/* 
   lab070420a.c

   Simple Neuron

   2007.4.19 A.Date

  %  gcc lab070420a.c -lm
 
 */

#include <stdio.h>
#include <stdlib.h> /* drand48 */
#include <math.h> /* log */
#define MAX_SAMPLES 1000
#define N_DIM 3


/* 以下のパラメータ値を変えて学習の様子がどう変化するか観測してみる */

#define RAND_SEED  1234567  /* 乱数の種 */
#define SLEEP 50000   /* 表示スピード */
int n_trials = 100;   /* 学習回数 */
double eta = 0.1;     /* 学習係数 */
double lambda=1.0;   /* シグモイド関数のパラメータ */
char data_file1[] = "apples.dat";   /* 入力データ．課題C,Dではファイル名を変える */
char data_file2[] = "oranges.dat";


int n_data, n_data1, n_data2;

double sigmoid(double x){
	return 1.0/(1.0 + exp(-lambda*x));
}


int main ()
{
	FILE *gp, *fp; 
	
	int a,i,j,l,t, n;
	int seed = RAND_SEED;
	
	int n_data;
	int teacher; 
	
	double y1, y2; 
	double dw[N_DIM];
	double w[N_DIM];
	double data[MAX_SAMPLES][N_DIM];
	double u,z,e,e2; 
	double x_start = -1.0, x_end=5.0;
	double y_start = -1.0, y_end=5.0;
	
	srand48(seed);

/* load data */
	fp = fopen(data_file1, "r");
	if (fp == NULL){
		fprintf(stderr, "Can't open data file: %s\n",data_file1);
		exit(-1);
	} 

	i=0;
	while( (n=fscanf(fp, "%lf %lf", &data[i][1], &data[i][2])) != EOF ){
		if( n == 2 ){
			data[i][0] = 1.0;
			i++;
		} 
		else {
			fprintf(stderr, "Something wrong in data1.\n");
			exit(-1);
		}
	}
	n_data1 = i;
	fclose(fp); 


	fp = fopen(data_file2, "r");
	if (fp == NULL){
		fprintf(stderr, "Can't open data file: %s\n",data_file2);
		exit(-1);
	} 

	while( (n=fscanf(fp, "%lf %lf", &data[i][1], &data[i][2])) != EOF ){
		if( n == 2 ){
			data[i][0] = 1.0;
			i++;
		}
		else {
			fprintf(stderr, "Something wrong in data2.\n");
			exit(-1);
		}
	}
	n_data = i;
	n_data2 = n_data - n_data1;
	fclose(fp); 

	
/* gnuplot へのコマンド． あまり気にしなくてよい．*/
	gp = popen("gnuplot -geometry 640x480","w");
	fprintf(gp, "set xlabel \"x1\"\n");
	fprintf(gp, "set ylabel \"x2\"\n");
	fprintf(gp, "set xrange[%.2lf:%.2lf]\n",x_start,x_end);
	fprintf(gp, "set yrange[%.2lf:%.2lf]\n",y_start,y_end);
	fprintf(gp, "set size square\n");
	//      fprintf(gp, "set terminal postscript\n");

		

/* 結合係数の初期値として乱数を代入 */
	for (i=0; i<N_DIM; i++) {
		w[i] = drand48() -0.5;
	}


/* loop over a training set */
	for (t=0; t<n_trials; t++) {

		dw[0] = 0.0;
		dw[1] = 0.0;
		dw[2] = 0.0;
		e2 = 0.0;

		for ( i = 0; i<n_data; i++) {
			u = w[0] + w[1]*data[i][1] +  w[2]*data[i][2];

			if ( i < n_data1 ){
				teacher = 1;
			}
			else{
				teacher = 0;
			}

			z = sigmoid(u);
			e = (double)teacher - z;
			e2 = e2 + e*e;

/* comment out if you want to see the dynamics of unit activity.*/
//		printf("t:%d\t pattern:%d\t z:%.5lf\t true:%d\t error:%.5lf\n", t, i, z, teacher, e);


			dw[0] = dw[0] + 2.0*eta*e*lambda*z*(1.0-z);
			dw[1] = dw[1] + 2.0*eta*e*lambda*z*(1.0-z)*data[i][1];
			dw[2] = dw[2] + 2.0*eta*e*lambda*z*(1.0-z)*data[i][2];
		}

/* comment out if you want to see something */
//		printf("t:%d \t e2:%.5lf\n", t, e2);


/* update weights */	
		w[0] = w[0] + dw[0];
		w[1] = w[1] + dw[1];
		w[2] = w[2] + dw[2]; 
			
/* comment out if you want to see dynamics of weights */
//			printf("%d: %.5lf\t%.5lf\t%.5lf\n",t, dw[0],dw[1],dw[2]);


/* plot at u=0 (hyper plane) */
		fprintf(gp, "set title 't =%d'\n", t);
		fprintf(gp, "plot '%s' with points 1, '%s' with points 3, %.3lf*x + %.3lf with lines\n", data_file1, data_file2, -w[1]/w[2], -w[0]/w[2]);
		fflush(gp); 

		usleep(SLEEP);

	}

}