def classTest():
ratio = 0.1
dataset = array(pd.read_csv('/home/plutolove/train.csv'))
data = dataset[:, 1:]
labels = dataset[:, 0]
normdata, ranges, minval = autoNorm(data)
size = data.shape[0]
numTestVecs = int(size * ratio)
errorCount = 0
for i in range(numTestVecs):
result = classify(normdata[i, :], normdata[numTestVecs:size, :], labels[numTestVecs:size], 3)
print 'the classifer came back with: %d, the real answer is: %d' %(result, labels[i])
if(result != labels[i]): errorCount += 1
print 'the total error rate is: %f' %(errorCount/float(numTestVecs))
import matplotlib.pyplot as plt
from autoNorm import *
from KNN import *
group, labels = file2matrix('/home/plutolove/machinelearninginaction/Ch02/datingTestSet2.txt')
data, ranges, min_val = autoNorm(group)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(data[:, 1], data[:, 2], 15*array(labels), 15*array(labels))
plt.show()
