matrix.c aenderungen

Reviewed-on: kachelto100370/info2Praktikum-NeuronalesNetz#1

.gitignore

@@ -1,4 +1,10 @@
 mnist
 runTests
 *.o
-*.exe
+*.exe
+.vscode/settings.json
+.vscode/launch.json
+.vscode/settings.json
+.vscode/settings.json
+runImageInputTests
+testFile.info2

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "makefile.configureOnOpen": false
+}

README.md

@@ -0,0 +1,2 @@
+# Projekt 2 für Informatik 2 Praktikum
+  

imageInput.c

@@ -1,22 +1,136 @@
+#include "imageInput.h"
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include "imageInput.h"
 
-#define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_image_file_format__"
+// define BUFFER 100
+// 10x10 pixel
 
-// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
-
-// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
-GrayScaleImageSeries *readImages(const char *path)
-{
-    GrayScaleImageSeries *series = NULL;
-    
-    return series;
+/* ----------------------------------------------------------
+   1. Header prüfen
+   ---------------------------------------------------------- */
+static int readHeader(FILE *file) {
+  char header[sizeof(FILE_HEADER_STRING)];
+  if (fread(header, 1, sizeof(FILE_HEADER_STRING) - 1, file) !=
+      sizeof(FILE_HEADER_STRING) - 1)
+    return 0;
+  header[sizeof(FILE_HEADER_STRING) - 1] = '\0';
+  return strcmp(header, FILE_HEADER_STRING) == 0;
 }
 
-// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
-void clearSeries(GrayScaleImageSeries *series)
-{
-}
+/* ----------------------------------------------------------
+   2. Meta-Daten lesen (unsigned short)
+   ---------------------------------------------------------- */
+static int readMeta(FILE *file, unsigned short *count, unsigned short *width,
+                    unsigned short *height) {
+  if (fread(count, sizeof(unsigned short), 1, file) != 1)
+    return 0;
+  if (fread(width, sizeof(unsigned short), 1, file) != 1)
+    return 0;
+  if (fread(height, sizeof(unsigned short), 1, file) != 1)
+    return 0;
+
+  return 1;
+}
+
+/* ----------------------------------------------------------
+   3. Einzelbild lesen
+   ---------------------------------------------------------- */
+static int readSingleImage(FILE *file, GrayScaleImage *img,
+                           unsigned short width, unsigned short height) {
+  img->width = width;
+  img->height = height;
+
+  size_t numPixels = (size_t)width * (size_t)height; // anzahl an pixeln
+  img->buffer = malloc(numPixels);
+  if (!img->buffer)
+    return 0;
+
+  if (fread(img->buffer, 1, numPixels, file) != numPixels) {
+    free(img->buffer);
+    img->buffer = NULL; // fehler bei ungültiger eingabe
+    return 0;
+  }
+  return 1;
+}
+
+/* ----------------------------------------------------------
+   4. Label lesen
+   ---------------------------------------------------------- */
+static int readLabel(FILE *file, unsigned char *label) {
+  return fread(label, 1, 1, file) == 1;
+}
+
+/* ----------------------------------------------------------
+   5. Komplette Bildserie lesen
+   ---------------------------------------------------------- */
+GrayScaleImageSeries *readImages(const char *path) {
+  FILE *file = fopen(path, "rb");
+  if (!file)
+    return NULL;
+
+  if (!readHeader(file)) {
+    fclose(file);
+    return NULL;
+  }
+
+  unsigned short count, width, height;
+  if (!readMeta(file, &count, &width, &height)) {
+
+    fclose(file);
+    return NULL;
+  }
+  // printf("%d, %d, %d", count, width, height);
+
+  GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
+  if (!series) {
+    fclose(file);
+    return NULL;
+  }
+
+  series->count = count;
+  series->images = malloc(count * sizeof(GrayScaleImage));
+  series->labels = malloc(count * sizeof(unsigned char));
+  if (!series->images || !series->labels) {
+    free(series->images);
+    free(series->labels);
+    free(series);
+    fclose(file);
+    return NULL;
+  }
+
+  for (unsigned int i = 0; i < count; i++) {
+    if (!readSingleImage(file, &series->images[i], width, height) ||
+        !readLabel(file, &series->labels[i])) {
+      // Aufräumen bei Fehler
+      for (unsigned int j = 0; j < i; j++) {
+        free(series->images[j].buffer);
+      }
+      free(series->images);
+      free(series->labels);
+      free(series);
+      fclose(file);
+      return NULL;
+    }
+  }
+
+  fclose(file);
+  return series;
+}
+
+/* ----------------------------------------------------------
+   6. Speicher komplett freigeben
+   ---------------------------------------------------------- */
+void clearSeries(GrayScaleImageSeries *series) {
+  if (!series)
+    return;
+
+  for (unsigned int i = 0; i < series->count; i++) {
+    free(series->images[i].buffer);
+  }
+
+  free(series->images);
+  free(series->labels);
+  free(series);
+}

