/*
===========================================================================

Created by Stefan Friese on 26.04.2018

===========================================================================
*/
// 
#include <stdio.h>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <string.h>
//#include <float.h> // DBL_MAX
#include "nlms_types.h" // added types

#define RGB_COLOR 255
#if defined(_MSC_VER)
#include <BaseTsd.h>
typedef SSIZE_T ssize_t;
#endif

typedef struct {
	double *weights;
	unsigned windowSize;
	unsigned samplesCount;
	double learnrate;
} mldata_t;

enum fileHeader{
	LOCAL_MEAN_HEADER,
	DIRECT_PREDECESSOR_HEADER,
	DIFFERENTIAL_PREDECESSOR_HEADER
};

double *xSamples;
mldata_t *mlData = NULL;	// Machine learning
point_t *points = NULL;		// Graphing			

/* *graph building* */
static imagePixel_t * rdPPM(char *fileName); 			// Read PPM file format
void mkPpmFile(char *fileName, imagePixel_t *image);		// Writes PPM file
int ppmColorChannel(FILE* fp, imagePixel_t *image, 		// Writes colorChannel from PPM file to log file
		char *colorChannel, mldata_t *mlData); 	
void colorSamples(FILE* fp, mldata_t *mlData); 			// Stores color channel values in xSamples

/* *file handling* */
char * mkFileName ( char* buffer, 							
		size_t max_len, int suffixId );			
char *fileSuffix ( int id );					
char *fileHeader ( int id );					// Header inside the logfiles
//void myLogger ( FILE* fp, point_t points[] );
void bufferLogger(char *buffer, point_t points[]);		// Writes points to graph template
void mkSvgGraph ( point_t points[] );				// Parses graph template and calls bufferLogger()
void weightsLogger ( double *weights, int suffix );		// Writes updated weights to a file

/* *rand seed* */
double r2 ( void );						// Random val between 0 and 1
double rndm ( void );

/* *args parser* */
void usage ( void ); 						// Help text called by args parser

/* *math* */
mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate);
double sum_array(double x[], int length);

void localMean ( mldata_t *mlData,point_t points[] );			// First,
void directPredecessor ( mldata_t *mlData, point_t points[] );		// Second,
void differentialPredecessor ( mldata_t *mlData, point_t points[] );	// Third filter implementation

double *popNAN(double *xError); 				// Returns array without NAN values, if any exist 
double windowXMean(int _arraylength, int xCount);		// Returns mean value of given window



int main( int argc, char **argv ) {
	char *colorChannel = (char *) malloc(sizeof(char)* 32);
	char *inputfile = (char *)malloc(sizeof(char) * 32);
	unsigned int *seed = NULL;
	unsigned k, xLength;
	unsigned int windowSize = 5;
	unsigned int samplesCount = 501;
	char *stdcolor = "green";
	colorChannel = stdcolor;
	unsigned int uint_buffer[1];
	double learnrate = 0.8;
	
	
	while( (argc > 1) && (argv[1][0] == '-')  ) {	// Parses parameters from stdin
			switch( argv[1][1] ) {
				case 'i':
					inputfile = &argv[1][3];
					++argv;
					--argc;
					break;
				case 'w':
					sscanf(&argv[1][3], "%u", &windowSize);	 
					++argv;
					--argc;
					break;
				case 'c':	
					colorChannel = &argv[1][3];	
					++argv;
					--argc;
					break;
				case 's':	
					sscanf(&argv[1][3], "%u", uint_buffer);
					seed = &uint_buffer[0];
					++argv;
					--argc;
					break;
				case 'n':
					sscanf(&argv[1][3], "%u", &samplesCount);
					++argv;
					--argc;
					break;			
				case'h':
					printf("Program name: %s\n", argv[0]);
					usage();
					break;
				case 'l':
					sscanf(&argv[1][3], "%lf", &learnrate);
					++argv;
					--argc;
					break;
				default:
					printf("Wrong Arguments: %s\n", argv[1]);
					usage();		
			}
		
