spark-instrumented-optimizer/python/pyspark/mllib/stat/distribution.py
zero323 01321bc0fe [SPARK-33252][PYTHON][DOCS] Migration to NumPy documentation style in MLlib (pyspark.mllib.*)
### What changes were proposed in this pull request?

This PR proposes migration of `pyspark.mllib` to NumPy documentation style.

### Why are the changes needed?

To improve documentation style.

Before:

![old](https://user-images.githubusercontent.com/1554276/100097941-90234980-2e5d-11eb-8b4d-c25d98d85191.png)

After:

![new](https://user-images.githubusercontent.com/1554276/100097966-987b8480-2e5d-11eb-9e02-07b18c327624.png)

### Does this PR introduce _any_ user-facing change?

Yes, this changes both rendered HTML docs and console representation (SPARK-33243).

### How was this patch tested?

`dev/lint-python` and manual inspection.

Closes #30413 from zero323/SPARK-33252.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-11-25 10:24:41 +09:00

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1.3 KiB
Python

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
#
from collections import namedtuple
__all__ = ['MultivariateGaussian']
class MultivariateGaussian(namedtuple('MultivariateGaussian', ['mu', 'sigma'])):
"""Represents a (mu, sigma) tuple
Examples
--------
>>> m = MultivariateGaussian(Vectors.dense([11,12]),DenseMatrix(2, 2, (1.0, 3.0, 5.0, 2.0)))
>>> (m.mu, m.sigma.toArray())
(DenseVector([11.0, 12.0]), array([[ 1., 5.],[ 3., 2.]]))
>>> (m[0], m[1])
(DenseVector([11.0, 12.0]), array([[ 1., 5.],[ 3., 2.]]))
"""