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From keypointt <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-13013][Docs] Replace example code in ml...
Date Sat, 20 Feb 2016 17:39:20 GMT
Github user keypointt commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11116#discussion_r53554064
  
    --- Diff: examples/src/main/python/mllib/latent_dirichlet_allocation_example.py ---
    @@ -0,0 +1,53 @@
    +#
    +# 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 __future__ import print_function
    +
    +from pyspark import SparkContext
    +# $example on$
    +from pyspark.mllib.clustering import LDA, LDAModel
    +from pyspark.mllib.linalg import Vectors
    +# $example off$
    +
    +if __name__ == "__main__":
    +    sc = SparkContext(appName="LatentDirichletAllocationExample")  # SparkContext
    +
    +    # $example on$
    +    # Load and parse the data
    +    data = sc.textFile("data/mllib/sample_lda_data.txt")
    +    parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split('
')]))
    +    # Index documents with unique IDs
    +    corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
    +
    +    # Cluster the documents into three topics using LDA
    +    ldaModel = LDA.train(corpus, k=3)
    +
    +    # Output topics. Each is a distribution over words (matching word count vectors)
    +    print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize())
    +          + " words):")
    --- End diff --
    
    got python check error "E128 continuation line under-indented for visual indent" after
running "./dev/lint-python", seems it should be indenting to the opening bracket


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