added stuff
This commit is contained in:
Friese
parent
2b1469f55e
commit
0a8a81677c
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@ -3,7 +3,7 @@
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// NLMSvariants.c
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//
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// Created by FBRDNLMS on 26.04.18.
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// Copyright © 2018 FBRDNLMS. All rights reserved.
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//
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//
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#include <stdio.h>
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@ -13,24 +13,26 @@
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#include <string.h>
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#include <float.h> // DBL_MAX
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#define M 1000
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#define NUMBER_OF_SAMPLES 1000
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#define WINDOWSIZE 5
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#define tracking 40 //Count of weights
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#define learnrate 1.0
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#define learnrate 0.8
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#define PURE_WEIGHTS 0
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#define USED_WEIGHTS 1
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#define RESULTS 3
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#define DIRECT_PREDECESSOR 2
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#define LOCAL_MEAN 4
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#define TEST_VALUES 5
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#define DIFFERENTIAL_PREDECESSOR 6
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#define RGB_COLOR 255
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#if defined(_MSC_VER)
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#include <BaseTsd.h>
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typedef SSIZE_T ssize_t;
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#endif
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double x[] = { 0 };
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double _x[M] = { 0 };
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double w[M][M] = { { 0 },{ 0 } };
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//double x[] = { 0.0 };
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double xSamples[NUMBER_OF_SAMPLES] = { 0.0 };
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double w[WINDOWSIZE][NUMBER_OF_SAMPLES] = { { 0.0 },{ 0.0 } };
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/* *svg graph building* */
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typedef struct {
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@ -38,9 +40,9 @@ typedef struct {
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double yVal[7];
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}point_t;
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point_t points[M]; // [0]=xActual, [1]=xPredicted from directPredecessor, [2]=xPredicted from localMean
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point_t points[NUMBER_OF_SAMPLES]; // [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
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/* *ppm reader/writer* */
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/* *ppm read, copy, write* */
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typedef struct {
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unsigned char red, green, blue;
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}colorChannel_t;
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@ -53,7 +55,7 @@ typedef struct {
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static imagePixel_t * rdPPM(char *fileName); // read PPM file format
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void mkPpmFile(char *fileName, imagePixel_t *image); // writes PPM file
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int ppmColorChannel(FILE* fp, imagePixel_t *image); // writes colorChannel from PPM file to log file
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void ppmTo_X( FILE* fp ); // stores color channel values in _x[]
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void colorSamples( FILE* fp ); // stores color channel values in xSamples[]
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/* *file handling* */
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char * mkFileName(char* buffer, size_t max_len, int suffixId);
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@ -69,30 +71,32 @@ double rndm(void);
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double sum_array(double x[], int length);
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void directPredecessor(void);
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void localMean(void);
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void differentialPredecessor( void );
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double *popNAN(double *xError, int xErrorLength); //return new array without NAN values
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double windowXMean( int _arraylength, int xCount );
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int main(int argc, char **argv) {
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char fileName[50];
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int i, xLength;
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int i, k, xLength;
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int *colorChannel;
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imagePixel_t *image;
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image = rdPPM("beaches.ppm");
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image = rdPPM("cow.ppm");
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mkFileName(fileName, sizeof(fileName), TEST_VALUES);
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FILE* fp5 = fopen(fileName, "w");
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xLength = ppmColorChannel(fp5, image);
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printf("%d\n", xLength);
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FILE* fp6 = fopen(fileName, "r");
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ppmTo_X ( fp6 );
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colorSamples ( fp6 );
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srand((unsigned int)time(NULL));
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for (i = 0; i < M; i++) {
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for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
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// _x[i] += ((255.0 / M) * i); // Init test values
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for (int k = 0; k < M; k++) {
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for (int k = 0; k < WINDOWSIZE; k++) {
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w[k][i]= rndm(); // Init weights
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}
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}
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@ -101,7 +105,7 @@ int main(int argc, char **argv) {
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// save plain test_array before math magic happens
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FILE *fp0 = fopen(fileName, "w");
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for (i = 0; i <= tracking; i++) {
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for (int k = 0; k < tracking; k++) {
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for ( k = 0; k < WINDOWSIZE; k++) {
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fprintf(fp0, "[%d][%d] %lf\n", k, i, w[k][i]);
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}
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}
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@ -110,14 +114,14 @@ int main(int argc, char **argv) {
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// math magic
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localMean();
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directPredecessor(); // TODO: used_weights.txt has gone missing!
