graphing is opt in now.
This commit is contained in:
gurkenhabicht
parent
25251cd572
commit
8ce167ff0d
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@ -64,13 +64,14 @@ int main( int argc, char **argv ) {
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char *colorChannel = (char *) malloc(sizeof(char)* 32);
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char *inputfile = (char *)malloc(sizeof(char) * 32);
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unsigned *seed = NULL;
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unsigned k, xclude = 0;
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unsigned k, include = 0;
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unsigned windowSize = 5;
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unsigned samplesCount = 512;
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char *stdcolor = "green", xBuffer[512];
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colorChannel = stdcolor;
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unsigned int uint_buffer[1], windowBuffer[1];
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double learnrate = 0.4;
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char *istrue = "true";
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while( (argc > 1) && (argv[1][0] == '-') ) { // Parses parameters from stdin
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@ -111,9 +112,14 @@ int main( int argc, char **argv ) {
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++argv;
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--argc;
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break;
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case 'x':
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case 'g':
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sscanf(&argv[1][3], "%s", xBuffer);
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xclude = 1;
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if ( strstr(xBuffer, istrue) ) {
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include = 1;
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} else {
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printf( "Wrong Argruments: %s\n", argv[1]);
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usage(argv);
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}
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++argv;
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--argc;
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break;
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@ -169,7 +175,7 @@ int main( int argc, char **argv ) {
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directPredecessor ( mlData, points);
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differentialPredecessor( mlData, points );
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if ( xclude == 0 ) {
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if ( include == 1 ) {
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mkSvgGraph(points); // Graph building
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}
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@ -480,8 +486,8 @@ char * fileSuffix ( int id ) {
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"_localMean.txt",
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"_testvalues.txt",
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"_differential_predecessor.txt",
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"_weights_used_local_mean",
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"_weights_used_diff_pred",
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"_weights_used_local_mean.txt",
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"_weights_used_diff_pred.txt",
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};
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return suffix[id];
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}
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@ -24,12 +24,12 @@ There are a bunch of options you can predefine but do not have to. The only para
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| Parameter | Description | StdVal |
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|:----------|:-----------------------------:|:-------|
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| -i | The inputfile, has to be PPM | none |
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| -n | Amount of input data used | 500 |
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| -w | Size of M (window) | 5 |
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| -i | The inputfile, has to be PPM. | none |
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| -n | Amount of input data used. | 500 |
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| -w | Size of M (window). | 5 |
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| -c | Choose RGB color channel, green has least noise. | green |
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| -l | Learnrate of machine learning | 0.4 |
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| -x | Exclude graph building. Logfiles only, choose for insane amount of input data. 10Mio. Pixels tested so far.| none|
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| -l | Learnrate of machine learning.| 0.4 |
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| -g true | include graph building. Choose for amount of input data lower than 1200.| none|
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| -s | Seed randomizing weights. Choose for repoducability. | time(NULL)|
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This code is ANSI compatible no POSIX, C99, C11 or GNU libs, because it had to be VS compatible . There are way easier methods like getline() or getopt(), I know ...
