From 1fca7598d6707af51e5ff9e58cbbe15c2178d4bd Mon Sep 17 00:00:00 2001 From: maxgrf Date: Mon, 24 Nov 2025 12:14:10 +0100 Subject: [PATCH] Daten kopiert --- imageInput.c | 82 +++++++++++++++++++++++++- imageInputTests.c | 3 +- matrix.c | 101 ++++++++++++++++++++++++++++++-- matrix.h | 5 ++ neuralNetworkTests.c | 134 +++++++++++++++++++++++++++++-------------- 5 files changed, 273 insertions(+), 52 deletions(-) diff --git a/imageInput.c b/imageInput.c index bb30de1..80ee0da 100644 --- a/imageInput.c +++ b/imageInput.c @@ -6,17 +6,95 @@ #define BUFFER_SIZE 100 #define FILE_HEADER_STRING "__info2_image_file_format__" -// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen GrayScaleImageSeries *readImages(const char *path) { - GrayScaleImageSeries *series = NULL; + // Initialisiert einen Zeiger zur struct und reserviert Speicherplatz + GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries)); + if(series == NULL){ + printf("Es ist nicht genügend Speicher übrig"); + return NULL; + } + + FILE * data = fopen(path, "rb"); + if (data == NULL){ + printf("Die Datei konnte nicht gelesen werden"); + return NULL; + } + + // Überprüfung, ob die Datei einen Header hat + char header[BUFFER_SIZE]; + fread(header, strlen(FILE_HEADER_STRING), 1, data); + header[strlen(FILE_HEADER_STRING)] ='\0'; + if(strncmp(header, FILE_HEADER_STRING, strlen(FILE_HEADER_STRING) )!= 0){ + printf("Die Datei hat keinen Header"); + fclose(data); + return NULL; + } + //liest die Anzahl der Bilder aus + fread(&series->count, sizeof(unsigned short),1, data); + series->images = malloc(series->count * sizeof(GrayScaleImage)); + if (series->images == NULL){ + printf("Es ist nicht genügend Speicher übrig"); + fclose(data); + return NULL; + } + + //liest die Höhe und Breite der Bilder aus + unsigned short height = 0, width = 0; + fread(&width, sizeof(unsigned short), 1, data); + fread(&height, sizeof(unsigned short), 1, data); + //reserviert Speicher für die Labels, die aber erst nach jedem Bild eingelesen werden + series->labels = malloc(sizeof(unsigned char) * series->count); + if (series->labels == NULL){ + printf("Es ist nicht genügend Speicher übrig"); + free(series->images); + fclose(data); + return NULL; + } + + //liest jedes Bild einzeln aus und speichert es in images + for(int counter_picture = 0 ; counter_picture < series->count; counter_picture++){ + + // für jedes Bild muss vorher eine Größe festgelegt werden, die jedoch in diesem Fall immer gleich ist + series->images[counter_picture].width = width; + series->images[counter_picture].height =height; + unsigned int size_picture = height * width; + + //reservieren des Speichers für Buffer, der die einzelnen Pixels speichert + series->images[counter_picture].buffer = malloc(size_picture* sizeof(GrayScalePixelType)); + if (series->images[counter_picture].buffer == NULL){ + printf("Es ist nicht genügend Speicher übrig"); + free(series->images); + free(series); + fclose(data); + return NULL; + } + + //einlesen der einzelnen Pixel in buffer + for(int counter_pixels = 0; counter_pixels < size_picture; counter_pixels++){ + fread(&series->images[counter_picture].buffer[counter_pixels], sizeof(unsigned char), 1, data); + } + + //einlesen der Labels + fread(&series->labels[counter_picture], sizeof(unsigned char), 1, data); + + } + + fclose(data); return series; } // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt void clearSeries(GrayScaleImageSeries *series) { + //erst den Speicherplatz der Pixel freigeben + for(int number= 0; number < series->count; number++){ + free(series->images[number].buffer); + } + // dann die Bilder freigeben + free(series-> images); + free(series); } \ No newline at end of file diff --git a/imageInputTests.c b/imageInputTests.c index 03240ab..c918b24 100644 --- a/imageInputTests.c +++ b/imageInputTests.c @@ -123,7 +123,8 @@ void setUp(void) { // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden } -void tearDown(void) { +void tearDown(void) +{ // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden } diff --git a/matrix.c b/matrix.c index ad00628..0f183f3 100644 --- a/matrix.c +++ b/matrix.c @@ -6,30 +6,119 @@ Matrix createMatrix(unsigned int rows, unsigned int cols) { - + Matrix matrix = {NULL, 0, 0}; + + if (rows == 0 || cols == 0) + return matrix; //gibt leere Matrix zurück + + matrix.