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//directPredecessor();
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//differentialPredecessor();
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// save test_array after math magic happened
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// memset( fileName, '\0', sizeof(fileName) );
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mkFileName(fileName, sizeof(fileName), USED_WEIGHTS);
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FILE *fp1 = fopen(fileName, "w");
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for (i = 0; i < tracking; i++) {
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for (int k = 0; k < tracking; k++) {
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for (int k = 0; k < WINDOWSIZE; k++) {
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fprintf(fp1, "[%d][%d] %lf\n", k, i, w[k][i]);
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}
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@ -131,62 +135,83 @@ int main(int argc, char **argv) {
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/*
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=======================================================================================
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======================================================================================================
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localMean
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Variant (1/3), substract local mean.
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=======================================================================================
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======================================================================================================
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*/
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void localMean(void) {
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char fileName[50];
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double xError[M]; // includes e(n)
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memset(xError, 0, M);// initialize xError-array with Zero
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int xCount = 0; // runtime var
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int i;
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double xError[NUMBER_OF_SAMPLES]; // includes e(n)
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memset(xError, 0.0, NUMBER_OF_SAMPLES);// initialize xError-array with Zero
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int xCount = 0, i; // runtime var;
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mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
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FILE* fp4 = fopen(fileName, "w");
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fprintf(fp4, "\n\n\n\n*********************LocalMean*********************\n");
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for (xCount = 1; xCount < M; xCount++) {
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//double xPartArray[xCount]; //includes all values at the size of runtime var
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double xMean = (xCount > 0) ? (sum_array(_x, xCount) / xCount) : 0;// xCount can not be zero
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fprintf(fp4, "\n=====================================LocalMean=====================================\n");
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double xMean = xSamples[0];
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double weightedSum = 0.0;
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double filterOutput = 0.0;
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double xSquared = 0.0;
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double xPredicted = 0.0;
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double xActual = _x[xCount + 1];
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double xActual = 0.0;
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
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double xPartArray[xCount]; //includes all values at the size of runtime var
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//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
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int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
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//printf("xCount:%d, length:%d\n", xCount, _arrayLength);
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xMean = ( xCount > 0 ) ? windowXMean(_arrayLength, xCount) : 0;
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// printf("WINDOWSIZE:%f\n", windowXMean(_arrayLength, xCount));
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xPredicted = 0.0;
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xActual = xSamples[xCount + 1];
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// weightedSum += _x[ xCount-1 ] * w[xCount][0];
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for (i = 1; i < _arrayLength ; i++) { //get predicted value
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xPredicted += (w[i][xCount] * ( xSamples[xCount - i] - xMean));
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for (i = 1; i < xCount; i++) { //get predicted value
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xPredicted += (w[i][xCount] * (_x[xCount - i] - xMean));
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}
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xPredicted += xMean;
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xError[xCount] = xActual - xPredicted;
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points[xCount].xVal[2] = xCount;
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points[xCount].yVal[2] = xPredicted;
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double xSquared = 0.0;
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printf("Pred: %f\t\tActual:%f\n", xPredicted,xActual);
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points[xCount].xVal[1] = xCount;
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points[xCount].yVal[1] = xPredicted;
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points[xCount].xVal[4] = xCount;
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points[xCount].yVal[4] = xError[xCount];
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for (i = 1; i < xCount; i++) { //get x squared
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xSquared = +pow(_x[xCount - i], 2);
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xSquared = 0.0;
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for (i = 1; i < _arrayLength; i++) { //get xSquared
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//xSquared += pow(xSamples[xCount - i], 2);
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xSquared += pow(xSamples[xCount - i] - xMean, 2);
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printf("xSquared:%f\n", xSquared);
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}
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for (i - 1; i < xCount; i++) { //update weights
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * (_x[xCount - i] / xSquared);
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if(xSquared == 0.0){ // returns Pred: -1.#IND00
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xSquared = 1.0;
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}
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//printf("%f\n", xSquared);
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for (i = 1; i < _arrayLength; i++) { //update weights
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - i] - xMean) / xSquared);
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}
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fprintf(fp4, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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}
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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double mean = sum_array(xError, xErrorLength) / M;
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printf("vor:%d", xErrorLength);
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popNAN(xError, xErrorLength);
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printf("nach:%d", xErrorLength);
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xErrorLength = sizeof(xError) / sizeof(xError[0]);
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double mean = sum_array(xError, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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// Mean square
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for (i = 0; i < M - 1; i++) {
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deviation += pow(xError[i], 2);
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for (i = 0; i < xErrorLength - 1; i++) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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@ -199,17 +224,15 @@ void localMean(void) {
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fclose(fp4);
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}
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/*
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===================================
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======================================================================================================
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directPredecessor
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Variant (2/3),
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substract direct predecessor
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===================================
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======================================================================================================
