def testKNN(filename, k):
from random import shuffle
from math import ceil
data = DataSet.readFromArff(filename)
data.normalize()
data.simb2num(-1)
d = data.data
shuffle(d)
ntrain = int(ceil(0.7 * len(d)))
k = KNN(k, d[:ntrain])
error_rate = 0
i = 0
for test in d[ntrain:len(d)]:
i += 1
answer = k.classify(test[:-1])
expected = test[-1]
error = abs(expected - answer)
error_rate += error
return float(error_rate) / float(i)
def testKNN(filename, k): from random import shuffle from math import ceil data = DataSet.readFromArff(filename) data.normalize() data.simb2num(-1) d = data.data shuffle(d) ntrain = int(ceil(0.7*len(d))) k = KNN(k, d[:ntrain]) error_rate = 0 i = 0 for test in d[ntrain:len(d)]: i += 1 answer = k.classify(test[:-1]) expected = test[-1] error = abs(expected - answer) error_rate += error return float(error_rate) / float(i)
def testPerceptron(filename, alpha, nepochs):
from random import shuffle
from math import ceil
data = DataSet.readFromArff(filename)
data.normalize()
data.simb2num(-1)
d = data.data
shuffle(d)
ntrain = int(ceil(0.7 * len(d)))
p = Perceptron(len(d[0]) - 1)
p.train(d[:ntrain], alpha, nepochs)
error_rate = 0
i = 0
for test in d[ntrain:len(d)]:
i += 1
answer = p.classify(test[:-1])
expected = test[-1]
error = abs(expected - answer)
error_rate += error
return float(error_rate) / float(i)
def testPerceptron(filename, alpha, nepochs): from random import shuffle from math import ceil data = DataSet.readFromArff(filename) data.normalize() data.simb2num(-1) d = data.data shuffle(d) ntrain = int(ceil(0.7*len(d))) p = Perceptron(len(d[0])-1) p.train(d[:ntrain], alpha, nepochs) error_rate = 0 i = 0 for test in d[ntrain:len(d)]: i += 1 answer = p.classify(test[:-1]) expected = test[-1] error = abs(expected - answer) error_rate += error return float(error_rate) / float(i)