imageInputTests.c

@@ -1,143 +1,210 @@
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <string.h>
-#include "unity.h"
 #include "imageInput.h"
+#include "unity.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
 
+/* ---------------------------------------------------------
+   Hilfsfunktion: Testdatei vorbereiten
+   --------------------------------------------------------- */
+static void prepareImageFile(const char *path, unsigned int width,
+                             unsigned int height, unsigned int numberOfImages,
+                             unsigned char label) {
+  FILE *file = fopen(path, "wb");
+  if (!file)
+    return;
 
-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");
+  // Header
+  const char *fileTag = "__info2_image_file_format__";
+  fwrite(fileTag, 1, strlen(fileTag), file);
 
-    if(file != NULL)
-    {
-        const char *fileTag = "__info2_image_file_format__";
-        GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
+  // Meta-Daten als unsigned short
+  unsigned short n = (unsigned short)numberOfImages;
+  unsigned short w = (unsigned short)width;
+  unsigned short h = (unsigned short)height;
+  fwrite(&n, sizeof(unsigned short), 1, file);
+  fwrite(&w, sizeof(unsigned short), 1, file);
+  fwrite(&h, sizeof(unsigned short), 1, file);
 
-        if(zeroBuffer != NULL)
-        {
-            fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
-            fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
-            fwrite(&width, sizeof(width), 1, file);
-            fwrite(&height, sizeof(height), 1, file);
+  // Pixelbuffer
+  GrayScalePixelType *buffer =
+      calloc(width * height, sizeof(GrayScalePixelType));
+  if (!buffer) {
+    fclose(file);
+    return;
+  }
+  for (unsigned int i = 0; i < width * height; i++)
+    buffer[i] = (GrayScalePixelType)i;
 
-            for(int i = 0; i < numberOfImages; i++)
-            {
-                fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
-                fwrite(&label, sizeof(unsigned char), 1, file);
-            }
+  // Jedes Bild schreiben: Pixel + Label
+  for (unsigned int img = 0; img < numberOfImages; img++) {
+    fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
+    fwrite(&label, sizeof(unsigned char), 1, file);
+  }
 
-            free(zeroBuffer);
-        }
-
-        fclose(file);
-    }
+  free(buffer);
+  fclose(file);
 }
 
+/* ---------------------------------------------------------
+   Unit Tests
+   --------------------------------------------------------- */
 
-void test_readImagesReturnsCorrectNumberOfImages(void)
-{
-    GrayScaleImageSeries *series = NULL;
-    const unsigned short expectedNumberOfImages = 2;
-    const char *path = "testFile.info2";
-    prepareImageFile(path, 8, 8, expectedNumberOfImages, 1);
-    series = readImages(path);
-    TEST_ASSERT_NOT_NULL(series);
-    TEST_ASSERT_EQUAL_UINT16(expectedNumberOfImages, series->count);
-    clearSeries(series);
-    remove(path);
+void test_readImagesReturnsCorrectNumberOfImages(void) {
+  GrayScaleImageSeries *series = NULL;
+  const unsigned int expectedNumberOfImages = 2;
+  const char *path = "testFile.info2";
+  prepareImageFile(path, 8, 8, expectedNumberOfImages, 1);
+  series = readImages(path);
+  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_EQUAL_UINT(expectedNumberOfImages, series->count);
+  clearSeries(series);
+  remove(path);
 }
 
