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added new stuff

This commit is contained in:
gurkenhabicht 2018-05-13 21:41:44 +02:00
parent 136aab9ecf
commit 121955bc5a
2 changed files with 903 additions and 102 deletions
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bin/NLMSsingleweights.c Normal file
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//
//
// NLMSvariants.c
//
// Created by FBRDNLMS on 26.04.18.
//
//
#include <stdio.h>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <string.h>
#include <float.h> // DBL_MAX
#define NUMBER_OF_SAMPLES 1000
#define WINDOWSIZE 5
#define tracking 40 //Count of weights
#define learnrate 0.8
#define PURE_WEIGHTS 0
#define USED_WEIGHTS 1
#define RESULTS 3
#define DIRECT_PREDECESSOR 2
#define LOCAL_MEAN 4
#define TEST_VALUES 5
#define DIFFERENTIAL_PREDECESSOR 6
#define RGB_COLOR 255
#if defined(_MSC_VER)
#include <BaseTsd.h>
typedef SSIZE_T ssize_t;
#endif
//double x[] = { 0.0 };
double xSamples[NUMBER_OF_SAMPLES] = { 0.0 };
/* *svg graph building* */
typedef struct {
double xVal[7];
double yVal[7];
}point_t;
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
/* *ppm read, copy, write* */
typedef struct {
unsigned char red, green, blue;
}colorChannel_t;
typedef struct {
int x, y;
colorChannel_t *data;
}imagePixel_t;
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
void colorSamples(FILE* fp); // stores color channel values in xSamples[]
/* *file handling* */
char * mkFileName(char* buffer, size_t max_len, int suffixId);
char *fileSuffix(int id);
void myLogger(FILE* fp, point_t points[]);
void mkSvgGraph(point_t points[]);
//void weightsLogger(double *weights, int var);
/* *rand seed* */
double r2(void);
double rndm(void);
/* *math* */
double sum_array(double x[], int length);
void directPredecessor(double *weights);
void localMean(double *weights);
//void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void differentialPredecessor(double *weights);
double *popNAN(double *xError, int xErrorLength); //return new array without NAN values
double windowXMean(int _arraylength, int xCount);
int main(int argc, char **argv) {
double weights[WINDOWSIZE] = { 0.0 };
// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
char fileName[50];
int i, xLength;
imagePixel_t *image;
image = rdPPM("beaches.ppm");
mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w");
xLength = ppmColorChannel(fp5, image);
printf("%d\n", xLength);
FILE* fp6 = fopen(fileName, "r");
colorSamples(fp6);
srand((unsigned int)time(NULL));
for (i = 0; i < WINDOWSIZE; i++) {
//_x[i] += ((255.0 / M) * i); // Init test values
// for (int k = 0; k < WINDOWSIZE; k++) {
weights[i] = rndm(); // Init weights
// }
}
mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS);
// save plain test_array before math magic happens
FILE *fp0 = fopen(fileName, "w");
for (i = 0; i < WINDOWSIZE; i++) {
// for (k = 0; k < WINDOWSIZE; k++) {
fprintf(fp0, "[%d]%lf\n", i, weights[i]);
}
// }
fclose(fp0);
// math magic
/* for (i = 0; i < NUMBER_OF_SAMPLES; i++){
for (k = 0; k < WINDOWSIZE; k++){
local_weights[k][i] = weights[k][i];
printf("ALT::%f\n", local_weights[k][i]);
}
}*/
localMean(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
directPredecessor(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
differentialPredecessor(weights);
mkSvgGraph(points);
// save test_array after math magic happened
// memset( fileName, '\0', sizeof(fileName) );
/* mkFileName(fileName, sizeof(fileName), USED_WEIGHTS);
FILE *fp1 = fopen(fileName, "w");
for (i = 0; i < tracking; i++) {
for (int k = 0; k < WINDOWSIZE; k++) {
fprintf(fp1, "[%d][%d] %lf\n", k, i, w[k][i]);
}
}
fclose(fp1);
*/
// getchar();
printf("\nDONE!\n");
}
/*
======================================================================================================
localMean
Variant (1/3), substract local mean.
