KNNPython代码

以下是一个简单的KNN分类器的Python代码示例：

import numpy as np
from collections import Counter

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):
        predictions = []
        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, i))
            distances = sorted(distances)
            k_nearest_neighbors = [self.y_train[i] for d, i in distances[:self.k]]
            most_common = Counter(k_nearest_neighbors).most_common(1)
            predictions.append(most_common[0][0])
        return predictions

这个KNN分类器使用欧几里得距离计算距离，并且返回最常见的k个邻居的分类标签的众数作为预测结果。在使用之前，需要将要训练的数据和对应的标签传递给`fit()`函数。在进行预测时，将测试数据传递给`predict()`函数，它会返回一个包含预测标签的列表。