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType)); + if (matrix.buffer == NULL) //auf verfügbaren Speicherplatz prüfen + return matrix; + + matrix.rows = rows; + matrix.cols = cols; + return matrix; //Matrix zurückgeben } void clearMatrix(Matrix *matrix) { - + + if (matrix != NULL) + { + free(matrix->buffer); //Speicherplatz bereinigen + matrix->buffer = NULL; //Werte auf 0 setzen + matrix->rows = 0; + matrix->cols = 0; + } } -void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx) +void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx) { - + if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL) //Prüft ob Zugriff möglich + matrix.buffer[rowIdx * matrix.cols + colIdx] = value; + //schreibt 2D element in 1D Liste: Element_Reihe*Matrix_Spalten + Element_Spalte } MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx) { - + if (rowIdx >= matrix.rows || colIdx >= matrix.cols || matrix.buffer == NULL) + return 0; // Sicherheitscheck + return matrix.buffer[rowIdx * matrix.cols + colIdx]; } +// TODO: Funktionen implementieren Matrix add(const Matrix matrix1, const Matrix matrix2) { - + // immer Probe, gleiche Zeilen der Matrizen + // "Elementweise Addition": Probe, ob matrix gleiche größe hat + if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols) + { + Matrix result_add = createMatrix(matrix1.rows, matrix1.cols); + for (int r = 0; r < matrix1.rows; r++) + { + for (int c = 0; c < matrix1.cols; c++) + { + // first version: matrix_add[r][c] = matrix1[r][c] + matrix2[r][c] + MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, c); + setMatrixAt(sum, result_add, r, c); + } + } + return result_add; + } + // "Broadcasting": matrix1 hat 1 Spalte + if (matrix1.rows == matrix2.rows && matrix1.cols == 1) + { + Matrix result_add = createMatrix(matrix1.rows, matrix2.cols); + for (int r = 0; r < matrix1.rows; r++) + { + for (int c = 0; c < matrix2.cols; c++) + { + MatrixType sum = getMatrixAt(matrix2, r, c) + getMatrixAt(matrix1, r, 0); + setMatrixAt(sum, result_add, r, c); + } + } + return result_add; + } + // "Broadcasting": matrix2 hat 1 Spalte + if (matrix1.rows == matrix2.rows && matrix2.cols == 1) + { + Matrix result_add = createMatrix(matrix1.rows, matrix1.cols); + for (int r = 0; r < matrix1.rows; r++) + { + for (int c = 0; c < matrix1.cols; c++) + { + MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, 0); + setMatrixAt(sum, result_add, r, c); + } + } + return result_add; + } + + return createMatrix(0, 0); } Matrix multiply(const Matrix matrix1, const Matrix matrix2) { + MatrixType buffer_add; + if (!matrix1.buffer || !matrix2.buffer) // Probe ob leere Matrize vorliegt + return createMatrix(0, 0); + if (matrix1.cols != matrix2.rows) // Probe ob Spalten1 = Zeilen2 + return createMatrix(0, 0); + + Matrix result_mul = createMatrix(matrix1.rows, matrix2.cols); + + for (unsigned int index = 0; index < matrix1.rows; index++) + { + for (unsigned int shift = 0; shift < matrix2.cols; shift++) + { + buffer_add = 0; + for (unsigned int skalar = 0; skalar < matrix1.cols; skalar++) + { + buffer_add += getMatrixAt(matrix1, index, skalar) * getMatrixAt(matrix2, skalar, shift); + } + setMatrixAt(buffer_add, result_mul, index, shift); + } + } + return result_mul; } \ No newline at end of file diff --git a/matrix.h b/matrix.h index cc640d1..ad9b2c6 100644 --- a/matrix.h +++ b/matrix.h @@ -6,6 +6,11 @@ typedef float MatrixType; // TODO Matrixtyp definieren +typedef struct { + MatrixType *buffer; + unsigned int rows; + unsigned int cols; +} Matrix; Matrix createMatrix(unsigned int rows, unsigned int cols); diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c index 21ab370..11ff12d 100644 --- a/neuralNetworkTests.c +++ b/neuralNetworkTests.c @@ -5,10 +5,56 @@ #include "unity.h" #include "neuralNetwork.h" - static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) { - // TODO + FILE *file = fopen(path, "wb"); + if (!file) + return; + + const char *tag = "__info2_neural_network_file_format__"; + fwrite(tag, 1, strlen(tag), file); + + // Schreibe die Anzahl der Layer + if (nn.numberOfLayers == 0) + { + fclose(file); + return; + } + + // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks + int input = nn.layers[0].weights.cols; + int output = nn.layers[0].weights.rows; + + fwrite(&input, sizeof(int), 1, file); + fwrite(&output, sizeof(int), 1, file); + + // Schreibe die Layer-Daten + for (int i = 0; i < nn.numberOfLayers; i++) + { + const Layer *layer = &nn.layers[i]; + int out = layer->weights.rows; + int in = layer->weights.cols; + + fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file); + + fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file); + + if (i + 1 < nn.numberOfLayers) + { + int nextOut = nn.layers[i + 1].weights.rows; + fwrite(&nextOut, sizeof(int), 1, file); + } + } + fclose(file); + + // Debuging-Ausgabe + printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers); + for (unsigned int i = 0; i < nn.numberOfLayers; i++) + { + Layer layer = nn.layers[i]; + printf("Layer %u: weights (%u x %u), biases (%u x %u)\n", + i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols); + } } void test_loadModelReturnsCorrectNumberOfLayers(void) @@ -16,15 +62,15 @@ void test_loadModelReturnsCorrectNumberOfLayers(void) const char *path = "some__nn_test_file.info2"; MatrixType buffer1[] = {1, 2, 3, 4, 5, 6}; MatrixType buffer2[] = {1, 2, 3, 4, 5, 6}; - Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2}; - Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3}; + Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2}; + Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3}; MatrixType buffer3[] = {1, 2, 3}; MatrixType buffer4[] = {1, 2}; - Matrix