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@ -23,58 +23,59 @@ double *xSamples; // Input values
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mldata_t *mlData = NULL; // Machine learning
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point_t *points = NULL; // Graphing
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/* *graph building* */
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/* *graph building* */
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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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char *colorChannel, mldata_t *mlData);
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void colorSamples(FILE* fp, mldata_t *mlData); // Stores color channel values in xSamples
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/* *file handling* */
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char * mkFileName(char* buffer,
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size_t max_len, int suffixId);
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char *fileSuffix(int id);
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char *fileHeader(int id); // Header inside the logfiles
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//void myLogger ( FILE* fp, point_t points[] );
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/* *file handling* */
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char * mkFileName ( char* buffer,
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size_t max_len, int suffixId );
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char *fileSuffix ( int id );
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char *fileHeader ( int id ); // Header inside the logfiles
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//void myLogger ( FILE* fp, point_t points[] );
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void bufferLogger(char *buffer, point_t points[]); // Writes points to graph template
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void mkSvgGraph(point_t points[]); // Parses graph template and calls bufferLogger()
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void weightsLogger(double *weights, int suffix); // Writes updated weights to a file
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void mkSvgGraph ( point_t points[] ); // Parses graph template and calls bufferLogger()
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void weightsLogger ( double *weights, int suffix ); // Writes updated weights to a file
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/* *rand seed* */
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double r2(void); // Random val between 0 and 1
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double rndm(void);
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/* *rand seed* */
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double r2 ( void ); // Random val between 0 and 1
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double rndm ( void );
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/* *args parser* */
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void usage(char **argv); // Help text called by args parser
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void usage ( char **argv ); // Help text called by args parser
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/* *math* */
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/* *math* */
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mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate);
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double sum_array(double x[], int length);
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void localMean(mldata_t *mlData, point_t points[]); // First,
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void directPredecessor(mldata_t *mlData, point_t points[]); // Second,
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void differentialPredecessor(mldata_t *mlData, point_t points[]); // Third filter implementation
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void localMean ( mldata_t *mlData,point_t points[] ); // First,
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void directPredecessor ( mldata_t *mlData, point_t points[] ); // Second,
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void differentialPredecessor ( mldata_t *mlData, point_t points[] ); // Third filter implementation
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double *popNAN(double *xError); // Returns array without NAN values, if any exist
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double windowXMean(int _arraylength, int xCount); // Returns mean value of given window
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int main(int argc, char **argv) {
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char *colorChannel = (char *)malloc(sizeof(char) * 32);
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int main( int argc, char **argv ) {
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char *colorChannel = (char *) malloc(sizeof(char)* 32);
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char *inputfile = (char *)malloc(sizeof(char) * 32);
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unsigned *seed = NULL;
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unsigned k, xclude = 0;
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unsigned k, include = 0;
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unsigned windowSize = 5;
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unsigned samplesCount = 512;
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char *stdcolor = "green", xBuffer[512];
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colorChannel = stdcolor;
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unsigned int uint_buffer[1], windowBuffer[1];
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double learnrate = 0.4;
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char *istrue = "true";
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while ((argc > 1) && (argv[1][0] == '-')) { // Parses parameters from stdin
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switch (argv[1][1]) {
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while( (argc > 1) && (argv[1][0] == '-') ) { // Parses parameters from stdin
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switch( argv[1][1] ) {
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case 'i':
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inputfile = &argv[1][3];
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++argv;
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@ -111,9 +112,14 @@ int main(int argc, char **argv) {
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++argv;
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--argc;
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break;
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case 'x':
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case 'g':
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sscanf(&argv[1][3], "%s", xBuffer);
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xclude = 1;
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if ( strstr(xBuffer, istrue) ) {
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include = 1;
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} else {
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printf( "Wrong Argruments: %s\n", argv[1]);
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usage(argv);
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}
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++argv;
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--argc;
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break;
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@ -126,9 +132,9 @@ int main(int argc, char **argv) {
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--argc;
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}
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init_mldata_t(windowSize, samplesCount, learnrate);
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xSamples = (double *)malloc(sizeof(double) * mlData->samplesCount); // Resize input values
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points = (point_t *)malloc(sizeof(point_t) * mlData->samplesCount); // Resize points
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init_mldata_t ( windowSize, samplesCount, learnrate );
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xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values
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points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points
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imagePixel_t *image;
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image = rdPPM(inputfile); // Set Pointer on input values
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@ -141,12 +147,11 @@ int main(int argc, char **argv) {
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FILE* fp6 = fopen(fileName, "r");
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colorSamples(fp6, mlData);
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if ((seed != NULL)) {
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srand(*seed); // Seed for random number generating
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if ( (seed != NULL) ){
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srand( *seed ); // Seed for random number generating
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printf("srand is reproducable\n");
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}
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else {
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srand((unsigned int)time(NULL));
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} else {
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srand( (unsigned int)time(NULL) );
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printf("srand depends on time\n"); // Default seed is time(NULL)
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}
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printf("generated weights:\n");
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@ -166,11 +171,11 @@ int main(int argc, char **argv) {
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fclose(fp0);
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/* *math magic* */
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localMean(mlData, points);
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directPredecessor(mlData, points);
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differentialPredecessor(mlData, points);
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localMean ( mlData, points );
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directPredecessor ( mlData, points);
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differentialPredecessor( mlData, points );
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if (xclude == 0) {
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if ( include == 1 ) {
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mkSvgGraph(points); // Graph building
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}
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@ -191,20 +196,20 @@ Variant (1/3), substract local mean.
