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150
iris.txt
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150
iris.txt
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@ -0,0 +1,150 @@
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5.0,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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5.0,3.4,1.5,0.2,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.4,3.7,1.5,0.2,Iris-setosa
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4.8,3.4,1.6,0.2,Iris-setosa
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4.8,3.0,1.4,0.1,Iris-setosa
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4.3,3.0,1.1,0.1,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.4,3.9,1.3,0.4,Iris-setosa
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5.1,3.5,1.4,0.3,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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5.1,3.7,1.5,0.4,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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4.8,3.4,1.9,0.2,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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5.0,3.4,1.6,0.4,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.2,3.4,1.4,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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5.5,4.2,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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4.4,3.0,1.3,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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5.0,3.5,1.3,0.3,Iris-setosa
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4.5,2.3,1.3,0.3,Iris-setosa
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.1,3.8,1.6,0.2,Iris-setosa
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4.6,3.2,1.4,0.2,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.4,3.2,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.5,2.8,4.6,1.5,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.3,3.3,4.7,1.6,Iris-versicolor
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4.9,2.4,3.3,1.0,Iris-versicolor
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6.6,2.9,4.6,1.3,Iris-versicolor
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5.2,2.7,3.9,1.4,Iris-versicolor
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5.0,2.0,3.5,1.0,Iris-versicolor
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5.9,3.0,4.2,1.5,Iris-versicolor
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6.0,2.2,4.0,1.0,Iris-versicolor
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6.1,2.9,4.7,1.4,Iris-versicolor
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5.6,2.9,3.6,1.3,Iris-versicolor
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.8,2.7,4.1,1.0,Iris-versicolor
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6.2,2.2,4.5,1.5,Iris-versicolor
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5.6,2.5,3.9,1.1,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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6.1,2.8,4.0,1.3,Iris-versicolor
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6.3,2.5,4.9,1.5,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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6.8,2.8,4.8,1.4,Iris-versicolor
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6.7,3.0,5.0,1.7,Iris-versicolor
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6.0,2.9,4.5,1.5,Iris-versicolor
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5.7,2.6,3.5,1.0,Iris-versicolor
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5.5,2.4,3.8,1.1,Iris-versicolor
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5.5,2.4,3.7,1.0,Iris-versicolor
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5.8,2.7,3.9,1.2,Iris-versicolor
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6.0,2.7,5.1,1.6,Iris-versicolor
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5.4,3.0,4.5,1.5,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
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6.7,3.1,4.7,1.5,Iris-versicolor
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.6,3.0,4.1,1.3,Iris-versicolor
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5.5,2.5,4.0,1.3,Iris-versicolor
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5.5,2.6,4.4,1.2,Iris-versicolor
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6.1,3.0,4.6,1.4,Iris-versicolor
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5.8,2.6,4.0,1.2,Iris-versicolor
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5.0,2.3,3.3,1.0,Iris-versicolor
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5.6,2.7,4.2,1.3,Iris-versicolor
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5.7,3.0,4.2,1.2,Iris-versicolor
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5.7,2.9,4.2,1.3,Iris-versicolor
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6.2,2.9,4.3,1.3,Iris-versicolor
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5.1,2.5,3.0,1.1,Iris-versicolor
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5.7,2.8,4.1,1.3,Iris-versicolor
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6.3,3.3,6.0,2.5,Iris-virginica
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5.8,2.7,5.1,1.9,Iris-virginica
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7.1,3.0,5.9,2.1,Iris-virginica
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6.3,2.9,5.6,1.8,Iris-virginica
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6.5,3.0,5.8,2.2,Iris-virginica
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7.6,3.0,6.6,2.1,Iris-virginica
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4.9,2.5,4.5,1.7,Iris-virginica
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7.3,2.9,6.3,1.8,Iris-virginica
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6.7,2.5,5.8,1.8,Iris-virginica
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7.2,3.6,6.1,2.5,Iris-virginica
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6.5,3.2,5.1,2.0,Iris-virginica
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6.4,2.7,5.3,1.9,Iris-virginica
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6.8,3.0,5.5,2.1,Iris-virginica
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5.7,2.5,5.0,2.0,Iris-virginica
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5.8,2.8,5.1,2.4,Iris-virginica
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6.4,3.2,5.3,2.3,Iris-virginica
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6.5,3.0,5.5,1.8,Iris-virginica
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7.7,3.8,6.7,2.2,Iris-virginica
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7.7,2.6,6.9,2.3,Iris-virginica
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6.0,2.2,5.0,1.5,Iris-virginica
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6.9,3.2,5.7,2.3,Iris-virginica
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5.6,2.8,4.9,2.0,Iris-virginica
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7.7,2.8,6.7,2.0,Iris-virginica
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6.3,2.7,4.9,1.8,Iris-virginica
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6.7,3.3,5.7,2.1,Iris-virginica
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7.2,3.2,6.0,1.8,Iris-virginica
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6.2,2.8,4.8,1.8,Iris-virginica
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6.1,3.0,4.9,1.8,Iris-virginica
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6.4,2.8,5.6,2.1,Iris-virginica
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7.2,3.0,5.8,1.6,Iris-virginica
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7.4,2.8,6.1,1.9,Iris-virginica
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7.9,3.8,6.4,2.0,Iris-virginica
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6.4,2.8,5.6,2.2,Iris-virginica
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6.3,2.8,5.1,1.5,Iris-virginica
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6.1,2.6,5.6,1.4,Iris-virginica
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7.7,3.0,6.1,2.3,Iris-virginica
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6.3,3.4,5.6,2.4,Iris-virginica
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6.4,3.1,5.5,1.8,Iris-virginica
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6.0,3.0,4.8,1.8,Iris-virginica
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6.9,3.1,5.4,2.1,Iris-virginica
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6.7,3.1,5.6,2.4,Iris-virginica
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6.9,3.1,5.1,2.3,Iris-virginica
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5.8,2.7,5.1,1.9,Iris-virginica
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6.8,3.2,5.9,2.3,Iris-virginica
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6.7,3.3,5.7,2.5,Iris-virginica
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6.7,3.0,5.2,2.3,Iris-virginica
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6.3,2.5,5.0,1.9,Iris-virginica
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6.5,3.0,5.2,2.0,Iris-virginica
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6.2,3.4,5.4,2.3,Iris-virginica
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5.9,3.0,5.1,1.8,Iris-virginica
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166
task2.py
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166
task2.py
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from csv import reader
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from random import seed
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from random import randrange
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from math import sqrt
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from math import exp
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from math import pi
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def read_csv(filename):
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dataset = list()
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with open(filename, 'r') as file:
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csv_reader = reader(file)
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for row in csv_reader:
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if not row:
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continue
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dataset.append(row)
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return dataset
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# convert string column to float
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def str_column_to_float(dataset, column):
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for row in dataset:
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row[column] = float(row[column].strip())
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# convert string column to integer
