逻辑回归(Logistic Regression)简介及C++实现

逻辑回归(Logistic Regression)：该模型用于分类而非回归，可以使用logistic sigmoid函数( 可参考：http://blog.csdn.net/fengbingchun/article/details/73848734 )将线性函数的输出压缩进区间(0,1)：

p(y=1| x;θ)=σ(θ^Tx).

逻辑回归是一种广义的线性回归分析(可参考： http://blog.csdn.net/fengbingchun/article/details/77892193 )模型，因此与多重线性回归分析有很多相同之处。它们的模型形式基本上相同，都具有w’x+b，其中w和b是待求参数，其区别在于它们的因变量不同，多重线性回归直接将w’x+b作为因变量，即y=w’x+b,而逻辑回归则通过函数L将w’x+b对应一个隐状态p, p=L(w’x+b)，然后根据p与1-p的大小决定因变量的值。如果L是logistic函数，就是logistic回归，如果L是多项式函数就是多项式回归。

Logistic回归的因变量可以是二分类的，也可以是多分类的。多类可以使用softmax方法(可参考 http://blog.csdn.net/fengbingchun/article/details/75220591 )进行处理。

优化逻辑回归的方法包括：梯度下降法、牛顿法、BFGS。优化的主要目标是找到一个方向，参数朝这个方向移动之后使得似然函数的值能够减少，这个方向往往由一阶偏导或者二阶偏导各种组合求得。这几种方法一般都是采用迭代的方式来逐步逼近极小值。

Logistic回归类似于线性回归模型，但更适用于因变量为二分变量的模型。逻辑回归中预测函数用到了逻辑函数即sigmoid函数(逻辑回归拟合函数)。逻辑回归中的损失函数(cost function)是基于最大似然估计推导的。

逻辑回归中的过拟合：对于逻辑回归或线性回归的损失函数构成的模型，可能会有些权重很大，有些权重很小，导致过拟合，使得模型的复杂度提高，泛化能力较差。一般多用正则化的方法来解决过拟合。

以下是参考OpenCV3.3中LogisticRegression类实现的二分类逻辑回归code：包括训练方法包括Batch和Mini Batch，并支持简单的正则化方法，实现code如下：

logistic_regression.hpp:

#ifndef FBC_NN_LOGISTICREGRESSION_HPP_#define FBC_NN_LOGISTICREGRESSION_HPP_#include <string>#include <memory>#include <vector>namespace ANN {template<typename T>class LogisticRegression { // two categoriespublic:LogisticRegression() = default;int init(const T* data, const T* labels, int train_num, int feature_length,int reg_kinds = -1, T learning_rate = 0.00001, int iterations = 10000, int train_method = 0, int mini_batch_size = 1);int train(const std::string& model);int load_model(const std::string& model);T predict(const T* data, int feature_length) const; // y = 1/(1+exp(-(wx+b)))// Regularization kindsenum RegKinds {REG_DISABLE = -1, // Regularization disabledREG_L1 = 0 // L1 norm};// Training methodsenum Methods {BATCH = 0,MINI_BATCH = 1};private:int store_model(const std::string& model) const;T calc_sigmoid(T x) const; // y = 1/(1+exp(-x))T norm(const std::vector<T>& v1, const std::vector<T>& v2) const;void batch_gradient_descent();void mini_batch_gradient_descent();void gradient_descent(const std::vector<std::vector<T>>& data_batch, const std::vector<T>& labels_batch, int length_batch);std::vector<std::vector<T>> data;std::vector<T> labels;int iterations = 1000;int train_num = 0; // train samples numint feature_length = 0;T learning_rate = 0.00001;std::vector<T> thetas; // coefficient//T epsilon = 0.000001; // termination conditionT lambda = (T)0.; // regularization methodint train_method = 0;int mini_batch_size = 1;};} // namespace ANN#endif // FBC_NN_LOGISTICREGRESSION_HPP_

logistic_regression.cpp:

#include "logistic_regression.hpp"#include <fstream>#include <algorithm>#include <functional>#include <numeric>#include "common.hpp"namespace ANN {template<typename T>int LogisticRegression<T>::init(const T* data, const T* labels, int train_num, int feature_length,int reg_kinds, T learning_rate, int iterations, int train_method, int mini_batch_size){if (train_num < 2) {fprintf(stderr, "logistic regression train samples num is too little: %d\n", train_num);return -1;}if (learning_rate <= 0) {fprintf(stderr, "learning rate must be greater 0: %f\n", learning_rate);return -1;}if (iterations <= 0) {fprintf(stderr, "number of iterations cannot be zero or a negative number: %d\n", iterations);return -1;}CHECK(reg_kinds == -1 || reg_kinds == 0);CHECK(train_method == 0 || train_method == 1);CHECK(mini_batch_size >= 1 && mini_batch_size < train_num);if (reg_kinds == REG_L1) this->lambda = (T)1.;if (train_method == MINI_BATCH) this->train_method = 1;this->mini_batch_size = mini_batch_size;this->learning_rate = learning_rate;this->iterations = iterations;this->train_num = train_num;this->feature_length = feature_length;this->data.resize(train_num);this->labels.resize(train_num);for (int i = 0; i < train_num; ++i) {const T* p = data + i * feature_length;this->data[i].resize(feature_length+1);this->data[i][0] = (T)1; // biasfor (int j = 0; j < feature_length; ++j) {this->data[i][j+1] = p[j];}this->labels[i] = labels[i];}this->thetas.resize(feature_length + 1, (T)0.); // bias + feature_lengthreturn 0;}template<typename T>int LogisticRegression<T>::train(const std::string& model){CHECK(data.size() == labels.size());if (train_method == BATCH) batch_gradient_descent();else mini_batch_gradient_descent();CHECK(store_model(model) == 0);return 0;}template<typename T>void LogisticRegression<T>::batch_gradient_descent(){for (int i = 0; i < iterations; ++i) {gradient_descent(data, labels, train_num);/*std::unique_ptr<T[]> z(new T[train_num]), gradient(new T[thetas.size()]);for (int j = 0; j < train_num; ++j) {z.get()[j] = (T)0.;for (int t = 0; t < feature_length + 1; ++t) {z.get()[j] += data[j][t] * thetas[t];}}std::unique_ptr<T[]> pcal_a(new T[train_num]), pcal_b(new T[train_num]), pcal_ab(new T[train_num]);for (int j = 0; j < train_num; ++j) {pcal_a.get()[j] = calc_sigmoid(z.get()[j]) - labels[j];pcal_b.get()[j] = data[j][0]; // biaspcal_ab.get()[j] = pcal_a.get()[j] * pcal_b.get()[j];}gradient.get()[0] = ((T)1. / train_num) * std::accumulate(pcal_ab.get(), pcal_ab.get() + train_num, (T)0.); // biasfor (int j = 1; j < thetas.size(); ++j) {for (int t = 0; t < train_num; ++t) {pcal_b.get()[t] = data[t][j];pcal_ab.get()[t] = pcal_a.get()[t] * pcal_b.get()[t];}gradient.get()[j] = ((T)1. / train_num) * std::accumulate(pcal_ab.get(), pcal_ab.get() + train_num, (T)0.) +(lambda / train_num) * thetas[j];}for (int i = 0; i < thetas.size(); ++i) {thetas[i] = thetas[i] - learning_rate / train_num * gradient.get()[i];}*/}}template<typename T>void LogisticRegression<T>::mini_batch_gradient_descent(){const int step = mini_batch_size;const int iter_batch = (train_num + step - 1) / step;for (int i = 0; i < iterations; ++i) {int pos{ 0 };for (int j = 0; j < iter_batch; ++j) {std::vector<std::vector<T>> data_batch;std::vector<T> labels_batch;int