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======================================================================================================
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*/
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void localMean(mldata_t *mlData, point_t points[]) {
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double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
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localWeights = mlData->weights; // Copy weights so they can be changed locally
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void localMean ( mldata_t *mlData, point_t points[] ) {
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double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1);
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localWeights = mlData->weights;
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char fileName[50];
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double *xError = (double *)malloc(sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
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double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
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memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero
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unsigned i, xCount = 0; // Runtime vars
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mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename
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FILE* fp4 = fopen(fileName, "w");
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fprintf(fp4, fileHeader(LOCAL_MEAN_HEADER));
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fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );
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mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
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mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
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FILE *fp9 = fopen(fileName, "w");
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@ -213,14 +218,14 @@ void localMean(mldata_t *mlData, point_t points[]) {
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double xPredicted = 0.0;
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double xActual = 0.0;
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for (xCount = 1; xCount < mlData->samplesCount-1; xCount++) { // First value will not get predicted
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unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount; // Ensures corect length at start
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for ( xCount = 1; xCount < mlData->samplesCount-1; xCount++ ) { // First value will not get predicted
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unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount; // Ensures corect length at start
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xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
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xPredicted = 0.0;
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xActual = xSamples[xCount];
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for (i = 1; i < _arrayLength; i++) { // Get predicted value
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xPredicted += (localWeights[i - 1] * (xSamples[xCount - i] - xMean));
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for ( i = 1; i < _arrayLength; i++ ) { // Get predicted value
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xPredicted += ( localWeights[i - 1] * (xSamples[xCount - i] - xMean) );
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}
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xPredicted += xMean;
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xError[xCount] = xActual - xPredicted; // Get error value
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@ -228,13 +233,13 @@ void localMean(mldata_t *mlData, point_t points[]) {
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for (i = 1; i < _arrayLength; i++) { // Get xSquared
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xSquared += pow(xSamples[xCount - i] - xMean, 2);
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}
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if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
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if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
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xSquared = 1.0;
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}
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for (i = 1; i < _arrayLength; i++) { // Update weights
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for ( i = 1; i < _arrayLength; i++ ) { // Update weights
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localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount] // Substract localMean
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* ((xSamples[xCount - i] - xMean) / xSquared);
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fprintf(fp9, "%lf\n", localWeights[i]);
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* ( (xSamples[xCount - i] - xMean) / xSquared );
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fprintf( fp9, "%lf\n", localWeights[i] );
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}
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fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
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@ -250,7 +255,7 @@ void localMean(mldata_t *mlData, point_t points[]) {
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double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
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double xErrorLength = *xErrorPtr; // Watch popNAN()!
