updated
This commit is contained in:
kbecke23
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@ -11,7 +11,7 @@ Created by Stefan Friese on 26.04.2018
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#include <time.h>
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#include <stdlib.h>
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#include <string.h>
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#include "nlms_types.h"
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#include "nlms_types.h" // added types
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#define RGB_COLOR 255
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#if defined(_MSC_VER)
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@ -19,24 +19,25 @@ Created by Stefan Friese on 26.04.2018
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typedef SSIZE_T ssize_t;
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#endif
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double *xSamples; // Input color values from PPM
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mldata_t *mlData = NULL; // Machine learning realted data
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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, // Date+suffix as filename
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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); // Filename ending of logs
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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[], char *templatePath); // Parses graph template and calls bufferLogger()
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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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@ -54,9 +55,11 @@ 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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char *inputfile = (char *)malloc(sizeof(char) * 32);
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@ -64,12 +67,12 @@ int main(int argc, char **argv) {
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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 = (char*)"green", xBuffer[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 = (char*)"true";
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char *templatePath = NULL;
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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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@ -114,10 +117,6 @@ int main(int argc, char **argv) {
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if (strstr(xBuffer, istrue)) {
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include = 1;
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}
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else if (xBuffer && !strstr(xBuffer, istrue)) {
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templatePath = xBuffer;
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include = 1;
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}
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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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@ -144,7 +143,8 @@ int main(int argc, char **argv) {
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char fileName[50]; // Logfiles and their names
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mkFileName(fileName, sizeof(fileName), TEST_VALUES);
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FILE* fp5 = fopen(fileName, "w");
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ppmColorChannel(fp5, image, colorChannel, mlData);
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//xLength =
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ppmColorChannel(fp5, image, colorChannel, mlData); // Returns length of ppm input values, debugging
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FILE* fp6 = fopen(fileName, "r");
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colorSamples(fp6, mlData);
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@ -163,19 +163,22 @@ int main(int argc, char **argv) {
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printf("[%d] %lf\n", k, mlData->weights[k]);
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}
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mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
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FILE *fp0 = fopen(fileName, "w");
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for (k = 0; k < mlData->windowSize; k++) {
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fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
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}
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fclose(fp0);
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localMean(mlData, points); // math magic functions
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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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if (include == 1) {
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mkSvgGraph(points, templatePath); // Graph building
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mkSvgGraph(points); // Graph building
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}
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@ -197,13 +200,12 @@ Variant (1/3), substract local mean.
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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;
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memcpy(localWeights, mlData->weights, sizeof(double) * mlData->windowSize + 1);
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//localWeights = mlData->weights;
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char fileName[50];
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unsigned xErrorLength = mlData->samplesCount;
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double *xError = (double *)malloc(sizeof(double) * xErrorLength+1);
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memset(xError, 0.0, sizeof(double) * xErrorLength);
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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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@ -232,13 +234,14 @@ void localMean(mldata_t *mlData, point_t points[]) {
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xError[xCount] = xActual - xPredicted; // Get error value
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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] - xMean, 2);
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double x = xSamples[xCount - i] - xMean;
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xSquared += x * x;
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}
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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] // Substract localMean
