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Created create_random_permute function #115

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Merged
merged 2 commits into from
Feb 2, 2023
Merged

Created create_random_permute function #115

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10fd247
Update functional.py
jiwonbuilds Feb 2, 2023
319aecf
Update __init__.py
jiwonbuilds Feb 2, 2023
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2 changes: 2 additions & 0 deletions torchhd/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
inverse,
negative,
cleanup,
create_random_permute,
randsel,
multirandsel,
soft_quantize,
Expand Down Expand Up @@ -71,6 +72,7 @@
"inverse",
"negative",
"cleanup",
"create_random_permute",
"randsel",
"multirandsel",
"soft_quantize",
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47 changes: 46 additions & 1 deletion torchhd/functional.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
import math
from typing import Type, Union
from typing import Type, Union, Callable
import torch
from torch import LongTensor, FloatTensor, Tensor
from collections import deque
Expand All @@ -25,6 +25,7 @@
"inverse",
"negative",
"cleanup",
"create_random_permute",
"hard_quantize",
"soft_quantize",
"hamming_similarity",
Expand Down Expand Up @@ -661,6 +662,50 @@ def permute(input: VSA_Model, *, shifts=1) -> VSA_Model:
return input.permute(shifts)



def create_random_permute(dim: int) -> Callable[[VSA_Model, int], VSA_Model]:
r"""Creates random permutation functions.

Args:
dim (int): dimension of the hypervectors

Examples::

>>> a = torchhd.random_hv(3, 10)
>>> a
tensor([[-1., 1., 1., 1., -1., -1., -1., -1., 1., -1.],
[-1., -1., -1., 1., -1., 1., -1., -1., 1., -1.],
[ 1., 1., 1., -1., -1., 1., -1., 1., 1., 1.]])
>>> p = torchhd.create_random_permute(10)
>>> p(a, 2)
tensor([[ 1., 1., -1., -1., -1., 1., -1., -1., 1., -1.],
[ 1., -1., -1., -1., 1., 1., -1., -1., -1., -1.],
[ 1., 1., 1., -1., 1., -1., -1., 1., 1., 1.]])
>>> p(a, -2)
tensor([[-1., 1., 1., 1., -1., -1., -1., -1., 1., -1.],
[-1., -1., -1., 1., -1., 1., -1., -1., 1., -1.],
[ 1., 1., 1., -1., -1., 1., -1., 1., 1., 1.]])

"""

forward = torch.randperm(dim)
backward = torch.empty_like(forward)
backward[forward] = torch.arange(dim)

def permute(input: VSA_Model, shifts: int = 1) -> VSA_Model:
y = input
if shifts > 0:
for _ in range(shifts):
y = y[..., forward]
elif shifts < 0:
for _ in range(shifts):
y = y[..., backward]
return y

return permute



def inverse(input: VSA_Model) -> VSA_Model:
r"""Inverse for the binding operation.

Expand Down