Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1628#discussion_r15563820
--- Diff: python/pyspark/mllib/random/RandomRDDGenerators.py ---
@@ -0,0 +1,201 @@
+#
+# 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 random import getrandbits
+
+from pyspark.rdd import RDD
+from pyspark.mllib._common import _deserialize_double,
_deserialize_double_vector
+from pyspark.serializers import NoOpSerializer
+
+
+def uniformRDD(sc, size, numPartitions=None, seed=None):
+ """
+ Generates an RDD comprised of i.i.d. samples from the
+ uniform distribution on [0.0, 1.0].
+
+ To transform the distribution in the generated RDD from U[0.0, 1.0]
+ to U[a, b], use
+ C{uniformRDD(sc, n, p, seed).map(lambda v: a + (b - a) * v)}
+
+ >>> x = uniformRDD(sc, 100).collect()
+ >>> len(x)
+ 100
+ >>> max(x) <= 1.0 and min(x) >= 0.0
+ True
+ >>> uniformRDD(sc, 100, 4).getNumPartitions()
+ 4
+ >>> parts = uniformRDD(sc, 100, seed=4).getNumPartitions()
+ >>> parts == sc.defaultParallelism
+ True
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI().uniformRDD(sc._jsc, size,
numPartitions, seed)
+ uniform = RDD(jrdd, sc, NoOpSerializer())
+ return uniform.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+
+def normalRDD(sc, size, numPartitions=None, seed=None):
+ """
+ Generates an RDD comprised of i.i.d samples from the standard normal
+ distribution.
+
+ To transform the distribution in the generated RDD from standard normal
+ to some other normal N(mean, sigma), use
+ C{normal(sc, n, p, seed).map(lambda v: mean + sigma * v)}
+
+ >>> x = normalRDD(sc, 1000, seed=1L).collect()
+ >>> from pyspark.statcounter import StatCounter
+ >>> stats = StatCounter(x)
+ >>> stats.count()
+ 1000L
+ >>> abs(stats.mean() - 0.0) < 0.1
+ True
+ >>> abs(stats.stdev() - 1.0) < 0.1
+ True
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI().normalRDD(sc._jsc, size,
numPartitions, seed)
+ normal = RDD(jrdd, sc, NoOpSerializer())
+ return normal.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+
+def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
+ """
+ Generates an RDD comprised of i.i.d samples from the Poisson
+ distribution with the input mean.
+
+ >>> mean = 100.0
+ >>> x = poissonRDD(sc, mean, 1000, seed=1L).collect()
+ >>> from pyspark.statcounter import StatCounter
+ >>> stats = StatCounter(x)
+ >>> stats.count()
+ 1000L
+ >>> abs(stats.mean() - mean) < 0.5
+ True
+ >>> from math import sqrt
+ >>> abs(stats.stdev() - sqrt(mean)) < 0.5
+ True
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI().poissonRDD(sc._jsc, mean, size,
numPartitions, seed)
+ poisson = RDD(jrdd, sc, NoOpSerializer())
+ return poisson.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+
+def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
+ """
+ Generates an RDD comprised of vectors containing i.i.d samples drawn
+ from the uniform distribution on [0.0 1.0].
+
+ >>> import numpy as np
+ >>> mat = np.matrix(uniformVectorRDD(sc, 10, 10).collect())
+ >>> mat.shape
+ (10, 10)
+ >>> mat.max() <= 1.0 and mat.min() >= 0.0
+ True
+ >>> uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
+ 4
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI()\
+ .uniformVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
+ uniform = RDD(jrdd, sc, NoOpSerializer())
+ return uniform.map(lambda bytes:
_deserialize_double_vector(bytearray(bytes)))
+
+def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
+ """
+ Generates an RDD comprised of vectors containing i.i.d samples drawn
+ from the standard normal distribution.
+
+ >>> import numpy as np
+ >>> mat = np.matrix(normalVectorRDD(sc, 100, 100, seed=1L).collect())
+ >>> mat.shape
+ (100, 100)
+ >>> abs(mat.mean() - 0.0) < 0.1
+ True
+ >>> abs(mat.std() - 1.0) < 0.1
+ True
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI() \
+ .normalVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
+ normal = RDD(jrdd, sc, NoOpSerializer())
+ return normal.map(lambda bytes:
_deserialize_double_vector(bytearray(bytes)))
+
+def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None,
seed=None):
+ """
+ Generates an RDD comprised of vectors containing i.i.d samples drawn
+ from the Poisson distribution with the input mean.
+
+ >>> import numpy as np
+ >>> mean = 100.0
+ >>> rdd = poissonVectorRDD(sc, mean, 100, 100, seed=1L)
+ >>> mat = np.mat(rdd.collect())
+ >>> mat.shape
+ (100, 100)
+ >>> abs(mat.mean() - mean) < 0.5
+ True
+ >>> from math import sqrt
+ >>> abs(mat.std() - sqrt(mean)) < 0.5
+ True
+ """
+ numPartitions, seed = _getDefaultArgs(sc, numPartitions, seed)
+ jrdd = sc._jvm.PythonMLLibAPI() \
+ .poissonVectorRDD(sc._jsc, mean, numRows, numCols, numPartitions,
seed)
+ poisson = RDD(jrdd, sc, NoOpSerializer())
+ return poisson.map(lambda bytes:
_deserialize_double_vector(bytearray(bytes)))
+
+def _getDefaultArgs(sc, numPartitions, seed):
+ """
+ Use sc.defaultParallelism for numPartitions and
+ a randomly generated long for seed if either has a value of C{None}
+
+ >>> _getDefaultArgs(sc, 3, 2)
+ (3, 2)
+ >>> _getDefaultArgs(sc, None, 2) == (sc.defaultParallelism, 2)
+ True
+ >>> from math import pow
+ >>> _getDefaultArgs(sc, None, None)[1] < pow(2, 63)
+ True
+ """
+ if not numPartitions:
+ numPartitions = sc.defaultParallelism
+ if not seed:
+ seed = _nextLong()
+ return numPartitions, seed
+
+def _nextLong():
+ """
+ Returns a random long to be used as RNG seed in the Java APIs.
+
+ Note: only 63 random bits are used here since Long.MAX_VALUE = 2 ^ 63
- 1
+ """
+ return long(getrandbits(63))
+
+
+def _test():
--- End diff --
For these tests to run automatically, you also need to add this file into
the `python/run-tests` script. Otherwise it won't automatically discover it,
e.g. in Jenkins.
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