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localWeights[i-1] = 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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}
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@ -253,17 +256,27 @@ void localMean(mldata_t *mlData, point_t points[]) {
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}
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fclose(fp9);
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double mean = sum_array(xError, xErrorLength) / xErrorLength; // Mean
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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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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength; // Mean
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double deviation = 0.0;
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for (i = 1; i < xErrorLength; i++) { // Mean square
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deviation += pow(xError[i] - mean, 2);
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double x = xError[i] - mean;
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deviation += x*x;
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}
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deviation /= xErrorLength; // Deviation
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printf("mean square err: %lf, variance: %lf\t\tlocal Mean\n", mean, 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(xErrorPtr);
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free(xError);
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fclose(fp4);
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//weightsLogger( local_weights, USED_WEIGHTS );
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}
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/*
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@ -278,12 +291,12 @@ substract direct predecessor
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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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localWeights = mlData->weights;
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memcpy(localWeights, mlData->weights, sizeof(double) * mlData->windowSize + 1);
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//localWeights = mlData->weights;
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char fileName[512];
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const unsigned xErrorLength = mlData->samplesCount;
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double *xError = (double *)malloc(sizeof(double) * xErrorLength);
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memset(xError, 0.0, sizeof(double) * xErrorLength);
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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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double xPredicted = 0.0;
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@ -309,13 +322,14 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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double xSquared = 0.0;
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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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double x = xSamples[xCount - 1] - xSamples[xCount - i - 1];
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xSquared += x*x; // 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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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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localWeights[i-1] = 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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@ -325,20 +339,31 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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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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// weightsLogger( fp, localWeights, USED_WEIGHTS );
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}
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fclose(fp9);
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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; // Stored length in [0] , won't be used anyway. Bit dirty
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//printf("Xerrorl:%lf", xErrorLength);
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double mean = sum_array(xError, xErrorLength) / xErrorLength; // Mean
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength; // Mean
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double deviation = 0.0;
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for (i = 1; i < xErrorLength; i++) {
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deviation += pow(xError[i] - mean, 2); // Mean square
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double x = xError[i] - mean;
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deviation += x*x; // Mean square
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}
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deviation /= xErrorLength; // Deviation
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printf("mean square err: %lf, variance: %lf\t\t\tdirect Predecessor\n", mean, deviation);
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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(xErrorPtr);
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free(xError);
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}
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/*
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@ -353,11 +378,12 @@ differential predecessor.
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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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localWeights = mlData->weights;
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const unsigned xErrorLength = mlData->samplesCount;
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memcpy(localWeights, mlData->weights, 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) * xErrorLength);
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memset(xError, 0.0, sizeof(double) * xErrorLength);
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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 xPredicted = 0.0;
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@ -384,14 +410,15 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
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double xSquared = 0.0;
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for (i = 1; i < _arrayLength; i++) {
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xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // Substract direct predecessor
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double x = xSamples[xCount - i] - xSamples[xCount - i - 1];
