added some alternatives, however non of them work

matrix.c

@@ -131,4 +131,17 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
             }
     }
     return result;
+}
+void writeMatrix(Matrix matrix, FILE *file)//Added for neuralNetworkTests
+{
+	//fprintf(file, "%d%d", matrix.rows, matrix.cols);
+	for(int i = 0; i < matrix.rows; i++)
+	{
+		for(int j = 0; j < matrix.cols; j++)
+		{
+			putw(*(matrix.buffer + (j+i)*sizeof(MatrixType)), file);
+			//fprintf(file, "%f", *(matrix.buffer + (j+i)*sizeof(MatrixType)));
+			printf("%f", *(matrix.buffer + (j+i)*sizeof(MatrixType)));
+		}
+	}
 }

matrix.h

@@ -21,6 +21,7 @@ void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
+void writeMatrix(Matrix matrix, FILE *file);//Added for neuralNetworkTests
 
 
 #endif

neuralNetworkTests.c

@@ -4,11 +4,23 @@
 #include <math.h>
 #include "unity.h"
 #include "neuralNetwork.h"
+#include "matrix.h"
 
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+	FILE *testDatei = fopen(path, "w");
+	fprintf(testDatei, "__info2_neural_network_file_format__");
+	fprintf(testDatei, "%d%d", nn.layers->weights.rows, nn.layers->weights.cols);
+	// fprintf(testDatei, (char*) nn.numberOfLayers);
+	for(int i = 0; i < nn.numberOfLayers; i++)
+	{
+		//fprintf(testDatei, "\n");
+		//putw((nn.layers + sizeof(Layer) * i), testDatei);
+		writeMatrix(nn.layers[i].weights, testDatei);
+		writeMatrix(nn.layers[i].biases, testDatei);
+	}
+	fclose(testDatei);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -31,7 +43,7 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
 
     netUnderTest = loadModel(path);
     remove(path);
-
+	printf("\n%d\n%d\n", netUnderTest.numberOfLayers, expectedNet.numberOfLayers);
     TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
     clearModel(&netUnderTest);
 }