C++实现AdaBoost算法(机器学习 Boost)

#include <cmath> #include <cstdio> #include <vector> using std::vector; #include "LBoost.h" /// @brief Boost分类规则 enum LBOOST_CLASSIFY_RULE { LARGER_SUN = 1, ///< 大于则分类为BOOST_SUN, 小于等于则分类为BOOST_MOON LARGER_MOON ///< 大于则分类为BOOST_MOON, 小于等于则分类为BOOST_SUN }; /// @brief Boost树桩分类器 /// /// 弱分类器 struct LBoostStump { unsigned int FeatureIndex; ///< 特征索引, 使用该特征索引对样本进行分类 float FeatureThreshold; ///< 特征阈值, 使用该特征阈值对样本进行分类 LBOOST_CLASSIFY_RULE ClassifyRule; ///< 分类规则 float Alpha; //< 该树桩的Alpha }; LBoostTree::LBoostTree() { this->m_featureNumber = 0; this->m_weakClassifierNum = 0; this->m_pWeakClassifierList = 0; this->m_maxWeakClassifierNum = 40; } LBoostTree::~LBoostTree() { if (this->m_pWeakClassifierList ) { delete[] m_pWeakClassifierList; m_pWeakClassifierList = 0; } } void LBoostTree::SetMaxClassifierNum(IN unsigned int num) { m_maxWeakClassifierNum = num; } bool LBoostTree::TrainModel(IN const LBoostProblem& problem) { if (problem.XMatrix.ColumnLen < 1) return false; if (problem.XMatrix.RowLen < 2) return false; if (problem.YVector.ColumnLen != 1) return false; if (problem.YVector.RowLen != problem.XMatrix.RowLen) return false; for (unsigned int i = 0; i < problem.YVector.RowLen; i++) { if (problem.YVector[i][0] != BOOST_SUN && problem.YVector[i][0] != BOOST_MOON) return false; } this->m_featureNumber = problem.XMatrix.ColumnLen; // 构造并且初始化权重向量(列向量) LBoostMatrix weightVector(problem.XMatrix.RowLen, 1); for (unsigned int i =0; i < weightVector.RowLen; i++) { weightVector[i][0] = 1.0f/(float)weightVector.RowLen; } // 构造累加类别向量(列向量)并且初始化 LBoostMatrix sumClassisVector(problem.XMatrix.RowLen, 1); for (unsigned int i = 0; i < sumClassisVector.RowLen; i++) { sumClassisVector[i][0] = 0.0f; } LBoostStump stump; LBoostMatrix classisVector; vector<LBoostStump> weakClassifierList; for (unsigned int i = 0; i < m_maxWeakClassifierNum; i++) { this->BuildStump(problem, weightVector, stump, classisVector); weakClassifierList.push_back(stump); // 使用alpha更新权重向量 float sumWeight = 0.0f; for (unsigned int m = 0; m < weightVector.RowLen; m++) { weightVector[m][0] = weightVector[m][0] * exp(-1 * stump.Alpha * classisVector[m][0] * problem.YVector[m][0]); sumWeight += weightVector[m][0]; } for (unsigned int m = 0; m < weightVector.RowLen; m++) { weightVector[m][0] = weightVector[m][0]/sumWeight; } // 计算累加类别向量 for (unsigned int m = 0; m < sumClassisVector.RowLen; m++) { sumClassisVector[m][0] += stump.Alpha * classisVector[m][0]; } // 计算累加错误率 int errorCount = 0; // 错误分类计数 for (unsigned int m = 0; m < sumClassisVector.RowLen; m++) { if (sumClassisVector[m][0] * problem.YVector[m][0] < 0) errorCount++; } float sumError = (float)errorCount/(float)sumClassisVector.RowLen; if (sumError == 0.0f) break; } if (weakClassifierList.size() > 0) { if (this->m_pWeakClassifierList != 0) { delete[] this->m_pWeakClassifierList; this->m_pWeakClassifierList = 0; } this->m_weakClassifierNum = weakClassifierList.size(); this->m_pWeakClassifierList = new LBoostStump[weakClassifierList.size()]; for (unsigned int