Python实现简单的KNN聚类算法

Python实现简单的KNN聚类算法

完整代码

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 鸢尾花
iris=load_iris()
df = pd.DataFrame(data=iris.data,columns=iris.feature_names)
df["class"]=iris.target
df["class"]=df["class"].map({0:iris.target_names[0], 1:iris.target_names[1], 2:iris.target_names[2]})

# 解析数据
x=iris.data
y=iris.target.reshape(-1,1)

# 划分训练集和测试集
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=35,stratify=y)

# 距离函数定义 欧式距离
def l1_distance(a, b):
    return np.sum(np.abs(a-b),axis=1)
# 距离函数定义 曼哈顿距离
def l2_distance(a, b):
    return np.sqrt(np.sum((a-b)**2,axis=1))

# 分类器
class KNN(object):
    def __init__(self,n_neighbors=1,dist_func=l1_distance):
        self.n_neighbors =n_neighbors
        self.dist_func = dist_func

    # 模型训练
    def fit(self,x,y):
        self.x_train =x
        self.y_train =y

    # 模型预测
    def predict(self,x):
        y_pred=np.zeros((x.shape[0],1),dtype=self.y_train.dtype)

        for i,x_test in enumerate(x):
            # 计算距离
            distance=self.dist_func(self.x_train,x_test)
            # 距离排序
            nn_index = np.argsort(distance)
            nn_y = self.y_train[nn_index[:self.n_neighbors]].ravel()

            y_pred[i] = np.argmax(np.bincount(nn_y))
        return y_pred

# knn=KNN(n_neighbors=3)
# # 训练
# knn.fit(x_train,y_train)
# # 预测
# y_pred=knn.predict(x_test)
# # 准确率
# accuracy=accuracy_score(y_test,y_pred)
# print("准确率：",accuracy)

knn=KNN()
# 返回结果
result_list=[]
# 训练
knn.fit(x_train,y_train)
# 分别计算欧式距离和曼哈顿距离
for p in [1,2]:
    knn.dist_func=l1_distance if p==1 else l2_distance
    # k取奇数 步长为2 计算不同k值下的准确率
    for k in range(1,10,2):
        knn.n_neighbors=k
        # 预测结果
        y_pred = knn.predict(x_test)
        # 准确率
        accuracy=accuracy_score(y_test,y_pred)
        result_list.append([k, "欧式" if p==1 else "曼哈顿",accuracy])

df=pd.DataFrame(result_list,columns=["k值","距离算法","准确率"])
print(df)

准确率结果