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Upgrade liblinear to 2.47

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This commit is contained in:
dmiller
2024-02-28 18:18:35 +00:00
parent 1fc984bc73
commit 34e0769329
22 changed files with 2883 additions and 1054 deletions
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49
liblinear/predict.c
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@@ -5,6 +5,10 @@
#include <errno.h>
#include "linear.h"
int print_null(const char *s,...) {return 0;}
static int (*info)(const char *fmt,...) = &printf;
struct feature_node *x;
int max_nr_attr = 64;
@@ -23,7 +27,7 @@ static int max_line_len;
static char* readline(FILE *input)
{
int len;
if(fgets(line,max_line_len,input) == NULL)
return NULL;
@@ -38,10 +42,12 @@ static char* readline(FILE *input)
return line;
}
void do_predict(FILE *input, FILE *output, struct model* model_)
void do_predict(FILE *input, FILE *output)
{
int correct = 0;
int total = 0;
double error = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
int nr_class=get_nr_class(model_);
double *prob_estimates=NULL;
@@ -65,7 +71,7 @@ void do_predict(FILE *input, FILE *output, struct model* model_)
labels=(int *) malloc(nr_class*sizeof(int));
get_labels(model_,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
fprintf(output,"labels");
fprintf(output,"labels");
for(j=0;j<nr_class;j++)
fprintf(output," %d",labels[j]);
fprintf(output,"\n");
@@ -77,7 +83,7 @@ void do_predict(FILE *input, FILE *output, struct model* model_)
while(readline(input) != NULL)
{
int i = 0;
int target_label, predict_label;
double target_label, predict_label;
char *idx, *val, *label, *endptr;
int inst_max_index = 0; // strtol gives 0 if wrong format
@@ -85,13 +91,13 @@ void do_predict(FILE *input, FILE *output, struct model* model_)
if(label == NULL) // empty line
exit_input_error(total+1);
target_label = (int) strtol(label,&endptr,10);
target_label = strtod(label,&endptr);
if(endptr == label || *endptr != '\0')
exit_input_error(total+1);
while(1)
{
if(i>=max_nr_attr-2) // need one more for index = -1
if(i>=max_nr_attr-2) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct feature_node *) realloc(x,max_nr_attr*sizeof(struct feature_node));
@@ -131,7 +137,7 @@ void do_predict(FILE *input, FILE *output, struct model* model_)
{
int j;
predict_label = predict_probability(model_,x,prob_estimates);
fprintf(output,"%d",predict_label);
fprintf(output,"%g",predict_label);
for(j=0;j<model_->nr_class;j++)
fprintf(output," %g",prob_estimates[j]);
fprintf(output,"\n");
@@ -139,14 +145,29 @@ void do_predict(FILE *input, FILE *output, struct model* model_)
else
{
predict_label = predict(model_,x);
fprintf(output,"%d\n",predict_label);
fprintf(output,"%.17g\n",predict_label);
}
if(predict_label == target_label)
++correct;
error += (predict_label-target_label)*(predict_label-target_label);
sump += predict_label;
sumt += target_label;
sumpp += predict_label*predict_label;
sumtt += target_label*target_label;
sumpt += predict_label*target_label;
++total;
}
printf("Accuracy = %g%% (%d/%d)\n",(double) correct/total*100,correct,total);
if(check_regression_model(model_))
{
info("Mean squared error = %g (regression)\n",error/total);
info("Squared correlation coefficient = %g (regression)\n",
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
);
}
else
info("Accuracy = %g%% (%d/%d)\n",(double) correct/total*100,correct,total);
if(flag_predict_probability)
free(prob_estimates);
}
@@ -156,7 +177,8 @@ void exit_with_help()
printf(
"Usage: predict [options] test_file model_file output_file\n"
"options:\n"
"-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0)\n"
"-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only\n"
"-q : quiet mode (no outputs)\n"
);
exit(1);
}
@@ -176,7 +198,10 @@ int main(int argc, char **argv)
case 'b':
flag_predict_probability = atoi(argv[i]);
break;
case 'q':
info = &print_null;
i--;
break;
default:
fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]);
exit_with_help();
@@ -207,7 +232,7 @@ int main(int argc, char **argv)
}
x = (struct feature_node *) malloc(max_nr_attr*sizeof(struct feature_node));
do_predict(input, output, model_);
do_predict(input, output);
free_and_destroy_model(&model_);
free(line);
free(x);