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def str_column_to_int(dataset, column):
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class_ids = [row[column] for row in dataset]
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unique = set(class_ids)
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lookup = dict()
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print("Class IDs:")
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for i, value in enumerate(unique):
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lookup[value] = i
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print('[%s] => %d' % (value, i))
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for row in dataset:
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row[column] = lookup[row[column]]
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return lookup
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# split a dataset into n folds
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def cross_validation_split(dataset, n_folds):
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dataset_split = list()
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dataset_copy = list(dataset)
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fold_size = int(len(dataset) / n_folds)
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for _ in range(n_folds):
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fold = list()
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while len(fold) < fold_size:
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index = randrange(len(dataset_copy))
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fold.append(dataset_copy.pop(index))
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dataset_split.append(fold)
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# print(dataset_split)
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return dataset_split
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# calculate accuracy (in per cent)
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def accuracy_metric(actual, predicted):
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correct = 0
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for i in range(len(actual)):
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if actual[i] == predicted[i]:
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correct += 1
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return correct / float(len(actual)) * 100.0
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# evaluate using a cross validation split
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def evaluate_algorithm(dataset, n_folds, algorithm, *args):
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folds = cross_validation_split(dataset, n_folds)
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scores = list()
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for fold in folds:
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train_set = list(folds)
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train_set.remove(fold)
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train_set = sum(train_set, [])
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test_set = list()
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for row in fold:
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row_copy = list(row)
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# get a row from each fold
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test_set.append(row_copy)
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# we don't know the class yet
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row_copy[-1] = None
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predicted = algorithm(train_set, test_set, *args)
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actual = [row[-1] for row in fold]
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# calculate accuracy
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accuracy = accuracy_metric(actual, predicted)
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scores.append(accuracy)
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return scores
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# split the dataset by class values, return a dictionary
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def separate_by_class(dataset):
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separated = dict()
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for i in range(len(dataset)):
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vector = dataset[i]
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class_id = vector[-1]
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if (class_id not in separated):
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separated[class_id] = list()
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separated[class_id].append(vector)
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return separated
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# mean of a list of numbers
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def mean(numbers):
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return sum(numbers)/float(len(numbers))
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# calculate the standard deviation (sigma) of a list of numbers
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def stdev(numbers):
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avg = mean(numbers)
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variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)
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return sqrt(variance)
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# summarize - the mean, standard deviation (sigma) and count for each column in a dataset
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def summarize_dataset(dataset):
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summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)]
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del(summaries[-1])
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return summaries
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# calculate statistics for each row of the dataset split by class
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def summarize_by_class(dataset):
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separated = separate_by_class(dataset)
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summaries = dict()
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for class_id, rows in separated.items():
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summaries[class_id] = summarize_dataset(rows)
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return summaries
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# Gaussian probability distribution function for x
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def gaussian_probability(x, mean, stdev):
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exponent = exp(-((x-mean)**2 / (2 * stdev**2 )))
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return (1 / (sqrt(2 * pi) * stdev)) * exponent
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# calculate the probabilities of predicting each class for a given row
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def class_probability_predictions(summaries, row):
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total_rows = sum([summaries[label][0][2] for label in summaries])
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probabilities = dict()
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for class_id, class_summaries in summaries.items():
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# for iris, we only care about 3 classes (0-2)
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probabilities[class_id] = summaries[class_id][0][2]/float(total_rows)
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for i in range(len(class_summaries)):
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mean, stdev, _ = class_summaries[i]
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probabilities[class_id] *= gaussian_probability(row[i], mean, stdev)
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return probabilities
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# predict the class for a given row
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def predict(summaries, row):
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probabilities = class_probability_predictions(summaries, row)
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best_label, best_probability = None, -1
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for class_id, probability in probabilities.items():
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if best_label is None or probability > best_probability:
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best_probability= probability
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best_label = class_id
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return best_label
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# da thing, the magic
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def naive_bayes(train, test):
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summarize = summarize_by_class(train)
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predictions = list()
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for row in test:
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output = predict(summarize, row)
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predictions.append(output)
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return(predictions)
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# run naive bayes on the example dataset
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seed(1)
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filename = 'iris.txt'
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dataset = read_csv(filename)
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for i in range(len(dataset[0])-1):
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str_column_to_float(dataset, i)
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# convert class column to integers
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str_column_to_int(dataset, len(dataset[0])-1)
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# evaluate algorithm
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n_folds = 10
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scores = evaluate_algorithm(dataset, n_folds, naive_bayes)
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print('Scores: %s' % scores)
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print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
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