NeuralNetworkTest fertig?

imageInput.c

@@ -5,12 +5,59 @@
 
 #define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_image_file_format__"
+#define FILE_HEADER_SIZE (sizeof(FILE_HEADER_STRING)-1)
+ 
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
+FILE *checkFile(const char *path)
+{
+    FILE *datei = fopen(path,"rb");
+    if (datei == NULL) 
+    {
+        perror("Datei konnte nicht geoeffnet werden");
+        return NULL;
+    }
+    char buffer[FILE_HEADER_SIZE+1];
+    if (fread(buffer,1,FILE_HEADER_SIZE,datei)!=FILE_HEADER_SIZE)
+    {
+        perror("Header konnte nicht eingelessen werden");
+        fclose(datei);
+        return NULL;
+    }
+    buffer[FILE_HEADER_SIZE] = '\0';
+    if (strcmp(buffer,FILE_HEADER_STRING)!=0)
+    {
+        printf("Falscher Dateikopf");
+        //printf("\n%s",buffer);
+        //printf("\n%s",FILE_HEADER_STRING);
+        //printf("\n%d",strcmp(buffer,FILE_HEADER_STRING));
+        fclose(datei);
+        return NULL;
+    }
+    return datei;
+}
 
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
+    FILE *datei = checkFile(path);
+    if (datei==NULL)
+    {
+        return NULL;
+    }
+    unsigned short image_count, width, height;
+    fread(&image_count,1,sizeof(unsigned short),datei);
+    fread(&width,1,sizeof(unsigned short),datei);
+    fread(&height,1,sizeof(unsigned short),datei);
+    //printf("%u Bilder und %u mal %u",image_count,width,height);
+    
+    fclose(datei);
+
+
+
+
+
+
     GrayScaleImageSeries *series = NULL;
     
     return series;

imageInput.h

@@ -19,5 +19,5 @@ typedef struct
 
 GrayScaleImageSeries *readImages(const char *path);
 void clearSeries(GrayScaleImageSeries *series);
-
+FILE *checkFile(const char *path);
 #endif

main.c

@@ -1,11 +1,13 @@
 #include <stdio.h>
 #include <stdlib.h>
 #include "imageInput.h"
-#include "mnistVisualization.h"
+//#include "mnistVisualization.h"
-#include "neuralNetwork.h"
+//#include "neuralNetwork.h"
 
 int main(int argc, char *argv[])
 {
+    readImages("mnist_test.info2");
+    /*
     const unsigned int windowWidth = 800;
     const unsigned int windowHeight = 600;
 
@@ -65,4 +67,5 @@ int main(int argc, char *argv[])
     }
 
     return exitCode;
+    */
 }

matrix.c

@@ -4,32 +4,104 @@
 
 // TODO Matrix-Funktionen implementieren
 
+// Matrix erzeugen
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
+    Matrix matrix;
+    matrix.buffer = NULL;
+    matrix.rows = 0;
+    matrix.cols = 0;
     
+
+    // Wenn die Dimensionen gültig sind, Speicher reservieren
+    if (rows > 0 && cols > 0) 
+    {
+        matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
+        if (matrix.buffer != NULL)
+        {
+            matrix.rows = rows;
+            matrix.cols = cols;
+        }
+    }
+    return matrix;
 }
 
+// Matrix Speicher freigeben
 void clearMatrix(Matrix *matrix)
 {
-    
+    if (matrix->buffer != NULL)
+    {
+        free(matrix->buffer);
+        matrix->buffer = NULL;
+    }
+    matrix->rows = 0;
+    matrix->cols = 0;
 }
 
+// Wert setzen
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (rowIdx < matrix.rows && colIdx < matrix.cols)
+    {
+        matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+    }
 }
 
+// Wert auslesen
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (rowIdx < matrix.rows && colIdx < matrix.cols)
+    {
+        return matrix.buffer[rowIdx * matrix.cols + colIdx];
+    }
+    return 0; // Fallback
 }
 
-Matrix add(const Matrix matrix1, const Matrix matrix2)
+// Matrizen addieren
+Matrix add(const Matrix m1, const Matrix m2)
 {
-    
+    if (m1.rows != m2.rows || m1.cols != m2.cols)
+    {
+        return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
+    }
+
+    Matrix result = createMatrix(m1.rows, m1.cols);
+    if (result.buffer == NULL) return result;
+
+    for (unsigned int r = 0; r < m1.rows; r++)
+    {
+        for (unsigned int c = 0; c < m1.cols; c++)
+        {
+            result.buffer[r * m1.cols + c] =
+                getMatrixAt(m1, r, c) + getMatrixAt(m2, r, c);
+        }
+    }
+    return result;
 }
 
-Matrix multiply(const Matrix matrix1, const Matrix matrix2)
+// Matrizen multiplizieren
+Matrix multiply(const Matrix m1, const Matrix m2)
 {
-    
+    if (m1.cols != m2.rows)
+    {
+        return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
+    }
+
+    Matrix result = createMatrix(m1.rows, m2.cols);
+    if (result.buffer == NULL) return result;
+
+    for (unsigned int r = 0; r < m1.rows; r++)
+    {
+        for (unsigned int c = 0; c < m2.cols; c++)
+        {
+            MatrixType sum = 0;
+            for (unsigned int k = 0; k < m1.cols; k++)
+            {
+                sum += getMatrixAt(m1, r, k) * getMatrixAt(m2, k, c);
+            }
+            result.buffer[r * m2.cols + c] = sum;
+        }
+    }
+
+    return result;
 }

matrix.h

@@ -6,6 +6,13 @@
 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);

neuralNetworkTests.c

@@ -8,7 +8,58 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    // TODO : Fehlerbehandlung
+    // Öffne die Datei zum Schreiben im Binärmodus
+  FILE *file = fopen(path, "wb");
+    if (!file) return;
+
+    // Schreibe den Datei-Tag
+    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)