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xSquared += x*x; // 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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xSquared = 1.0;
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}
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for (i = 1; i < _arrayLength; i++) {
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localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
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localWeights[i-1] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
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* ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
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fprintf(fp9, "%lf\n", localWeights[i]);
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@ -405,17 +432,29 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
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}
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fclose(fp9);
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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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double mean = sum_array(xError, xErrorLength) / xErrorLength;
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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for (i = 1; i < xErrorLength; i++) { // Mean square
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deviation += pow(xError[i] - mean, 2);
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double x = xError[i] - mean;
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deviation += x*x;
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}
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deviation /= xErrorLength;
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printf("mean square err: %lf, variance: %lf\t\t\tdifferential Predecessor\n", mean, deviation);
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printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation);
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fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
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fclose(fp6);
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//free(localWeights);
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free(xErrorPtr);
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free(xError);
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// weightsLogger( localWeights, USED_WEIGHTS );
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}
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/*
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@ -451,15 +490,15 @@ Contains and returns every suffix for all existing filenames
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======================================================================================================
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*/
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char * fileSuffix(int id) {
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char * suffix[] = { (char*)"_weights_pure.txt",
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(char*)"_weights_used_dir_pred_.txt",
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(char*)"_direct_predecessor.txt",
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(char*)"_ergebnisse.txt",
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(char*)"_localMean.txt",
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(char*)"_testvalues.txt",
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(char*)"_differential_predecessor.txt",
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(char*)"_weights_used_local_mean.txt",
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(char*)"_weights_used_diff_pred.txt",
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char * suffix[] = { "_weights_pure.txt",
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"_weights_used_dir_pred_.txt",
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"_direct_predecessor.txt",
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"_ergebnisse.txt",
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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.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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@ -474,9 +513,9 @@ Contains and returns header from logfiles
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======================================================================================================
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*/
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char * fileHeader(int id) {
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char * header[] = { (char*)"\n=========================== Local Mean ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
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(char*)"\n=========================== Direct Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
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(char*)"\n=========================== Differential Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n"
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char * header[] = { "\n=========================== Local Mean ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
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"\n=========================== Direct Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
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"\n=========================== Differential Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n"
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};
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return header[id];
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}
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@ -486,7 +525,7 @@ char * fileHeader(int id) {
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weightsLogger
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Logs used weights to logfile - not used right now
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Logs used weights to logfile
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======================================================================================================
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*/
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@ -519,7 +558,8 @@ formats output of mkSvgGraph -- Please open graphResults.html to see the output-
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*/
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void bufferLogger(char *buffer, point_t points[]) {
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unsigned i;
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char _buffer[512] = "";
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char _buffer[512] = ""; // TODO: resize buffer and _buffer so greater sampleval can be choosen