======================================================================================================
*/
void localMean(double *weights) {
double local_weights[WINDOWSIZE];
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE);
char fileName[50];
double xError[2048]; // includes e(n)
memset(xError, 0.0, NUMBER_OF_SAMPLES);// initialize xError-array with Zero
int xCount = 0, i; // runtime var;
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
FILE* fp4 = fopen(fileName, "w");
fprintf(fp4, "\n=====================================LocalMean=====================================\n");
double xMean = xSamples[0];
double xSquared = 0.0;
double xPredicted = 0.0;
double xActual = 0.0;
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
//double xPartArray[1000]; //includes all values at the size of runtime var
//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
int _arrayLength = ( xCount > WINDOWSIZE ) ? WINDOWSIZE + 1 : xCount;
//printf("xCount:%d, length:%d\n", xCount, _arrayLength);
xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
// printf("WINDOWSIZE:%f\n", windowXMean(_arrayLength, xCount));
xPredicted = 0.0;
xActual = xSamples[xCount + 1];
// weightedSum += _x[ xCount-1 ] * w[xCount][0];
for (i = 1; i < _arrayLength; i++) { //get predicted value
xPredicted += (local_weights[i] * (xSamples[xCount - i] - xMean));
}
xPredicted += xMean;
xError[xCount] = xActual - xPredicted;
printf("Pred: %f\t\tActual:%f\n", xPredicted, xActual);
points[xCount].xVal[1] = xCount;
points[xCount].yVal[1] = xPredicted;
points[xCount].xVal[4] = xCount;
points[xCount].yVal[4] = xError[xCount];
xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) { //get xSquared
xSquared += pow(xSamples[xCount - i] - xMean, 2);
// printf("xSquared:%f\n", xSquared);
}
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
}
//printf("%f\n", xSquared);
for (i = 1; i < _arrayLength; i++) { //update weights
local_weights[i] = local_weights[i] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
// printf("NEU::%lf\n", local_weights[i]);
}
fprintf(fp4, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
}
int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0;
// Mean square
for (i = 0; i < xErrorLength - 1; i++) {
deviation += pow(xError[i] - mean, 2);
}
deviation /= xErrorLength;
// write in file
mkFileName(fileName, sizeof(fileName), RESULTS);
FILE *fp2 = fopen(fileName, "w");
fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
fclose(fp2);
fclose(fp4);
// weightsLogger( local_weights, USED_WEIGHTS );
//mkSvgGraph(points);
}
/*
======================================================================================================
directPredecessor
Variant (2/3),
substract direct predecessor
======================================================================================================
*/
void directPredecessor(double *weights) {
double local_weights[WINDOWSIZE];
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE );
char fileName[512];
double xError[2048];
int xCount = 0, i;
double xActual = 0.0;
double xPredicted = 0.0;
// File handling
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
FILE *fp3 = fopen(fileName, "w");
fprintf(fp3, "\n=====================================DirectPredecessor=====================================\n");
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
//double xPartArray[1000]; //includes all values at the size of runtime var
//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
//printf("xCount:%d, length:%d\n", xCount, _arrayLength);
// printf("WINDOWSIZE:%f\n", windowXMean(_arrayLength, xCount));
xPredicted = 0.0;
xActual = xSamples[xCount + 1];
// weightedSum += _x[ xCount-1 ] * w[xCount][0];
for (i = 1; i < _arrayLength; i++) {
xPredicted += (local_weights[i] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
}
xPredicted += xSamples[xCount - 1];
xError[xCount] = xActual - xPredicted;
fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
points[xCount].xVal[2] = xCount;
points[xCount].yVal[2] = xPredicted;
points[xCount].xVal[5] = xCount;
points[xCount].yVal[5] = xError[xCount];
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++) {
local_weights[i] = local_weights[i] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
}
}
int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0;
for (i = 0; i < xErrorLength - 1; i++) {
deviation += pow(xError[i] - mean, 2);
}
deviation /= xErrorLength;
// mkSvgGraph(points);
fprintf(fp3, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
fclose(fp3);
}
/*
======================================================================================================
differentialPredecessor
variant (3/3),
differenital predecessor.
