Modify linear.cpp so that it is consistent to /poly2/linear.cpp

diff --git a/linear.cpp b/linear.cpp
index 4810da241eef9d08398967c797d058c40a074935..55479741f6ef09d010c7bdd3b5fa4015d9f1d2ad 100644 (file)
--- a/linear.cpp
+++ b/linear.cpp
@@ -100,7 +100,7 @@ double l2_lr_fun::fun(double *w)
        double f=0;
        int *y=prob->y;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();
 
        Xv(w, z);
        for(i=0;i<l;i++)
@@ -112,7 +112,7 @@ double l2_lr_fun::fun(double *w)
                        f += C[i]*(-yz+log(1 + exp(yz)));
        }
        f = 2*f;
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                f += w[i]*w[i];
        f /= 2.0;
 
@@ -124,7 +124,7 @@ void l2_lr_fun::grad(double *w, double *g)
        int i;
        int *y=prob->y;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();
 
        for(i=0;i<l;i++)
        {
@@ -134,7 +134,7 @@ void l2_lr_fun::grad(double *w, double *g)
        }
        XTv(z, g);
 
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                g[i] = w[i] + g[i];
 }
 
@@ -147,7 +147,7 @@ void l2_lr_fun::Hv(double *s, double *Hs)
 {
        int i;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();
        double *wa = new double[l];
 
        Xv(s, wa);
@@ -155,7 +155,7 @@ void l2_lr_fun::Hv(double *s, double *Hs)
                wa[i] = C[i]*D[i]*wa[i];
 
        XTv(wa, Hs);
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                Hs[i] = s[i] + Hs[i];
        delete[] wa;
 }
@@ -182,10 +182,10 @@ void l2_lr_fun::XTv(double *v, double *XTv)
 {
        int i;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();
        feature_node **x=prob->x;
 
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                XTv[i]=0;
        for(i=0;i<l;i++)
        {
@@ -259,7 +259,7 @@ double l2loss_svm_fun::fun(double *w)
        double f=0;
        int *y=prob->y;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();   
 
        Xv(w, z);
        for(i=0;i<l;i++)
@@ -270,7 +270,7 @@ double l2loss_svm_fun::fun(double *w)
                        f += C[i]*d*d;
        }
        f = 2*f;
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                f += w[i]*w[i];
        f /= 2.0;
 
@@ -282,7 +282,7 @@ void l2loss_svm_fun::grad(double *w, double *g)
        int i;
        int *y=prob->y;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();   
 
        sizeI = 0;
        for (i=0;i<l;i++)
@@ -294,7 +294,7 @@ void l2loss_svm_fun::grad(double *w, double *g)
                }
        subXTv(z, g);
 
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                g[i] = w[i] + 2*g[i];
 }
 
@@ -307,7 +307,7 @@ void l2loss_svm_fun::Hv(double *s, double *Hs)
 {
        int i;
        int l=prob->l;
-       int n=prob->n;
+       int w_size=get_nr_variable();
        double *wa = new double[l];
 
        subXv(s, wa);
@@ -315,7 +315,7 @@ void l2loss_svm_fun::Hv(double *s, double *Hs)
                wa[i] = C[I[i]]*wa[i];
 
        subXTv(wa, Hs);
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                Hs[i] = s[i] + 2*Hs[i];
        delete[] wa;
 }
@@ -358,10 +358,10 @@ void l2loss_svm_fun::subXv(double *v, double *Xv)
 void l2loss_svm_fun::subXTv(double *v, double *XTv)
 {
        int i;
-       int n=prob->n;
+       int w_size=get_nr_variable();
        feature_node **x=prob->x;
 
-       for(i=0;i<n;i++)
+       for(i=0;i<w_size;i++)
                XTv[i]=0;
        for(i=0;i<sizeI;i++)
        {
@@ -401,7 +401,7 @@ class Solver_MCSVM_CS
                void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new);
                bool be_shrunk(int m, int yi, double alpha_i, double minG);
                double *B, *C, *G;
-               int n, l;
+               int w_size, l;
                int nr_class;
                int max_iter;
                double eps;
@@ -410,7 +410,7 @@ class Solver_MCSVM_CS
 
