knn算法python代码

下面是一个简单的 Python 实现 KNN 算法的例子：

from collections import Counter
import math

def euclidean_distance(x1, x2):
    distance = 0
    for i in range(len(x1)):
        distance += (x1[i] - x2[i]) ** 2
    return math.sqrt(distance)

class KNN:
    def __init__(self, k=3):
        self.k = k
        
    def fit(self, X, y):
        self.X_train = X
        self.y_train = y
        
    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return y_pred
    
    def _predict(self, x):
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        k_indices = sorted(range(len(distances)), key=lambda i: distances[i])[: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：

# 创建 KNN 对象并拟合数据
knn = KNN(k=3)
knn.fit(X_train, y_train)

# 预测测试集数据
y_pred = knn.predict(X_test)

其中，`X_train` 和 `y_train` 分别是训练集的特征和标签，`X_test` 是测试集的特征，`y_pred` 是预测结果。`k` 是 KNN 算法中的超参数，表示选取最近邻的个数。