标签在前K个近邻中的出现次数

import sys
from collections import Counter

def KNN_Classification():
    input_tokens = sys.stdin.read().strip()
    input_tokens = input_tokens.split()
    token_iter = iter(input_tokens)
    k = int(next(token_iter))
    SampleNums = int(next(token_iter))
    FeatureDim = int(next(token_iter))
    ClassNums  = int(next(token_iter))

    test_inputs = [float(next(token_iter)) for _ in range(FeatureDim)]
    train_inputs = []
    train_labels = []
    for _ in range(SampleNums):
        sample_data = [float(next(token_iter)) for _ in range(FeatureDim + 1)]
        train_inputs.append(sample_data[:FeatureDim])
        train_labels.append(int(sample_data[-1]))

    index_list =[]
    distance_list = []
    for i  in range(SampleNums):
        current_feat = train_inputs[i]
        distance = 0
        for j in range(FeatureDim):
            delta = current_feat[j] - test_inputs[j]
            distance += delta*delta
        #L2_norm = 
        distance_list.append((distance,i))
    distance_list.sort(key = lambda x:x[0])
    top_k_index = [distance_list[i][1] for i in range(k)]

    top_k_labels = [train_labels[index] for index in top_k_index]

    labels_counts = Counter(top_k_labels)
    max_frequency = max(labels_counts.values())

    tiled_labels = [labels for labels,freq in labels_counts.items() if freq ==max_frequency] 
    select_label = None
    for i in range(SampleNums):
        current_label = train_labels[distance_list[i][1]]
        if current_label in tiled_labels:
            select_label = current_label
            break
    print(select_label,max_frequency)

if __name__ == '__main__':
    KNN_Classification()