用python实现knn（k，in_data，out_data）函数

以下是用Python实现KNN函数的代码：

import numpy as np

def knn(k, in_data, out_data):
    """
    K-Nearest Neighbors algorithm implementation.
    
    Parameters:
        k (int): Number of nearest neighbors to consider.
        in_data (numpy.ndarray): Input data, shape (n_samples, n_features).
        out_data (numpy.ndarray): Output data, shape (n_samples,).
    
    Returns:
        numpy.ndarray: Predicted output data, shape (n_samples,).
    """
    
    # Calculate pairwise distances between input samples and output samples.
    dists = np.sqrt(np.sum((in_data[:, None] - out_data) ** 2, axis=2))
    
    # Find indices of k nearest neighbors for each output sample.
    nn_indices = np.argsort(dists, axis=0)[:k]
    
    # Get corresponding output values for each nearest neighbor.
    nn_outputs = in_data[nn_indices]
    
    # Calculate the mean of the nearest neighbors for each output sample.
    pred_outputs = np.mean(nn_outputs, axis=1)
    
    return pred_outputs

这个函数接受三个参数：k（要考虑的最近邻居数），in_data（输入数据）和out_data（输出数据）。该函数使用欧几里得距离计算输入样本和输出样本之间的距离，并找到每个输出样本的k个最近邻居的索引。然后，它获取每个最近邻居的相应输出值，并计算每个输出样本的最近邻居的平均值，作为该输出样本的预测值。最后，该函数返回一个预测输出数据的数组。