knn算法python代码

下面是一个简单的 Python 代码实现 KNN 算法：

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k=1):
        self.k = k
        
    def fit(self, X, y):
        self.X_train = X
        self.y_train = y
        
    def euclidean_distance(self, x1, x2):
        return np.sqrt(np.sum((x1 - x2)**2))
    
    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)
    
    def _predict(self, x):
        distances = [self.euclidean_distance(x, x_train) for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

使用方法：

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

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
knn = KNN(k=3)
knn.fit(X_train, y_train)

# 预测
y_pred = knn.predict(X_test)

# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

注意：此代码为 KNN 的基本实现，可能存在一些问题。在实际使用中，需要根据具体情况进行调整和优化。