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// char *_buffer = (char *) malloc ( sizeof(char) * 512 + 1);
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for (i = 1; i < mlData->samplesCount - 1; i++) { // xActual
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sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
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strcat(buffer, _buffer);
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@ -530,7 +570,7 @@ void bufferLogger(char *buffer, point_t points[]) {
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strcat(buffer, _buffer);
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}
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strcat(buffer, "\" fill=\"none\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
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for (i = 1; i <= mlData->samplesCount - 2; i++) { //xPredicted from directPredecessor
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for (i = 1; i <= mlData->samplesCount - 1; i++) { //xPredicted from directPredecessor
|
||||
sprintf(_buffer, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]);
|
||||
strcat(buffer, _buffer);
|
||||
}
|
||||
|
|
@ -566,6 +606,40 @@ double sum_array(double x[], int xlength) {
|
|||
/*
|
||||
======================================================================================================
|
||||
|
||||
popNan
|
||||
|
||||
returns new array without NAN values
|
||||
|
||||
======================================================================================================
|
||||
*/
|
||||
double *popNAN(double *xError) {
|
||||
unsigned i, counter = 1;
|
||||
double tmpLength = 0.0;
|
||||
double *tmp = NULL;
|
||||
double *more_tmp = NULL;
|
||||
|
||||
for (i = 0; i < mlData->samplesCount - 1; i++) {
|
||||
counter++;
|
||||
more_tmp = (double *)realloc(tmp, counter*(sizeof(double)));
|
||||
if (!isnan(xError[i])) {
|
||||
tmp = more_tmp;
|
||||
tmp[counter - 1] = xError[i];
|
||||
//printf("xERROR:%lf\n", tmp[counter - 1]);
|
||||
tmpLength++;
|
||||
}
|
||||
}
|
||||
counter += 1;
|
||||
more_tmp = (double *)realloc(tmp, counter * sizeof(double));
|
||||
tmp = more_tmp;
|
||||
*tmp = tmpLength; // Length of array is stored inside tmp[0]. tmp[0] is never used anyways
|
||||
|
||||
return tmp;
|
||||
|
||||
}
|
||||
|
||||
/*
|
||||
======================================================================================================
|
||||
|
||||
r2
|
||||
|
||||
returns a random double value between 0 and 1
|
||||
|
|
@ -599,26 +673,24 @@ parses template.svg and writes results in said template
|
|||
|
||||
======================================================================================================
|
||||
*/
|
||||
void mkSvgGraph(point_t points[], char *templatePath) {
|
||||
FILE* input = NULL;
|
||||
void mkSvgGraph(point_t points[]) {
|
||||
FILE *input = fopen("graphResults_template.html", "r");
|
||||
FILE *target = fopen("graphResults.html", "w");
|
||||
if (templatePath) {
|
||||
printf("\ngraph template src at: %s\n", templatePath);
|
||||
input = fopen(templatePath, "r");
|
||||
}
|
||||
else {
|
||||
input = fopen("graphResults_template.html", "r");
|
||||
}
|
||||
|
||||
char line[512];
|
||||
char firstGraph[15] = { "<path d=\"M0 0" }; // Position where points will be written after
|
||||
|
||||
if (input == NULL) {
|
||||
printf("\nNo inputfile at mkSvgGraph()\n");
|
||||
printf("No inputfile at mkSvgGraph()");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
char buffer[131072] = ""; // Really really dirty
|
||||
fseek(input, 0, SEEK_END);
|
||||
long fpLength = ftell(input);
|
||||
fseek(input, 0, SEEK_SET);
|
||||
|
||||
|
||||
char buffer[131072] = ""; // Bit dirty
|
||||
// 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
|
||||
|
|
@ -656,7 +728,7 @@ static imagePixel_t *rdPPM(char *fileName) {
|
|||
perror(fileName);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
if (buffer[0] != 'P' || buffer[1] != '6') { // PPM files start with P6
|
||||
if (buffer[0] != 'P' || buffer[1] != '6') {
|
||||
fprintf(stderr, "No PPM file format\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
|
@ -665,7 +737,7 @@ static imagePixel_t *rdPPM(char *fileName) {
|
|||
fprintf(stderr, "malloc() failed");
|
||||
}
|
||||
c = getc(fp);
|
||||
while (c == '#') { // PPM Comments start with #
|
||||
while (c == '#') {
|
||||
while (getc(fp) != '\n');
|
||||
c = getc(fp);
|
||||
}
|
||||
|
|
@ -691,7 +763,7 @@ static imagePixel_t *rdPPM(char *fileName) {
|
|||
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);
|
||||
mlData->samplesCount = (image->x * image->y) / sizeof(double);
|
||||
}
|
||||
if (fread(image->data, 3 * image->x, image->y, fp) != image->y) {
|
||||
fprintf(stderr, "Loading image failed");
|
||||
|
|
@ -780,6 +852,7 @@ void colorSamples(FILE* fp, mldata_t *mlData) {
|
|||
while (!feof(fp)) {
|
||||
if (fgets(buffer, mlData->samplesCount, fp) != NULL) {
|
||||
sscanf(buffer, "%lf", &xSamples[i]);
|
||||
//printf("%lf\n", xSamples[i] );
|
||||
points[i].yVal[0] = xSamples[i]; // Fills points so actual input values can be seen as a graph
|
||||
points[i].xVal[0] = i;
|
||||
++i;
|
||||
|
|
@ -801,7 +874,7 @@ double windowXMean(int _arraylength, int xCount) {
|
|||
double sum = 0.0;
|
||||
double *ptr;
|
||||
|
||||
for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { // Set ptr to beginning of window and iterate through array
|
||||
for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { // Set ptr to beginning of window
|
||||
sum += *ptr;
|
||||
}
|
||||
return sum / (double)_arraylength;
|
||||
|
|
@ -825,10 +898,10 @@ void usage(char **argv) {
|
|||
printf("\t-c <color>\t\tUse this color channel from inputfile.\n");
|
||||
printf("\t-w <digit>\t\tCount of used weights (windowSize).\n");
|
||||
printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\n");
|
||||
printf("\t-g <path>\t\t\tGraph building. If template is located in another folder use path, otherwise true.\n\t\t\t\tChoose for n < 1200.\n");
|
||||
printf("\t-x true\t\t\tLogfiles only, no graph building.\n\t\t\t\tChoose for intense amount of input data.\n");
|
||||
printf("\t-s <digit>\t\tDigit for random seed generator.\n\t\t\t\tSame Digits produce same random values. Default is srand by time.\n");
|
||||
printf("\n\n");
|
||||
printf("%s compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\t%s -i myimage.ppm -w 3 -c green -s 5 -g true\n", &argv[0][0], &argv[0][0]);
|
||||
printf("%s compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\t%s -i myimage.ppm -w 3 -c green -s 5 -x true\n", &argv[0][0], &argv[0][0]);
|
||||
exit(8);
|
||||
}
|
||||
|
||||
|
|
@ -838,7 +911,7 @@ void usage(char **argv) {
|
|||
init_mldata_t
|
||||
|
||||
|
||||
Init meachine learning data
|
||||
Contains meachine learning data
|
||||
|
||||
======================================================================================================
|
||||
*/
|
||||
|
|
|
|||
Loading…
Reference in New Issue