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# -*- coding: utf-8 -*- import sklearn from sklearn import neighbors import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets import pandas as pd import numpy def getData_1(): iris = datasets.load_iris() X = iris.data #样本特征矩阵,150*4矩阵,每行一个样本,每个样本维度是4 y = iris.target #样本类别矩阵,150维行向量,每个元素代表一个样本的类别 df1=pd.DataFrame(X, columns =['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']) df1['target']=y return df1 df=getData_1() X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:3],df['target'], test_size=0.3, random_state=42) print X_train, X_test, y_train, y_test model = neighbors.KNeighborsClassifier(n_neighbors=5, n_jobs=1) # knn """参数 --- n_neighbors: 使用邻居的数目 n_jobs:并行任务数 """ model.fit(X_train,y_train) predict=model.predict(X_test) print predict print y_test.values
print 'knn分类:{:.3f}'.format(model.score(X_test, y_test))
结果:
预测值:[2 0 2 1 1 0 1 1 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1 0 0 0 2 1 1 0 0] 真实值:[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1 0 0 0 2 1 1 0 0] knn分类准确度:0.956