 Solver_MCSVM_CS::Solver_MCSVM_CS(const problem *prob, int nr_class, double *C, double eps, int max_iter)
 {
-       this->n = prob->n;
+       this->w_size = prob->n;
        this->l = prob->l;
        this->nr_class = nr_class;
        this->eps = eps;
@@ -490,7 +490,7 @@ void Solver_MCSVM_CS::Solve(double *w)
        // initial
        for(i=0;i<l*nr_class;i++)
                alpha[i] = 0;
-       for(i=0;i<n*nr_class;i++)
+       for(i=0;i<w_size*nr_class;i++)
                w[i] = 0; 
        for(i=0;i<l;i++)
        {
@@ -647,7 +647,7 @@ void Solver_MCSVM_CS::Solve(double *w)
        // calculate objective value
        double v = 0;
        int nSV = 0;
-       for(i=0;i<n*nr_class;i++)
+       for(i=0;i<w_size*nr_class;i++)
                v += w[i]*w[i];
        v = 0.5*v;
        for(i=0;i<l*nr_class;i++)
@@ -701,7 +701,7 @@ static void solve_linear_c_svc(
        double Cp, double Cn, int solver_type)
 {
        int l = prob->l;
-       int n = prob->n;
+       int w_size = prob->n;
        int i, s, iter = 0;
        double C, d, G;
        double *QD = new double[l];
@@ -726,7 +726,7 @@ static void solve_linear_c_svc(
                upper_bound_p = Cp; upper_bound_n = Cn;
        }
 
-       for(i=0; i<n; i++)
+       for(i=0; i<w_size; i++)
                w[i] = 0;
        for(i=0; i<l; i++)
        {
@@ -867,7 +867,7 @@ static void solve_linear_c_svc(
 
        double v = 0;
        int nSV = 0;
-       for(i=0; i<n; i++)
+       for(i=0; i<w_size; i++)
                v += w[i]*w[i];
        for(i=0; i<l; i++)
        {
@@ -997,6 +997,7 @@ model* train(const problem *prob, const parameter *param)
        int i,j;
        int l = prob->l;
        int n = prob->n;
+       int w_size = prob->n;
        model *model_ = Malloc(model,1);
 
        if(prob->bias>=0)
@@ -1064,7 +1065,7 @@ model* train(const problem *prob, const parameter *param)
        {
                if(nr_class == 2)
                {
-                       model_->w=Malloc(double, n);
+                       model_->w=Malloc(double, w_size);
 
                        int e0 = start[0]+count[0];
                        k=0;
@@ -1077,8 +1078,8 @@ model* train(const problem *prob, const parameter *param)
                }
                else
                {
-                       model_->w=Malloc(double, n*nr_class);
-                       double *w=Malloc(double, n);
+                       model_->w=Malloc(double, w_size*nr_class);
+                       double *w=Malloc(double, w_size);
                        for(i=0;i<nr_class;i++)
                        {
                                int si = start[i];
@@ -1094,7 +1095,7 @@ model* train(const problem *prob, const parameter *param)
 
                                train_one(&sub_prob, param, w, weighted_C[i], param->C);
 
-                               for(int j=0;j<n;j++)
+                               for(int j=0;j<w_size;j++)
                                        model_->w[j*nr_class+i] = w[j];
                        }
                        free(w);
@@ -1138,6 +1139,7 @@ int save_model(const char *model_file_name, const struct model *model_)
                n=nr_feature+1;
        else
                n=nr_feature;
+       int w_size = n;
        FILE *fp = fopen(model_file_name,"w");
        if(fp==NULL) return -1;
 
@@ -1159,7 +1161,7 @@ int save_model(const char *model_file_name, const struct model *model_)
        fprintf(fp, "bias %.16g\n", model_->bias);
 
        fprintf(fp, "w\n");
-       for(i=0; i<n; i++)
+       for(i=0; i<w_size; i++)
        {
                int j;
                for(j=0; j<nr_w; j++)
@@ -1249,15 +1251,15 @@ struct model *load_model(const char *model_file_name)
                n=nr_feature+1;
        else
                n=nr_feature;
-
+       int w_size = n;
        int nr_w;
        if(nr_class==2 && param.solver_type != MCSVM_CS)
                nr_w = 1;
        else
                nr_w = nr_class;
 
-       model_->w=Malloc(double, n*nr_w);
-       for(i=0; i<n; i++)
+       model_->w=Malloc(double, w_size*nr_w);
+       for(i=0; i<w_size; i++)
        {
                int j;
                for(j=0; j<nr_w; j++)