remainder{ 0 };if (pos + step < train_num) remainder = step;else remainder = train_num - pos;data_batch.resize(remainder);labels_batch.resize(remainder, (T)0.);for (int t = 0; t < remainder; ++t) {data_batch[t].resize(thetas.size(), (T)0.);for (int m = 0; m < thetas.size(); ++m) {data_batch[t][m] = data[pos + t][m];}labels_batch[t] = labels[pos + t];}gradient_descent(data_batch, labels_batch, remainder);pos += step;}}}template<typename T>void LogisticRegression<T>::gradient_descent(const std::vector<std::vector<T>>& data_batch, const std::vector<T>& labels_batch, int length_batch){std::unique_ptr<T[]> z(new T[length_batch]), gradient(new T[this->thetas.size()]);for (int j = 0; j < length_batch; ++j) {z.get()[j] = (T)0.;for (int t = 0; t < this->thetas.size(); ++t) {z.get()[j] += data_batch[j][t] * this->thetas[t];}}std::unique_ptr<T[]> pcal_a(new T[length_batch]), pcal_b(new T[length_batch]), pcal_ab(new T[length_batch]);for (int j = 0; j < length_batch; ++j) {pcal_a.get()[j] = calc_sigmoid(z.get()[j]) - labels_batch[j];pcal_b.get()[j] = data_batch[j][0]; // biaspcal_ab.get()[j] = pcal_a.get()[j] * pcal_b.get()[j];}gradient.get()[0] = ((T)1. / length_batch) * std::accumulate(pcal_ab.get(), pcal_ab.get() + length_batch, (T)0.); // biasfor (int j = 1; j < this->thetas.size(); ++j) {for (int t = 0; t < length_batch; ++t) {pcal_b.get()[t] = data_batch[t][j];pcal_ab.get()[t] = pcal_a.get()[t] * pcal_b.get()[t];}gradient.get()[j] = ((T)1. / length_batch) * std::accumulate(pcal_ab.get(), pcal_ab.get() + length_batch, (T)0.) +(this->lambda / length_batch) * this->thetas[j];}for (int i = 0; i < thetas.size(); ++i) {this->thetas[i] = this->thetas[i] - this->learning_rate / length_batch * gradient.get()[i];}}template<typename T>int LogisticRegression<T>::load_model(const std::string& model){std::ifstream file;file.open(model.c_str(), std::ios::binary);if (!file.is_open()) {fprintf(stderr, "open file fail: %s\n", model.c_str());return -1;}int length{ 0 };file.read((char*)&length, sizeof(length));thetas.resize(length);file.read((char*)thetas.data(), sizeof(T)*thetas.size());file.close();return 0;}template<typename T>T LogisticRegression<T>::predict(const T* data, int feature_length) const{CHECK(feature_length + 1 == thetas.size());T value{(T)0.};for (int t = 1; t < thetas.size(); ++t) {value += data[t - 1] * thetas[t];}return (calc_sigmoid(value + thetas[0]));}template<typename T>int LogisticRegression<T>::store_model(const std::string& model) const{std::ofstream file;file.open(model.c_str(), std::ios::binary);if (!file.is_open()) {fprintf(stderr, "open file fail: %s\n", model.c_str());return -1;}int length = thetas.size();file.write((char*)&length, sizeof(length));file.write((char*)thetas.data(), sizeof(T) * thetas.size());file.close();return 0;}template<typename T>T LogisticRegression<T>::calc_sigmoid(T x) const{return ((T)1 / ((T)1 + exp(-x)));}template<typename T>T LogisticRegression<T>::norm(const std::vector<T>& v1, const std::vector<T>& v2) const{CHECK(v1.size() == v2.size());T sum{ 0 };for (int i = 0; i < v1.size(); ++i) {T minus = v1[i] - v2[i];sum += (minus * minus);}return std::sqrt(sum);}template class LogisticRegression<float>;template class LogisticRegression<double>;} // namespace ANN

