knn聚类的python代码

以下是使用Python实现KNN聚类的示例代码：

import numpy as np
from collections import Counter

class KNN():
    def __init__(self, k=5):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        predicted_labels = [self._predict(x) for x in X]
        return np.array(predicted_labels)

    def _predict(self, x):
        distances = [self._distance(x, x_train) for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_labels = [self.y_train[i] for i in k_indices]
        most_common = Counter(k_labels).most_common(1)
        return most_common[0][0]

    def _distance(self, x1, x2):
        return np.sqrt(np.sum((x1 - x2) ** 2))

使用示例：

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

X, y = make_classification(n_samples=100, n_features=10, n_classes=2, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

knn = KNN(k=5)
knn.fit(X_train, y_train)

y_pred = knn.predict(X_test)

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