generated from freudenreichan/info2Praktikum-NeuronalesNetz
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5bf5b8971c |
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+4
-126
@@ -6,139 +6,17 @@
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#define BUFFER_SIZE 100
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#define FILE_HEADER_STRING "__info2_image_file_format__"
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// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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static int checkFileHeader(FILE *file)
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{
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char buffer[BUFFER_SIZE] = {0};
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size_t length = strlen(FILE_HEADER_STRING);
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if (length >= BUFFER_SIZE)
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{
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return 0;
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}
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if (fread(buffer, 1, length, file) != length)
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{
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return 0;
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}
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return strcmp(buffer, FILE_HEADER_STRING) == 0;
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}
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GrayScaleImageSeries *readImages(const char *path)
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{
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FILE *file = fopen(path, "rb");
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if (file == NULL) {
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return NULL;
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}
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if (!checkFileHeader(file))
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{
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fclose(file);
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return NULL;
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}
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unsigned short numberOfImages;
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unsigned short width;
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unsigned short height;
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if (fread(&numberOfImages, sizeof(unsigned short), 1, file) != 1 ||
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fread(&width, sizeof(unsigned short), 1, file) != 1 ||
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fread(&height, sizeof(unsigned short), 1, file) != 1)
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{
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fclose(file);
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return NULL;
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}
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if (numberOfImages == 0 || width == 0 || height == 0)
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{
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fclose(file);
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return NULL;
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}
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GrayScaleImageSeries *series =
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calloc(1, sizeof(GrayScaleImageSeries));
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if (series == NULL)
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{
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fclose(file);
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return NULL;
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}
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series->count = numberOfImages;
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series->images =
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calloc(numberOfImages, sizeof(GrayScaleImage));
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series->labels =
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calloc(numberOfImages, sizeof(unsigned char));
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if (series->images == NULL || series->labels == NULL)
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{
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clearSeries(series);
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fclose(file);
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return NULL;
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}
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for (unsigned int i = 0; i < numberOfImages; i++)
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{
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GrayScaleImage *image = &series->images[i];
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image->width = width;
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image->height = height;
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image->buffer = malloc(
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(size_t)width * height * sizeof(GrayScalePixelType)
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);
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if (image->buffer == NULL)
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{
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clearSeries(series);
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fclose(file);
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return NULL;
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}
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if (fread(image->buffer,
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sizeof(GrayScalePixelType),
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(size_t)width * height,
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file) != (size_t)width * height)
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{
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clearSeries(series);
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fclose(file);
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return NULL;
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}
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if (fread(&series->labels[i],
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sizeof(unsigned char),
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1,
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file) != 1)
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{
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clearSeries(series);
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fclose(file);
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return NULL;
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}
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}
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fclose(file);
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return series;
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GrayScaleImageSeries *series = NULL;
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return series;
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}
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// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
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void clearSeries(GrayScaleImageSeries *series)
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{
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if (series == NULL)
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{
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return;
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}
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if (series->images != NULL)
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{
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for (unsigned int i = 0; i < series->count; i++)
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{
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free(series->images[i].buffer);
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}
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}
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free(series->images);
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free(series->labels);
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free(series);
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}
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}
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+2
-5
@@ -1,8 +1,5 @@
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CC = gcc
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CFLAGS = -g -Wall
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mnist: main.o matrix.o neuralNetwork.o imageInput.o mnistVisualization.o ./windows/libmnist_complete.a ./windows/libraylib.a
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gcc -o mnist main.o matrix.o neuralNetwork.o imageInput.o mnistVisualization.o ./windows/libmnist_complete.a ./windows/libraylib.a -lopengl32 -lgdi32 -lwinmm
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ifeq ($(OS),Windows_NT)
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include makefile_windows.variables
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@@ -21,8 +18,8 @@ unityfolder = ./unity
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# --------------------------
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# Initiales Programm bauen (zum ausprobieren)
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# --------------------------
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mnist_initial: $(BINARIES)/libmnist_complete.a
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$(CC) -o mnist $(BINARIES)/libmnist_complete.a $(BINARIES)/libraylib.a ${LDFLAGS}
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mnist_initial: ./windows/libmnist_complete.a
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$(CC) -o mnist ./windows/libmnist_complete.a ./windows/libraylib.a ${LDFLAGS}
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# --------------------------
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# Selbst implementiertes Programm bauen
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+4
-72
@@ -44,92 +44,24 @@ void clearMatrix(Matrix *matrix)
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matrix->cols = 0;
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}
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void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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if (matrix.buffer == NULL ||
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rowIdx >= matrix.rows ||
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colIdx >= matrix.cols) {
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return;
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}
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matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
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}
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MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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if (matrix.buffer == NULL ||
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rowIdx >= matrix.rows ||
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colIdx >= matrix.cols) {
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return UNDEFINED_MATRIX_VALUE;
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}
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return matrix.buffer[rowIdx * matrix.cols + colIdx];
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}
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Matrix add(const Matrix matrix1, const Matrix matrix2)
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{
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if (matrix1.rows != matrix2.rows ||
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(matrix1.cols != matrix2.cols &&
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matrix1.cols != 1 &&
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matrix2.cols != 1)) {
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return createMatrix(0, 0);
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}
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// Ungültige Eingabematrizen abfangen
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if (matrix1.buffer == NULL || matrix2.buffer == NULL) {
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return createMatrix(0, 0);
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}
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// Größere Spaltenanzahl bestimmen
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unsigned int cols = matrix1.cols > matrix2.cols
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? matrix1.cols : matrix2.cols;
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Matrix result = createMatrix(matrix1.rows, cols);
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if (result.buffer == NULL) {
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return result;
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}
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for (unsigned int i = 0; i < result.rows; i++) {
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for (unsigned int j = 0; j < result.cols; j++) {
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// Bei einer Spalte immer Spalte 0 verwenden
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unsigned int j1 = matrix1.cols == 1 ? 0 : j;
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unsigned int j2 = matrix2.cols == 1 ? 0 : j;
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result.buffer[i * result.cols + j] =
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matrix1.buffer[i * matrix1.cols + j1] +
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matrix2.buffer[i * matrix2.cols + j2];
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}
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}
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return result;
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}
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Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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{
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if (matrix1.cols != matrix2.rows) {
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return createMatrix(0, 0);
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}
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// Ergebnismatrix erstellen
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Matrix result = createMatrix(matrix1.rows, matrix2.cols);
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// Matrizen multiplizieren
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for (unsigned int rowIdx = 0; rowIdx < matrix1.rows; rowIdx++) {
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for (unsigned int colIdx = 0; colIdx < matrix2.cols; colIdx++) {
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for (unsigned int k = 0; k < matrix1.cols; k++) {
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result.buffer[rowIdx * result.cols + colIdx] +=
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matrix1.buffer[rowIdx * matrix1.cols + k] *
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matrix2.buffer[k * matrix2.cols + colIdx];
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}
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}
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}
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return result;
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}
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