From 44f0bfc16d88e4a5902ab077211f9ee0d366b793 Mon Sep 17 00:00:00 2001
From: Simon <simon.d.may@proton.me>
Date: Fri, 14 Nov 2025 13:01:03 +0100
Subject: [PATCH] sync

---
 matrix.c             |  4 ++--
 matrix.h             |  2 +-
 neuralNetworkTests.c | 38 +++++++++++++++++++++++++++++++++++++-
 3 files changed, 40 insertions(+), 4 deletions(-)

diff --git a/matrix.c b/matrix.c
index f9ea09a..85fc869 100644
--- a/matrix.c
+++ b/matrix.c
@@ -6,13 +6,13 @@
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    Matrix m = {0, 0, NULL};
+    Matrix m = {NULL, 0, 0};
 
     if (rows > 0 && cols > 0)
     {
+        m.buffer = malloc(rows * cols * sizeof(int));
         m.rows = rows;
         m.cols = cols;
-        m.buffer = malloc(rows * cols * sizeof(int));
     }
 
     return m;
diff --git a/matrix.h b/matrix.h
index a27c11a..8ceedc9 100644
--- a/matrix.h
+++ b/matrix.h
@@ -9,9 +9,9 @@ typedef float MatrixType;
 
 typedef struct 
 {
+    MatrixType *buffer;    // Zeiger auf die Matrixdaten 
     unsigned int rows;   // Anzahl der Zeilen
     unsigned int cols;   // Anzahl der Spalten
-    MatrixType *buffer;    // Zeiger auf die Matrixdaten 
 } Matrix;
 
 
diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 21ab370..cbe0737 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -8,7 +8,43 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (file == NULL) {
+        perror("Fehler beim Erstellen der Testdatei");
+        exit(EXIT_FAILURE);
+    }
+
+    // Dateikopf speichern
+    const char *fileTag = "info2_neural_network_file_format";
+    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+    // Dimensionen der Eingabe und Ausgabe für den ersten Layer speichern
+    unsigned int inputDimension = nn.layers[0].weights.rows;  // Eingabedimension ist die Anzahl der Eingabeneuronen im ersten Layer
+    unsigned int outputDimension = nn.layers[0].weights.cols; // Ausgabedimension ist die Anzahl der Ausgabeneuronen im ersten Layer
+    fwrite(&inputDimension, sizeof(unsigned int), 1, file);
+    fwrite(&outputDimension, sizeof(unsigned int), 1, file);
+
+    // Alle Layer speichern
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
+        // Layer-Dimensionen speichern
+        inputDimension = nn.layers[i].weights.rows;
+        outputDimension = nn.layers[i].weights.cols;
+        
+        fwrite(&inputDimension, sizeof(unsigned int), 1, file);
+        fwrite(&outputDimension, sizeof(unsigned int), 1, file);
+
+        // Gewichte speichern
+        fwrite(&nn.layers[i].weights.rows, sizeof(unsigned int), 1, file);
+        fwrite(&nn.layers[i].weights.cols, sizeof(unsigned int), 1, file);
+        fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
+
+        // Biases speichern
+        fwrite(&nn.layers[i].biases.rows, sizeof(unsigned int), 1, file);
+        fwrite(&nn.layers[i].biases.cols, sizeof(unsigned int), 1, file);
+        fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
+    }
+
+    fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)