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"""
# -*- coding: utf-8 -*-
#
# Copyright 2021 Michael Büsch <m@bues.ch>
#
# Licensed under the Apache License version 2.0
# or the MIT license, at your option.
# SPDX-License-Identifier: Apache-2.0 OR MIT
#
"""
__all__ = [
"minibatches",
"proportion_equal",
"array_to_boolint",
"collapse_bool_nodes",
]
from typing import Generator, Iterator, Tuple
import numpy as np
def cut_batch(batch: np.ndarray,
minibatch_size: int) -> Generator:
size = batch.shape[0]
return (
batch[i:i+minibatch_size]
for i in range(0, size, minibatch_size)
)
def minibatches(x_batch: np.ndarray,
y_batch: np.ndarray,
minibatch_size: int = 128)\
-> Iterator[Tuple[Generator, Generator]]:
"""Split a batch into mini batches.
"""
assert x_batch.shape[0] == y_batch.shape[0]
return zip(cut_batch(x_batch, minibatch_size),
cut_batch(y_batch, minibatch_size))
def proportion_equal(a: np.ndarray,
b: np.ndarray,
rtol: float = 1e-05,
atol: float = 1e-08) -> float:
"""Returns the proportion of equal elements in a and b
as a number between 0.0 and 1.0.
"""
return np.mean(np.isclose(a, b, rtol, atol))
def array_to_boolint(y, thres=0.75):
"""Convert an array to a new array with {0, 1} values.
Values below the threshold are translated to 0.
Other values are translated to 1.
"""
return (y >= thres).astype(np.int)
def collapse_bool_nodes(y, thres=0.75):
"""Collapse the multiple boolean nodes into one integer node.
"""
assert y.ndim == 2
nr_nodes = y.shape[1]
y = array_to_boolint(y, thres)
# Scale nodes.
y *= np.fromiter((1 << i for i in range(nr_nodes)),
dtype=y.dtype)
# Collapse nodes.
y = np.sum(y, axis=1)
return y
# vim: ts=4 sw=4 expandtab
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