All/C_CPP/001/svm/svm-predict.c

#include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include "svm.h"

int print_null(const char *s, ...) { return 0; }

static int (*info)(const char *fmt, ...) = &printf;

struct svm_node *x;
int max_nr_attr = 64;

struct svm_model *model;
int predict_probability = 0;

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;
}

void exit_input_error(int line_num) {
    fprintf(stderr, "Wrong input format at line %d\n", line_num);
    exit(1);
}

void 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 svm_type = svm_get_svm_type(model);
    int nr_class = svm_get_nr_class(model);
    double *prob_estimates = NULL;
    int j;

    if (predict_probability) {
        if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
            info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",
                 svm_get_svr_probability(model));
        else if (svm_type == ONE_CLASS) {
            // nr_class = 2 for ONE_CLASS
            prob_estimates = (double *) malloc(nr_class * sizeof(double));
            fprintf(output, "label normal outlier\n");
        } else {
            int *labels = (int *) malloc(nr_class * sizeof(int));
            svm_get_labels(model, labels);
            prob_estimates = (double *) malloc(nr_class * sizeof(double));
            fprintf(output, "labels");
            for (j = 0; j < nr_class; j++)
                fprintf(output, " %d", labels[j]);
            fprintf(output, "\n");
            free(labels);
        }
    }

    max_line_len = 1024;
    line = (char *) malloc(max_line_len * sizeof(char));
    while (readline(input) != NULL) {
        int i = 0;
        double target_label, predict_label;
        char *idx, *val, *label, *endptr;
        int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0

        label = strtok(line, " \t\n");
        if (label == NULL) // empty line
            exit_input_error(total + 1);

        target_label = strtod(label, &endptr);
        if (endptr == label || *endptr != '\0')
            exit_input_error(total + 1);

        while (1) {
            if (i >= max_nr_attr - 1)    // need one more for index = -1
            {
                max_nr_attr *= 2;
                x = (struct svm_node *) realloc(x, max_nr_attr * sizeof(struct svm_node));
            }

            idx = strtok(NULL, ":");
            val = strtok(NULL, " \t");

            if (val == NULL)
                break;
            errno = 0;
            x[i].index = (int) strtol(idx, &endptr, 10);
            if (endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
                exit_input_error(total + 1);
            else
                inst_max_index = x[i].index;

            errno = 0;
            x[i].value = strtod(val, &endptr);
            if (endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
                exit_input_error(total + 1);

            ++i;
        }
        x[i].index = -1;

        if (predict_probability && (svm_type == C_SVC || svm_type == NU_SVC || svm_type == ONE_CLASS)) {
            predict_label = svm_predict_probability(model, x, prob_estimates);
            fprintf(output, "%g", predict_label);
            for (j = 0; j < nr_class; j++)
                fprintf(output, " %g", prob_estimates[j]);
            fprintf(output, "\n");
        } else {
            predict_label = svm_predict(model, x);
            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;
    }
    if (svm_type == NU_SVR || svm_type == EPSILON_SVR) {
        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) (classification)\n",
             (double) correct / total * 100, correct, total);
    if (predict_probability)
        free(prob_estimates);
}

void exit_with_help() {
    printf(
            "Usage: svm-predict [options] test_file model_file output_file\n"
            "options:\n"
            "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n"
            "-q : quiet mode (no outputs)\n"
    );
    exit(1);
}

int main(int argc, char **argv) {
    FILE *input, *output;
    int i;
    // parse options
    for (i = 1; i < argc; i++) {
        if (argv[i][0] != '-') break;
        ++i;
        switch (argv[i - 1][1]) {
            case 'b':
                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();
        }
    }

    if (i >= argc - 2)
        exit_with_help();

    input = fopen(argv[i], "r");
    if (input == NULL) {
        fprintf(stderr, "can't open input file %s\n", argv[i]);
        exit(1);
    }

    output = fopen(argv[i + 2], "w");
    if (output == NULL) {
        fprintf(stderr, "can't open output file %s\n", argv[i + 2]);
        exit(1);
    }

    if ((model = svm_load_model(argv[i + 1])) == 0) {
        fprintf(stderr, "can't open model file %s\n", argv[i + 1]);
        exit(1);
    }

    x = (struct svm_node *) malloc(max_nr_attr * sizeof(struct svm_node));
    if (predict_probability) {
        if (svm_check_probability_model(model) == 0) {
            fprintf(stderr, "Model does not support probabiliy estimates\n");
            exit(1);
        }
    } else {
        if (svm_check_probability_model(model) != 0)
            info("Model supports probability estimates, but disabled in prediction.\n");
    }

    predict(input, output);
    svm_free_and_destroy_model(&model);
    free(x);
    free(line);
    fclose(input);
    fclose(output);
    return 0;
}