knn算法python代码

以下是一个简单的KNN算法Python代码示例：

import numpy as np

# 计算距离函数
def distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2)**2))

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 = []
        for x in X:
            # 计算当前点与训练集中所有点的距离
            distances = [distance(x, x_train) for x_train in self.X_train]
            # 将距离从小到大排序，取前k个点
            k_indices = np.argsort(distances)[:self.k]
            # 找到这k个点所对应的类别
            k_labels = [self.y_train[i] for i in k_indices]
            # 将出现次数最多的类别作为预测结果
            most_common = max(set(k_labels), key=k_labels.count)
            y_pred.append(most_common)
        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

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

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

# 进行预测
y_pred = knn.predict(X_test)

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

注意：这只是一个简单的KNN算法示例，实际应用中可能需要进行更多的数据处理和特征工程。