		++argv;
		--argc;
	}
	init_mldata_t(windowSize, samplesCount, learnrate);
	xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount );
	points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount);
	imagePixel_t *image;	
	char fileName[50];

	image = rdPPM(inputfile);
	mkFileName(fileName, sizeof(fileName), TEST_VALUES);
	FILE* fp5 = fopen(fileName, "w");
	xLength = ppmColorChannel(fp5, image, colorChannel, mlData); // Returns length of ppm input values
	printf("%d\n", xLength);
	FILE* fp6 = fopen(fileName, "r");
	colorSamples(fp6, mlData);

	if ( (seed != NULL) ){ 			
		srand( *seed );					// Seed for random number generating
		printf("srand is reproducable : %u", seed);
	} else {
		srand( (unsigned int)time(NULL) );
		printf("srand from time\n");			// Default seed is time(NULL)
	}
//	for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
		for (k = 0; k < mlData->windowSize; k++) {
		mlData->weights[k] = rndm(); 		// Init random weights
		printf("%lf\n", mlData->weights[k]);
		}
//	}

	mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS);
	FILE *fp0 = fopen(fileName, "w");
//	for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
		for (k = 0; k < mlData->windowSize; k++) {
			fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);	// Save generated weights to to file
		}
//	}
	fclose(fp0);

	// math magic
    	localMean ( mlData, points );
	directPredecessor ( mlData, points);
	differentialPredecessor( mlData, points );
	
	mkSvgGraph(points);
	free(xSamples);
	free(points);
	printf("\nDONE!\n");
}

/*
======================================================================================================

localMean

Variant (1/3), substract local mean.

======================================================================================================
*/
void localMean ( mldata_t *mlData, point_t points[] ) {
//	double (*local_weights)[WINDOWSIZE] =(double (*)[WINDOWSIZE]) malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
	//memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
	double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1);
	memcpy ( localWeights, mlData->weights, sizeof(mlData->windowSize) ); // TODO: check size !!!

	char fileName[50];
	double xError[2048]; // includes e(n)
	memset(xError, 0.0, mlData->samplesCount);// initialize xError-array with Zero
	unsigned i, xCount = 0; // runtime var
	mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
	FILE* fp4 = fopen(fileName, "w");
	fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );

	double xMean = xSamples[0];
	double xSquared = 0.0;
	double xPredicted = 0.0;
	double xActual = 0.0;

	for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted

		unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
		xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
		xPredicted = 0.0;
		xActual = xSamples[xCount];

		for (i = 1; i < _arrayLength; i++) { //get predicted value
			xPredicted += (localWeights[i] * (xSamples[xCount - i] - xMean));
		}
		xPredicted += xMean;
		xError[xCount] = xActual - xPredicted;
		xSquared = 0.0;
		for (i = 1; i < _arrayLength; i++) { //get xSquared
			xSquared += pow(xSamples[xCount - i] - xMean, 2);
		}
		if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
			xSquared = 1.0;
		}
		for ( i = 1; i < _arrayLength; i++ ) { //update weights
			localWeights[ i + 1 ] = localWeights[i] + mlData->learnrate * xError[xCount] * ( (xSamples[xCount - i] - xMean) / xSquared );
		}
		
		fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);	
		
		points[xCount].xVal[1] = xCount;
		points[xCount].yVal[1] = xPredicted;
		points[xCount].xVal[4] = xCount;
		points[xCount].yVal[4] = xError[xCount];


	}
	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
  	xErrorPtr[0] = 0.0;
//	printf("Xerrorl:%lf", xErrorLength);
	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "w");
	fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	//fclose(fp2);
	free(localWeights);
	fclose(fp4);