-void test_readImagesReturnsCorrectImageWidth(void)
-{
-    GrayScaleImageSeries *series = NULL;
-    const unsigned short expectedWidth = 10;
-    const char *path = "testFile.info2";
-    prepareImageFile(path, expectedWidth, 8, 2, 1);
-    series = readImages(path);
-    TEST_ASSERT_NOT_NULL(series);
-    TEST_ASSERT_NOT_NULL(series->images);
-    TEST_ASSERT_EQUAL_UINT16(2, series->count);
-    TEST_ASSERT_EQUAL_UINT16(expectedWidth, series->images[0].width);
-    TEST_ASSERT_EQUAL_UINT16(expectedWidth, series->images[1].width);
-    clearSeries(series);
-    remove(path);
+void test_readImagesReturnsCorrectImageWidth(void) {
+  GrayScaleImageSeries *series = NULL;
+  const unsigned int expectedWidth = 10;
+  const char *path = "testFile.info2";
+  prepareImageFile(path, expectedWidth, 8, 2, 1);
+  series = readImages(path);
+  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_NOT_NULL(series->images);
+  TEST_ASSERT_EQUAL_UINT(2, series->count);
+  TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[0].width);
+  TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[1].width);
+  clearSeries(series);
+  remove(path);
 }
 
-void test_readImagesReturnsCorrectImageHeight(void)
-{
-    GrayScaleImageSeries *series = NULL;
-    const unsigned short expectedHeight = 10;
-    const char *path = "testFile.info2";
-    prepareImageFile(path, 8, expectedHeight, 2, 1);
-    series = readImages(path);
-    TEST_ASSERT_NOT_NULL(series);
-    TEST_ASSERT_NOT_NULL(series->images);
-    TEST_ASSERT_EQUAL_UINT16(2, series->count);
-    TEST_ASSERT_EQUAL_UINT16(expectedHeight, series->images[0].height);
-    TEST_ASSERT_EQUAL_UINT16(expectedHeight, series->images[1].height);
-    clearSeries(series);
-    remove(path);
+void test_readImagesReturnsCorrectImageHeight(void) {
+  GrayScaleImageSeries *series = NULL;
+  const unsigned int expectedHeight = 10;
+  const char *path = "testFile.info2";
+  prepareImageFile(path, 8, expectedHeight, 2, 1);
+  series = readImages(path);
+  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_NOT_NULL(series->images);
+  TEST_ASSERT_EQUAL_UINT(2, series->count);
+  TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[0].height);
+  TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[1].height);
+  clearSeries(series);
+  remove(path);
 }
 
-void test_readImagesReturnsCorrectLabels(void)
-{
-    const unsigned char expectedLabel = 15;
+void test_readImagesReturnsCorrectLabels(void) {
+  const unsigned char expectedLabel = 15;
 
-    GrayScaleImageSeries *series = NULL;
-    const char *path = "testFile.info2";
-    prepareImageFile(path, 8, 8, 2, expectedLabel);
-    series = readImages(path);
-    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++) {
-        TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
-    }
-    clearSeries(series);
-    remove(path);
+  GrayScaleImageSeries *series = NULL;
+  const char *path = "testFile.info2";
+  prepareImageFile(path, 8, 8, 2, expectedLabel);
+  series = readImages(path);
+  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_NOT_NULL(series->labels);
+  TEST_ASSERT_EQUAL_UINT(2, series->count);
+  for (int i = 0; i < 2; i++) {
+    TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
+  }
+  clearSeries(series);
+  remove(path);
 }
 
-void test_readImagesReturnsNullOnNotExistingPath(void)
-{
-    const char *path = "testFile.txt";
-    remove(path);
+void test_readImagesReturnsNullOnNotExistingPath(void) {
+  const char *path = "testFile.txt";
+  remove(path);
+  TEST_ASSERT_NULL(readImages(path));
+}
+
+void test_readImagesFailsOnWrongFileTag(void) {
+  const char *path = "testFile.info2";
+  FILE *file = fopen(path, "w");
+  if (file != NULL) {
+    fprintf(file, "some_tag                  ");
+    fclose(file);
     TEST_ASSERT_NULL(readImages(path));
+  }
+  remove(path);
 }
 
