Algorithm-LibLinear

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src/liblinear/linear.cpp  view on Meta::CPAN

				// primal_solver_tol obtained from eps for dual may be too loose
				primal_solver_tol *= 0.001;
				l2r_l2_svr_fun fun_obj(prob, param, C);
				NEWTON newton_obj(&fun_obj, primal_solver_tol);
				newton_obj.set_print_string(liblinear_print_string);
				newton_obj.newton(w);
			}
			break;
		}
		default:
			fprintf(stderr, "ERROR: unknown solver_type\n");
			break;
	}

	delete[] C;
}

// Calculate the initial C for parameter selection
static double calc_start_C(const problem *prob, const parameter *param)
{
	int i;

src/liblinear/linear.cpp  view on Meta::CPAN

		// calculate weighted C
		double *weighted_C = Malloc(double, nr_class);
		for(i=0;i<nr_class;i++)
			weighted_C[i] = param->C;
		for(i=0;i<param->nr_weight;i++)
		{
			for(j=0;j<nr_class;j++)
				if(param->weight_label[i] == label[j])
					break;
			if(j == nr_class)
				fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
			else
				weighted_C[j] *= param->weight[i];
		}

		// constructing the subproblem
		feature_node **x = Malloc(feature_node *,l);
		for(i=0;i<l;i++)
			x[i] = prob->x[perm[i]];

		int k;

src/liblinear/linear.cpp  view on Meta::CPAN

void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target)
{
	int i;
	int *fold_start;
	int l = prob->l;
	int *perm = Malloc(int,l);
	if (nr_fold > l)
	{
		nr_fold = l;
		fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n");
	}
	fold_start = Malloc(int,nr_fold+1);
	for(i=0;i<l;i++) perm[i]=i;
	for(i=0;i<l;i++)
	{
		int j = i+rand()%(l-i);
		swap(perm[i],perm[j]);
	}
	for(i=0;i<=nr_fold;i++)
		fold_start[i]=i*l/nr_fold;

src/liblinear/linear.cpp  view on Meta::CPAN

	int i;
	int *fold_start;
	int l = prob->l;
	int *perm = Malloc(int, l);
	struct problem *subprob = Malloc(problem,nr_fold);

	if (nr_fold > l)
	{
		nr_fold = l;
		fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n");
	}
	fold_start = Malloc(int,nr_fold+1);
	for(i=0;i<l;i++) perm[i]=i;
	for(i=0;i<l;i++)
	{
		int j = i+rand()%(l-i);
		swap(perm[i],perm[j]);
	}
	for(i=0;i<=nr_fold;i++)
		fold_start[i]=i*l/nr_fold;

src/liblinear/linear.cpp  view on Meta::CPAN

// FSCANF helps to handle fscanf failures.
// Its do-while block avoids the ambiguity when
// if (...)
//    FSCANF();
// is used
//
#define FSCANF(_stream, _format, _var)do\
{\
	if (fscanf(_stream, _format, _var) != 1)\
	{\
		fprintf(stderr, "ERROR: fscanf failed to read the model\n");\
		EXIT_LOAD_MODEL()\
	}\
}while(0)
// EXIT_LOAD_MODEL should NOT end with a semicolon.
#define EXIT_LOAD_MODEL()\
{\
	setlocale(LC_ALL, old_locale);\
	free(model_->label);\
	free(model_);\
	free(old_locale);\

src/liblinear/linear.cpp  view on Meta::CPAN

			for(i=0;solver_type_table[i];i++)
			{
				if(strcmp(solver_type_table[i],cmd)==0)
				{
					param.solver_type=i;
					break;
				}
			}
			if(solver_type_table[i] == NULL)
			{
				fprintf(stderr,"unknown solver type.\n");
				EXIT_LOAD_MODEL()
			}
		}
		else if(strcmp(cmd,"nr_class")==0)
		{
			FSCANF(fp,"%d",&nr_class);
			model_->nr_class=nr_class;
		}
		else if(strcmp(cmd,"nr_feature")==0)
		{

src/liblinear/linear.cpp  view on Meta::CPAN

		}
		else if(strcmp(cmd,"label")==0)
		{
			int nr_class = model_->nr_class;
			model_->label = Malloc(int,nr_class);
			for(int i=0;i<nr_class;i++)
				FSCANF(fp,"%d",&model_->label[i]);
		}
		else
		{
			fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
			EXIT_LOAD_MODEL()
		}
	}

	nr_feature=model_->nr_feature;
	if(model_->bias>=0)
		n=nr_feature+1;
	else
		n=nr_feature;
	int w_size = n;

src/liblinear/linear.cpp  view on Meta::CPAN

{
	if(feat_idx > model_->nr_feature)
		return 0;
	return get_w_value(model_, feat_idx-1, label_idx);
}

double get_decfun_bias(const struct model *model_, int label_idx)
{
	if(check_oneclass_model(model_))
	{
		fprintf(stderr, "ERROR: get_decfun_bias can not be called for a one-class SVM model\n");
		return 0;
	}
	int bias_idx = model_->nr_feature;
	double bias = model_->bias;
	if(bias <= 0)
		return 0;
	else
		return bias*get_w_value(model_, bias_idx, label_idx);
}

double get_decfun_rho(const struct model *model_)
{
	if(check_oneclass_model(model_))
		return model_->rho;
	else
	{
		fprintf(stderr, "ERROR: get_decfun_rho can be called only for a one-class SVM model\n");
		return 0;
	}
}

void free_model_content(struct model *model_ptr)
{
	free(model_ptr->w);
	model_ptr->w = NULL;
	free(model_ptr->label);
	model_ptr->label = NULL;