如何使用Python'；s loop从头开始开发KNN alogarithm

我正在学习Python并练习在不使用库的情况下开发KNN

下面是我想采取的3个主要步骤，但我的代码中充满了错误

我正在使用的数据有4个特性和两个类

请看我在下面尝试做什么并帮助改进它-我遇到的主要错误是：

TypeError: only size-1 arrays can be converted to Python scalars

计划分3个阶段进行：

要准备数据，请拆分（用于评估）：

从KNN测量所有距离

from math import sqrt
from collections import Counter
#new_measure=(X_new)
#X_new= [1,2,3,4]
distance=[]
for group in X_train:
    for features in X_train:
       Eu_dis= sqrt( (X_new [0]- X_train[0])**2 + (X_new [1]- X_train[1])**2+(X_new [2]- X_train[2])**2+(X_new [3]- X_train[3])**2)

确定最近的KNN和最可能的类别

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在此之后如何继续？

以下是所有需要的功能： 1.计算两个向量之间的欧氏距离 2.找到最相似的邻居 3.利用邻域进行分类预测

# calculate the Euclidean distance between two vectors
def euclidean_distance(row1, row2):
    distance = 0.0
    for i in range(len(row1)-1):
        distance += (row1[i] - row2[i])**2
    return sqrt(distance)

# Locate the most similar neighbors
def get_neighbors(train, test_row, num_neighbors):
    distances = list()
    for train_row in train:
        dist = euclidean_distance(test_row, train_row)
        distances.append((train_row, dist))
    distances.sort(key=lambda tup: tup[1])
    neighbors = list()
    for i in range(num_neighbors):
        neighbors.append(distances[i][0])
    return neighbors

# Make a classification prediction with neighbors
def predict_classification(train, test_row, num_neighbors):
    neighbors = get_neighbors(train, test_row, num_neighbors)
    output_values = [row[-1] for row in neighbors]
    prediction = max(set(output_values), key=output_values.count)
    return prediction

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谢谢，非常有帮助。我使用了你的提示，并尝试将其应用于虹膜数据。我还有一个错误，你可以从中找到解决办法。这就是错误，您认为发生了什么。范围内的i（len（row1）-1）的10距离=0.0 11：-->12距离+=（row1[i]-row2[i]）**2 13返回sqrt（distance）14索引器：索引4超出大小为4的轴0的范围

# calculate the Euclidean distance between two vectors
def euclidean_distance(row1, row2):
    distance = 0.0
    for i in range(len(row1)-1):
        distance += (row1[i] - row2[i])**2
    return sqrt(distance)

# Locate the most similar neighbors
def get_neighbors(train, test_row, num_neighbors):
    distances = list()
    for train_row in train:
        dist = euclidean_distance(test_row, train_row)
        distances.append((train_row, dist))
    distances.sort(key=lambda tup: tup[1])
    neighbors = list()
    for i in range(num_neighbors):
        neighbors.append(distances[i][0])
    return neighbors

# Make a classification prediction with neighbors
def predict_classification(train, test_row, num_neighbors):
    neighbors = get_neighbors(train, test_row, num_neighbors)
    output_values = [row[-1] for row in neighbors]
    prediction = max(set(output_values), key=output_values.count)
    return prediction