简单:
一、手动写一个KNN算法解决分类问题
from sklearn import datasets from collections import Counter # 为了做投票 from sklearn.model_selection import train_test_split import numpy as np # 导入iris数据 iris = datasets.load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2003) def euc_dis(instance1, instance2): """ 计算两个样本instance1和instance2之间的欧式距离 instance1: 第一个样本, array型 instance2: 第二个样本, array型 """ # TODO dist = np.sqrt(sum((instance1-instance2)**2)) return dist def knn_classify(X, y, testInstance, k): """ 给定一个测试数据testInstance, 通过KNN算法来预测它的标签。 X: 训练数据的特征 y: 训练数据的标签 testInstance: 测试数据,这里假定一个测试数据 array型 k: 选择多少个neighbors? """ # TODO 返回testInstance的预测标签 = {0,1,2} distances = [euc_dis(x,testInstance) for x in X] kneighbors = np.argsort(distances)[:k] count = Counter(y[kneighbors]) return count. most_common()[0][0] # 预测结果。 predictions = [knn_classify(X_train, y_train, data, 3) for data in X_test] correct = np.count_nonzero((predictions==y_test)==True) print ("Accuracy is: %.3f" %(correct/len(X_test)))