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*/
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void directPredecessor(void) {
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@ -217,46 +240,56 @@ void directPredecessor(void) {
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double xError[2048];
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int xCount = 0, i;
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double xActual;
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int xPredicted = 0.0;
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// File handling
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mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
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FILE *fp3 = fopen(fileName, "w");
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fprintf(fp3, "\n\n\n\n*********************DirectPredecessor*********************\n");
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fprintf(fp3, "\n=====================================DirectPredecessor=====================================\n");
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for (xCount = 1; xCount < M + 1; xCount++) {
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xActual = _x[xCount + 1];
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double xPredicted = 0.0;
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES + 1; xCount++) {
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double xPartArray[xCount]; //includes all values at the size of runtime var
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//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
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int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
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printf("xCount:%d, length:%d\n", xCount, _arrayLength);
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double xMean = ( xCount > 0 ) ? windowXMean(_arrayLength, xCount) : 0;
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printf("%f\n", windowXMean(_arrayLength, xCount));
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xPredicted = 0.0;
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xActual = xSamples[xCount + 1];
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for (i = 1; i < xCount; i++) {
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xPredicted += (w[i][xCount] * (_x[xCount - i] - _x[xCount - i - 1]));
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for (i = 1; i < _arrayLength; i++) {
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xPredicted += (w[i][xCount] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
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}
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xPredicted += _x[xCount - 1];
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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points[xCount].xVal[0] = xCount;
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points[xCount].yVal[0] = xActual;
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points[xCount].xVal[1] = xCount;
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points[xCount].yVal[1] = xPredicted;
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points[xCount].xVal[2] = xCount;
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points[xCount].yVal[2] = xPredicted;
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points[xCount].xVal[5] = xCount;
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points[xCount].yVal[5] = xError[xCount];
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double xSquared = 0.0;
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for (i = 1; i < xCount; i++) {
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xSquared += pow(_x[xCount - i] - _x[xCount - i - 1], 2); // substract direct predecessor
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for (i = 1; i < _arrayLength; i++) {
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xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
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}
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for (i = 1; i < xCount; i++) {
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((_x[xCount - i] - _x[xCount - i - 1]) / xSquared); //TODO: double val out of bounds
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for (i = 1; i < _arrayLength; i++) {
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
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}
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}
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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printf("vor:%d", xErrorLength);
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popNAN(xError, xErrorLength);
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printf("nach:%d", xErrorLength);
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xErrorLength = sizeof(xError) / sizeof(xError[0]);
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double mean = sum_array(xError, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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for (i = 0; i < xErrorLength - 1; i++) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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mkSvgGraph(points);
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fprintf(fp3, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
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@ -264,19 +297,80 @@ void directPredecessor(void) {
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}
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/*
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======================================================================================================
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differentialPredecessor
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variant (3/3),
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differenital predecessor.
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======================================================================================================
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*/
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void differentialPredecessor ( void ) {
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char fileName[512];
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double xError[2048];
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int xCount = 0, i;
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double xActual;
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// File handling
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mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR);
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FILE *fp6 = fopen(fileName, "w");
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fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\n");
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES + 1; xCount++) {
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xActual = xSamples[xCount + 1];
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double xPredicted = 0.0;
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for (i = 1; i < xCount; i++) {
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xPredicted += (w[i][xCount] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
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}
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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points[xCount].xVal[3] = xCount;
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points[xCount].yVal[3] = xPredicted;
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points[xCount].xVal[6] = xCount;
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points[xCount].yVal[6] = xError[xCount];
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double xSquared = 0.0;
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for (i = 1; i < xCount; i++) {
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xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
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}
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for (i = 1; i < xCount; i++) {
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
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}
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}
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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double mean = sum_array(xError, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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for (i = 0; i < xErrorLength - 1; i++) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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mkSvgGraph(points);
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fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
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fclose(fp6);
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}
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/*
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=========================================================================
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======================================================================================================
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mkFileName
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Writes the current date plus the suffix with index suffixId
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into the given buffer. If[M ?K the total length is longer than max_len,
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into the given buffer. If the total length is longer than max_len,
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only max_len characters will be written.
