使用python实现knn算法_使用python实现KNN算法

KNN（K-Nearest Neighbors）算法是一种非常简单但又非常有效的分类和回归方法。它的原理是：在训练集中找出与测试数据最接近的K个数据，然后根据这K个数据的分类，确定测试数据的分类。

下面是使用Python实现KNN算法的步骤：

1. 导入必要的库

import numpy as np
from collections import Counter

2. 定义KNN类

class KNN:
    def __init__(self, k=3):
        self.k = k

3. 定义距离函数

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

4. 训练模型

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

5. 预测函数

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

6. 内部预测函数

    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]

完整代码如下：

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k=3):
        self.k = k

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

    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 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KNN算法进行分类的示例代码：

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)

# 构建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：KNN算法的效果非常依赖于数据的质量和特征的选取，因此在实际应用中需要进行多次尝试和调整。