maekawatoshiki
3/24/2015 - 4:43 AM
NeuralNetworks_Weather
NeuralNetworks_Weather
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <ctime>
#include <iostream>
#include <unistd.h>
using namespace std;
#define NUM_LEARN 80000000
#define NUM_SAMPLE 310
#define NUM_INPUT 3
#define NUM_HIDDEN 8
#define NUM_OUTPUT 1
#define RTMAX 50
double sigmoid(double x)
{
return 1.0 / (1.0+exp(-x));
}
struct NN
{
double x[NUM_INPUT];
double y[NUM_OUTPUT];
};
unsigned int w = 123426572;
unsigned int xor128()
{
static unsigned int x = 123456789, y = 362436069, z = 521288629;
unsigned int t = (x ^ (x << 11) );
x=y; y=z; z=w;
return ( w=(w^(w>>19)) ^ ( t ^ ( t >>8)) );
}
NN txy[NUM_SAMPLE] = { 0 };
double x[NUM_INPUT+1],h[NUM_HIDDEN+1],y[NUM_OUTPUT];
double w1[NUM_INPUT+1][NUM_HIDDEN],w2[NUM_HIDDEN+1][NUM_OUTPUT];
double h_back[NUM_HIDDEN+1],y_back[NUM_OUTPUT];
int main(int argc, char *argv[])
{
//if(argc < 2) return -1;
FILE *fp = fopen("data.csv", "r");
int learn, sample, i = 0, j, k = 0;
double net_input, starttm, endtm;
double emax;
double EPSILON = 0.006;
cin >> emax;
srand((unsigned)time(NULL));
// goto Break;
fscanf(fp, "%lf %lf %lf %lf",
&txy[0].x[0], &txy[0].x[1], &txy[0].x[2],
&txy[0].y[0] );
txy[0].x[0] = (fabs(txy[0].x[0]) ) / RTMAX;
txy[0].x[1] = (fabs(txy[0].x[1]) ) / RTMAX;
txy[0].x[2] = (fabs(txy[0].x[2]) ) / RTMAX;
txy[0].y[0] = (fabs(txy[0].y[0]) ) / RTMAX;
for(i = 1; i < NUM_SAMPLE-1; i++ )
{
txy[i].x[0] = txy[i-1].x[1];
txy[i].x[1] = txy[i-1].x[2];
txy[i].x[2] = txy[i-1].y[0];
fscanf(fp, "%lf ", &txy[i].y[0] );
txy[i].y[0] = (fabs(txy[i].y[0]) ) / RTMAX;
cout << txy[i].x[0] * RTMAX << endl;
}
getchar(); getchar();
for(i = 0; i < NUM_INPUT+1; i++)
for(j = 0;j < NUM_HIDDEN; j++)
{
w1[i][j]= (double) ( (double) xor128() / 0xFFFFFFFF - 0.5) / 10.0;
cout << xor128() << endl;
}
for(i = 0; i < NUM_HIDDEN+1; i++)
for(j = 0;j < NUM_OUTPUT; j++)
w2[i][j]= (double) ( (double) xor128() / 0xFFFFFFFF - 0.5) / 10.0;
starttm = clock();
for(learn = 0; learn < NUM_LEARN; learn++)
{
double e = 0.0;
for(sample = 0; sample < NUM_SAMPLE; sample++)
{
for(i = 0;i < NUM_INPUT; i++)
x[i] = txy[sample].x[i];
x[NUM_INPUT] = (double)1.0;
for( j = 0; j < NUM_HIDDEN;j++)
{
net_input = 0;
for( i = 0; i < NUM_INPUT+1; i++ )
net_input = net_input + w1[i][j] * x[i];
h[j] = tanh(net_input);
}
h[NUM_HIDDEN] = (double)1.0;
for (j = 0; j < NUM_OUTPUT;j++)
{
net_input = 0;
for(i = 0; i < NUM_HIDDEN + 1; i++)
net_input = net_input + w2[i][j] * h[i];
y[j] = sigmoid(net_input);
e += 0.5 * (txy[sample].y[j] - y[j]) * (txy[sample].y[j] - y[j]);
if(learn % 10000 == 0)
{
printf("> %lf %lf error=%lf Epsilon=%lf\n", y[j] * RTMAX, fabs(txy[sample].y[j] - y[j]),
e, EPSILON);
}
}
for(i = 0; i < NUM_HIDDEN;i++)
{
net_input = 0;
for(j = 0; j < NUM_OUTPUT; j++)
{
y_back[j] = ( y[j] - txy[sample].y[j] ) * ( 1.0 - y[j] ) * y[j];
net_input = net_input + w2[i][j] * y_back[j];
}
h_back[i] = net_input * ((double)1.0 - h[i]) * h[i];
}
for(i = 0; i < NUM_INPUT+1; i++)
for(j = 0; j < NUM_HIDDEN; j++)
w1[i][j] = w1[i][j] - EPSILON * x[i] * h_back[j];
for(i = 0; i < NUM_HIDDEN+1; i++)
for(j = 0; j < NUM_OUTPUT; j++)
w2[i][j] = w2[i][j] - EPSILON * h[i] * y_back[j];
}
// if(learn % 10000 == 0)
/*if(e - 0.01 < emax)
EPSILON = e - emax + 0.001;*/
if(e <= emax)
{
cout << "error = " << e << endl;
break;
}
}
Break:
fclose(fp);
fseek(fp, 0L, SEEK_SET);
fp = fopen("w_io.wd", "wb");
if(!fp) cout << "err" << endl;
fwrite(w1, sizeof(double) , (NUM_INPUT+1) * NUM_HIDDEN, fp);
fwrite(w2, sizeof(double) , (NUM_HIDDEN+1) * NUM_OUTPUT, fp);
fclose(fp);
endtm = clock();
cout << "End of learning : " << (endtm-starttm) / CLOCKS_PER_SEC / 60 << "minutes" << endl;
for(int w = 0; w < 10; w++)
{
for(i = 0;i < NUM_INPUT; i++)
{
cin >> x[i];
x[i] /= RTMAX;
}
for(k=0; k<30; k++)
{
h[NUM_HIDDEN] = x[NUM_INPUT] = (double)1.0;
for( j = 0; j < NUM_HIDDEN;j++)
{
net_input = 0;
for(i = 0; i < NUM_INPUT+1; i++)
{
net_input = net_input + w1[i][j] * x[i];
}
h[j] = tanh(net_input);
}
for (j = 0; j < NUM_OUTPUT;j++)
{
net_input = 0;
for(i = 0;i < NUM_HIDDEN+1; i++)
net_input += w2[i][j] * h[i];
y[j] = sigmoid(net_input);
printf("> %lf\n", y[j] * RTMAX);
}
for (j = 0; j < NUM_INPUT-1; j++)
{
x[j] = x[j+1];
}
x[NUM_INPUT-1] = y[0];
}
}
return 0;
}
/*
0.3.2
0.3.4
0.3.7
0.3.8
0.3.5
0.3.8
0.3.6
0.3.9
0.3.8
0.3.4
0.3.7
0.3.3
0.3.2
0.2.9
0.2.8
0.2.9
0.2.6
0.2.8
0.2.5
0.2.7
0.1.9
0.2.4
0.2.4
0.2.2
0.2.3
0.2.1
0.2.1
0.2.1
0.2.3
0.2.3
0.2.3
0.2.4
0.2.1
0.2.5
0.2.7
0.2.9
0.3.2
0.3.0
0.3.0
0.3.0
0.3.1
0.3.0
0.3.1
0.3.1
0.3.0
0.3.1
0.3.2
0.3.3
0.3.2
0.3.4
0.3.5
0.3.6
0.4.0
0.4.4
0.5.1
0.5.4
0.6.0
0.5.8
0.6.6
0.7.6
0.8.0
0.8.0
0.7.7
0.7.9
0.19.2
0.9.5
0.18.1
0.18.7
0.8.7
0.9.7
0.19.7
0.110.
0.110.
0.10.
0.11.
0.10.
0.10.
0.110.
0.11.
0.12.
0.11.
0.11.
0.11.
0.11.
0.10.
0.10.
0.97
0.96
0.95
0.95
0.96
0.91
0.87
0.85
0.84
0.83
0.82
0.81
0.78
0.77
0.75
0.72
0.70
0.73
0.77
0.79
0.77
0.76
0.76
0.77
0.73
0.70
0.68
0.70
0.73
0.70
0.68
0.72
0.69
0.70
0.72
0.68
0.67
0.69
0.72
0.65
0.67
0.70
0.68
0.66
0.64
0.66
0.65
0.64
0.63
0.61
0.65
0.63
0.56
0.58
0.55
0.51
0.53
*/