80 lines
3.1 KiB
Python
80 lines
3.1 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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from numpy import array, dot
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from math import sqrt
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from pyspark import SparkContext
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from pyspark.mllib._common import \
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_get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
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_serialize_double_matrix, _deserialize_double_matrix, \
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_serialize_double_vector, _deserialize_double_vector, \
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_get_initial_weights, _serialize_rating, _regression_train_wrapper
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class KMeansModel(object):
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"""A clustering model derived from the k-means method.
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>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
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>>> clusters = KMeans.train(sc, sc.parallelize(data), 2, maxIterations=10, runs=30, initialization_mode="random")
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>>> clusters.predict(array([0.0, 0.0])) == clusters.predict(array([1.0, 1.0]))
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True
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>>> clusters.predict(array([8.0, 9.0])) == clusters.predict(array([9.0, 8.0]))
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True
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>>> clusters = KMeans.train(sc, sc.parallelize(data), 2)
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"""
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def __init__(self, centers_):
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self.centers = centers_
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def predict(self, x):
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"""Find the cluster to which x belongs in this model."""
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best = 0
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best_distance = 1e75
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for i in range(0, self.centers.shape[0]):
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diff = x - self.centers[i]
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distance = sqrt(dot(diff, diff))
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if distance < best_distance:
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best = i
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best_distance = distance
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return best
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class KMeans(object):
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@classmethod
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def train(cls, sc, data, k, maxIterations=100, runs=1,
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initialization_mode="k-means||"):
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"""Train a k-means clustering model."""
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dataBytes = _get_unmangled_double_vector_rdd(data)
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ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd,
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k, maxIterations, runs, initialization_mode)
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if len(ans) != 1:
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raise RuntimeError("JVM call result had unexpected length")
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elif type(ans[0]) != bytearray:
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raise RuntimeError("JVM call result had first element of type "
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+ type(ans[0]) + " which is not bytearray")
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return KMeansModel(_deserialize_double_matrix(ans[0]))
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def _test():
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import doctest
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs,
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optionflags=doctest.ELLIPSIS)
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globs['sc'].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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