Logistics算法实现

原理参考：【DL笔记3】一步步用python实现Logistic回归
logistic回归——PYTHON实现
第二篇原理比较易懂，代码好像有点问题。

python实现：

import numpy as np

#Logistic回归模型
class LogisticRegression():
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.w = np.zeros(self.x.shape[1])
        self.b = 0

    def Logistic_sigmoid(self, y):
        #非线性层，将值域空间映射为（0，1）
        return np.exp(y)/(1+np.exp(y))

    def Logistic_cost(self, p, y):
        #损失函数
        return np.sum(-y*np.log(p)-(1-y)*np.log(1-p))

    def Logistic_BP(self, alpha, iters):
        #反向传播函数
        for i in range(iters):
            p = np.dot(self.x, self.w.T)+self.b
            a = self.Logistic_sigmoid(p)
            print('iters:', i,' cost:',  self.Logistic_cost(a, y))
            dz = a -self.y
            self.w -= alpha*np.dot(dz.T, self.x)
            self.b -= alpha*sum(dz)
        return self.w, self.b

    def Logistic_predict(self, x):
        #预测函数
        return self.Logistic_sigmoid(np.dot(x, self.w.T)+self.b)


if __name__ == '__main__':
    x = np.array([[0], [1], [2], [3]])
    y = np.array([0, 0, 1, 1])
    lg = LogisticRegression(x, y)
    w, b = lg.Logistic_BP(alpha=0.1, iters=100)
    print('最终训练得到的w和b为：', w, ',', b)
    pre = lg.Logistic_predict(np.array([[2.9]]))
    print('预测结果为：', pre)

python调包：

import numpy as np
from sklearn.linear_model import LogisticRegression

#Logistic回归模型
class MyLogisticRegression():
    def __init__(self, x, y):
        self.x = x
        self.y = y  
        self.clf = LogisticRegression()    
    
    def train(self):
        self.clf.fit(self.x, self.y)   
        w = self.clf.coef_
        b = self.clf.intercept_ 
        return w, b

    def predict(self, x):
        return self.clf.predict_proba(x)
        

if __name__ == '__main__':
    x = np.array([[0], [1], [2], [3]])
    y = np.array([[0], [0], [1], [1]])
    lr = MyLogisticRegression(x, y)

    w, b = lr.train()
    print('最终训练得到的w和b为：', w, ',', b)   
    print('预测结果为：', lr.predict([[2.9]]))

C++实现：

#include 
#include 
#include 
#include 

//k邻近模型
class KNN
{
public:
	KNN(std::vector> x, std::vector y, int k, float p) : m_x(x), m_y(y), m_k(k), m_p(p) {};

	int predict(std::vector> x)
	{
		x.resize(m_x.size());
		for (size_t i = 0; i < x.size(); i++)
		{
			x[i] = x[0];
		}

		//计算预测数据和训练数据的差值
		std::vector> diff = x;
		for (size_t i = 0; i < diff.size(); i++)
		{
			for (size_t j = 0; j < diff[0].size(); j++)
			{
				diff[i][j] -= m_x[i][j];
			}
		}

		//计算范数
		std::vector dist(diff.size(), 0);
		for (size_t i = 0; i < diff.size(); i++)
		{
			for (size_t j = 0; j < diff[0].size(); j++)
			{
				dist[i] += pow(diff[i][j], m_p);
			}
			dist[i] = pow(dist[i], 1.0 / m_p);
		}

		//返回从小到大排序的索引
		std::vector  dist_sorted(dist.size());
		for (size_t i = 0; i != dist_sorted.size(); ++i) dist_sorted[i] = i;
		std::sort(dist_sorted.begin(), dist_sorted.end(), [&dist](size_t i, size_t j) {return dist[i] <  dist[j]; });

		//分类投票
		std::map count;
		for (size_t i = 0; i < m_k; i++)
		{
			float vote = m_y[dist_sorted[i]];
			count[vote] += 1;
		}

		//返回投票最多的类别标签
		return count.rbegin()->first;
	}
	
private:
	std::vector> m_x;
	std::vector m_y;
	int m_k;
	float m_p;
};


int main(int argc, char* argv[])
{
	std::vector> x = { { 0, 10 },{ 1, 8 },{ 10, 1 },{ 7, 4 } };
	std::vector y = { 0, 0, 1, 1 };

	KNN knn = KNN(x, y, 3, 2);
	std::cout << "预测值为：" << knn.predict({ {6,2} }) << std::endl;

	system("pause");
	return EXIT_SUCCESS;
}

Logistics算法实现

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