KNN实现(Python)

kNN算法是一种分类算法，对于给定标签的训练集，计算新数据与训练集示例的距离，统计最近的k个示例，如果多数属于某一类，则新示例属于该类。

1.自定义函数实现kNN算法

import numpy as np from sklearn.model_selection import train_test_split from sklearn import datasets

准备数据

iris = datasets.load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=100)

定义kNN类

class kNN(): ''' k:近邻个数 X_train:训练集 y_train:训练标签 x:新示例 ''' def __init__(self, k, X_train, y_train, x): self.k = k self.X = X_train self.y = y_train self.x = x #计算未分类样本与已知类别样本的欧式距离 def distance(self): from numpy import sqrt,sum distances = [] for x_train in self.X: d = sqrt(sum((self.x - x_train)**2)) distances.append(d) return distances #对距离进行排序，选出最近的k个示例 def sort(self): from numpy import argsort distances = kNN.distance(self) d_sorted = argsort(distances)[:self.k] return self.y[d_sorted] #找到最多的类别 def knn_classify(self): y_knn = kNN.sort(self) from collections import Counter y_knn = kNN.sort(self) votes = Counter(y_knn) return votes.most_common(1)[0][0]

实例化

print('预测值\t实际值') for x, y_ in zip(X_test, y_test): knn = kNN(6, X_train, y_train, x) yhat = knn.knn_classify() print(f'{yhat}\t{y_}'

预测值 实际值 2 2 0 0 2 2 0 0 2 2 2 2 0 0 0 0 2 2 0 0 0 0 2 2 0 0 0 0 2 2

2.scikit-learn中的kNN算法

准备数据

from sklearn import datasets iris = datasets.load_iris() X = iris.data y = iris.target

划分训练集和测试集

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)

数据归一化

from sklearn.preprocessing import StandardScaler standardscaler = StandardScaler() standardscaler.fit(X_train) X_train_s = standardscaler.transform(X_train) X_test_s = standardscaler.transform(X_test)

预测

from sklearn.neighbors import KNeighborsClassifier kNN_classify = KNeighborsClassifier(6) kNN_classify.fit(X_train_s, y_train) yhat = kNN_classify.predict(X_test_s) print(f'预测值：{yhat}') print(f'实际值：{y_test}') print(f'准确率：{kNN_classify.score(X_test_s, y_test)}')

预测值：[2 0 2 0 1 2 0 0 2 0 0 2 0 0 2 1 1 1 2 2 2 0 2 0 1 2 1 0 1 2] 实际值：[2 0 2 0 2 2 0 0 2 0 0 2 0 0 2 1 1 1 2 2 2 0 2 0 1 2 1 0 1 2] 准确率：0.9666666666666667