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=========================================================================
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======================================================================================================
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*/
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char *mkFileName(char* buffer, size_t max_len, int suffixId) {
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|
|
@ -292,100 +386,115 @@ char *mkFileName(char* buffer, size_t max_len, int suffixId) {
|
|||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
=========================================================================
|
||||
======================================================================================================
|
||||
|
||||
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" };
|
||||
char * suffix[] = { "_weights_pure.txt", "_weights_used.txt", "_direct_predecessor.txt", "_ergebnisse.txt", "_localMean.txt","_testvalues.txt", "_differential_predecessor.txt" };
|
||||
return suffix[id];
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
|
||||
myLogger
|
||||
|
||||
Logs x,y points to svg graph
|
||||
|
||||
Logs on filepointer, used for svg graphing
|
||||
|
||||
==========================================================================
|
||||
*/
|
||||
/*
|
||||
void myLogger(FILE* fp, point_t points[]) {
|
||||
int i;
|
||||
for (i = 0; i <= M; i++) { // xActual
|
||||
fprintf(fp, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
|
||||
}
|
||||
fprintf(fp, "\" fill=\"none\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i < M - 1; i++) { // xPred from directPredecessor
|
||||
fprintf(fp, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]);
|
||||
}
|
||||
fprintf(fp, "\" fill=\"none\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i <= M; i++) { //xPred from lastMean
|
||||
fprintf(fp, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]);
|
||||
}
|
||||
}
|
||||
======================================================================================================
|
||||
*/
|
||||
void bufferLogger(char *buffer, point_t points[]) {
|
||||
int i;
|
||||
char _buffer[512] = "";
|
||||
|
||||
for (i = 0; i <= M; i++) { // xActual
|
||||
for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { // xActual
|
||||
sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
|
||||
strcat(buffer, _buffer);
|
||||
}
|
||||
strcat(buffer, "\" fill=\"none\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i < M - 1; i++) { // xPred from directPredecessor
|
||||
strcat(buffer, "\" fill=\"none\" stroke=\"black\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { // xPrediceted from localMean
|
||||
sprintf(_buffer, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]);
|
||||
strcat(buffer, _buffer);
|
||||
}
|
||||
strcat(buffer, "\" fill=\"none\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i <= M; i++) { //xPred from lastMean
|
||||
for (i = 0; i <= NUMBER_OF_SAMPLES - 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\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
|
||||
for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++ ){ //xPredicted from diff Pred
|
||||
sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].xVal[3]);
|
||||
strcat(buffer, _buffer);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
=========================================================================
|
||||
======================================================================================================
|
||||
|
||||
sum_array
|
||||
|
||||
|
||||
Sum of all elements in x within a defined length
|
||||
|
||||
=========================================================================
|
||||
======================================================================================================
|
||||
*/
|
||||
|
||||
double sum_array(double x[], int length) {
|
||||
double sum_array(double x[], int xlength) {
|
||||
int i = 0;
|
||||
double sum = 0.0;
|
||||
|
||||
for (i = 0; i< length; i++) {
|
||||
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,int xErrorLength ) {
|
||||
int i, counter;
|
||||
double noNAN [xErrorLength];
|
||||
|
||||
for ( i = 0; i < xErrorLength; i++) {
|
||||
if ( !isnan(xError[i]) ) {
|
||||
noNAN[i] = xError[i];
|
||||
counter++;
|
||||
}
|
||||
}
|
||||
realloc(noNAN, counter * sizeof(double));
|
||||
int noNANLength = sizeof(noNAN)/ sizeof(noNAN[0]);
|
||||
memcpy(xError, noNAN, noNANLength);
|
||||
return xError;
|
||||
|
||||
}
|
||||
/*
|
||||
======================================================================================================
