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imageInput.c

@@ -8,15 +8,127 @@
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
+static int read_header(FILE *file, unsigned short *count, unsigned short *width, unsigned short *height)
+{
+    size_t headerLEN = strlen(FILE_HEADER_STRING);
+    char buffer[BUFFER_SIZE];
+
+    if (headerLEN >= BUFFER_SIZE)
+    {
+        return 0;
+    }
+
+    if (fread(buffer, 1, headerLEN, file) != headerLEN)
+    {
+        return 0;
+    }
+
+    buffer[headerLEN] = '\0';
+
+    if (strcmp(buffer, FILE_HEADER_STRING) != 0)
+    {
+        return 0;
+    }
+
+    if (fread(count, sizeof(unsigned short), 1, file) != 1 || fread(width, sizeof(unsigned short), 1, file) != 1 ||
+        fread(height, sizeof(unsigned short), 1, file) != 1)
+    {
+        return 0;
+    }
+
+    return 1;
+}
+
+static int read_single_image(FILE *file, GrayScaleImage *image)
+{
+    unsigned int number_of_pixel = image->width * image->height;
+
+    if (fread(image->buffer, sizeof(GrayScalePixelType), number_of_pixel, file) != number_of_pixel) // fehler beim lesen
+    {
+        return 0;
+    }
+    return 1;
+}
+
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
-    GrayScaleImageSeries *series = NULL;
+    FILE *file = fopen(path, "rb");
+    if (!file)
+    {
+        return 0;
+    }
 
+    unsigned short count, width, height;
+
+    if (!read_header(file, &count, &width, &height))
+    {
+        fclose(file);
+        return 0;
+    }
+
+    GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
+
+    if (!series)
+    {
+        fclose(file);
+        return 0;
+    }
+
+    series->count = count;
+    series->images = malloc(count * sizeof(GrayScaleImage));
+    series->labels = malloc(count * sizeof(unsigned char));
+
+    if (!series->images || !series->labels)
+    {
+        clearSeries(series);
+        fclose(file);
+        return 0;
+    }
+
+    for (int i = 0; i < count; i++)
+    {
+        series->images[i].width = width;
+        series->images[i].height = height;
+        series->images[i].buffer = malloc(width * height * sizeof(GrayScalePixelType));
+
+        if (!series->images[i].buffer)
+        {
+            clearSeries(series);
+            fclose(file);
+            return 0;
+        }
+
+        if (!read_single_image(file, &series->images[i]))
+        {
+            clearSeries(series);
+            fclose(file);
+            return 0;
+        }
+
+        if (fread(&series->labels[i], 1, 1, file) != 1)
+        {
+            clearSeries(series);
+            fclose(file);
+            return 0;
+        }
+    }
+
+    fclose(file);
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
+    if (series)
+    {
+        for (int i = 0; i < series->count; i++)
+        {
+            free(series->images[i].buffer);
+        }
+        free(series->images);
+        free(series->labels);
+        free(series);
+    }
 }

imageInputTests.c

@@ -5,37 +5,40 @@
 #include "unity.h"
 #include "imageInput.h"
 
-
 static void prepareImageFile(const char *path, unsigned short int width, unsigned short int height, unsigned int short numberOfImages, unsigned char label)
 {
     FILE *file = fopen(path, "wb");
 
-    if(file != NULL)
+    if (file != NULL)
     {
         const char *fileTag = "__info2_image_file_format__";
-        GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
+        GrayScalePixelType *buffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
 
-        if(zeroBuffer != NULL)
+        if (buffer != NULL)
         {
-            fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
+            for (int i = 0; i < width * height; i++)
+            {    
+            buffer[i] = (GrayScalePixelType)i;                      // füllen des buffers mit Graustufen des Pixel für Test                              
+            }
+
+            fwrite(fileTag, 1, strlen(fileTag), file);
             fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
             fwrite(&width, sizeof(width), 1, file);
             fwrite(&height, sizeof(height), 1, file);
 
-            for(int i = 0; i < numberOfImages; i++)
+            for (int i = 0; i < numberOfImages; i++)
             {
-                fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
+                fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
                 fwrite(&label, sizeof(unsigned char), 1, file);
             }
 
