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#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Functions to create and manipulate arrays.
"""
from sys import version_info
from .library import *
from .array import *
from .util import *
def constant(val, d0, d1=None, d2=None, d3=None, dtype=Dtype.f32):
"""
Create a multi dimensional array whose elements contain the same value.
Parameters
----------
val : scalar.
Value of each element of the constant array.
d0 : int.
Length of first dimension.
d1 : optional: int. default: None.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose elements are of value `val`.
- If d1 is None, `out` is 1D of size (d0,).
- If d1 is not None and d2 is None, `out` is 2D of size (d0, d1).
- If d1 and d2 are not None and d3 is None, `out` is 3D of size (d0, d1, d2).
- If d1, d2, d3 are all not None, `out` is 4D of size (d0, d1, d2, d3).
"""
out = Array()
out.arr = constant_array(val, d0, d1, d2, d3, dtype.value)
return out
# Store builtin range function to be used later
_brange = range
def range(d0, d1=None, d2=None, d3=None, dim=0, dtype=Dtype.f32):
"""
Create a multi dimensional array using length of a dimension as range.
Parameters
----------
val : scalar.
Value of each element of the constant array.
d0 : int.
Length of first dimension.
d1 : optional: int. default: None.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
dim : optional: int. default: 0.
The dimension along which the range is calculated.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose elements are along `dim` fall between [0 - self.dims[dim]-1]
- If d1 is None, `out` is 1D of size (d0,).
- If d1 is not None and d2 is None, `out` is 2D of size (d0, d1).
- If d1 and d2 are not None and d3 is None, `out` is 3D of size (d0, d1, d2).
- If d1, d2, d3 are all not None, `out` is 4D of size (d0, d1, d2, d3).
Examples
--------
>>> import arrayfire as af
>>> a = af.range(3, 2) # dim is not specified, range is along first dimension.
>>> af.display(a) # The data ranges from [0 - 2] (3 elements along first dimension)
[3 2 1 1]
0.0000 0.0000
1.0000 1.0000
2.0000 2.0000
>>> a = af.range(3, 2, dim=1) # dim is 1, range is along second dimension.
>>> af.display(a) # The data ranges from [0 - 1] (2 elements along second dimension)
[3 2 1 1]
0.0000 1.0000
0.0000 1.0000
0.0000 1.0000
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
safe_call(backend.get().af_range(ct.pointer(out.arr), 4, ct.pointer(dims), dim, dtype.value))
return out
def iota(d0, d1=None, d2=None, d3=None, dim=-1, tile_dims=None, dtype=Dtype.f32):
"""
Create a multi dimensional array using the number of elements in the array as the range.
Parameters
----------
val : scalar.
Value of each element of the constant array.
d0 : int.
Length of first dimension.
d1 : optional: int. default: None.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
tile_dims : optional: tuple of ints. default: None.
The number of times the data is tiled.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose elements are along `dim` fall between [0 - self.elements() - 1].
Examples
--------
>>> import arrayfire as af
>>> import arrayfire as af
>>> a = af.iota(3,3) # tile_dim is not specified, data is not tiled
>>> af.display(a) # the elements range from [0 - 8] (9 elements)
[3 3 1 1]
0.0000 3.0000 6.0000
1.0000 4.0000 7.0000
2.0000 5.0000 8.0000
>>> b = af.iota(3,3,tile_dims(1,2)) # Asking to tile along second dimension.
>>> af.display(b)
[3 6 1 1]
0.0000 3.0000 6.0000 0.0000 3.0000 6.0000
1.0000 4.0000 7.0000 1.0000 4.0000 7.0000
2.0000 5.0000 8.0000 2.0000 5.0000 8.0000
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
td=[1]*4
if tile_dims is not None:
for i in _brange(len(tile_dims)):
td[i] = tile_dims[i]
tdims = dim4(td[0], td[1], td[2], td[3])
safe_call(backend.get().af_iota(ct.pointer(out.arr), 4, ct.pointer(dims),
4, ct.pointer(tdims), dtype.value))
return out
def randu(d0, d1=None, d2=None, d3=None, dtype=Dtype.f32):
"""
Create a multi dimensional array containing values from a uniform distribution.
Parameters
----------
d0 : int.
Length of first dimension.
d1 : optional: int. default: None.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose elements are sampled uniformly between [0, 1].
- If d1 is None, `out` is 1D of size (d0,).
- If d1 is not None and d2 is None, `out` is 2D of size (d0, d1).
- If d1 and d2 are not None and d3 is None, `out` is 3D of size (d0, d1, d2).
- If d1, d2, d3 are all not None, `out` is 4D of size (d0, d1, d2, d3).
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
safe_call(backend.get().af_randu(ct.pointer(out.arr), 4, ct.pointer(dims), dtype.value))
return out
def randn(d0, d1=None, d2=None, d3=None, dtype=Dtype.f32):
"""
Create a multi dimensional array containing values from a normal distribution.
Parameters
----------
d0 : int.
Length of first dimension.
d1 : optional: int. default: None.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose elements are sampled from a normal distribution with mean 0 and sigma of 1.
- If d1 is None, `out` is 1D of size (d0,).
- If d1 is not None and d2 is None, `out` is 2D of size (d0, d1).
- If d1 and d2 are not None and d3 is None, `out` is 3D of size (d0, d1, d2).
- If d1, d2, d3 are all not None, `out` is 4D of size (d0, d1, d2, d3).
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
safe_call(backend.get().af_randn(ct.pointer(out.arr), 4, ct.pointer(dims), dtype.value))
return out
def set_seed(seed=0):
"""
Set the seed for the random number generator.
Parameters
----------
seed: int.
Seed for the random number generator
"""
safe_call(backend.get().af_set_seed(ct.c_ulonglong(seed)))
def get_seed():
"""
Get the seed for the random number generator.
Returns
----------
seed: int.
Seed for the random number generator
"""
seed = ct.c_ulonglong(0)
safe_call(backend.get().af_get_seed(ct.pointer(seed)))
return seed.value
def identity(d0, d1, d2=None, d3=None, dtype=Dtype.f32):
"""
Create an identity matrix or batch of identity matrices.
Parameters
----------
d0 : int.
Length of first dimension.
d1 : int.
Length of second dimension.
d2 : optional: int. default: None.
Length of third dimension.
d3 : optional: int. default: None.
Length of fourth dimension.
dtype : optional: af.Dtype. default: af.Dtype.f32.
Data type of the array.
Returns
-------
out : af.Array
Multi dimensional array whose first two dimensions form a identity matrix.
- If d2 is None, `out` is 2D of size (d0, d1).
- If d2 is not None and d3 is None, `out` is 3D of size (d0, d1, d2).
- If d2, d3 are not None, `out` is 4D of size (d0, d1, d2, d3).
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
safe_call(backend.get().af_identity(ct.pointer(out.arr), 4, ct.pointer(dims), dtype.value))
return out
def diag(a, num=0, extract=True):
"""
Create a diagonal matrix or Extract the diagonal from a matrix.
Parameters
----------
a : af.Array.
1 dimensional or 2 dimensional arrayfire array.
num : optional: int. default: 0.
The index of the diagonal.
- num == 0 signifies the diagonal.
- num > 0 signifies super diagonals.
- num < 0 signifies sub diagonals.
extract : optional: bool. default: True.
- If True , diagonal is extracted. `a` has to be 2D.
- If False, diagonal matrix is created. `a` has to be 1D.
Returns
-------
out : af.Array
- if extract is True, `out` contains the num'th diagonal from `a`.
- if extract is False, `out` contains `a` as the num'th diagonal.
"""
out = Array()
if extract:
safe_call(backend.get().af_diag_extract(ct.pointer(out.arr), a.arr, ct.c_int(num)))
else:
safe_call(backend.get().af_diag_create(ct.pointer(out.arr), a.arr, ct.c_int(num)))
return out
def join(dim, first, second, third=None, fourth=None):
"""
Join two or more arrayfire arrays along a specified dimension.
Parameters
----------
dim: int.
Dimension along which the join occurs.
first : af.Array.
Multi dimensional arrayfire array.
second : af.Array.
Multi dimensional arrayfire array.
third : optional: af.Array. default: None.
Multi dimensional arrayfire array.
fourth : optional: af.Array. default: None.
Multi dimensional arrayfire array.
Returns
-------
out : af.Array
An array containing the input arrays joined along the specified dimension.
Examples
-------
>>> import arrayfire as af
>>> a = af.randu(2, 3)
>>> b = af.randu(2, 3)
>>> c = af.join(0, a, b)
>>> d = af.join(1, a, b)
>>> af.display(a)
[2 3 1 1]
0.9508 0.2591 0.7928
0.5367 0.8359 0.8719
>>> af.display(b)
[2 3 1 1]
0.3266 0.6009 0.2442
0.6275 0.0495 0.6591
>>> af.display(c)
[4 3 1 1]
0.9508 0.2591 0.7928
0.5367 0.8359 0.8719
0.3266 0.6009 0.2442
0.6275 0.0495 0.6591
>>> af.display(d)
[2 6 1 1]
0.9508 0.2591 0.7928 0.3266 0.6009 0.2442
0.5367 0.8359 0.8719 0.6275 0.0495 0.6591
"""
out = Array()
if (third is None and fourth is None):
safe_call(backend.get().af_join(ct.pointer(out.arr), dim, first.arr, second.arr))
else:
c_void_p_4 = ct.c_void_p * 4
c_array_vec = c_void_p_4(first.arr, second.arr, 0, 0)
num = 2
if third is not None:
c_array_vec[num] = third.arr
num+=1
if fourth is not None:
c_array_vec[num] = fourth.arr
num+=1
safe_call(backend.get().af_join_many(ct.pointer(out.arr), dim, num, ct.pointer(c_array_vec)))
return out
def tile(a, d0, d1=1, d2=1, d3=1):
"""
Tile an array along specified dimensions.
