KNN _ K近邻算法 的实现 ----- 机器学习

标签：KNN iris pred 近邻 np train ----- test self

导入相关包

import numpy as np
import pandas as pd

# 引入 sklearn 里的数据集，iris(鸢尾花)
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split # 切分为训练集和测试集
from sklearn.metrics import accuracy_score # 计算分类预测的准确率

1.数据加载预处理

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]})
df.describe() # 描述

x = iris.data
y = iris.target.reshape(-1,1)
print(x.shape, y.shape)

# 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)

print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

2. 核心算法实现

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

# 分类器实现
class kNN(object):
    # 定义一个初始化方法：__init__是类的构造方法
    def __init__(self, n_neighbors = 1, dist_func = l1_distance):
        self.n_neighbors = n_neighbors
        self.dist_func = dist_func
    
    # 调整模型方法
    def fit(self, x, y):
        self.x_train = x
        self.y_train = y
    
    # 模型预测方法
    def predict(self, x):
        # 初始化预测分类数组
        y_pred = np.zeros((x.shape[0], 1), dtype = self.y_train.dtype)
        
        # 遍历输入X的数据点 (每一个测试点的下标序号i和数据)
        for i, x_test in enumerate(x):
            # 测试数据x_test和训练数据计算距离
            distances = self.dist_func(self.x_train,x_test)
            
            # 由近到远排序,取得索引值
            nn_index = np.argsort(distances) # 输出索引值
            
            # 选取最近的K个点， 保存它们对应的分类类别
            nn_y = self.y_train[nn_index[:self.n_neighbors] ].ravel() # 变成一维数组
            
            #统计类别中出现频率最高的那个， 赋给y_pred[i]
            y_pred[i] = np.argmax(np.bincount(nn_y))# binnary count 统计每个值出现的次数 输出成数组
            
        return y_pred

 

3. 测试

# 定义实例
knn = kNN(n_neighbors = 3)
# 训练模型
knn.fit(x_train, y_train)
# 传入测试数据， 做预测
y_pred = knn.predict(x_test)

# 求出预测准确率
accuracy = accuracy_score(y_test, y_pred)

print("预测准确率：",accuracy)

 

 

# 定义实例
knn = kNN()
# 训练模型
knn.fit(x_train, y_train)

# list保存结果
result_list = []

# 针对不同的参数选取，做预测
for p in [1, 2]:
knn.dist_func = l1_distance if p == 1 else l2_distance

# 考虑不同的K取值. 步长为2 ，避免二元分类 偶数打平
for k in range(1, 10, 2):
knn.n_neighbors = k
# 传入测试数据， 做预测
y_pred = knn.predict(x_test)
# 求出预测准确率
accuracy = accuracy_score(y_test, y_pred)
result_list.append([k, 'l1_distance' if p == 1 else 'l2_distance', accuracy])
df= pd.DataFrame(result_list, columns=['k', '距离函数', '预测准确率'])
df

 

 

 

 

标签：KNN,iris,pred,近邻,np,train,-----,test,self
From： https://www.cnblogs.com/slowlydance2me/p/16857906.html