测试code:

#include "funset.hpp"#include <iostream>#include "perceptron.hpp"#include "BP.hpp""#include "CNN.hpp"#include "linear_regression.hpp"#include "naive_bayes_classifier.hpp"#include "logistic_regression.hpp"#include "common.hpp"#include <opencv2/opencv.hpp>// ================================ logistic regression =====================int test_logistic_regression_train(){const std::string image_path{ "E:/GitCode/NN_Test/data/images/digit/handwriting_0_and_1/" };cv::Mat data, labels;for (int i = 1; i < 11; ++i) {const std::vector<std::string> label{ "0_", "1_" };for (const auto& value : label) {std::string name = std::to_string(i);name = image_path + value + name + ".jpg";cv::Mat image = cv::imread(name, 0);if (image.empty()) {fprintf(stderr, "read image fail: %s\n", name.c_str());return -1;}data.push_back(image.reshape(0, 1));}}data.convertTo(data, CV_32F);std::unique_ptr<float[]> tmp(new float[20]);for (int i = 0; i < 20; ++i) {if (i % 2 == 0) tmp[i] = 0.f;else tmp[i] = 1.f;}labels = cv::Mat(20, 1, CV_32FC1, tmp.get());ANN::LogisticRegression<float> lr;const float learning_rate{ 0.00001f };const int iterations{ 250 };int reg_kinds = lr.REG_DISABLE; //ANN::LogisticRegression<float>::REG_L1;int train_method = lr.MINI_BATCH; //ANN::LogisticRegression<float>::BATCH;int mini_batch_size = 5;int ret = lr.init((float*)data.data, (float*)labels.data, data.rows, data.cols/*,reg_kinds, learning_rate, iterations, train_method, mini_batch_size*/);if (ret != 0) {fprintf(stderr, "logistic regression init fail: %d\n", ret);return -1;}const std::string model{ "E:/GitCode/NN_Test/data/logistic_regression.model" };ret = lr.train(model);if (ret != 0) {fprintf(stderr, "logistic regression train fail: %d\n", ret);return -1;}return 0;}int test_logistic_regression_predict(){const std::string image_path{ "E:/GitCode/NN_Test/data/images/digit/handwriting_0_and_1/" };cv::Mat data, labels, result;for (int i = 11; i < 21; ++i) {const std::vector<std::string> label{ "0_", "1_" };for (const auto& value : label) {std::string name = std::to_string(i);name = image_path + value + name + ".jpg";cv::Mat image = cv::imread(name, 0);if (image.empty()) {fprintf(stderr, "read image fail: %s\n", name.c_str());return -1;}data.push_back(image.reshape(0, 1));}}data.convertTo(data, CV_32F);std::unique_ptr<int[]> tmp(new int[20]);for (int i = 0; i < 20; ++i) {if (i % 2 == 0) tmp[i] = 0;else tmp[i] = 1;}labels = cv::Mat(20, 1, CV_32SC1, tmp.get());CHECK(data.rows == labels.rows);const std::string model{ "E:/GitCode/NN_Test/data/logistic_regression.model" };ANN::LogisticRegression<float> lr;int ret = lr.load_model(model);if (ret != 0) {fprintf(stderr, "load logistic regression model fail: %d\n", ret);return -1;}for (int i = 0; i < data.rows; ++i) {float probability = lr.predict((float*)(data.row(i).data), data.cols);fprintf(stdout, "probability: %.6f, ", probability);if (probability > 0.5) fprintf(stdout, "predict result: 1, ");else fprintf(stdout, "predict result: 0, ");fprintf(stdout, "actual result: %d\n", ((int*)(labels.row(i).data))[0]);}return 0;}

训练数据集为从MNIST(可以参考：http://blog.csdn.net/fengbingchun/article/details/49611549  )中train中随机选取的0、1各10个图像；测试数据集为从MNIST中test中随机选取的0、1各10个图像，如下图，其中第一排前10个0用于训练，后10个0用于测试；第二排前10个1用于训练，后10个1用于测试：

测试结果如下：

由执行结果可知：测试图像全部识别正确，并且与OpenCV3.3中结果一致。

GitHub：https://github.com/fengbingchun/NN_Test