	//weightsLogger( local_weights, USED_WEIGHTS );
}

/*
======================================================================================================

directPredecessor

Variant (2/3),
substract direct predecessor

======================================================================================================
*/
void directPredecessor( mldata_t *mlData, point_t points[]) {
	double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
	memcpy ( localWeights, mlData->weights, sizeof(mlData->windowSize) );
	char fileName[512];
	double xError[2048];
	unsigned xCount = 0, i;
	double xActual = 0.0;
	double xPredicted = 0.0;

	mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
	FILE *fp3 = fopen(fileName, "w");
	fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) );
	mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS);
	FILE *fp9 = fopen(fileName, "w");


	for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted
		unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
		xPredicted = 0.0;
		xActual = xSamples[xCount];
		
		for (i = 1; i < _arrayLength; i++) {
			xPredicted += ( localWeights[i - 1] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
		}
		
		xPredicted += xSamples[xCount - 1];
		xError[xCount] = xActual - xPredicted;

		double xSquared = 0.0;
		for (i = 1; i < _arrayLength; i++) {
			xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
		}
		for (i = 1; i < _arrayLength; i++) {
		localWeights[i + 1] = localWeights[i] + mlData->learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
		fprintf( fp9, "%lf\n", localWeights[i] );
		}
		
	        fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
		points[xCount].xVal[2] = xCount; 		// Fill point_t array for graph building
		points[xCount].yVal[2] = xPredicted;
		points[xCount].xVal[5] = xCount;
		points[xCount].yVal[5] = xError[xCount];
	//	weightsLogger( fp, localWeights, USED_WEIGHTS );


	}
	fclose(fp9);
	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	//printf("%lf", xErrorPtr[499]);
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
    xErrorPtr[0] = 0.0;
	//printf("Xerrorl:%lf", xErrorLength);

	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "wa");
	fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	fclose(fp3);
	//fclose(fp2);
	free(localWeights);

}

/*
======================================================================================================

differentialPredecessor

variant (3/3),
differential predecessor.

======================================================================================================
*/
void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
	double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
	memcpy( localWeights, mlData->weights, sizeof(mlData->windowSize) );
	char fileName[512];
	double xError[2048];
	unsigned xCount = 0, i;
	double xPredicted = 0.0;
	double xActual = 0.0;

	// File handling
	mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR);
	FILE *fp6 = fopen(fileName, "w");
	fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) );

		for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted

		unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount;
		xPredicted = 0.0;
		xActual = xSamples[xCount];

		for (i = 1; i < _arrayLength; i++) {
			xPredicted += ( localWeights[i - 1] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
		}
		xPredicted += xSamples[xCount - 1];
		xError[xCount] = xActual - xPredicted;

		double xSquared = 0.0;

		for (i = 1; i < _arrayLength; i++) {
			xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
		}
		for (i = 1; i < _arrayLength; i++) {
			localWeights[i+1] = localWeights[i] + mlData->learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
		}
       		fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
		
		points[xCount].xVal[3] = xCount;
		points[xCount].yVal[3] = xPredicted;
		points[xCount].xVal[6] = xCount;
		points[xCount].yVal[6] = xError[xCount];

	}

	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	//printf("%lf", xErrorPtr[499]);
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
    	xErrorPtr[0] = 0.0;
//	printf("Xerrorl:%lf", xErrorLength);

	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "wa");
	fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	//fclose(fp2);
	fclose(fp6);
	free(localWeights);

//	weightsLogger( localWeights, USED_WEIGHTS );
}

/*
======================================================================================================

mkFileName

Writes the current date plus the suffix with index suffixId
into the given buffer. If the total length is longer than max_len,
only max_len characters will be written.