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xErrorPtr[0] = 0.0;
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// printf("Xerrorl:%lf", xErrorLength);
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// printf("Xerrorl:%lf", xErrorLength);
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength; // Mean
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double deviation = 0.0;
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@ -260,7 +265,7 @@ void localMean(mldata_t *mlData, point_t points[]) {
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deviation /= xErrorLength; // Deviation
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printf("mean:%lf, devitation:%lf\t\tlocal Mean\n", mean, deviation);
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fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean); // Write to logfile
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//free(localWeights);
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free(localWeights);
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free(xErrorPtr);
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free(xError);
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@ -279,11 +284,12 @@ substract direct predecessor
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======================================================================================================
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*/
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void directPredecessor(mldata_t *mlData, point_t points[]) {
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double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
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void directPredecessor( mldata_t *mlData, point_t points[]) {
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double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
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localWeights = mlData->weights;
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char fileName[512];
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double *xError = (double *)malloc(sizeof(double) * mlData->samplesCount + 1);
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double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
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memset(xError, 0.0, mlData->samplesCount);
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unsigned xCount = 0, i;
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double xActual = 0.0;
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@ -291,18 +297,18 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
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FILE *fp3 = fopen(fileName, "w");
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fprintf(fp3, fileHeader(DIRECT_PREDECESSOR_HEADER));
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fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) );
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mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
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mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
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FILE *fp9 = fopen(fileName, "w");
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for (xCount = 1; xCount < mlData->samplesCount-1; xCount++) { // first value will not get predicted
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unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount;
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unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
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xPredicted = 0.0;
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xActual = xSamples[xCount];
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for (i = 1; i < _arrayLength; i++) {
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xPredicted += (localWeights[i - 1] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
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xPredicted += ( localWeights[i - 1] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
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}
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xPredicted += xSamples[xCount - 1];
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@ -312,13 +318,13 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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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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if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
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if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
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xSquared = 1.0;
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}
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for (i = 1; i < _arrayLength; i++) { // Update weights
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localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
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* ((xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
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fprintf(fp9, "%lf\n", localWeights[i]);
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for ( i = 1; i < _arrayLength; i++ ) { // Update weights
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localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
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* ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
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fprintf( fp9, "%lf\n", localWeights[i] );
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}
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fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
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@ -347,7 +353,7 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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printf("mean:%lf, devitation:%lf\t\tdirect Predecessor\n", mean, deviation);
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fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
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fclose(fp3);
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//free(localWeights);
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free(localWeights);
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free(xErrorPtr);
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free(xError);
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}
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@ -362,21 +368,23 @@ differential predecessor.
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======================================================================================================
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*/
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void differentialPredecessor(mldata_t *mlData, point_t points[]) {
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double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
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void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
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double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
|
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localWeights = mlData->weights;
|
||||
|
||||
char fileName[512];
|
||||
double *xError = (double *)malloc(sizeof(double) * mlData->samplesCount + 1);
|
||||
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
|
||||
memset(xError, 0.0, mlData->samplesCount);
|
||||
|
||||
unsigned xCount = 0, i;
|
||||
double xPredicted = 0.0;
|
||||
double xActual = 0.0;
|
||||
|
||||
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
|
||||
FILE *fp6 = fopen(fileName, "w");
|
||||
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER));
|
||||
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) );
|
||||
|
||||
mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
|
||||
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
|
||||
FILE *fp9 = fopen(fileName, "w");
|
||||
|
||||
for (xCount = 1; xCount < mlData->samplesCount-1; xCount++) { // First value will not get predicted
|
||||
|
|
@ -386,7 +394,7 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
|
|||
xActual = xSamples[xCount];
|
||||
|
||||
for (i = 1; i < _arrayLength; i++) {
|
||||
xPredicted += (localWeights[i - 1] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
|
||||
xPredicted += ( localWeights[i - 1] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
|
||||
}
|
||||
xPredicted += xSamples[xCount - 1];
|
||||
xError[xCount] = xActual - xPredicted;
|
||||
|
|
@ -395,14 +403,14 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
|
|||
for (i = 1; i < _arrayLength; i++) {
|
||||
xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // Substract direct predecessor
|
||||
}
|
||||
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
|
||||
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
|
||||
xSquared = 1.0;
|
||||
}
|
||||
|
||||
for (i = 1; i < _arrayLength; i++) {
|
||||
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
|
||||
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
|
||||
* ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
|
||||
fprintf(fp9, "%lf\n", localWeights[i]);
|
||||
fprintf( fp9, "%lf\n", localWeights[i] );
|
||||
|
||||
}
|
||||
fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
|
||||
|
|
@ -417,7 +425,7 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
|
|||
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
|
||||
double xErrorLength = *xErrorPtr; // Watch popNAN()!
