a15ca5533d
## What changes were proposed in this pull request? Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13242 from WeichenXu123/python_doctest_update_sparksession.
429 lines
16 KiB
Python
429 lines
16 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Python package for random data generation.
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"""
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from functools import wraps
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from pyspark import since
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from pyspark.mllib.common import callMLlibFunc
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__all__ = ['RandomRDDs', ]
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def toArray(f):
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@wraps(f)
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def func(sc, *a, **kw):
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rdd = f(sc, *a, **kw)
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return rdd.map(lambda vec: vec.toArray())
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return func
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class RandomRDDs(object):
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"""
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Generator methods for creating RDDs comprised of i.i.d samples from
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some distribution.
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.. versionadded:: 1.1.0
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"""
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@staticmethod
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@since("1.1.0")
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def uniformRDD(sc, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the
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uniform distribution U(0.0, 1.0).
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To transform the distribution in the generated RDD from U(0.0, 1.0)
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to U(a, b), use
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C{RandomRDDs.uniformRDD(sc, n, p, seed)\
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.map(lambda v: a + (b - a) * v)}
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:param sc: SparkContext used to create the RDD.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ `U(0.0, 1.0)`.
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>>> x = RandomRDDs.uniformRDD(sc, 100).collect()
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>>> len(x)
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100
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>>> max(x) <= 1.0 and min(x) >= 0.0
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True
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>>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
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4
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>>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
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>>> parts == sc.defaultParallelism
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True
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"""
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return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
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@staticmethod
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@since("1.1.0")
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def normalRDD(sc, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the standard normal
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distribution.
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To transform the distribution in the generated RDD from standard normal
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to some other normal N(mean, sigma^2), use
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C{RandomRDDs.normal(sc, n, p, seed)\
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.map(lambda v: mean + sigma * v)}
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:param sc: SparkContext used to create the RDD.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0).
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>>> x = RandomRDDs.normalRDD(sc, 1000, seed=1)
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>>> stats = x.stats()
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>>> stats.count()
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1000
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>>> abs(stats.mean() - 0.0) < 0.1
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True
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>>> abs(stats.stdev() - 1.0) < 0.1
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True
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"""
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return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
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@staticmethod
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@since("1.3.0")
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def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the log normal
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distribution with the input mean and standard distribution.
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:param sc: SparkContext used to create the RDD.
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:param mean: mean for the log Normal distribution
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:param std: std for the log Normal distribution
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ log N(mean, std).
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>>> from math import sqrt, exp
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>>> mean = 0.0
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>>> std = 1.0
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>>> expMean = exp(mean + 0.5 * std * std)
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>>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
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>>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)
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>>> stats = x.stats()
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>>> stats.count()
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1000
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>>> abs(stats.mean() - expMean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(stats.stdev() - expStd) < 0.5
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True
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"""
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return callMLlibFunc("logNormalRDD", sc._jsc, float(mean), float(std),
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size, numPartitions, seed)
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@staticmethod
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@since("1.1.0")
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def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the Poisson
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distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or lambda, for the Poisson distribution.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ Pois(mean).
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>>> mean = 100.0
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>>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2)
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>>> stats = x.stats()
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>>> stats.count()
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1000
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>>> abs(stats.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(stats.stdev() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("poissonRDD", sc._jsc, float(mean), size, numPartitions, seed)
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@staticmethod
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@since("1.3.0")
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def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the Exponential
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distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or 1 / lambda, for the Exponential distribution.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ Exp(mean).
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>>> mean = 2.0
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>>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2)
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>>> stats = x.stats()
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>>> stats.count()
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1000
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>>> abs(stats.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(stats.stdev() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)
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@staticmethod
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@since("1.3.0")
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def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the Gamma
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distribution with the input shape and scale.
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:param sc: SparkContext used to create the RDD.
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:param shape: shape (> 0) parameter for the Gamma distribution
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:param scale: scale (> 0) parameter for the Gamma distribution
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ Gamma(shape, scale).
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>>> from math import sqrt
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>>> shape = 1.0
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>>> scale = 2.0
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>>> expMean = shape * scale
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>>> expStd = sqrt(shape * scale * scale)
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>>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)
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>>> stats = x.stats()
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>>> stats.count()
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1000
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>>> abs(stats.mean() - expMean) < 0.5
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True
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>>> abs(stats.stdev() - expStd) < 0.5
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True
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"""
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return callMLlibFunc("gammaRDD", sc._jsc, float(shape),
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float(scale), size, numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.1.0")
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def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the uniform distribution U(0.0, 1.0).
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:param sc: SparkContext used to create the RDD.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD.
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:param seed: Seed for the RNG that generates the seed for the generator in each partition.
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:return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
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>>> import numpy as np
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>>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
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>>> mat.shape
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(10, 10)
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>>> mat.max() <= 1.0 and mat.min() >= 0.0
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True
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>>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
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4
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"""
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return callMLlibFunc("uniformVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.1.0")
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def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the standard normal distribution.
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:param sc: SparkContext used to create the RDD.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.
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>>> import numpy as np
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>>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1).collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - 0.0) < 0.1
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True
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>>> abs(mat.std() - 1.0) < 0.1
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True
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"""
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return callMLlibFunc("normalVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.3.0")
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def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the log normal distribution.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean of the log normal distribution
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:param std: Standard Deviation of the log normal distribution
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ log `N(mean, std)`.
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>>> import numpy as np
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>>> from math import sqrt, exp
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>>> mean = 0.0
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>>> std = 1.0
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>>> expMean = exp(mean + 0.5 * std * std)
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>>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
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>>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()
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>>> mat = np.matrix(m)
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - expMean) < 0.1
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True
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>>> abs(mat.std() - expStd) < 0.1
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True
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"""
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return callMLlibFunc("logNormalVectorRDD", sc._jsc, float(mean), float(std),
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numRows, numCols, numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.1.0")
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def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the Poisson distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or lambda, for the Poisson distribution.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`)
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ Pois(mean).
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>>> import numpy as np
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>>> mean = 100.0
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>>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1)
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>>> mat = np.mat(rdd.collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(mat.std() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("poissonVectorRDD", sc._jsc, float(mean), numRows, numCols,
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numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.3.0")
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def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the Exponential distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or 1 / lambda, for the Exponential distribution.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`)
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ Exp(mean).
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>>> import numpy as np
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>>> mean = 0.5
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>>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1)
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>>> mat = np.mat(rdd.collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(mat.std() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("exponentialVectorRDD", sc._jsc, float(mean), numRows, numCols,
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numPartitions, seed)
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@staticmethod
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@toArray
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@since("1.3.0")
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def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the Gamma distribution.
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:param sc: SparkContext used to create the RDD.
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:param shape: Shape (> 0) of the Gamma distribution
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:param scale: Scale (> 0) of the Gamma distribution
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ Gamma(shape, scale).
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>>> import numpy as np
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>>> from math import sqrt
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>>> shape = 1.0
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>>> scale = 2.0
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>>> expMean = shape * scale
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>>> expStd = sqrt(shape * scale * scale)
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>>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - expMean) < 0.1
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True
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>>> abs(mat.std() - expStd) < 0.1
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True
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"""
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return callMLlibFunc("gammaVectorRDD", sc._jsc, float(shape), float(scale),
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numRows, numCols, numPartitions, seed)
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def _test():
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import doctest
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from pyspark.sql import SparkSession
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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spark = SparkSession.builder\
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.master("local[2]")\
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.appName("mllib.random tests")\
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.getOrCreate()
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globs['sc'] = spark.sparkContext
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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spark.stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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