======================================================================================================
*/
char *mkFileName(char* buffer, size_t max_len, int suffixId) {
	const char * format_str = "%Y-%m-%d_%H_%M_%S";
	size_t date_len;
	const char * suffix = fileSuffix(suffixId);
	time_t now = time(NULL);

	strftime(buffer, max_len, format_str, localtime(&now));
	date_len = strlen(buffer);
	strncat(buffer, suffix, max_len - date_len);
	return buffer;
}

/*
======================================================================================================

fileSuffix

Contains and returns every suffix for all existing filenames

======================================================================================================
*/
char * fileSuffix ( int id ) {
	char * suffix[] = { 	"_weights_pure.txt", 
				"_weights_used.txt", 
				"_direct_predecessor.txt", 
				"_ergebnisse.txt", 
				"_localMean.txt",
				"_testvalues.txt", 
				"_differential_predecessor.txt" 
	};
	return suffix[id];
}

/*
======================================================================================================

fileHeader

Contains and returns header for logfiles 

======================================================================================================
*/
char * fileHeader ( int id ) {
	char * header[] = {	"\n=========================== Local Mean ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
				"\n=========================== Direct Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
				"\n=========================== Differential Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n"
	};
	return header[id];
}

/*
======================================================================================================

myLogger

Logs x,y points to svg graph

======================================================================================================
*/
void weightsLogger (double *weights, int val ) {
	char fileName[512];
	unsigned i;
	mkFileName(fileName, sizeof(fileName), val);
	FILE* fp = fopen(fileName, "wa");
	for (i = 0; i < mlData->windowSize - 1; i++) {
	//	for (int k = 0; k < WINDOWSIZE; k++) {
			fprintf(fp, "[%d]%lf\n", i, weights[i]);
	//	}
	}
	fprintf(fp,"\n\n\n\n===================== NEXT =====================\n");
	fclose(fp);
}

/*
======================================================================================================

bufferLogger

formats output of mkSvgGraph -- Please open graphResults.html to see the output--	
	[0] = xActual, 
	[1] = xPredicted from localMean, 
	[2] = xPredicted from directPredecessor, 
	[3] = xPredicted from differentialpredecessor, 
	[4] = xError from localMean, 
	[5] = xError from directPredecessor, 
	[6] = xError from differentialPredecessor

======================================================================================================
*/
void bufferLogger(char *buffer, point_t points[]) {
	unsigned i;
	char _buffer[512] = "";

	for (i = 0; i < mlData->samplesCount - 1; i++) { // xActual
		sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_1\" stroke=\"black\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i < mlData->samplesCount - 1; i++) { // xPredicted from localMean
		sprintf(_buffer, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i <= mlData->samplesCount - 1; i++) { //xPreddicted from directPredecessor
		sprintf(_buffer, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_3\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i < mlData->samplesCount - 1; i++) { //xPredicted from diff Pred
		sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"red\" stroke-width=\"0.4px\"/>\n");
}

/*
======================================================================================================

sum_array

Sum of all elements in x within a defined length

======================================================================================================
*/
double sum_array(double x[], int xlength) {
	int i = 0;
	double sum = 0.0;

	if (xlength != 0) {
		for (i = 0; i < xlength; i++) {
			sum += x[i];
		}
	}
	return sum;
}

/*
======================================================================================================

popNanLength

returns length of new array without NAN values

======================================================================================================
*/
double *popNAN(double *xError) {
	unsigned i, counter = 1;
	double tmpLength = 0.0;
	double *tmp = NULL;
	double *more_tmp = NULL;

	for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
		counter ++;
		more_tmp = (double *) realloc ( tmp, counter*(sizeof(double) ));
			if ( !isnan(xError[i]) ) {
				tmp = more_tmp;
				tmp[counter - 1] = xError[i];
				//printf("xERROR:%lf\n", tmp[counter - 1]);
				tmpLength++;
			}
	}
	counter += 1;
	more_tmp = (double *) realloc ( tmp, counter * sizeof(double) );
	tmp = more_tmp;
	*tmp = tmpLength; // Length of array has to be stored in tmp[0],
				    // Cause length is needed later on in the math functions.
				    // xError counting has to begin with 1 in the other functions !
	printf("tmpLength in tmp:%lf, %lf\n", tmp[counter-2], *tmp);
	return tmp;