|
||||
xErrorPtr[0] = 0.0;
|
||||
// printf("Xerrorl:%lf", xErrorLength);
|
||||
// printf("Xerrorl:%lf", xErrorLength);
|
||||
|
||||
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
|
||||
double deviation = 0.0;
|
||||
|
|
@ -430,12 +438,12 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
|
|||
printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation);
|
||||
fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
|
||||
fclose(fp6);
|
||||
//free(localWeights);
|
||||
free(localWeights);
|
||||
free(xErrorPtr);
|
||||
free(xError);
|
||||
|
||||
|
||||
// weightsLogger( localWeights, USED_WEIGHTS );
|
||||
// weightsLogger( localWeights, USED_WEIGHTS );
|
||||
}
|
||||
|
||||
/*
|
||||
|
|
@ -470,7 +478,7 @@ Contains and returns every suffix for all existing filenames
|
|||
|
||||
======================================================================================================
|
||||
*/
|
||||
char * fileSuffix(int id) {
|
||||
char * fileSuffix ( int id ) {
|
||||
char * suffix[] = { "_weights_pure.txt",
|
||||
"_weights_used_dir_pred_.txt",
|
||||
"_direct_predecessor.txt",
|
||||
|
|
@ -478,8 +486,8 @@ char * fileSuffix(int id) {
|
|||
"_localMean.txt",
|
||||
"_testvalues.txt",
|
||||
"_differential_predecessor.txt",
|
||||
"_weights_used_local_mean",
|
||||
"_weights_used_diff_pred",
|
||||
"_weights_used_local_mean.txt",
|
||||
"_weights_used_diff_pred.txt",
|
||||
};
|
||||
return suffix[id];
|
||||
}
|
||||
|
|
@ -493,7 +501,7 @@ Contains and returns header from logfiles
|
|||
|
||||
======================================================================================================
|
||||
*/
|
||||
char * fileHeader(int id) {
|
||||
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"
|
||||
|
|
@ -510,7 +518,7 @@ Logs used weights to logfile
|
|||
|
||||
======================================================================================================
|
||||
*/
|
||||
void weightsLogger(double *weights, int val) {
|
||||
void weightsLogger (double *weights, int val ) {
|
||||
char fileName[512];
|
||||
unsigned i;
|
||||
mkFileName(fileName, sizeof(fileName), val);
|
||||
|
|
@ -527,20 +535,20 @@ void weightsLogger(double *weights, int val) {
|
|||
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
|
||||
[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] = ""; // TODO: resize buffer and _buffer so greater sampleval can be choosen
|
||||
// char *_buffer = (char *) malloc ( sizeof(char) * 512 + 1);
|
||||
// char *_buffer = (char *) malloc ( sizeof(char) * 512 + 1);
|
||||
for (i = 1; i < mlData->samplesCount - 1; i++) { // xActual
|
||||
sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
|
||||
strcat(buffer, _buffer);
|
||||
|
|
@ -599,10 +607,10 @@ double *popNAN(double *xError) {
|
|||
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])) {
|
||||
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]);
|
||||
|
|
@ -610,7 +618,7 @@ double *popNAN(double *xError) {
|
|||
}
|
||||
}
|
||||
counter += 1;
|
||||
more_tmp = (double *)realloc(tmp, counter * sizeof(double));
|
||||
more_tmp = (double *) realloc ( tmp, counter * sizeof(double) );
|
||||
tmp = more_tmp;
|
||||
*tmp = tmpLength; // Length of array is stored inside tmp[0]. tmp[0] is never used anyways
|
||||
|
||||
|
|
@ -671,7 +679,7 @@ void mkSvgGraph(point_t points[]) {
|
|||
|
||||
|
||||
char buffer[131072] = ""; // Bit dirty
|
||||
