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Merge pull request #437 from dysomni/master
homework 24
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‎submissions/Brock/kMeansBonus.txt‎

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Source of data:
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https://www.dataquest.io/blog/k-means-clustering-us-senators/
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The data represents the votes of US Senators on various bills.
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Sample is 100 rows long and more than 3 columns, and the k value in the middle of the other two performed the best.
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5 - my data is normalized, which is >= 0 and <= 1

‎submissions/Brock/kMeansVoting.csv‎

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1,0,0,0,1,1,1,1,0,0,1,1,1,0,0,0,0,0
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1,0,0,0,1,1,1,1,0,0,1,0,1,0,1,0,1,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0.5,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,0,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0.5,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,1
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0,0,1,0,0,0,1,0,1,1,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,0,1,0,1,0,1,0,1,0
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0,0,1,1,0,0,1,0,1,0,1,0,0.5,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0.5,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,0.5,0.5,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,1,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,0
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0,1,0,0,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0
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0,0,1,0,0,0,1,0,1,0,1,0,0,1,1,0,1,0
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1,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,0,1,0,1,1,0.5,0.5,0,0,1,0,0
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0,0,1,0,1,0,1,0,0,1,1,1,0,0,1,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,0,1
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1,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,0.5
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0,1,0,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,1,0,0
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0,0,1,1,1,0,1,0,1,0,1,0,0,1,1,0,1,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1
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1,0,0,0,1,1,1,1,0,1,0,1,1,0,0,1,0,0
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0,0,1,1,0,0,1,0,1,0,1,0,0,1,1,0,1,1

‎submissions/Brock/myKMeans.py‎

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import csv, numpy, keras_applications
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with open('kMeansVoting.csv', newline='') as file:
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data = numpy.array(list(csv.reader(file)))
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data[0][0] = '0'
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data = data.astype(numpy.float)
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Examples = {
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'Voing Data': {
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'data': data,
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'k': [7, 2, 9],
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},
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}

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