Parameters
----------
a : af.Array.
Multi dimensional array.
d0: int.
The number of times `a` has to be tiled along first dimension.
d1: optional: int. default: 1.
The number of times `a` has to be tiled along second dimension.
d2: optional: int. default: 1.
The number of times `a` has to be tiled along third dimension.
d3: optional: int. default: 1.
The number of times `a` has to be tiled along fourth dimension.
Returns
-------
out : af.Array
An array containing the input after tiling the the specified number of times.
Examples
-------
>>> import arrayfire as af
>>> a = af.randu(2, 3)
>>> b = af.tile(a, 2)
>>> c = af.tile(a, 1, 2)
>>> d = af.tile(a, 2, 2)
>>> af.display(a)
[2 3 1 1]
0.9508 0.2591 0.7928
0.5367 0.8359 0.8719
>>> af.display(b)
[4 3 1 1]
0.4107 0.9518 0.4198
0.8224 0.1794 0.0081
0.4107 0.9518 0.4198
0.8224 0.1794 0.0081
>>> af.display(c)
[2 6 1 1]
0.4107 0.9518 0.4198 0.4107 0.9518 0.4198
0.8224 0.1794 0.0081 0.8224 0.1794 0.0081
>>> af.display(d)
[4 6 1 1]
0.4107 0.9518 0.4198 0.4107 0.9518 0.4198
0.8224 0.1794 0.0081 0.8224 0.1794 0.0081
0.4107 0.9518 0.4198 0.4107 0.9518 0.4198
0.8224 0.1794 0.0081 0.8224 0.1794 0.0081
"""
out = Array()
safe_call(backend.get().af_tile(ct.pointer(out.arr), a.arr, d0, d1, d2, d3))
return out
def reorder(a, d0=1, d1=0, d2=2, d3=3):
"""
Reorder the dimensions of the input.
Parameters
----------
a : af.Array.
Multi dimensional array.
d0: optional: int. default: 1.
The location of the first dimension in the output.
d1: optional: int. default: 0.
The location of the second dimension in the output.
d2: optional: int. default: 2.
The location of the third dimension in the output.
d3: optional: int. default: 3.
The location of the fourth dimension in the output.
Returns
-------
out : af.Array
- An array containing the input aftern reordering its dimensions.
Note
------
- `af.reorder(a, 1, 0)` is the same as `transpose(a)`
Examples
--------
>>> import arrayfire as af
>>> a = af.randu(5, 5, 3)
>>> af.display(a)
[5 5 3 1]
0.4107 0.0081 0.6600 0.1046 0.8395
0.8224 0.3775 0.0764 0.8827 0.1933
0.9518 0.3027 0.0901 0.1647 0.7270
0.1794 0.6456 0.5933 0.8060 0.0322
0.4198 0.5591 0.1098 0.5938 0.0012
0.8703 0.9250 0.4387 0.6530 0.4224
0.5259 0.3063 0.3784 0.5476 0.5293
0.1443 0.9313 0.4002 0.8577 0.0212
0.3253 0.8684 0.4390 0.8370 0.1103
0.5081 0.6592 0.4718 0.0618 0.4420
0.8355 0.6767 0.1033 0.9426 0.9276
0.4878 0.6742 0.2119 0.4817 0.8662
0.2055 0.4523 0.5955 0.9097 0.3578
0.1794 0.1236 0.3745 0.6821 0.6263
0.5606 0.7924 0.9165 0.6056 0.9747
>>> b = af.reorder(a, 2, 0, 1)
>>> af.display(b)
[3 5 5 1]
0.4107 0.8224 0.9518 0.1794 0.4198
0.8703 0.5259 0.1443 0.3253 0.5081
0.8355 0.4878 0.2055 0.1794 0.5606
0.0081 0.3775 0.3027 0.6456 0.5591
0.9250 0.3063 0.9313 0.8684 0.6592
0.6767 0.6742 0.4523 0.1236 0.7924
0.6600 0.0764 0.0901 0.5933 0.1098
0.4387 0.3784 0.4002 0.4390 0.4718
0.1033 0.2119 0.5955 0.3745 0.9165
0.1046 0.8827 0.1647 0.8060 0.5938
0.6530 0.5476 0.8577 0.8370 0.0618
0.9426 0.4817 0.9097 0.6821 0.6056
0.8395 0.1933 0.7270 0.0322 0.0012
0.4224 0.5293 0.0212 0.1103 0.4420
0.9276 0.8662 0.3578 0.6263 0.9747
"""
out = Array()
safe_call(backend.get().af_reorder(ct.pointer(out.arr), a.arr, d0, d1, d2, d3))
return out
def shift(a, d0, d1=0, d2=0, d3=0):
"""
Shift the input along each dimension.
