FinalVersion

neuralNetworkTests.c

@@ -9,50 +9,48 @@
 
 #define NN_HEADER "__info2_neural_network_file_format__\n"
 
-//das auch zu machen
-static void writeMatrix(FILE *file, const Matrix *m) {
-    unsigned short rows = (unsigned short)m->rows;
-    unsigned short cols = (unsigned short)m->cols;
-
-    fwrite(&rows, sizeof(unsigned short), 1, file);
-    fwrite(&cols, sizeof(unsigned short), 1, file);
-    fwrite(m->buffer, sizeof(MatrixType), rows * cols, file);
-}
-
 
  //erste FKt war zu machen!
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
-FILE *file = fopen(path, "wb");
-    if (!file) return;
+    if (path == NULL || nn.layers == NULL || nn.numberOfLayers == 0)
+        return;
 
-    // 1. Header (ohne \n!)
-    const char *header = "__info2_neural_network_file_format__";
-    fwrite(header, sizeof(char), strlen(header), file);
+    FILE *file = fopen(path, "wb");
+    if (file == NULL)
+        return;
 
-    // 2. Schichten durchgehen
-    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
-        unsigned int inputDim  = (unsigned int)nn.layers[i].weights.cols;
-        unsigned int outputDim = (unsigned int)nn.layers[i].weights.rows;
+    // 1) Header schreiben – muss zu FILE_HEADER_STRING in neuralNetwork.c passen
+    const char *fileTag = "__info2_neural_network_file_format__";
+    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
 
-        // 3. Dimensionen schreiben
-        fwrite(&inputDim, sizeof(unsigned int), 1, file);
-        fwrite(&outputDim, sizeof(unsigned int), 1, file);
+    // 2) Erste Eingabedimension: Spalten der ersten Gewichtsmatrix
+    int inputDim = (int)nn.layers[0].weights.cols;
+    fwrite(&inputDim, sizeof(int), 1, file);
 
-        // 4. Matrizen schreiben
-        writeMatrix(file, &nn.layers[i].weights);
-        writeMatrix(file, &nn.layers[i].biases);
+    // 3) Für jede Schicht: Output-Dimension + Gewichte + Biases schreiben
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+    {
+        Matrix weights = nn.layers[i].weights;
+        Matrix biases  = nn.layers[i].biases;
+
+        int outputDim = (int)weights.rows;
+        fwrite(&outputDim, sizeof(int), 1, file);
+
+        int nWeights = (int)(weights.rows * weights.cols);
+        int nBiases  = (int)(biases.rows * biases.cols);
+
+        fwrite(weights.buffer, sizeof(MatrixType), nWeights, file);
+        fwrite(biases.buffer, sizeof(MatrixType), nBiases, file);
     }
 
-    // 5. Endsignal: letzte Output-Dimension = 0
-    unsigned int zero = 0;
-    fwrite(&zero, sizeof(unsigned int), 1, file);
+    // 4) Terminierende 0-Dimension, damit loadModel die Schleife beendet
+    int zero = 0;
+    fwrite(&zero, sizeof(int), 1, file);
 
     fclose(file);
 }
 
-
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";