-void test_readImagesFailsOnWrongFileTag(void)
-{
-    const char *path = "testFile.info2";
-    FILE *file = fopen(path, "w");
-    if(file != NULL)
-    {
-        fprintf(file, "some_tag                  ");
-        fclose(file);
-        TEST_ASSERT_NULL(readImages(path));
-    }
-    remove(path);
+// Test
+
+void test_read_GrayScale_Pixel(
+    void) { // testet das einlesen eines graustufenbildes von readImages()
+  GrayScaleImageSeries *series = NULL; // enthält später das Bild
+  const char *path = "testFile.info2";
+
+  prepareImageFile(path, 8, 8, 1,
+                   1); // Höhe x Breite in Pixel, Anzahl Bilder und Kategorie
+  series = readImages(path);
+
+  TEST_ASSERT_NOT_NULL(series);             // Speicher reservieren
+  TEST_ASSERT_NOT_NULL(series->images);     // Inhalt ist da
+  TEST_ASSERT_EQUAL_UINT(1, series->count); // Anzahl der Bilder stimmt
+
+  for (int i = 0; i < (8 * 8); i++) {
+    TEST_ASSERT_EQUAL_UINT8(
+        (GrayScalePixelType)i,
+        series->images[0].buffer[i]); // alle Pixelwerte prüfen
+  }
+
+  clearSeries(series);
+  remove(path);
 }
 
-void setUp(void) {
-    // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
+/* ---------------------------------------------------------
+   Optional: Mehrere Bilder gleichzeitig testen
+   --------------------------------------------------------- */
+
+void test_readImagesMultipleImagesContent(void) {
+  GrayScaleImageSeries *series = NULL;
+  const char *path = "testFile.info2";
+  const unsigned int numberOfImages = 3;
+  const unsigned int width = 4;
+  const unsigned int height = 4;
+  const unsigned char label = 7;
+
+  prepareImageFile(path, width, height, numberOfImages, label);
+
+  series = readImages(path);
+  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_NOT_NULL(series->images);
+  TEST_ASSERT_NOT_NULL(series->labels);
+  TEST_ASSERT_EQUAL_UINT(numberOfImages, series->count);
+
+  for (unsigned int img = 0; img < numberOfImages; img++) {
+    for (unsigned int i = 0; i < width * height; i++)
+      TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i,
+                              series->images[img].buffer[i]);
+    TEST_ASSERT_EQUAL_UINT8(label, series->labels[img]);
+  }
+
+  clearSeries(series);
+  remove(path);
 }
 
-void tearDown(void) {
-    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+/* ---------------------------------------------------------
+   Setup / Teardown
+   --------------------------------------------------------- */
+void setUp(void) {}
+void tearDown(void) {}
+
+/* ---------------------------------------------------------
+   main()
+   --------------------------------------------------------- */
+int main(void) {
+  UNITY_BEGIN();
+
+  printf("\n============================\nImage input "
+         "tests\n============================\n");
+
+  RUN_TEST(test_readImagesReturnsCorrectNumberOfImages);
+  RUN_TEST(test_readImagesReturnsCorrectImageWidth);
+  RUN_TEST(test_readImagesReturnsCorrectImageHeight);
+  RUN_TEST(test_readImagesReturnsCorrectLabels);
+  RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
+  RUN_TEST(test_readImagesFailsOnWrongFileTag);
+  RUN_TEST(test_read_GrayScale_Pixel);
+  RUN_TEST(test_readImagesMultipleImagesContent);
+
+  return UNITY_END();
 }
-
-int main()
-{
-    UNITY_BEGIN();
-    
-    printf("\n============================\nImage input tests\n============================\n");
-    RUN_TEST(test_readImagesReturnsCorrectNumberOfImages);
-    RUN_TEST(test_readImagesReturnsCorrectImageWidth);
-    RUN_TEST(test_readImagesReturnsCorrectImageHeight);
-    RUN_TEST(test_readImagesReturnsCorrectLabels);
-    RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
-    RUN_TEST(test_readImagesFailsOnWrongFileTag);
-
-    return UNITY_END();
-}

makefile

@@ -59,8 +59,8 @@ imageInputTests: imageInput.o imageInputTests.c $(unityfolder)/unity.c
 # --------------------------
 clean:
 ifeq ($(OS),Windows_NT)
-	del /f *.o *.exe
-else
 	rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
+else
+	del /f *.o *.exe
 endif
-
+ 

matrix.c

@@ -1,35 +1,212 @@
+#include "matrix.h"
+#include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include "matrix.h"
 
