//compute the central point of each filterbank on mel-scale
//for M filterbanks, the central point shoule be extended to M+2
int* MelCentralPoint(int Fl, int Fh, int M,
int Fs, int N)
{
int m;
double F;
int *CentralPoint=new int[M+2];
for(m=0 ; m<=M+1 ; m++)
{
//central frequency 中心频率，高-低加低，乘以m数/点数
F=b2f(f2b(Fl)+(f2b(Fh)-f2b(Fl))*m/(M+1));
//central point
CentralPoint[m]=f2n(F, Fs, N);
}
return CentralPoint;
}
void main(void)
{
f0(D, I, J);
s1 = f1(D, I, D);
f2a(G510);
f2b(G511);
}
void classic_BP_test()
{
int neuronsOfLayer[] = {2, 10, 9, 1}; // first = input layer, last = output layer
int numLayers = sizeof (neuronsOfLayer) / sizeof (int);
NNET *Net = create_NN(numLayers, neuronsOfLayer);
LAYER lastLayer = Net->layers[numLayers - 1];
double errors[dim_K];
int userKey = 0;
#define M 50 // how many errors to record for averaging
double errors1[M], errors2[M]; // two arrays for recording errors
double sum_err1 = 0.0, sum_err2 = 0.0; // sums of errors
int tail = 0; // index for cyclic arrays (last-in, first-out)
for (int i = 0; i < M; ++i) // clear errors to 0.0
errors1[i] = errors2[i] = 0.0;
// start_NN_plot();
start_W_plot();
// start_K_plot();
start_output_plot();
start_LogErr_plot();
// plot_ideal();
printf("Press 'Q' to quit\n\n");
start_timer();
char status[1024], *s;
for (int i = 1; 1; ++i)
{
s = status + sprintf(status, "[%05d] ", i);
// Create random K vector
for (int k = 0; k < 2; ++k)
K[k] = (rand() / (float) RAND_MAX);
// printf("*** K = <%lf, %lf>\n", K[0], K[1]);
// if ((i % 4) == 0)
// K[0] = 1.0, K[1] = 0.0;
// if ((i % 4) == 1)
// K[0] = 0.0, K[1] = 0.0;
// if ((i % 4) == 2)
// K[0] = 0.0, K[1] = 1.0;
// if ((i % 4) == 3)
// K[0] = 1.0, K[1] = 1.0;
ForwardPropMethod(Net, 2, K); // dim K = 2
// Desired value = K_star
double training_err = 0.0;
for (int k = 0; k < 1; ++k) // output has only 1 component
{
// double ideal = K[k]; /* identity function */
#define f2b(x) (x > 0.5f ? 1 : 0) // convert float to binary
// ^ = binary XOR
double ideal = ((double) (f2b(K[0]) ^ f2b(K[1]))); // ^ f2b(K[2]) ^ f2b(K[3])))
// #define Ideal ((double) (f2b(K[k]) ^ f2b(K[2]) ^ f2b(K[3])))
// double ideal = 1.0f - (0.5f - K[0]) * (0.5f - K[1]);
// printf("*** ideal = %lf\n", ideal);
// Difference between actual outcome and desired value:
double error = ideal - lastLayer.neurons[k].output;
errors[k] = error; // record this for back-prop
training_err += fabs(error); // record sum of errors
}
// printf("sum of squared error = %lf ", training_err);
// update error arrays cyclically
// (This is easier to understand by referring to the next block of code)
sum_err2 -= errors2[tail];
sum_err2 += errors1[tail];
sum_err1 -= errors1[tail];
sum_err1 += training_err;
// printf("sum1, sum2 = %lf %lf\n", sum_err1, sum_err2);
double mean_err = (i < M) ? (sum_err1 / i) : (sum_err1 / M);
if (mean_err < 2.0)
s += sprintf(s, "mean |e|=%1.06lf, ", mean_err);
else
s += sprintf(s, "mean |e|=%e, ", mean_err);
// record new error in cyclic arrays
errors2[tail] = errors1[tail];
errors1[tail] = training_err;
++tail;
if (tail == M) // loop back in cycle
tail = 0;
// plot_W(Net);
back_prop(Net, errors);
// plot_W(Net);
// pause_graphics();
if ((i % 200) == 0)
{
// Testing set
double test_err = 0.0;
#define numTests 50
for (int j = 0; j < numTests; ++j)
{
// Create random K vector
for (int k = 0; k < 2; ++k)
K[k] = ((double) rand() / (double) RAND_MAX);
// plot_tester(K[0], K[1]);
ForwardPropMethod(Net, 2, K);
// Desired value = K_star
double single_err = 0.0;
for (int k = 0; k < 1; ++k)
{
// double ideal = 1.0f - (0.5f - K[0]) * (0.5f - K[1]);
double ideal = (double) (f2b(K[0]) ^ f2b(K[1]));
// double ideal = K[k]; /* identity function */
// Difference between actual outcome and desired value:
double error = ideal - lastLayer.neurons[k].output;
single_err += fabs(error); // record sum of errors
}
test_err += single_err;
}
test_err /= ((double) numTests);
if (test_err < 2.0)
s += sprintf(s, "random test |e|=%1.06lf, ", test_err);
else
s += sprintf(s, "random test |e|=%e, ", test_err);
if (test_err < ErrorThreshold)
break;
}
if (i > 50 && (isnan(mean_err) || mean_err > 10.0))
{
re_randomize(Net, numLayers, neuronsOfLayer);
sum_err1 = 0.0; sum_err2 = 0.0;
tail = 0;
for (int j = 0; j < M; ++j) // clear errors to 0.0
errors1[j] = errors2[j] = 0.0;
i = 1;
restart_LogErr_plot();
start_timer();
printf("\n****** Network re-randomized.\n");
}
if ((i % 50) == 0)
{
double ratio = (sum_err2 - sum_err1) / sum_err1;
if (ratio > 0)
s += sprintf(s, "|e| ratio=%e", ratio);
else
s += sprintf(s, "|e| ratio=\x1b[31m%e\x1b[39;49m", ratio);
//if (isnan(ratio))
// break;
}
if ((i % 10) == 0) // display status periodically
{
printf("%s\n", status);
// plot_NN(Net);
plot_W(Net);
plot_LogErr(mean_err, ErrorThreshold);
plot_output(Net, ForwardPropMethod);
flush_output();
// plot_trainer(0); // required to clear the window
// plot_K();
userKey = delay_vis(0);
}
// if (ratio - 0.5f < 0.0000001) // ratio == 0.5 means stationary
// if (test_err < 0.01)
if (userKey == 1)
break;
else if (userKey == 3) // Re-start with new random weights
{
re_randomize(Net, numLayers, neuronsOfLayer);
sum_err1 = 0.0; sum_err2 = 0.0;
tail = 0;
for (int j = 0; j < M; ++j) // clear errors to 0.0
errors1[j] = errors2[j] = 0.0;
i = 1;
restart_LogErr_plot();
start_timer();
printf("\n****** Network re-randomized.\n");
userKey = 0;
beep();
// pause_key();
}
}
end_timer(NULL);
beep();
// plot_output(Net, ForwardPropMethod);
flush_output();
plot_W(Net);
if (userKey == 0)
pause_graphics();
else
quit_graphics();
free_NN(Net, neuronsOfLayer);
}