======================================================================================================
*/
void differentialPredecessor(double *weights) {
double local_weights[WINDOWSIZE];
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE );
char fileName[512];
double xError[2048];
int 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, "\n=====================================DifferentialPredecessor=====================================\n");
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
xActual = xSamples[xCount +1];
xPredicted = 0.0;
int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
for (i = 1; i < _arrayLength; i++) {
xPredicted += (local_weights[i] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
}
xPredicted += xSamples[xCount - 1];
xError[xCount] = xActual - xPredicted;
fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xSamples[xCount], xError[xCount]);
points[xCount].xVal[3] = xCount;
points[xCount].yVal[3] = xPredicted;
points[xCount].xVal[6] = xCount;
points[xCount].yVal[6] = xError[xCount];
double xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) {
xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
}
if (xSquared == 0.0 ){
xSquared = 1.0;
}
for (i = 1; i < _arrayLength; i++) {
local_weights[i] = local_weights[i] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
printf("NEU::%lf\n", local_weights[i]);
}
}
int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0;
for (i = 0; i < xErrorLength - 1; i++) {
deviation += pow(xError[i] - mean, 2);
}
deviation /= xErrorLength;
//mkSvgGraph(points);
fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
fclose(fp6);
}
/*
======================================================================================================
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];
}
/*
======================================================================================================
myLogger
Logs x,y points to svg graph
======================================================================================================
*/
/*
void weightsLogger (double weights[WINDOWSIZE], int val ) {
char fileName[512];
int i;
mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa");
for (i = 0; i < WINDOWSIZE; 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);
}
*/
void bufferLogger(char *buffer, point_t points[]) {
int i;
char _buffer[512] = "";
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\" id=\"svg_1\" 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\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
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\" id=\"svg_3\" 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);
}
strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"blue\" 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, int xErrorLength) {
int i, counter;
double *tmp = NULL;
double *more_tmp = NULL;
//tmp = realloc( noNAN, xErrorLength * sizeof(double) );
for ( i = 0; i < xErrorLength; i++ ) {
counter ++;
more_tmp = (double *) realloc ( tmp, counter*(sizeof(double) ));
if ( !isnan(xError[i]) ) {
tmp = more_tmp;
tmp[counter - 1] = xError[i];
}
}
/* for (i = 0; i < xErrorLength; i++) {
if (!isnan(xError[i])) {
tmp[i] = xError[i];
counter++;
}
}
*/
//realloc(noNAN, counter * sizeof(double));
//int tmpLength = sizeof(noNAN) / sizeof(noNAN[0]);
//memcpy(xError, tmp, tmpLength);
//return xError;
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));
// printf("%s\n", line);
if (strstr(line, firstGraph) != NULL) {
bufferLogger(buffer, points);
}
}
fprintf(target, buffer);
//puts(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) {
// int length = 1000; // (image->x * image->y) / 3;
int i = 0;
if (image) {
for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) {
fprintf(fp, "%d\n", image->data[i].green);
}
}
fclose(fp);
return NUMBER_OF_SAMPLES;
}
/*
======================================================================================================
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;
char buffer[NUMBER_OF_SAMPLES];
while (!feof(fp)) {
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) {
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;
}

225
bin/NLMSvariants.c
View File

@ -13,7 +13,7 @@
#include <string.h>
#include <float.h> // DBL_MAX
#define NUMBER_OF_SAMPLES 1000
#define NUMBER_OF_SAMPLES 500
#define WINDOWSIZE 5
#define tracking 40 //Count of weights
#define learnrate 0.8
@ -33,8 +33,6 @@ typedef SSIZE_T ssize_t;
//double x[] = { 0.0 };
double xSamples[NUMBER_OF_SAMPLES] = { 0.0 };
/* *svg graph building* */
typedef struct {
double xVal[7];
@ -58,35 +56,35 @@ 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
void colorSamples(FILE* fp); // stores color channel values in xSamples[]
/* *file handling* */
/* *file handling* */
char * mkFileName(char* buffer, size_t max_len, int suffixId);
char *fileSuffix(int id);
void myLogger(FILE* fp, point_t points[]);
void mkSvgGraph(point_t points[]);
//void weightsLogger(double *weights, int var);
/* *rand seed* */
double r2(void);
double rndm(void);
/* *math* */
double sum_array(double x[], int length);
void directPredecessor(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void localMean(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void differentialPredecessor(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
double *popNAN(double *xError, int xErrorLength); //return new array without NAN values