Parameters
----------
a : af.Array.
Multi dimensional array.
d0: int.
The amount of shift along first dimension.
d1: optional: int. default: 0.
The amount of shift along second dimension.
d2: optional: int. default: 0.
The amount of shift along third dimension.
d3: optional: int. default: 0.
The amount of shift along fourth dimension.
Returns
-------
out : af.Array
- An array the same shape as `a` after shifting it by the specified amounts.
Examples
--------
>>> import arrayfire as af
>>> a = af.randu(3, 3)
>>> b = af.shift(a, 2)
>>> c = af.shift(a, 1, -1)
>>> af.display(a)
[3 3 1 1]
0.7269 0.3569 0.3341
0.7104 0.1437 0.0899
0.5201 0.4563 0.5363
>>> af.display(b)
[3 3 1 1]
0.7104 0.1437 0.0899
0.5201 0.4563 0.5363
0.7269 0.3569 0.3341
>>> af.display(c)
[3 3 1 1]
0.4563 0.5363 0.5201
0.3569 0.3341 0.7269
0.1437 0.0899 0.7104
"""
out = Array()
safe_call(backend.get().af_shift(ct.pointer(out.arr), a.arr, d0, d1, d2, d3))
return out
def moddims(a, d0, d1=1, d2=1, d3=1):
"""
Modify the shape of the array without changing the data layout.
Parameters
----------
a : af.Array.
Multi dimensional array.
d0: int.
The first dimension of output.
d1: optional: int. default: 1.
The second dimension of output.
d2: optional: int. default: 1.
The third dimension of output.
d3: optional: int. default: 1.
The fourth dimension of output.
Returns
-------
out : af.Array
- An containing the same data as `a` with the specified shape.
- The number of elements in `a` must match `d0 x d1 x d2 x d3`.
"""
out = Array()
dims = dim4(d0, d1, d2, d3)
safe_call(backend.get().af_moddims(ct.pointer(out.arr), a.arr, 4, ct.pointer(dims)))
return out
def flat(a):
"""
Flatten the input array.
Parameters
----------
a : af.Array.
Multi dimensional array.
Returns
-------
out : af.Array
- 1 dimensional array containing all the elements from `a`.
"""
out = Array()
safe_call(backend.get().af_flat(ct.pointer(out.arr), a.arr))
return out
def flip(a, dim=0):
"""
Flip an array along a dimension.
Parameters
----------
a : af.Array.
Multi dimensional array.
dim : optional: int. default: 0.
The dimension along which the flip is performed.
Returns
-------
out : af.Array
The output after flipping `a` along `dim`.
Examples
-------
>>> import arrayfire as af
>>> a = af.randu(3, 3)
>>> af.display(a)
[3 3 1 1]
0.7269 0.3569 0.3341
0.7104 0.1437 0.0899
0.5201 0.4563 0.5363
>>> af.display(b)
[3 3 1 1]
0.5201 0.4563 0.5363
0.7104 0.1437 0.0899
0.7269 0.3569 0.3341
>>> af.display(c)
[3 3 1 1]
0.3341 0.3569 0.7269
0.0899 0.1437 0.7104
0.5363 0.4563 0.5201
"""
out = Array()
safe_call(backend.get().af_flip(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
return out
def lower(a, is_unit_diag=False):
"""
Extract the lower triangular matrix from the input.
Parameters
----------
a : af.Array.
Multi dimensional array.
is_unit_diag: optional: bool. default: False.
Flag specifying if the diagonal elements are 1.
Returns
-------
out : af.Array
An array containing the lower triangular elements from `a`.
"""
out = Array()
safe_call(backend.get().af_lower(ct.pointer(out.arr), a.arr, is_unit_diag))
return out
def upper(a, is_unit_diag=False):
"""
Extract the upper triangular matrix from the input.
Parameters
----------
a : af.Array.
Multi dimensional array.
is_unit_diag: optional: bool. default: False.
Flag specifying if the diagonal elements are 1.
Returns
-------
out : af.Array
An array containing the upper triangular elements from `a`.
"""
out = Array()
safe_call(backend.get().af_upper(ct.pointer(out.arr), a.arr, is_unit_diag))
return out