#!/usr/bin/python
# coding = utf-8
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
def plot(dataMat, labels, x1, x2):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(dataMat[:, x1], dataMat[:, x2], 15.0 * array(labels), 15.0 * array(labels))
plt.show()
if __name__ == "__main__":
import importData
dataset, label = importData.dataFrmFile("./optdigits.tes")
dataSet, ranges, minVals = importData.nomilze(dataset)
plot(dataSet, label, 3, 4)
#!/usr/bin/python
#coding = utf-8
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
def plot(dataMat, labels, x1, x2):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(dataMat[:,x1], dataMat[:,x2], 15.0*array(labels), 15.0*array(labels))
plt.show()
if __name__ == '__main__':
import importData
dataset, label = importData.dataFrmFile('./optdigits.tes')
dataSet, ranges, minVals = importData.nomilze(dataset)
plot(dataSet, label, 3, 4)
#!/usr/bin/python
#coding = utf-8
from numpy import *
import importData
import plot
import knn
dataSet_tra, label_tra = importData.dataFrmFile('./optdigits.tra')
dataSet_tes, label_tes = importData.dataFrmFile('./optdigits.tes')
index = -1
cnt = 0
for i in label_tes:
index += 1
if i != knn.classify(dataSet_tes[index], dataSet_tra, label_tra, 10):
cnt += 1
print cnt
plot.plot(dataSet_tes, label_tes, 4, 5)
#!/usr/bin/python
#coding = utf-8
from numpy import *
import operator
def classify(inX, dataSet, labels, k):
dataLen = len(labels)
diff = tile(inX, (dataLen, 1)) - dataSet
diff **= 2
distances = diff.sum(1)
distances **= 0.5
classCnt = {}
disSortedIndex = distances.argsort()
for i in xrange(k):
votelabel = labels[disSortedIndex[i]]
classCnt[votelabel] = classCnt.get(votelabel, 0) + 1
sortedClassCnt = sorted(classCnt.iteritems(), key = operator.itemgetter(1), reverse = True)
return sortedClassCnt[0][0]
if __name__ == '__main__':
import importData
dataSet, labels = importData.dataFrmFile('./optdigits.tes')
print classify(dataSet[0], dataSet, labels, 4)