void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
//void differentialPredecessor(double *weights);
double *popNAN(double *xError); //return new array without NAN values
double windowXMean(int _arraylength, int xCount);
int main(int argc, char **argv) {
double w[WINDOWSIZE][NUMBER_OF_SAMPLES] = { { 0.0 },{ 0.0 } };
double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
//int main(int argc, char **argv) {
int main( void ) {
double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} };
// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
char fileName[50];
int i, k, xLength;
int *colorChannel;
int i,k, xLength;
imagePixel_t *image;
image = rdPPM("cow.ppm");
image = rdPPM("beaches.ppm");
mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w");
xLength = ppmColorChannel(fp5, image);
@ -98,47 +96,39 @@ int main(int argc, char **argv) {
srand((unsigned int)time(NULL));
for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
// _x[i] += ((255.0 / M) * i); // Init test values
//_x[i] += ((255.0 / M) * i); // Init test values
for (int k = 0; k < WINDOWSIZE; k++) {
w[k][i] = rndm(); // Init weights
weights[k][i] = rndm(); // Init weights
}
}
mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS);
// save plain test_array before math magic happens
FILE *fp0 = fopen(fileName, "w");
for (i = 0; i <= tracking; i++) {
for (i = 0; i < tracking; i++) {
for (k = 0; k < WINDOWSIZE; k++) {
fprintf(fp0, "[%d][%d] %lf\n", k, i, w[k][i]);
fprintf(fp0, "[%d][%d]%lf\n", k, i, weights[k][i]);
}
}
fclose(fp0);
// math magic
for (i = 0; i < NUMBER_OF_SAMPLES; i++){
for (k = 0; k < WINDOWSIZE; k++){
local_weights[k][i] = w[k][i];
}
}
localMean(local_weights);
for (i = 0; i < NUMBER_OF_SAMPLES; i++){
for (k = 0; k < WINDOWSIZE; k++){
local_weights[k][i] = w[k][i];
}
}
//directPredecessor(local_weights);
for (i = 0; i < NUMBER_OF_SAMPLES; i++){
for (k = 0; k < WINDOWSIZE; k++){
local_weights[k][i] = w[k][i];
}
}
//differentialPredecessor(local_weights);
/* for (i = 0; i < NUMBER_OF_SAMPLES; i++){
for (k = 0; k < WINDOWSIZE; k++){
local_weights[k][i] = weights[k][i];
printf("ALT::%f\n", local_weights[k][i]);
}
}*/
localMean(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
// directPredecessor(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
// differentialPredecessor(weights);
mkSvgGraph(points);
// save test_array after math magic happened
// memset( fileName, '\0', sizeof(fileName) );
mkFileName(fileName, sizeof(fileName), USED_WEIGHTS);
/* mkFileName(fileName, sizeof(fileName), USED_WEIGHTS);
FILE *fp1 = fopen(fileName, "w");
for (i = 0; i < tracking; i++) {
for (int k = 0; k < WINDOWSIZE; k++) {
@ -147,10 +137,9 @@ int main(int argc, char **argv) {
}
fclose(fp1);
*/
// getchar();
printf("DONE!");
printf("\nDONE!\n");
}
@ -164,8 +153,11 @@ Variant (1/3), substract local mean.
======================================================================================================
*/
void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
//double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
double (*local_weights)[WINDOWSIZE] = malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
// double *local_weights[WINDOWSIZE];
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
char fileName[50];
double xError[2048]; // includes e(n)
memset(xError, 0.0, NUMBER_OF_SAMPLES);// initialize xError-array with Zero
@ -175,17 +167,14 @@ void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
fprintf(fp4, "\n=====================================LocalMean=====================================\n");
double xMean = xSamples[0];
double weightedSum = 0.0;
double filterOutput = 0.0;
double xSquared = 0.0;
double xPredicted = 0.0;
double xActual = 0.0;
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
//double xPartArray[1000]; //includes all values at the size of runtime var
//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
int _arrayLength = ( xCount > WINDOWSIZE ) ? WINDOWSIZE + 1 : xCount;
//printf("xCount:%d, length:%d\n", xCount, _arrayLength);
xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
// printf("WINDOWSIZE:%f\n", windowXMean(_arrayLength, xCount));
@ -199,7 +188,7 @@ void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
}
xPredicted += xMean;
xError[xCount] = xActual - xPredicted;
printf("Pred: %f\t\tActual:%f\n", xPredicted, xActual);
// printf("Pred: %f\t\tActual:%f\n", xPredicted, xActual);
points[xCount].xVal[1] = xCount;
points[xCount].yVal[1] = xPredicted;
points[xCount].xVal[4] = xCount;
@ -207,31 +196,32 @@ void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) { //get xSquared
//xSquared += pow(xSamples[xCount - i], 2);
xSquared += pow(xSamples[xCount - i] - xMean, 2);
printf("xSquared:%f\n", xSquared);
// printf("xSquared:%f\n", xSquared);
}
if (xSquared == 0.0) { // returns Pred: -1.#IND00
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
}
//printf("%f\n", xSquared);
for (i = 1; i < _arrayLength; i++) { //update weights
local_weights[i][xCount + 1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
// printf("NEU::%lf\n", local_weights[i][xCount]);
}
fprintf(fp4, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
}
/* int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
// int xErrorLength = sizeof(xError) / sizeof(xError[0]);
// printf("vor:%d", xErrorLength);
popNAN(xError); // delete NAN values from xError[]
// printf("%lf", xError[499]);
double xErrorLength = xError[0]; // Watch popNAN()!
