KNN算法Demo

1， 数据集介绍：

使用系统自带的数据集。

虹膜

Virginia_Iris.png

150个实例

萼片长度，萼片宽度，花瓣长度，花瓣宽度

(sepal length, sepal width, petal length and petal width）

类别：

Iris setosa, Iris versicolor, Iris virginica.

kahi2.jpg

2. 利用Python的机器学习库sklearn: SkLearnExample.py

 from sklearn import neighbors

from sklearn import datasets

knn = neighbors.KNeighborsClassifier()
#得到分类器
iris = datasets.load_iris()
#得到数据集
print( iris)

knn.fit(iris.data, iris.target)
#训练模型
predictedLabel = knn.predict([[0.1, 0.2, 0.3, 0.4]])

print (predictedLabel)

3,自己动手写KNN算法
加载数据

def loadDataset(filename, split, trainingSet = [], testSet = []):
    with open(filename, 'r') 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])

计算距离

def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x]-instance2[x]), 2)
    return math.sqrt(distance)

计算准确率

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

将训练集和需要测试的样例和K传入，得到与测试样例最近的k个样例，将其类型放入neighbors[]中，返回neighbors

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))
        #distances.append(dist)
    distances.sort(key=operator.itemgetter(1))
    neighbors = []
    for x in range(k):
        neighbors.append(distances[x][0])
        return neighbors

用一个字典classVotes，以少数服从多数，将类型最多的一种结果返回

def getResponse(neighbors):
    classVotes = {}
    for x in range(len(neighbors)):
        response = neighbors[x][-1]
        if response in classVotes:
            classVotes[response] += 1
        else:
            classVotes[response] = 1
        #按降序pai lie
    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
    return sortedVotes[0][0]

main函数调用

def main():
    #prepare data
    trainingSet = []
    testSet = []
    split = 0.67
    loadDataset(r'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()

运行结果

屏幕快照 2018-09-03 下午12.43.05.png