// char *buffer = (char *) malloc ( sizeof(char) * ( ( 3 * mlData->samplesCount ) + fpLength + 1 ) );
|
||||
// char *buffer = (char *) malloc ( sizeof(char) * ( ( 3 * mlData->samplesCount ) + fpLength + 1 ) );
|
||||
|
||||
memset(buffer, '\0', sizeof(buffer));
|
||||
while (!feof(input)) { // parses file until "firstGraph" has been found
|
||||
|
|
@ -723,30 +731,30 @@ static imagePixel_t *rdPPM(char *fileName) {
|
|||
c = getc(fp);
|
||||
}
|
||||
ungetc(c, fp);
|
||||
if (fscanf(fp, "%d %d", &image->x, &image->y) != 2) {
|
||||
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) {
|
||||
if ( fscanf(fp, "%d", &rgbColor) != 1 ) {
|
||||
fprintf(stderr, "Invalid rgb component in %s\n", fileName);
|
||||
}
|
||||
if (rgbColor != RGB_COLOR) {
|
||||
if ( rgbColor != RGB_COLOR ) {
|
||||
fprintf(stderr, "Invalid image color range in %s\n", fileName);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
while (fgetc(fp) != '\n');
|
||||
while ( fgetc(fp) != '\n' );
|
||||
image->data = (colorChannel_t *)malloc(image->x * image->y * sizeof(imagePixel_t));
|
||||
if (!image) {
|
||||
fprintf(stderr, "malloc() on image->data failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
if ((image->x * image->y) < mlData->samplesCount) {
|
||||
printf("Changing \"-n\" to %d, image max data size\n", (image->x * image->y));
|
||||
tmp = (double *)realloc(xSamples, sizeof(double) * (image->x * image->y));
|
||||
if ( (image->x * image->y) < mlData->samplesCount) {
|
||||
printf("Changing \"-n\" to %d, image max data size\n", ( image->x * image->y ) );
|
||||
tmp = (double *) realloc ( xSamples, sizeof(double) * (image->x * image->y) );
|
||||
xSamples = tmp;
|
||||
mlData->samplesCount = (image->x * image->y) / sizeof(double);
|
||||
mlData->samplesCount = (image->x * image->y ) / sizeof(double);
|
||||
}
|
||||
if (fread(image->data, 3 * image->x, image->y, fp) != image->y) {
|
||||
if ( fread( image->data, 3 * image->x, image->y, fp) != image->y) {
|
||||
fprintf(stderr, "Loading image failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
|
@ -790,26 +798,23 @@ int ppmColorChannel(FILE* fp, imagePixel_t *image, char *colorChannel, mldata_t
|
|||
unsigned i = 0;
|
||||
|
||||
printf("colorChannel : %s\n", colorChannel);
|
||||
if (image) { // RGB channel can be set through args from cli
|
||||
if (strcmp(colorChannel, "green") == 0) {
|
||||
for (i = 0; i < mlData->samplesCount - 1; i++) {
|
||||
fprintf(fp, "%d\n", image->data[i].green);
|
||||
if ( image ) { // RGB channel can be set through args from cli
|
||||
if ( strcmp(colorChannel, "green") == 0 ){
|
||||
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
|
||||
fprintf ( fp, "%d\n", image->data[i].green );
|
||||
}
|
||||
}
|
||||
else if (strcmp(colorChannel, "red") == 0) {
|
||||
for (i = 0; i < mlData->samplesCount - 1; i++) {
|
||||
fprintf(fp, "%d\n", image->data[i].red);
|
||||
} else if ( strcmp(colorChannel, "red") == 0 ){
|
||||
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
|
||||
fprintf ( fp, "%d\n", image->data[i].red );
|
||||
}
|
||||
|
||||
} else if ( strcmp(colorChannel, "blue") == 0 ) {
|
||||
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
|
||||
fprintf ( fp, "%d\n", image->data[i].blue );
|
||||
}
|
||||
else if (strcmp(colorChannel, "blue") == 0) {
|
||||
for (i = 0; i < mlData->samplesCount - 1; i++) {
|
||||
fprintf(fp, "%d\n", image->data[i].blue);
|
||||
}
|
||||
}
|
||||
else {
|
||||
} else {
|
||||