printf("Xerrorl:%lf", xErrorLength);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0;
// Mean square
for (i = 0; i < xErrorLength - 1; i++) {
for (i = 1; i < xErrorLength; i++) {
deviation += pow(xError[i] - mean, 2);
}
deviation /= xErrorLength;
@ -241,9 +231,12 @@ void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
FILE *fp2 = fopen(fileName, "w");
fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
fclose(fp2);
free(local_weights);
fclose(fp4);
*/
//mkSvgGraph(points);
// weightsLogger( local_weights, USED_WEIGHTS );
mkSvgGraph(points);
}
/*
@ -257,12 +250,16 @@ substract direct predecessor
======================================================================================================
*/
void directPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
double (*local_weights)[WINDOWSIZE] = malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES );
char fileName[512];
double xError[2048];
int xCount = 0, i;
double xActual = 0.0;
int xPredicted = 0.0;
double xPredicted = 0.0;
// File handling
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
FILE *fp3 = fopen(fileName, "w");
@ -297,13 +294,13 @@ void directPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
}
for (i = 1; i < _arrayLength; i++) {
local_weights[i][xCount + 1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
}
}
/*
int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
popNAN(xError);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
@ -316,7 +313,7 @@ void directPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
// mkSvgGraph(points);
fprintf(fp3, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
*/
fclose(fp3);
}
@ -331,8 +328,11 @@ differenital predecessor.
======================================================================================================
*/
void differentialPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
double (*local_weights)[WINDOWSIZE] = malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES );
char fileName[512];
double xError[2048];
int xCount = 0, i;
@ -347,7 +347,8 @@ void differentialPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
double xPredicted = 0.0;
xPredicted = 0.0;
xActual = xSamples[xCount + 1];
for (i = 1; i < _arrayLength; i++) {
xPredicted += (local_weights[i][xCount] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
@ -366,13 +367,13 @@ void differentialPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]
xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
}
for (i = 1; i < _arrayLength; i++) {
local_weights[i][xCount + 1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
}
}
/*
int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength);
popNAN(xError, xErrorLength);
popNAN(xError);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
@ -385,7 +386,7 @@ void differentialPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]
//mkSvgGraph(points);
fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
*/
fclose(fp6);
@ -443,6 +444,22 @@ Logs x,y points to svg graph
======================================================================================================
*/
void weightsLogger (double weights[WINDOWSIZE], int val ) {
char fileName[512];
int i;
mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa");
for (i = 0; i < WINDOWSIZE; 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);
}
void bufferLogger(char *buffer, point_t points[]) {
int i;
char _buffer[512] = "";
@ -503,21 +520,32 @@ returns length of new array without NAN values
======================================================================================================
*/
double *popNAN(double *xError, int xErrorLength) {
int i, counter;
double noNAN[10];
realloc(noNAN, xErrorLength);
double *popNAN(double *xError) {
int i, counter = 1;
double tmpLength = 0.0;
double *tmp = NULL;
double *more_tmp = NULL;
for (i = 0; i < xErrorLength; i++) {
if (!isnan(xError[i])) {
noNAN[i] = xError[i];
counter++;
}
// printf("LENGTH: %d", xErrorLength);
for ( i = 0; i < NUMBER_OF_SAMPLES; 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++;
}
}
realloc(noNAN, counter * sizeof(double));
int noNANLength = sizeof(noNAN) / sizeof(noNAN[0]);
memcpy(xError, noNAN, noNANLength);
return xError;
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;
}
/*
@ -562,8 +590,8 @@ 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");
FILE *input = fopen("graphResults_template.html", "r");
FILE *target = fopen("graphResults.html", "w");
char line[512];
char firstGraph[15] = { "<path d=\"M0 0" };
@ -713,8 +741,6 @@ creating the SVG graph
*/
void colorSamples(FILE* fp) {
int i = 0;
int d, out;
double f;
char buffer[NUMBER_OF_SAMPLES];
while (!feof(fp)) {
@ -730,20 +756,21 @@ void colorSamples(FILE* 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);
//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;
}