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最新推荐文章于 2024-01-27 22:12:32 发布

ixtgtg

最新推荐文章于 2024-01-27 22:12:32 发布

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分类专栏：机器学习算法

本文链接：https://blog.youkuaiyun.com/ixtgtg/article/details/81583174

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本文介绍了一个基于Python的逻辑回归分类器实现，该分类器使用梯度下降法进行参数更新，并通过sigmoid函数预测鸢尾花数据集中的类别。文章详细展示了如何将原始数据分为训练集和测试集，再利用训练数据拟合模型，最终在测试集上评估模型准确率。

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
import numpy as np

def sGradDescent(x,y):
    alpha=0.01
    deviation = 1
    iter = 0    
    w=np.random.rand(4)
    while True:
        for i in range(112): 
            deviation = 0
            h = w[0] * x[i][0] + w[1] * x[i][1]+w[2] * x[i][2] + w[3] * x[i][3]
            for j in range(4):
                w[j] = w[j] + alpha * (y[i] - h)*x[i][j]
        iter = iter + 1
        for i in range(112):
            deviation = deviation + (y[i] - (w[0] * x[i][0] + w[1] * x[i][1]+w[2] * x[i][2] + w[3] * x[i][3])) ** 2
        if iter >150 :           
            break
    return w

def sigmoid(x):
    return (1/(1+np.exp(-x)) )      

if __name__=="__main__":
 
    data = load_iris()   
    X = data.data  
    y = data.target    
    features = data.feature_names  
    targets = data.target_names  
    X_train_raw, X_test_raw, y_train, y_test = train_test_split(X,y)#112,38
    weight1 = []  
    ih=[]
    for i in range(3):
        labelMat1 = []  
        for j in range(112):
            if y_train[j] == i:  
                labelMat1.append(  1)
            else:  
                labelMat1.append(0)  
        weight1.append(sGradDescent(X_train_raw,labelMat1))
    for j in range(38):  
        ob=0
        h=np.zeros(3)
        for i in range(3):  
            ob=X_test_raw[j][0]*weight1[i][0]+X_test_raw[j][1]*weight1[i][1]+X_test_raw[j][2]*weight1[i][2]+X_test_raw[j][3]*weight1[i][3]
            h[i] = sigmoid(ob)
        voteResult=h.tolist()
        ih.append(voteResult.index(max(voteResult))  )
      #  print(np.where(h==np.max(h)),y_test[j])
    error = 0.0  
    for j in range(38):  
        if ih[j] != y_test[j]:  
            error = error +1      
    pro = 1 - error / 38
    print (pro)