-            free(zeroBuffer);
+            free(buffer);
         }
 
         fclose(file);
     }
 }
 
-
 void test_readImagesReturnsCorrectNumberOfImages(void)
 {
     GrayScaleImageSeries *series = NULL;
@@ -92,7 +95,8 @@ void test_readImagesReturnsCorrectLabels(void)
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->labels);
     TEST_ASSERT_EQUAL_UINT16(2, series->count);
-    for (int i = 0; i < 2; i++) {
+    for (int i = 0; i < 2; i++)
+    {
         TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
     }
     clearSeries(series);
@@ -110,7 +114,7 @@ void test_readImagesFailsOnWrongFileTag(void)
 {
     const char *path = "testFile.info2";
     FILE *file = fopen(path, "w");
-    if(file != NULL)
+    if (file != NULL)
     {
         fprintf(file, "some_tag                  ");
         fclose(file);
@@ -119,11 +123,36 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
-void setUp(void) {
+// Test der Hilfsfunktionen
+
+void test_read_GrayScale_Pixel(void)
+{
+    GrayScaleImageSeries *series = NULL;
+    const char *path = "testFile.info2";
+
+    prepareImageFile(path, 8, 8, 1, 1);
+    series = readImages(path);
+
+    TEST_ASSERT_NOT_NULL(series);
+    TEST_ASSERT_NOT_NULL(series->images);
+    TEST_ASSERT_EQUAL_UINT16(1, series->count);
+
+    for (int i = 0; i < (8 * 8); i++)
+    {
+        TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images->buffer[i]);
+    }
+
+    clearSeries(series);
+    remove(path);
+}
+
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
@@ -138,6 +167,7 @@ int main()
     RUN_TEST(test_readImagesReturnsCorrectLabels);
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
+    RUN_TEST(test_read_GrayScale_Pixel);
 
     return UNITY_END();
 }

matrix.c

@@ -1,35 +1,178 @@
 #include <stdlib.h>
 #include <string.h>
 #include "matrix.h"
+#include <stdio.h>
 
 // TODO Matrix-Funktionen implementieren
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
+    Matrix matrix;
 
+    if (rows == 0 || cols == 0)
+    {
+        matrix.rows = 0;
+        matrix.cols = 0;
+        matrix.buffer = NULL;
+        return matrix;
+    }
+
+    matrix.rows = rows;
+    matrix.cols = cols;
+
+    matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
+
+    if (matrix.buffer == NULL)
+    {
+
+        matrix.rows = 0;
+        matrix.cols = 0;
+
+        return matrix;
+    }
+
+    for (int i = 0; i < rows; i++)
+    {
+        for (int j = 0; j < cols; j++)
+        {
+            matrix.buffer[i * matrix.cols + j] = UNDEFINED_MATRIX_VALUE;
+        }
+    }
+    return matrix;
 }
 
 void clearMatrix(Matrix *matrix)
 {
+    if (matrix->buffer != NULL)
+    {
+        free(matrix->buffer);
+        matrix->buffer = NULL;
+    }
 
+    matrix->rows = 0;
+    matrix->cols = 0;
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
+    if (rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    {
+        fprintf(stderr, "Fehler: Ungültiger Index (%u, %u) bei Matrixgröße %u x %u\n", rowIdx, colIdx, matrix.rows, matrix.cols);
+        return; // abbruch falls fehler
+    }
 
+    matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
+    if (rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    {
+        fprintf(stderr, "Fehler: Ungültiger Index (%u, %u) bei Matrixgröße %u x %u\n", rowIdx, colIdx, matrix.rows, matrix.cols);
+        return UNDEFINED_MATRIX_VALUE;
+    }
 
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
+    if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols) // gleiche Dimension
+    {
+        Matrix result = createMatrix(matrix1.rows, matrix1.cols);
 
