/* ExtRec.c
* Experimental Character Recognition Program
* Using Neural Nets and Subsumption
* (c) Logan Kearsley
*/

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include "mlayer.h"

#define ROWS 10
#define COLS 8
#define CHARS 95

//WINDOW *scrn;

typedef struct charnet {
	neuro_t* w;
	char id;
	struct charnet* next;
} cdata;

cdata* Nbase;

void cteol(){while(getchar()!='\n');}

void load(neuro_t* w, char* ftrn, float ls, float m){
	FILE* f;
	setLearningSpeed(w,ls);
	if(m!=0){
		momentumOn(w);
		setMomentum(w,m);
	}
	f=fopen(ftrn,"rb");
	bldnet(f,w);
	fclose(f);
}

void init_neuro(){
	char t;
	char ln[10];
	cdata* net;
	FILE* nlist = fopen("chars.config", "r");

	fscanf(nlist,"%c",&t);
	fscanf(nlist,"%s",ln);
	Nbase = (cdata*)malloc(sizeof(cdata));
	mknet(Nbase->w);
	load(Nbase->w,ln,0.7,0.2);
	Nbase->id = t;
	net = Nbase;

	//printf("Loaded net for character '%c'\n", t);

	while(fscanf(nlist,"\n%c",&t) != EOF) {// read one character from the pipe
		net->next = (cdata*)malloc(sizeof(cdata));
		net = net->next;
		fscanf(nlist,"%s",ln);
		mknet(net->w);
		load(net->w,ln,0.7,0.2);
		net->id = t;
		net->next = NULL;
		//printf("Loaded net for character '%c'\n", t);
	}
	close(nlist);
}

void save_neuro(){
	int i=0;
	cdata* net;
	char fname[10];
	FILE* f;
	FILE* conf = fopen("chars.config","w");
	net = Nbase;
	while(net->next!=NULL){
		sprintf(fname,"%d",i);
		strcat(fname,".net");
		f=fopen(fname,"w");
		bsvnet(f,net->w);
		fclose(f);
		fprintf(conf,"%c %s\n",net->id,fname);
		net = net->next;
	}
	fclose(conf);
}

char recChar(float* img){
	cdata* c;
	cdata* net;
	float p=0,t;
	net = Nbase;
	while(net->next!=NULL){
		loadInput(net->w,img);
		mquery(net->w);
		t = getOutput(net->w)[0];
		if(t>p){
			p=t;
			c=net;
		}
		net = net->next;
	}
	return c->id;
}

void addChar(cdata* N, char t, float* bit){
	int d[] = {81,40,20,10,5,1};
	float e;
	int i;
	float one=1;

	N->next = (cdata*)malloc(sizeof(cdata));
	N = N->next;
	mknet(N->w);
	mrndnet(N->w,d,5);

	printf("New character '%c'...\n", t); 

	loadInput(N->w, bit);
	loadTarget(N->w, &one);
	for(i=0,e=1.0;e>0.1&&i<1000;i++) mquery(N->w),e=backprop(N->w);
	//printf("training took %d epochs\n", epochs);

	N->id = t;
	N->next = NULL;
}

void comp(){
	int i,l=0,p=0,s=500,cyc=0;
	float one=1,zero=0,acc=0,oldacc=0,avrg=0,e=1;
	float bit[81];
	char comp_c, rec_c;
	char c[82];
	char infile[20];
	char compfile[20];
	char* text = (char*)malloc(sizeof(char)*500);
	FILE* tf;
	FILE* cf;
	cdata* net;
	cdata* net_2;

	printf("Text Image to Recognize: ");
	scanf("%s",infile);
	tf = fopen(infile,"r");
	printf("Comparison File: ");
	scanf("%s",compfile);
	cf = fopen(compfile,"r");


	while(1){
		while(1){
			if(fscanf(tf,"%s\n",c)==EOF) break;
			for(i=0;i<81;i++) bit[i]=(c[i]=='1'?1.0:0.0);

			//refresh();
			rec_c=recChar(bit);
			l++;
			if(l>=s) text = (char*)realloc(text,sizeof(char)*(s+=500));
			text[l-1]=rec_c;
			text[l]='\0';
	
			comp_c=fgetc(cf);
			net=Nbase;
			if(comp_c==rec_c){
				p++;
				while(net->id != comp_c) net=net->next;
				loadInput(net->w, bit); //should've been taken care of in recChar, but just in case....
				loadTarget(net->w,&one);
				for(i=0;i<10;i++) mquery(net->w),backprop(net->w); //reinforce it to avoid backsliding
			}
			else{
				while(net->id != rec_c) net=net->next;

				//e=1.0;
				//for(i=0,e=1.0;e>0.1&&i<1000;i++) mquery(net->w),e=backprop(net->w);

				net_2=Nbase;
				while(net_2->id != comp_c){
					if(net_2->next==NULL) break;
					else net_2=net_2->next;
				}
				if(net_2->id != comp_c){ addChar(net_2, comp_c, bit),net_2=net_2->next; } //not a good way to test; keep as a back-up only

				loadInput(net->w, bit);
				loadInput(net_2->w, bit);
				loadTarget(net->w,&zero);
				loadTarget(net_2->w,&one);

				mquery(net->w);
				mquery(net_2->w);
				e=backprop(net->w);
				e+=backprop(net_2->w);

				if(e/2<.2){
					for(i=0;getOutput(net->w)[0] > getOutput(net_2->w)[0] && i<1000;i++){
						mquery(net->w);
						mquery(net_2->w);
						backprop(net->w);
						backprop(net_2->w);
					}
				}else{
					for(i=0;e/2 > .2 && i<1000;i++){
						mquery(net->w);
						mquery(net_2->w);
						e=backprop(net->w);
						e+=backprop(net_2->w);
					}
				}
			}
		}
		printf("\n%s\n",text);
		acc=(float)p/l*100.0;
		if(acc>85){
			printf("Accuracy: %f\%\n(%f Improvement)\nRerun [y/n]?",acc,acc-oldacc);
			while((rec_c=getchar())!='y' && rec_c!='n');
			if(rec_c=='n') break;
		}
		else{
			avrg=(avrg*cyc+acc-oldacc)/(cyc+1);
			//move(10,0);
			printf("Accuracy: %f\%\n(%f Improvement)\t Trend: %f\n",acc,acc-oldacc,avrg);
		}
		oldacc=acc;
		rewind(cf);
		rewind(tf);
		l=0;
		p=0;
		cyc++;
	}
	close(tf);
	close(cf);
	
	save_neuro();
}

int main(int argc, char *argv[]){
	init_neuro();
	comp();
	return 0;
}