}

/*
======================================================================================================

r2

returns a random double value between 0 and 1

======================================================================================================
*/
double r2(void) {
	return((rand() % 10000) / 10000.0);
}

/*
======================================================================================================

rndm

fills a double variable with random value and returns it

======================================================================================================
*/
double rndm(void) {
	double rndmval = r2();
	return rndmval;
}

/*
======================================================================================================

mkSvgGraph

parses template.svg and writes results in said template

======================================================================================================
*/
void mkSvgGraph(point_t points[]) {
	FILE *input = fopen("graphResults_template.html", "r");
	FILE *target = fopen("graphResults.html", "w");
	char line[512];
	char firstGraph[15] = { "<path d=\"M0 0" };

	if (input == NULL) {
		exit(EXIT_FAILURE);
	}

	char buffer[131072] = "";

	memset(buffer, '\0', sizeof(buffer));
	while (!feof(input)) {
		fgets(line, 512, input);
		strncat(buffer, line, strlen(line));
		if (strstr(line, firstGraph) != NULL) {
			bufferLogger(buffer, points);
		}

	}
	fprintf(target, buffer);
}

/*
======================================================================================================

rdPPM

reads data from file of type PPM, stores colorchannels in a struct in the
size of given picture

======================================================================================================
*/
static imagePixel_t *rdPPM(char *fileName) {
	char buffer[16];
	imagePixel_t *image;
	int c, rgbColor;

	FILE *fp = fopen(fileName, "rb");
	if (!fp) {
		exit(EXIT_FAILURE);
	}
	if (!fgets(buffer, sizeof(buffer), fp)) {
		perror(fileName);
		exit(EXIT_FAILURE);
	}
	if (buffer[0] != 'P' || buffer[1] != '6') {
		fprintf(stderr, "No PPM file format\n");
		exit(EXIT_FAILURE);
	}
	image = (imagePixel_t *)malloc(sizeof(imagePixel_t));
	if (!image) {
		fprintf(stderr, "malloc() failed");
	}
	c = getc(fp);
	while (c == '#') {
		while (getc(fp) != '\n');
		c = getc(fp);
	}
	ungetc(c, fp);
	if (fscanf(fp, "%d %d", &image->x, &image->y) != 2) {
		fprintf(stderr, "Invalid image size in %s\n", fileName);
		exit(EXIT_FAILURE);
	}
	if (fscanf(fp, "%d", &rgbColor) != 1) {
		fprintf(stderr, "Invalid rgb component in %s\n", fileName);
	}
	if (rgbColor != RGB_COLOR) {
		fprintf(stderr, "Invalid image color range in %s\n", fileName);
		exit(EXIT_FAILURE);
	}
	while (fgetc(fp) != '\n');
	image->data = (colorChannel_t *)malloc(image->x * image->y * sizeof(imagePixel_t));
	if (!image) {
		fprintf(stderr, "malloc() failed");
		exit(EXIT_FAILURE);
	}
	if (fread(image->data, 3 * image->x, image->y, fp) != image->y) {
		fprintf(stderr, "Loading image failed");
		exit(EXIT_FAILURE);
	}
	fclose(fp);
	return image;
}

/*
======================================================================================================

mkPpmFile

gets output from the result of rdPpmFile and writes a new PPM file. Best Case is a
carbon copy of the source image. Build for debugging

======================================================================================================
*/
void mkPpmFile(char *fileName, imagePixel_t *image) {
	FILE* fp = fopen(fileName, "wb");
	if (!fp) {
		fprintf(stderr, "Opening file failed.");
		exit(EXIT_FAILURE);
	}
	fprintf(fp, "P6\n");
	fprintf(fp, "%d %d\n", image->x, image->y);
	fprintf(fp, "%d\n", RGB_COLOR);
	fwrite(image->data, 3 * image->x, image->y, fp);
	fclose(fp);
}