printf("Colorchannels are red, green and blue. Pick one of them!");
|
||||
exit(EXIT_FAILURE);
|
||||
exit( EXIT_FAILURE );
|
||||
}
|
||||
}
|
||||
fclose(fp);
|
||||
|
|
@ -826,9 +831,9 @@ creating the SVG graph
|
|||
|
||||
======================================================================================================
|
||||
*/
|
||||
void colorSamples(FILE* fp, mldata_t *mlData) {
|
||||
void colorSamples ( FILE* fp, mldata_t *mlData ) {
|
||||
int i = 0;
|
||||
char *buffer = (char *)malloc(sizeof(char) * mlData->samplesCount + 1);
|
||||
char *buffer = (char *) malloc(sizeof(char) * mlData->samplesCount + 1);
|
||||
|
||||
while (!feof(fp)) {
|
||||
if (fgets(buffer, mlData->samplesCount, fp) != NULL) {
|
||||
|
|
@ -864,13 +869,13 @@ double windowXMean(int _arraylength, int xCount) {
|
|||
/*
|
||||
======================================================================================================
|
||||
|
||||
usage
|
||||
usage
|
||||
|
||||
used in conjunction with the args parser. Returns help section of "-h"
|
||||
used in conjunction with the args parser. Returns help section of "-h"
|
||||
|
||||
======================================================================================================
|
||||
*/
|
||||
void usage(char **argv) {
|
||||
void usage ( char **argv ) {
|
||||
printf("Usage: %s [POSIX style options] -i file ...\n", &argv[0][0]);
|
||||
printf("POSIX options:\n");
|
||||
printf("\t-h\t\t\tDisplay this information.\n");
|
||||
|
|
@ -889,18 +894,18 @@ void usage(char **argv) {
|
|||
/*
|
||||
======================================================================================================
|
||||
|
||||
init_mldata_t
|
||||
init_mldata_t
|
||||
|
||||
|
||||
Contains meachine learning data
|
||||
Contains meachine learning data
|
||||
|
||||
======================================================================================================
|
||||
*/
|
||||
mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate) {
|
||||
mlData = (mldata_t *)malloc(sizeof(mldata_t));
|
||||
mlData = (mldata_t *) malloc( sizeof(mldata_t) );
|
||||
mlData->windowSize = windowSize;
|
||||
mlData->samplesCount = samplesCount;
|
||||
mlData->learnrate = learnrate;
|
||||
mlData->weights = (double *)malloc(sizeof(double) * windowSize + 1);
|
||||
mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
|
||||
return mlData;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -23,11 +23,11 @@ There are a bunch of options you can predefine but do not have to. The only para
|
|||
|
||||
| Parameter | Description | StdVal |
|
||||
|:----------|:-----------------------------:|:-------|
|
||||
| -i | The inputfile, has to be PPM | none |
|
||||
| -n | Amount of input data used | 500 |
|
||||
| -w | Size of M (window) | 5 |
|
||||
| -i | The inputfile, has to be PPM. | none |
|
||||
| -n | Amount of input data used. | 500 |
|
||||
| -w | Size of M (window). | 5 |
|
||||
| -c | Choose RGB color channel, green has least noise. | green |
|
||||
| -l | Learnrate of machine learning | 0.4 |
|
||||
| -x | Exclude graph building. Logfiles only, choose for insane amount of input data. 10Mio. Pixels tested so far.| none|
|
||||
| -l | Learnrate of machine learning. | 0.4 |
|
||||
| -g true | include graph building. Choose for amount of input data lower than 1200.| none|
|
||||
| -s | Seed randomizing weights. Choose for repoducability. | time(NULL)|
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue