Я пытаюсь написать классификацию программ с помощью svm в windows форме gui c ++. Но это превратилось в ошибку, и я не знаю, как ее решить. Вот программа, вызывающая ошибку:
predict(input, output);
svm_free_and_destroy_model(&model);
free(x);
free(row);
fclose(input);
fclose(output);
Ошибка говорит:
Ошибка 25, ошибка C2664: 'void svm_free_and_destroy_model (svm_model **)': невозможно преобразовать аргумент 1 из 'cli :: interior_ptr 'to' svm_model ** '
Вот полная программа:
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 *row;
static int max_line_len;
static char* readline(FILE *input)
{
int len;
if (fgets(row, max_line_len, input) == NULL)
return NULL;
while (strrchr(row, '\n') == NULL)
{
max_line_len *= 2;
row = (char *)realloc(row, max_line_len);
len = (int)strlen(row);
if (fgets(row + len, max_line_len - len, input) == NULL)
break;
}
return row;
}
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) //svm predict
{
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
{
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;
row = (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(baris, " \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))
{
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);
classification_s = 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;
}
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);
}
private: System::Void button10_Click(System::Object^ sender, System::EventArgs^ e) {
FILE *input, *output;
int i;
input = fopen("data-testing.txt", "r");
if (input == NULL)
{
//fprintf(stderr, "can't open input file %s\n", argv[i]);
exit(1);
}
output = fopen("output.txt", "w");
if (output == NULL)
{
//fprintf(stderr, "can't open output file %s\n", argv[i + 2]);
exit(1);
}
if ((model = svm_load_model("datatraining.model")) == 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(baris);
fclose(input);
fclose(output);
if (classification_s == 1)
textBox3->Text += "Middle " + Environment::NewLine;
else if (classification_s == 2)
textBox3->Text += "Big " + Environment::NewLine;
else
textBox3->Text += "Small " + Environment::NewLine;
А вот svm_free_and_destroy_model в svm.h:
void svm_free_and_destroy_model(svm_model** model_ptr_ptr)
{
if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL)
{
svm_free_model_content(*model_ptr_ptr);
free(*model_ptr_ptr);
*model_ptr_ptr = NULL;
}
}
И я не разобрался с указателем в этом случае и не знаю, как получить к нему доступ. Пожалуйста, помогите мне решить эту проблему. Мне это действительно нужно для моего последнего экзамена. Большое спасибо