/*
======================================================================================================

ppmColorChannel

gets one of the rgb color channels and writes them to a file

======================================================================================================
*/
int ppmColorChannel(FILE* fp, imagePixel_t *image, char *colorChannel, mldata_t *mlData) {
	// int length = (image->x * image->y) / 3;
	unsigned i = 0;

	printf("colorChannel in Function: %s", colorChannel);
	if (image) {
	
		if (strcmp(colorChannel, "green") == 0){
			for (i = 0; i < mlData->samplesCount - 1; i++) {
				fprintf(fp, "%d\n", image->data[i].green);
				printf("|");
			}
		} else if (strcmp(colorChannel, "red") == 0){				
			for (i = 0; i < mlData->samplesCount - 1; i++) {
				fprintf(fp, "%d\n", image->data[i].red);
				printf(".");
			}	
			
		} else if (strcmp(colorChannel, "blue") == 0 ) {
			for (i = 0; i < mlData->samplesCount - 1; i++ ) {
				fprintf(fp, "%d\n", image->data[i].blue);
				printf("/");
			}
		} else { 
			printf("Colorchannels are red, green and blue. Pick one of them!");
			exit(EXIT_FAILURE);
		}
	}
	fclose(fp);
	return mlData->samplesCount;
}

/*
======================================================================================================

colorSamples

reads colorChannel values from file and stores them in xSamples as well as in points datatype for
creating the SVG graph

======================================================================================================
*/
void colorSamples ( FILE* fp, mldata_t *mlData ) {
	int i = 0;
	//char  buffer[NUMBER_OF_SAMPLES];
	char *buffer = (char *) malloc(sizeof(char) * mlData->samplesCount);
	while (!feof(fp)) {
		if (fgets(buffer, mlData->samplesCount, fp) != NULL) {
			sscanf(buffer, "%lf", &xSamples[i]);
			//printf("%lf\n", xSamples[i] );
			points[i].yVal[0] = xSamples[i];
			points[i].xVal[0] = i;
			++i;
		}
	}
	fclose(fp);
}

/*
======================================================================================================

windowXMean

returns mean value of given input, which has a length of WINDOWSIZE

======================================================================================================
*/
double windowXMean(int _arraylength, int xCount) {
	double sum = 0.0;
	double *ptr;

	for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { //set ptr to beginning of window
        sum += *ptr;
	}
	return sum / (double)_arraylength;
}

/*
======================================================================================================

 usage 
 
 used in conjunction with the args parser. Returns help section of "-h"

======================================================================================================
*/
void usage ( void ) {
	printf("Usage: lms [POSIX style options] -i file ...\n");
	printf("POSIX options:\n");
	printf("\t-h\t\t\tDisplay this information.\n");
	printf("\t-i <filename>\t\tName of inputfile. Must be PPM image.\n");
	printf("\t-n <digit>\t\tAmount of input data used.\n");	
	printf("\t-c <color>\t\tUse this color channel from inputfile.\n");
	printf("\t-w <digit>\t\tCount of used weights (windowSize).\n");
	printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\n");
	printf("\t-s <digit>\t\tDigit for random seed generator.\n\t\t\t\tSame Digits produce same random values. Default is srand by time.");
	printf("\n\n");
	printf("lms compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\tlms -i myimage.ppm -w 3 -c green -s 5\n"); 
	exit(8);
}

/*
======================================================================================================

 init_mldata_t 
 
 
 Contains meachine learning data

======================================================================================================
*/
mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate) {
	mlData = (mldata_t *) malloc( sizeof(mldata_t) );
	mlData->windowSize = windowSize;
	mlData->samplesCount = samplesCount;
	mlData->learnrate = learnrate;
	mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
	return mlData;
}