int main() {
size_t i, j;
size_t nencode = 3, ninput = pow(2, nencode); //Set the number of vectors
size_t iter_times = 50000; // Complex network requires more iteration times
double rate = 0.5, momentum = 0.5; // Learning parameters;
size_t hidden[1] = { nencode };
char *ch = "sigmoid";
char *chs[1] = {ch};
double **input = alloc_2d(ninput, ninput), **output = alloc_2d(ninput, ninput);
for (i = 0; i < ninput; ++i) {
for (j = 0; j < ninput; ++j) {
if (i == j)
input[i][j] = output[i][j] = 1;
else
input[i][j] = output[i][j] = 0;
}
}
/* Set the configuration of neural network */
struct nn_config config = {
.dim_h = hidden,
.ahfunc = chs,
.n_hidden = 1,
.dim_i = ninput,
.dim_o = ninput,
.aofunc = ch
};
struct neural_network* nn = create_nn(&config);
train(nn, input, output, ninput, rate, momentum, iter_times);
double *res = alloc_1d(ninput);
for (i = 0; i < ninput; ++i) {
for (j = 0; j < ninput; ++j)
printf("%d ", (int)input[i][j]);
printf("=> ");
predict_hidden(nn, input[i], res, 0);
for (j = 0; j < nencode; ++j) {
printf("%.3f ", res[j]); // Actually value
// printf("%d ", (int)(res[j]+0.5)); // 0-1 value
}
predict(nn, input[i], res);
printf("=> ");
for (j = 0; j < ninput; ++j)
printf("%.3f ", res[j]);
printf("\n");
}
free_2d(input, ninput);
free_2d(output, ninput);
free_1d(res);
destroy_nn(nn);
return 0;
}
int main()
{
int i, j, m=4, n=5;
int **p = alloc_2d(m, n);
for (i=0; i<m; i++)
for (j=0; j<n; j++)
p[i][j] = i+j;
for (i=0; i<m; i++) {
for (j=0; j<n; j++)
printf("(%d,%d)=%d ", i, j, p[i][j]);
printf("\n");
}
free(p);
return 0;
}
/*Builds the CSF filter banks for each channel and contrast level.*/
static void build_csf_tables(csf_filter_bank _filters[5][8]){
csf_ctx csf_ctxs[5]={
{
(csf_func)csfl,
&Y_CTX,
FREQUENCY_MAX,
3.74695975597140362
},
{
(csf_func)csfc,
&CR_CTX,
FREQUENCY_MAX,
0
},
{
(csf_func)csfc,
&CR_CTX,
0.5*FREQUENCY_MAX,
0
},
{
(csf_func)csfc,
&CB_CTX,
FREQUENCY_MAX,
0
},
{
(csf_func)csfc,
&CB_CTX,
0.5*FREQUENCY_MAX,
0
}
};
double **scratch;
int chi;
int ci;
/*Allocate the scratch space needed for our least-squares problem.*/
scratch=(double **)alloc_2d(NFREQS*NPHASES*8,NTAPS+1,sizeof(double));
for(chi=0;chi<5;chi++){
for(ci=0;ci<8;ci++){
csf_ctxs[chi].contrast=pow(CONTRASTS[ci]*CONTRAST_SCALE,1.0/CONTRAST_EXP);
/*Find the normalization constant for this contrast level.*/
csf_ctxs[chi].scale=csf_ctxs[chi].contrast+
1/csf_ctxs[chi].c0csf(csf_ctxs[chi].c0csf_ctx,csf_ctxs[chi].peakf);
find_csf_filters(_filters[chi][ci],(csf_func)csf,csf_ctxs+chi,scratch);
}
}
}