i = 0; i < m_weakClassifierNum; i++) { this->m_pWeakClassifierList[i] = weakClassifierList[i]; } } return true; } float LBoostTree::Predict(IN const LBoostMatrix& sample) { if (sample.RowLen != 1) return 0.0f; LBoostMatrix classisVector(1, 1); bool bRet = this->Predict(sample, classisVector); if (!bRet) return 0.0f; return classisVector[0][0]; } bool LBoostTree::Predict(IN const LBoostMatrix& sampleMatrix, OUT LBoostMatrix& classisVector) { if (this->m_weakClassifierNum < 1) return false; if (sampleMatrix.RowLen < 1) return false; if (sampleMatrix.ColumnLen != this->m_featureNumber) return false; LBoostMatrix classisVectorTemp(sampleMatrix.RowLen, 1); LBoostMatrix sumClassisVector(sampleMatrix.RowLen, 1); for (unsigned int m = 0; m < sumClassisVector.RowLen; m++) { sumClassisVector[m][0] = 0.0f; } for (unsigned int i = 0; i < this->m_weakClassifierNum; i++) { LBoostStump& stump = this->m_pWeakClassifierList[i]; this->StumpClassify(sampleMatrix, stump, classisVectorTemp); for (unsigned int m = 0; m < classisVectorTemp.RowLen; m++) { sumClassisVector[m][0] += stump.Alpha * classisVectorTemp[m][0]; } } classisVector.Reset(sampleMatrix.RowLen, 1); for (unsigned int m = 0; m < sumClassisVector.RowLen; m++) { if (sumClassisVector[m][0] >= 0.0f) classisVector[m][0] = BOOST_SUN; else classisVector[m][0] = BOOST_MOON; } return true; } void LBoostTree::BuildStump( IN const LBoostProblem& problem, IN const LBoostMatrix& weightVector, OUT LBoostStump& stump, OUT LBoostMatrix& classisVector) { const float CLASSIFY_RIGHT = 0.0f; ///< 分类正确标记 const float CLASSIFY_ERROR = 1.0f; ///< 分类错误标记 const LBoostMatrix& X = problem.XMatrix; // 样本矩阵 const LBoostMatrix& Y = problem.YVector; // 标签矩阵 const unsigned int M = X.RowLen; // 样本数量 const unsigned int N = X.ColumnLen; // 样本特征数量 const int StepNum = 10; LBoostMatrix classisVectorTemp(M, 1); // 分类出的向量(列向量) LBoostMatrix errorVector(M, 1); // 错误向量(列向量), 矩阵行数为样本数目, 矩阵列为1, //被正确分类的样本对应在错误向量中会被标记为0.0f, 被错误分类的样本对应在错误向量中会被标记为1.0f LBoostStump stumpTemp; // 用于分类的树桩 LBoostMatrix weightErrorMatrix(1, 1); float minWeightError = 1.0f; // 最小权重错误率 LBoostStump bestStump; // 最好的树桩 LBoostMatrix bestClassisVector(M, 1); // 最好类别向量 // 对每一个特征 for (unsigned int n = 0; n < N; n++) { stumpTemp.FeatureIndex = n; float rangeMin = X[0][n]; // 所有样本中列i(特征)中的最小值 float rangeMax = X[0][n]; // 所有样本中列i(特征)中的最大值 for (unsigned int m = 0; m < M; m++) { if (X[m][n] < rangeMin) rangeMin = X[m][n]; if (X[m][n] > rangeMax) rangeMax = X[m][n]; } float stepSize = (rangeMax - rangeMin)/(float)StepNum; for (int k = -1; k <= StepNum + 1; k++) { stumpTemp.FeatureThreshold = rangeMin + k * stepSize; stumpTemp.ClassifyRule = LARGER_SUN; this->StumpClassify(X, stumpTemp, classisVectorTemp); for (unsigned int m = 0; m < M; m++) { if (classisVectorTemp[m][0] == Y[m][0]) errorVector[m][0] = CLASSIFY_RIGHT; else errorVector[m][0] = CLASSIFY_ERROR; } weightErrorMatrix = weightVector.T() * errorVector; float weightError = weightErrorMatrix[0][0]; if (weightError < minWeightError) { minWeightError = weightError; best