最近隣接する分類のアルゴリズムKNNは実現します--麦の学院の彭亮の機械の学習の基礎を参考にします4.2
2644 ワード
import csv
import random
import math
import operator
# ,
def loadDataset(filename, split, trainingSet = [], testSet = []):
with open(filename, 'rt') as csvfile:
lines = csv.reader(csvfile)
dataset = list(lines)
for x in range(len(dataset)-1):
for y in range(4):
dataset[x][y] = float(dataset[x][y])
if random.random() < split:
trainingSet.append(dataset[x])
else:
testSet.append(dataset[x])
#
def euclideanDistance(instance1, instance2, length):
distance = 0
for x in range(length):
distance += pow((instance1[x]-instance2[x]), 2)
return math.sqrt(distance)
# (K ),testInstance
def getNeighbors(trainingSet, testInstance, k):
distances = []
length = len(testInstance)-1
for x in range(len(trainingSet)):
#testinstance
dist = euclideanDistance(testInstance, trainingSet[x], length)
distances.append((trainingSet[x], dist))#distance list
#distances.append(dist)
distances.sort(key=operator.itemgetter(1))# dist
neighbors = []
for x in range(k):
neighbors.append(distances[x][0])#
return neighbors
#
def getResponse(neighbors):
classVotes = {}#key-- value--
for x in range(len(neighbors)):
response = neighbors[x][-1]
if response in classVotes:
classVotes[response] += 1
else:
classVotes[response] = 1
sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
return sortedVotes[0][0]
#
def getAccuracy(testSet, predictions):
correct = 0
for x in range(len(testSet)):
if testSet[x][-1] == predictions[x]:
correct += 1
return (correct/float(len(testSet)))*100.0
def main():
#prepare data
trainingSet = []
testSet = []
split = 1/3
loadDataset(r'E:\pycharmcode\irisdata.txt', split, trainingSet, testSet)
print('Train set: '+ repr(len(trainingSet)))
print('Test set: ' + repr(len(testSet)))
#generate predictions
predictions = []
k = 3
for x in range(len(testSet)):
# trainingsettrainingSet[x]
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
print ('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
accuracy = getAccuracy(testSet, predictions)
print('Accuracy: ' + repr(accuracy) + '%')
if __name__ == '__main__':
main()
#main()