+        if (result.buffer == NULL)
+        {
+            fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
+            return result;
+        }
+
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i * matrix2.cols + j];
+            }
+        }
+        return result;
+    }
+        if (matrix1.rows == matrix2.rows && matrix2.cols == 1) // Matrix 2 hat eine Spalte
+        {
+            Matrix result = createMatrix(matrix1.rows, matrix1.cols);
+
+            if(result.buffer == NULL)
+            {
+                fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
+                return result;
+            }
+            for (int i = 0; i < matrix1.rows; i++)
+            {
+                for (int j = 0; j < matrix1.cols; j++)
+                {
+                    result.buffer[i * result.cols + j] = matrix1.buffer[i * matrix1.cols + j] + matrix2.buffer[i];
+                }
+            }
+            return result;
+        }
+
+        if (matrix1.rows == matrix2.rows && matrix1.cols == 1) // Matrix 1 hat eine Spalte
+        {
+            Matrix result = createMatrix(matrix2.rows, matrix2.cols);
+
+            if(result.buffer == NULL)
+            {
+                fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
+                return result;
+            }
+            for (int i = 0; i < matrix2.rows; i++)
+            {
+                for (int j = 0; j < matrix2.cols; j++)
+                {
+                    result.buffer[i * result.cols + j] = matrix1.buffer[i] + matrix2.buffer[i * matrix2.cols + j];
+                }
+            }
+            return result;
+        }
+    
+    // passt nicht
+    fprintf(stderr, "Fehler: Matrizen haben unterschiedliche Größen (%u x %u) und (%u x %u)\n",
+            matrix1.rows, matrix1.cols, matrix2.rows, matrix2.cols);
+
+    Matrix empty = {NULL, 0, 0};
+    return empty;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
+    if (matrix1.cols != matrix2.rows)
+    {
+        fprintf(stderr, "Fehler: Matrizen der Dimension (%u x %u) und (%u x %u) koennen nicht multipliziert werden\n",
+                matrix1.rows, matrix1.cols, matrix2.rows, matrix2.cols);
 
+        Matrix empty = {NULL, 0, 0};
+        return empty;
+    }
+
+    Matrix result = createMatrix(matrix1.rows, matrix2.cols);
+
+    if (result.buffer == NULL)
+    {
+        fprintf(stderr, "Fehler: Speicher konnte nicht reserviert werden!\n");
+        return result;
+    }
+
+    for (int i = 0; i < matrix1.rows; i++)
+    {
+        for (int j = 0; j < matrix2.cols; j++)
+        {
+            MatrixType sum = 0.0;
+
+            for (int k = 0; k < matrix1.cols; k++)
+            {
+                sum += matrix1.buffer[i * matrix1.cols + k] * matrix2.buffer[k * matrix2.cols + j];
+            }
+            result.buffer[i * result.cols + j] = sum;
+        }
+    }
+    return result;
 }

matrix.h

@@ -7,6 +7,13 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 
+typedef struct Matrix {
+    MatrixType *buffer;
+    unsigned int rows;
+    unsigned int cols;
+}   Matrix;
+
+
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);
 void clearMatrix(Matrix *matrix);

matrixTests.c

@@ -164,7 +164,7 @@ void test_setMatrixAtFailsOnIndicesOutOfRange(void)
     Matrix matrixToTest = {.rows=2, .cols=3, .buffer=buffer};
 
     setMatrixAt(-1, matrixToTest, 2, 3);
-    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, matrixToTest.cols * matrixToTest.rows);
 }
 
 void setUp(void) {

neuralNetworkTests.c

@@ -5,10 +5,49 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *f = fopen(path, "wb");
+    if (!f) return;
+
+    const char *tag = "__info2_neural_network_file_format__";
+    fwrite(tag, 1, strlen(tag), f);
+
+    if (nn.numberOfLayers == 0) {
+        fclose(f);
+        return;
+    }                                                               // In localmodel Struktur Testdateu aufruf:
+                                                                    // Header --> Input Dim --> Output Dim
+                                                                    // i. Layer weights --> biases --> nächste Dim
+
+  
+    int input  = nn.layers[0].weights.cols;
+    int output = nn.layers[0].weights.rows;
+
+    fwrite(&input,  sizeof(int), 1, f);
+    fwrite(&output, sizeof(int), 1, f);
+
+    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, f);
+
+        
+        fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, f);
+
+        
+        if (i + 1 < nn.numberOfLayers)
+        {
+            int nextOut = nn.layers[i + 1].weights.rows;
+            fwrite(&nextOut, sizeof(int), 1, f);
+        }
+    }
+
+    fclose(f);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -16,15 +55,15 @@ 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 weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
-    Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3};
+    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 biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
-    Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1};
+    Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
+    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 +79,12 @@ 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};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    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 +102,12 @@ 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};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    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 +125,12 @@ 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};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    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 +150,12 @@ 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};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    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 +177,7 @@ 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 +198,12 @@ 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};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    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 +220,7 @@ 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 +231,23 @@ 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 weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
-    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
+    Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
-    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
+    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 biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
-    Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
+    Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
-    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
+    Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights = weights2, .biases = biases2, .activation = someActivation},
-                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
+                      {.weights = weights3, .biases = biases3, .activation = someActivation}};
-    NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
+    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 +255,13 @@ 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
 }