biases1 = {.buffer=buffer3, .rows=3, .cols=1}; - Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1}; - Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}}; + Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1}; + Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1}; + Layer layers[] = {{.weights = weights1, .biases = biases1}, {.weights = weights2, .biases = biases2}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -40,12 +86,12 @@ void test_loadModelReturnsCorrectWeightDimensions(void) { const char *path = "some__nn_test_file.info2"; MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6}; - Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2}; + Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2}; MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -63,12 +109,12 @@ void test_loadModelReturnsCorrectBiasDimensions(void) { const char *path = "some__nn_test_file.info2"; MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6}; - Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2}; + Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2}; MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -86,12 +132,12 @@ void test_loadModelReturnsCorrectWeights(void) { const char *path = "some__nn_test_file.info2"; MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6}; - Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2}; + Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2}; MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -111,12 +157,12 @@ void test_loadModelReturnsCorrectBiases(void) { const char *path = "some__nn_test_file.info2"; MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6}; - Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2}; + Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2}; MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -138,7 +184,7 @@ void test_loadModelFailsOnWrongFileTag(void) NeuralNetwork netUnderTest; FILE *file = fopen(path, "wb"); - if(file != NULL) + if (file != NULL) { const char *fileTag = "info2_neural_network_file_format"; @@ -159,12 +205,12 @@ void test_clearModelSetsMembersToNull(void) { const char *path = "some__nn_test_file.info2"; MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6}; - Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2}; + Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2}; MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -181,7 +227,7 @@ void test_clearModelSetsMembersToNull(void) static void someActivation(Matrix *matrix) { - for(int i = 0; i < matrix->rows * matrix->cols; i++) + for (int i = 0; i < matrix->rows * matrix->cols; i++) { matrix->buffer[i] = fabs(matrix->buffer[i]); } @@ -192,23 +238,23 @@ void test_predictReturnsCorrectLabels(void) const unsigned char expectedLabels[] = {4, 2}; GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17}; GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128}; - GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}}; + GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}}; MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8}; MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14}; MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29}; - Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4}; - Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2}; - Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3}; + Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4}; + Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2}; + Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3}; MatrixType biasBuffer1[] = {200, 0}; MatrixType biasBuffer2[] = {0, -100, 0}; MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0}; - Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1}; - Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1}; - Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1}; - Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \ - {.weights=weights2, .biases=biases2, .activation=someActivation}, \ - {.weights=weights3, .biases=biases3, .activation=someActivation}}; - NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3}; + Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1}; + Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1}; + Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1}; + Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation}, + {.weights = weights2, .biases = biases2, .activation = someActivation}, + {.weights = weights3, .biases = biases3, .activation = someActivation}}; + NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3}; unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2); TEST_ASSERT_NOT_NULL(predictedLabels); int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0])); @@ -216,11 +262,13 @@ void test_predictReturnsCorrectLabels(void) free(predictedLabels); } -void setUp(void) { +void setUp(void) +{ // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden } -void tearDown(void) { +void tearDown(void) +{ // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden }