# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import shutil
import tempfile
from contextlib import contextmanager
from typing import Tuple
import numpy as np
[docs]@contextmanager
def TemporaryDirectory():
name = tempfile.mkdtemp()
try:
yield name
finally:
shutil.rmtree(name)
[docs]def chunks(l, n):
"""
Yield successive n-sized chunks from l.
"""
for i in range(0, len(l), n):
yield l[i : i + n]
[docs]def empirical_cdf(
samples: np.ndarray, num_bins: int = 100
) -> Tuple[np.ndarray, np.ndarray]:
"""
Calculate the empricial cdf from the given samples.
Parameters
----------
samples
Tensor of samples of shape (num_samples, batch_shape)
Returns
-------
Tensor
Emprically calculated cdf values. shape (num_bins, batch_shape)
Tensor
Bin edges corresponding to the cdf values. shape (num_bins + 1,
batch_shape)
"""
# calculate histogram separately for each dimension in the batch size
cdfs = []
edges = []
batch_shape = samples.shape[1:]
agg_batch_dim = np.prod(batch_shape)
samples = samples.reshape((samples.shape[0], -1))
for i in range(agg_batch_dim):
s = samples[:, i]
bins = np.linspace(s.min(), s.max(), num_bins + 1)
hist, edge = np.histogram(s, bins=bins)
cdfs.append(np.cumsum(hist / len(s)))
edges.append(edge)
empirical_cdf = np.stack(cdfs, axis=-1).reshape(num_bins, *batch_shape)
edges = np.stack(edges, axis=-1).reshape(num_bins + 1, *batch_shape)
return empirical_cdf, edges