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examples/src/main/python/mllib/pca_rowmatrix_example.py
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MechCoder
[SPARK-6227][MLLIB][PYSPARK] Implement PySpark wrappers for SVD and PCA (v2)
03 май 2017, 11:58
03 май 2017, 11:58
db2fb84
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# # 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 pyspark import SparkContext # $example on$ from pyspark.mllib.linalg import Vectors from pyspark.mllib.linalg.distributed import RowMatrix # $example off$ if __name__ == "__main__": sc = SparkContext(appName="PythonPCAOnRowMatrixExample") # $example on$ rows = sc.parallelize([ Vectors.sparse(5, {1: 1.0, 3: 7.0}), Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) ]) mat = RowMatrix(rows) # Compute the top 4 principal components. # Principal components are stored in a local dense matrix. pc = mat.computePrincipalComponents(4) # Project the rows to the linear space spanned by the top 4 principal components. projected = mat.multiply(pc) # $example off$ collected = projected.rows.collect() print("Projected Row Matrix of principal component:") for vector in collected: print(vector) sc.stop()