, axisc)
else:
# cp0 = a1 * b2 - 0 (a2 = 0)
# cp1 = 0 - a0 * b2 (a2 = 0)
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a1, b2, out=cp0)
afnumpy.multiply(a0, b2, out=cp1)
negative(cp1, out=cp1)
afnumpy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
elif a.shape[-1] == 3:
if b.shape[-1] == 3:
# cp0 = a1 * b2 - a2 * b1
# cp1 = a2 * b0 - a0 * b2
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a1, b2, out=cp0)
tmp = afnumpy.array(a2 * b1)
cp0 -= tmp
afnumpy.multiply(a2, b0, out=cp1)
afnumpy.multiply(a0, b2, out=tmp)
cp1 -= tmp
afnumpy.multiply(a0, b1, out=cp2)
afnumpy.multiply(a1, b0, out=tmp)
cp2 -= tmp
else:
# cp0 = 0 - a2 * b1 (b2 = 0)
# cp1 = a2 * b0 - 0 (b2 = 0)
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a2, b1, out=cp0)
negative(cp0, out=cp0)
afnumpy.multiply(a2, b0, out=cp1)
afnumpy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
if cp.ndim == 1:
return cp
else:
# This works because we are moving the last axis
return rollaxis(cp, -1, axisc)
def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):
if axis is not None:
axisa, axisb, axisc = (axis, ) * 3
a = asarray(a)
b = asarray(b)
# Move working axis to the end of the shape
a = rollaxis(a, axisa, a.ndim)
b = rollaxis(b, axisb, b.ndim)
msg = ("incompatible dimensions for cross product\n"
"(dimension must be 2 or 3)")
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
raise ValueError(msg)
# Create the output array
shape = broadcast(a[..., 0], b[..., 0]).shape
if a.shape[-1] == 3 or b.shape[-1] == 3:
shape += (3, )
dtype = afnumpy.promote_types(a.dtype, b.dtype)
cp = afnumpy.empty(shape, dtype)
# create local aliases for readability
a0 = a[..., 0]
a1 = a[..., 1]
if a.shape[-1] == 3:
a2 = a[..., 2]
b0 = b[..., 0]
b1 = b[..., 1]
if b.shape[-1] == 3:
b2 = b[..., 2]
if cp.ndim != 0 and cp.shape[-1] == 3:
cp0 = cp[..., 0]
cp1 = cp[..., 1]
cp2 = cp[..., 2]
if a.shape[-1] == 2:
if b.shape[-1] == 2:
# a0 * b1 - a1 * b0
afnumpy.multiply(a0, b1, out=cp)
cp -= a1 * b0
if cp.ndim == 0:
return cp
else:
# This works because we are moving the last axis
return rollaxis(cp, -1, axisc)
else:
# cp0 = a1 * b2 - 0 (a2 = 0)
# cp1 = 0 - a0 * b2 (a2 = 0)
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a1, b2, out=cp0)
afnumpy.multiply(a0, b2, out=cp1)
negative(cp1, out=cp1)
afnumpy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
elif a.shape[-1] == 3:
if b.shape[-1] == 3:
# cp0 = a1 * b2 - a2 * b1
# cp1 = a2 * b0 - a0 * b2
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a1, b2, out=cp0)
tmp = afnumpy.array(a2 * b1)
cp0 -= tmp
afnumpy.multiply(a2, b0, out=cp1)
afnumpy.multiply(a0, b2, out=tmp)
cp1 -= tmp
afnumpy.multiply(a0, b1, out=cp2)
afnumpy.multiply(a1, b0, out=tmp)
cp2 -= tmp
else:
# cp0 = 0 - a2 * b1 (b2 = 0)
# cp1 = a2 * b0 - 0 (b2 = 0)
# cp2 = a0 * b1 - a1 * b0
afnumpy.multiply(a2, b1, out=cp0)
negative(cp0, out=cp0)
afnumpy.multiply(a2, b0, out=cp1)
afnumpy.multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
if cp.ndim == 1:
return cp
else:
# This works because we are moving the last axis
return rollaxis(cp, -1, axisc)
def test_empty():
a = afnumpy.empty((2, 3))
b = numpy.array(a)
a[:] = 1
b[:] = 1
fassert(a, b)