New translations svm-train.c (Chinese Simplified)

lang/zh/gklearn/gedlib/lib/libsvm.3.22/svm-train.c

@@ -0,0 +1,380 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <ctype.h>
+#include <errno.h>
+#include "svm.h"
+#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
+
+void print_null(const char *s) {}
+
+void exit_with_help()
+{
+	printf(
+	"Usage: svm-train [options] training_set_file [model_file]\n"
+	"options:\n"
+	"-s svm_type : set type of SVM (default 0)\n"
+	"	0 -- C-SVC		(multi-class classification)\n"
+	"	1 -- nu-SVC		(multi-class classification)\n"
+	"	2 -- one-class SVM\n"
+	"	3 -- epsilon-SVR	(regression)\n"
+	"	4 -- nu-SVR		(regression)\n"
+	"-t kernel_type : set type of kernel function (default 2)\n"
+	"	0 -- linear: u'*v\n"
+	"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
+	"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
+	"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
+	"	4 -- precomputed kernel (kernel values in training_set_file)\n"
+	"-d degree : set degree in kernel function (default 3)\n"
+	"-g gamma : set gamma in kernel function (default 1/num_features)\n"
+	"-r coef0 : set coef0 in kernel function (default 0)\n"
+	"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
+	"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
+	"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
+	"-m cachesize : set cache memory size in MB (default 100)\n"
+	"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
+	"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
+	"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
+	"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
+	"-v n: n-fold cross validation mode\n"
+	"-q : quiet mode (no outputs)\n"
+	);
+	exit(1);
+}
+
+void exit_input_error(int line_num)
+{
+	fprintf(stderr,"Wrong input format at line %d\n", line_num);
+	exit(1);
+}
+
+void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
+void read_problem(const char *filename);
+void do_cross_validation();
+
+struct svm_parameter param;		// set by parse_command_line
+struct svm_problem prob;		// set by read_problem
+struct svm_model *model;
+struct svm_node *x_space;
+int cross_validation;
+int nr_fold;
+
+static char *line = NULL;
+static int max_line_len;
+
+static char* readline(FILE *input)
+{
+	int len;
+	
+	if(fgets(line,max_line_len,input) == NULL)
+		return NULL;
+
+	while(strrchr(line,'\n') == NULL)
+	{
+		max_line_len *= 2;
+		line = (char *) realloc(line,max_line_len);
+		len = (int) strlen(line);
+		if(fgets(line+len,max_line_len-len,input) == NULL)
+			break;
+	}
+	return line;
+}
+
+int main(int argc, char **argv)
+{
+	char input_file_name[1024];
+	char model_file_name[1024];
+	const char *error_msg;
+
+	parse_command_line(argc, argv, input_file_name, model_file_name);
+	read_problem(input_file_name);
+	error_msg = svm_check_parameter(&prob,&param);
+
+	if(error_msg)
+	{
+		fprintf(stderr,"ERROR: %s\n",error_msg);
+		exit(1);
+	}
+
+	if(cross_validation)
+	{
+		do_cross_validation();
+	}
+	else
+	{
+		model = svm_train(&prob,&param);
+		if(svm_save_model(model_file_name,model))
+		{
+			fprintf(stderr, "can't save model to file %s\n", model_file_name);
+			exit(1);
+		}
+		svm_free_and_destroy_model(&model);
+	}
+	svm_destroy_param(&param);
+	free(prob.y);
+	free(prob.x);
+	free(x_space);
+	free(line);
+
+	return 0;
+}
+
+void do_cross_validation()
+{
+	int i;
+	int total_correct = 0;
+	double total_error = 0;
+	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
+	double *target = Malloc(double,prob.l);
+
+	svm_cross_validation(&prob,&param,nr_fold,target);
+	if(param.svm_type == EPSILON_SVR ||
+	   param.svm_type == NU_SVR)
+	{
+		for(i=0;i<prob.l;i++)
+		{
+			double y = prob.y[i];
+			double v = target[i];
+			total_error += (v-y)*(v-y);
+			sumv += v;
+			sumy += y;
+			sumvv += v*v;
+			sumyy += y*y;
+			sumvy += v*y;
+		}
+		printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
+		printf("Cross Validation Squared correlation coefficient = %g\n",
+			((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
+			((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
+			);
+	}
+	else
+	{
+		for(i=0;i<prob.l;i++)
+			if(target[i] == prob.y[i])
+				++total_correct;
+		printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
+	}
+	free(target);
+}
+
+void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
+{
+	int i;
+	void (*print_func)(const char*) = NULL;	// default printing to stdout
+
+	// default values
+	param.svm_type = C_SVC;
+	param.kernel_type = RBF;
+	param.degree = 3;
+	param.gamma = 0;	// 1/num_features
+	param.coef0 = 0;
+	param.nu = 0.5;
+	param.cache_size = 100;
+	param.C = 1;
+	param.eps = 1e-3;
+	param.p = 0.1;
+	param.shrinking = 1;
+	param.probability = 0;
+	param.nr_weight = 0;
+	param.weight_label = NULL;
+	param.weight = NULL;
+	cross_validation = 0;
+
+	// parse options
+	for(i=1;i<argc;i++)
+	{
+		if(argv[i][0] != '-') break;
+		if(++i>=argc)
+			exit_with_help();
+		switch(argv[i-1][1])
+		{
+			case 's':
+				param.svm_type = atoi(argv[i]);
+				break;
+			case 't':
+				param.kernel_type = atoi(argv[i]);
+				break;
+			case 'd':
+				param.degree = atoi(argv[i]);
+				break;
+			case 'g':
+				param.gamma = atof(argv[i]);
+				break;
+			case 'r':
+				param.coef0 = atof(argv[i]);
+				break;
+			case 'n':
+				param.nu = atof(argv[i]);
+				break;
+			case 'm':
+				param.cache_size = atof(argv[i]);
+				break;
+			case 'c':
+				param.C = atof(argv[i]);
+				break;
+			case 'e':
+				param.eps = atof(argv[i]);
+				break;
+			case 'p':
+				param.p = atof(argv[i]);
+				break;
+			case 'h':
+				param.shrinking = atoi(argv[i]);
+				break;
+			case 'b':
+				param.probability = atoi(argv[i]);
+				break;
+			case 'q':
+				print_func = &print_null;
+				i--;
+				break;
+			case 'v':
+				cross_validation = 1;
+				nr_fold = atoi(argv[i]);
+				if(nr_fold < 2)
+				{
+					fprintf(stderr,"n-fold cross validation: n must >= 2\n");
+					exit_with_help();
+				}
+				break;
+			case 'w':
+				++param.nr_weight;
+				param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
+				param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
+				param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
+				param.weight[param.nr_weight-1] = atof(argv[i]);
+				break;
+			default:
+				fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
+				exit_with_help();
+		}
+	}
+
+	svm_set_print_string_function(print_func);
+
+	// determine filenames
+
+	if(i>=argc)
+		exit_with_help();
+
+	strcpy(input_file_name, argv[i]);
+
+	if(i<argc-1)
+		strcpy(model_file_name,argv[i+1]);
+	else
+	{
+		char *p = strrchr(argv[i],'/');
+		if(p==NULL)
+			p = argv[i];
+		else
+			++p;
+		sprintf(model_file_name,"%s.model",p);
+	}
+}
+
+// read in a problem (in svmlight format)
+
+void read_problem(const char *filename)
+{
+	int max_index, inst_max_index, i;
+	size_t elements, j;
+	FILE *fp = fopen(filename,"r");
+	char *endptr;
+	char *idx, *val, *label;
+
+	if(fp == NULL)
+	{
+		fprintf(stderr,"can't open input file %s\n",filename);
+		exit(1);
+	}
+
+	prob.l = 0;
+	elements = 0;
+
+	max_line_len = 1024;
+	line = Malloc(char,max_line_len);
+	while(readline(fp)!=NULL)
+	{
+		char *p = strtok(line," \t"); // label
+
+		// features
+		while(1)
+		{
+			p = strtok(NULL," \t");
+			if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
+				break;
+			++elements;
+		}
+		++elements;
+		++prob.l;
+	}
+	rewind(fp);
+
+	prob.y = Malloc(double,prob.l);
+	prob.x = Malloc(struct svm_node *,prob.l);
+	x_space = Malloc(struct svm_node,elements);
+
+	max_index = 0;
+	j=0;
+	for(i=0;i<prob.l;i++)
+	{
+		inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
+		readline(fp);
+		prob.x[i] = &x_space[j];
+		label = strtok(line," \t\n");
+		if(label == NULL) // empty line
+			exit_input_error(i+1);
+
+		prob.y[i] = strtod(label,&endptr);
+		if(endptr == label || *endptr != '\0')
+			exit_input_error(i+1);
+
+		while(1)
+		{
+			idx = strtok(NULL,":");
+			val = strtok(NULL," \t");
+
+			if(val == NULL)
+				break;
+
+			errno = 0;
+			x_space[j].index = (int) strtol(idx,&endptr,10);
+			if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
+				exit_input_error(i+1);
+			else
+				inst_max_index = x_space[j].index;
+
+			errno = 0;
+			x_space[j].value = strtod(val,&endptr);
+			if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
+				exit_input_error(i+1);
+
+			++j;
+		}
+
+		if(inst_max_index > max_index)
+			max_index = inst_max_index;
+		x_space[j++].index = -1;
+	}
+
+	if(param.gamma == 0 && max_index > 0)
+		param.gamma = 1.0/max_index;
+
+	if(param.kernel_type == PRECOMPUTED)
+		for(i=0;i<prob.l;i++)
+		{
+			if (prob.x[i][0].index != 0)
+			{
+				fprintf(stderr,"Wrong input format: first column must be 0:sample_serial_number\n");
+				exit(1);
+			}
+			if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
+			{
+				fprintf(stderr,"Wrong input format: sample_serial_number out of range\n");
+				exit(1);
+			}
+		}
+
+	fclose(fp);
+}