Table Of Contents
Table Of Contents

Source code for gluonts.testutil

# 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.

# Standard library imports
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