|
||||
|
||||
r2
|
||||
|
||||
returns a random double value between 0 and 1
|
||||
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
*/
|
||||
|
||||
double r2(void) {
|
||||
|
|
@ -393,15 +502,14 @@ double r2(void) {
|
|||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
|
||||
rndm
|
||||
|
||||
fills a double variable with random value and returns it
|
||||
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
*/
|
||||
|
||||
double rndm(void) {
|
||||
|
|
@ -410,15 +518,14 @@ double rndm(void) {
|
|||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
|
||||
mkSvgGraph
|
||||
|
||||
parses template.svg and writes results in said template
|
||||
|
||||
==========================================================================
|
||||
======================================================================================================
|
||||
*/
|
||||
|
||||
void mkSvgGraph(point_t points[]) {
|
||||
|
|
@ -448,16 +555,15 @@ void mkSvgGraph(point_t points[]) {
|
|||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
===========================================================================
|
||||
======================================================================================================
|
||||
|
||||
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) {
|
||||
|
|
@ -513,16 +619,15 @@ static imagePixel_t *rdPPM(char *fileName) {
|
|||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
=======================================================================================
|
||||
======================================================================================================
|
||||
|
||||
mkPpmFile
|
||||
|
||||
gets output from the result of rdPpmFile and writes a new mkPpmFile. Best Case is a
|
||||
carbon copy of the source image
|
||||
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) {
|
||||
|
|
@ -538,42 +643,74 @@ void mkPpmFile(char *fileName, imagePixel_t *image) {
|
|||
fclose(fp);
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
======================================================================================
|
||||
======================================================================================================
|
||||
|
||||
ppmColorChannel
|
||||
|
||||
gets one of the rgb color channels and returns the array
|
||||
gets one of the rgb color channels and writes them to a file
|
||||
|
||||
======================================================================================
|
||||
======================================================================================================
|
||||
*/
|
||||
|
||||
int ppmColorChannel(FILE* fp, imagePixel_t *image) {
|
||||
int length = 1000; // (image->x * image->y) / 3;
|
||||
// int length = 1000; // (image->x * image->y) / 3;
|
||||
int i = 0;
|
||||
|
||||
if (image) {
|
||||
for ( i = 0; i <= length; i++ ){
|
||||
for ( i = 0; i < NUMBER_OF_SAMPLES - 1; i++ ){
|
||||
fprintf(fp,"%d\n", image->data[i].green);
|
||||
}
|
||||
}
|
||||
fclose(fp);
|
||||
return length;
|
||||
return NUMBER_OF_SAMPLES;
|
||||
}
|
||||
|
||||
void ppmTo_X( FILE* fp ) {
|
||||
|
||||
/*
|
||||
======================================================================================================
|
||||
|
||||
colorSamples
|
||||
|
||||
reads colorChannel values from file and stores them in xSamples as well as points datatype for
|
||||
creating the SVG graph
|
||||
|
||||
======================================================================================================
|
||||
*/
|
||||
void colorSamples( FILE* fp ) {
|
||||
int i = 0;
|
||||
int d, out;
|
||||
double f;
|
||||
int length = 1000;
|
||||
char buffer[length];
|
||||
char buffer[NUMBER_OF_SAMPLES];
|
||||
|
||||
while ( !feof(fp) ) {
|
||||
if ( fgets(buffer, length, fp) != NULL ) {
|
||||
sscanf(buffer,"%lf", &_x[i]);
|
||||
printf("%lf\n", _x[i] );
|
||||
if ( fgets(buffer, NUMBER_OF_SAMPLES, 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);
|
||||
}
|
||||
|
||||
double windowXMean (int _arraylength, int xCount) {
|
||||
int count;
|
||||
double sum = 0.0;
|
||||
double *ptr;
|
||||
// printf("*window\t\t*base\t\txMean\n\n");
|
||||
for ( ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { //set ptr to beginning of window
|
||||
//window = xCount - _arraylength
|
||||
//base = window - _arraylength;
|
||||
//sum = 0.0;
|
||||
//for( count = 0; count < _arraylength; count++){
|
||||
sum += *ptr;
|
||||
// printf("%f\n", *base);
|
||||
|
||||
//}
|
||||
}
|
||||
//printf("\n%lf\t%lf\t%lf\n", *ptr, *ptr2, (sum/(double)WINDOW));
|
||||
return sum/(double)_arraylength;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue