如何用python实现KNN算法

KNN是一种简单而有效的分类算法，这里提供一个Python实现的KNN算法示例：

import numpy as np

class KNN:
    def __init__(self, k=3):
        self.k = k
        
    def fit(self, X_train, y_train):
        self.X_train = X_train
        self.y_train = y_train
        
    def predict(self, X_test):
        y_pred = []
        for x in X_test:
            distances = []
            for i, x_train in enumerate(self.X_train):
                distance = np.sqrt(np.sum(np.square(x - x_train)))
                distances.append((distance, self.y_train[i]))
            distances.sort()
            
            k_nearest_neighbors = distances[:self.k]
            k_nearest_labels = [label for _, label in k_nearest_neighbors]
            
            y_pred.append(max(set(k_nearest_labels), key=k_nearest_labels.count))
        return y_pred

使用示例：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

knn = KNN(k=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

这里使用了sklearn中的鸢尾花数据集进行测试，将数据集划分为训练集和测试集，然后使用KNN算法进行分类，并计算分类准确率。