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spark:spark-introduction [2014/11/11 09:08]
straka 		spark:spark-introduction [2014/11/11 09:13]
straka
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 ===== K-Means Example ===== ===== K-Means Example =====
-An example implementing [[http://en.wikipedia.org/wiki/K-means_clustering|Standard iterative K-Means algorithm]] follows. Try copying it to open Python shell. Note that this wiki formats empty lines as lines with one space, which is confusing for ''pyspark'' used without ''IPYTHON=1'', so either use ''IPYTHON=1'' or copy the text paragraph-by-paragraph.+An example implementing [[http://en.wikipedia.org/wiki/K-means_clustering|Standard iterative K-Means algorithm]] follows. Try copying it to open Python shell. Note that this wiki is formating empty lines as lines with one space, which is confusing for ''pyspark'' used without ''IPYTHON=1'', so either use ''IPYTHON=1'' or copy the text paragraph-by-paragraph.
 <file python> <file python>
 import numpy as np import numpy as np
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 lines = sc.textFile("/net/projects/spark-example-data/points", sc.defaultParallelism) lines = sc.textFile("/net/projects/spark-example-data/points", sc.defaultParallelism)
-data = lines.map(lambda line: map(float, line.split())).cache()+data = lines.map(lambda line: np.array(map(float, line.split()))).cache()
  
 K = 50 K = 50
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 print "Final centers: " + str(centers) print "Final centers: " + str(centers)
 </file> </file>
-The implementation starts by loading the data and caching them in memory using ''cache''. Then, standard iterative algorithm is performed, running in parallel. +The implementation starts by loading the data points and caching them in memory using ''cache''. Then, standard iterative algorithm is performed, running in parallel and synchronizing where necessary.
  
 Note that explicit broadcasting used for ''centers'' object is not strictly needed -- if we used ''old_centers = centers'', the example would work too, but it would send a copy of ''old_centers'' to //every distributed task//, instead of once to every machine. Note that explicit broadcasting used for ''centers'' object is not strictly needed -- if we used ''old_centers = centers'', the example would work too, but it would send a copy of ''old_centers'' to //every distributed task//, instead of once to every machine.

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