knn回归算法python代码

可以如下实现：

from sklearn.neighbors import KNeighborsRegressor

# 训练集数据和标签
X_train = [[0, 0], [1, 1], [2, 2], [3, 3]]
y_train = [0, 1, 2, 3]

# 创建模型
knn = KNeighborsRegressor(n_neighbors=2)

# 拟合模型
knn.fit(X_train, y_train)

# 预测
X_test = [[0.5, 0.5], [2.5, 2.5]]
y_pred = knn.predict(X_test)

print('预测值：', y_pred)

希望对您有帮助。

相关问题

knn算法的python代码

K最近邻（K-nearest neighbors，KNN）算法是种常用的分类和回归算法。它的基本思想是通过计算待分类样本与训练集中各个样本之间的距离，选取距离最近的K个样本作为邻居，然后根据邻居的标签进行投票或者计算平均值来确定待分类样本的类别或者数值。

下面是一个简单的KNN算法的Python代码示例：

import numpy as np
from collections import Counter

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

    def _predict(self, x):
        distances = [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]

使用示例：

# 创建训练集和标签
X_train = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])
y_train = np.array(['red', 'red', 'blue', 'blue', 'red', 'blue'])

# 创建KNN分类器对象
knn = KNN(k=3)

# 训练模型
knn.fit(X_train, y_train)

# 创建测试集
X_test = np.array([[1, 1], [2, 3], [6, 9], [8, 9]])

# 预测测试集的标签
y_pred = knn.predict(X_test)

print(y_pred)  # 输出预测结果

希望以上代码能够帮助到你！

knn算法python实现代码

KNN算法是一种常用的机器学习算法，可以用于分类和回归。其原理是通过计算待分类数据与训练数据之间的距离，选取距离最近的k个数据进行分类或回归。

以下是一个简单的KNN算法Python实现代码：

import numpy as np
from collections import Counter

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 np.array(y_pred)

    def _predict(self, x):
        distances = [np.sqrt(np.sum((x - x_train)**2)) 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

其中，fit方法用于训练模型，predict方法用于预测结果，_predict方法用于预测单个样本。