int main(int argc, char* argv[]) {
//输入测参数至少包括程序本身的名字、feature_file、label_file、regWeight（coefficient of l1 regularizer）、output_file五个参数
//output_file中存的是结果参数值向量
//如果输入的参数少于5个或者第一个参数中包含help字符，则打印帮助并退出
if (argc < 5 || !strcmp(argv[1], "-help") || !strcmp(argv[1], "--help") ||
!strcmp(argv[1], "-h") || !strcmp(argv[1], "-usage")) {
printUsageAndExit();
}
//读入feature_file、label_file、regWeight（coefficient of l1 regularizer）、output_file
const char* feature_file = argv[1];
const char* label_file = argv[2];
double regweight = atof(argv[3]);//l1正则化项
const char* output_file = argv[4];
if (regweight < 0) {
cout << "L1 regularization weight must be non-negative." << endl;
exit(1);
}
//给出默认值
bool leastSquares = false, quiet = false;
double tol = 1e-4, l2weight = 0;
int m = 10;
//对于可选的配置信息
for (int i=5; i<argc; i++) {
if (!strcmp(argv[i], "-ls")) leastSquares = true; //判断是否使用least square
else if (!strcmp(argv[i], "-q")) quiet = true; //判断是否静默输出
else if (!strcmp(argv[i], "-tol")) {
//读取tolerance
++i;
if (i >= argc || (tol = atof(argv[i])) <= 0) {
cout << "-tol (convergence tolerance) flag requires 1 positive real argument." << endl;
exit(1);
}
} else if (!strcmp(argv[i], "-l2weight")) {
//读取l2正则化项的权重
++i;
if (i >= argc || (l2weight = atof(argv[i])) < 0) {
cout << "-l2weight flag requires 1 non-negative real argument." << endl;
exit(1);
}
} else if (!strcmp(argv[i], "-m")) {
//读取记忆项的个数
++i;
if (i >= argc || (m = atoi(argv[i])) == 0) {
cout << "-m (L-BFGS memory param) flag requires 1 positive int argument." << endl;
exit(1);
}
} else {
cerr << "unrecognized argument: " << argv[i] << endl;
exit(1);
}
}
if (!quiet) {
cout << argv[0] << " called with arguments " << endl << " ";
for (int i=1; i<argc; i++) {
cout << argv[i] << " ";
}
cout << endl;
}
DifferentiableFunction *obj;
size_t size;
if (leastSquares) {
LeastSquaresProblem *prob = new LeastSquaresProblem(feature_file, label_file);
obj = new LeastSquaresObjective(*prob, l2weight);
size = prob->NumFeats();
} else {
//将数据导入到逻辑回归问题中
LogisticRegressionProblem *prob = new LogisticRegressionProblem(feature_file, label_file);
obj = new LogisticRegressionObjective(*prob, l2weight);
size = prob->NumFeats();
}
//size为特征的维度，init为初始参数值向量，ans为结果参数值向量
DblVec init(size), ans(size);
OWLQN opt(quiet);
//输入依次是LogisticRegressionObjective（包含了样本数据、l2正则化项的系数、损失函数）、
//参数的初始化值、参数最终的结果、l1正则化项的系数、允许的误差、lbfgs的记忆的项数
opt.Minimize(*obj, init, ans, regweight, tol, m);
int nonZero = 0;
for (size_t i = 0; i<ans.size(); i++) {
if (ans[i] != 0) nonZero++;
}
if (!quiet) cout << "Finished with optimization. " << nonZero << "/" << size << " non-zero weights." << endl;
printVector(ans, output_file);
return 0;
}
int main(int argc, char* argv[]) {
if (argc < 5 || !strcmp(argv[1], "-help") || !strcmp(argv[1], "--help") ||
!strcmp(argv[1], "-h") || !strcmp(argv[1], "-usage")) {
printUsageAndExit();
}
const char* feature_file = argv[1];
const char* label_file = argv[2];
double regweight = atof(argv[3]);
const char* output_file = argv[4];
if (regweight < 0) {
cout << "L1 regularization weight must be non-negative." << endl;
exit(1);
}
bool leastSquares = false, quiet = false;
double tol = 1e-4, l2weight = 0;
int m = 10;
for (int i=5; i<argc; i++) {
if (!strcmp(argv[i], "-ls")) leastSquares = true;
else if (!strcmp(argv[i], "-q")) quiet = true;
else if (!strcmp(argv[i], "-tol")) {
++i;
if (i >= argc || (tol = atof(argv[i])) <= 0) {
cout << "-tol (convergence tolerance) flag requires 1 positive real argument." << endl;
exit(1);
}
} else if (!strcmp(argv[i], "-l2weight")) {
++i;
if (i >= argc || (l2weight = atof(argv[i])) < 0) {
cout << "-l2weight flag requires 1 non-negative real argument." << endl;
exit(1);
}
} else if (!strcmp(argv[i], "-m")) {
++i;
if (i >= argc || (m = atoi(argv[i])) == 0) {
cout << "-m (L-BFGS memory param) flag requires 1 positive int argument." << endl;
exit(1);
}
} else {
cerr << "unrecognized argument: " << argv[i] << endl;
exit(1);
}
}
if (!quiet) {
cout << argv[0] << " called with arguments " << endl << " ";
for (int i=1; i<argc; i++) {
cout << argv[i] << " ";
}
cout << endl;
}
DifferentiableFunction *obj;
size_t size;
if (leastSquares) {
LeastSquaresProblem *prob = new LeastSquaresProblem(feature_file, label_file);
obj = new LeastSquaresObjective(*prob, l2weight);
size = prob->NumFeats();
} else {
LogisticRegressionProblem *prob = new LogisticRegressionProblem(feature_file, label_file);
obj = new LogisticRegressionObjective(*prob, l2weight);
size = prob->NumFeats();
}
DblVec init(size), ans(size);
OWLQN opt(quiet);
opt.Minimize(*obj, init, ans, regweight, tol, m);
int nonZero = 0;
for (size_t i = 0; i<ans.size(); i++) {
if (ans[i] != 0) nonZero++;
}
if (!quiet) cout << "Finished with optimization. " << nonZero << "/" << size << " non-zero weights." << endl;
printVector(ans, output_file);
return 0;
}