-// TODO Matrix-Funktionen implementieren
+/*typedef struct {
+    unsigned int rows;  //Zeilen
+    unsigned int cols; //Spalten
+    MatrixType *buffer; //Zeiger auf Speicherbereich Reihen*Spalten
+} Matrix;*/
 
-Matrix createMatrix(unsigned int rows, unsigned int cols)
-{
-    
+Matrix createMatrix(const unsigned int rows, const unsigned int cols) {
+  if (cols == 0 || rows == 0) {
+    Matrix errorMatrix = {0, 0, NULL};
+    return errorMatrix;
+  }
+  MatrixType *buffer =
+      malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc
+                                                // liefert Zeiger auf Speicher
+  Matrix newMatrix = {rows, cols, buffer};      // neue Matrix nach struct
+  return newMatrix;
+}
+void clearMatrix(Matrix *matrix) {
+
+  if (matrix->buffer != NULL) {
+    free((*matrix).buffer);
+    matrix->buffer = NULL;
+  }
+  matrix->rows = 0;
+  matrix->cols = 0;
 }
 
-void clearMatrix(Matrix *matrix)
-{
-    
-}
+void setMatrixAt(const MatrixType value, Matrix matrix,
+                 const unsigned int rowIdx, // Kopie der Matrix wird übergeben
+                 const unsigned int colIdx) {
 
-void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
-{
-    
+  if (rowIdx >= matrix.rows || colIdx >= matrix.cols) {
+    // Speichergröße nicht überschreiten
+    return;
+  }
+  matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+  // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
+  // innerhalb der Zeile
 }
+MatrixType
+getMatrixAt(const Matrix matrix,
+            const unsigned int rowIdx, // Kopie der Matrix wird übergeben
+            const unsigned int colIdx) {
+  if (rowIdx >= matrix.rows || colIdx >= matrix.cols ||
+      matrix.buffer == NULL) { // Speichergröße nicht überschreiten
+    return UNDEFINED_MATRIX_VALUE;
+  }
 
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
-{
-    
+  MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx];
+
+  return value;
 }
-
-Matrix add(const Matrix matrix1, const Matrix matrix2)
-{
-    
+Matrix broadCastCols(const Matrix matrix, const unsigned int cols) {
+  Matrix copy1 = createMatrix(matrix.rows, cols);
+  for (int r = 0; r < matrix.rows; r++) {
+    MatrixType valueMatrix1 = getMatrixAt(matrix, r, 0);
+    for (int c = 0; c < cols; c++) {
+      setMatrixAt(valueMatrix1, copy1, r, c);
+    }
+  }
+  return copy1;
 }
+Matrix broadCastRows(const Matrix matrix, const unsigned int rows) {
+  Matrix copy1 = createMatrix(rows, matrix.cols);
+  for (int c = 0; c < matrix.cols; c++) {
+    MatrixType valueMatrix1 = getMatrixAt(matrix, 0, c);
+    for (int r = 0; r < rows; r++) {
+      setMatrixAt(valueMatrix1, copy1, r, c);
+    }
+  }
+  return copy1;
+}
+Matrix add(const Matrix matrix1, const Matrix matrix2) {
 
-Matrix multiply(const Matrix matrix1, const Matrix matrix2)
-{
-    
-}
+  // Ergebnismatrix
+  Matrix result;
+  const int cols1 = matrix1.cols;
+  const int rows1 = matrix1.rows;
+  const int cols2 = matrix2.cols;
+  const int rows2 = matrix2.rows;
+
+  const int rowsEqual = (matrix1.rows == matrix2.rows) ? 1 : 0;
+  const int colsEqual = (matrix1.cols == matrix2.cols) ? 1 : 0;
+
+  // Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender
+  // Matrix
+  if (rowsEqual == 1 && colsEqual == 1) {
+    Matrix result = createMatrix(matrix1.rows, matrix1.cols);
+    if (result.buffer == NULL) {
+      return (Matrix){0, 0, NULL};
+    }
+    for (int i = 0; i < rows1; i++) {
+      for (int j = 0; j < cols1; j++) {
+        int valueM1 = getMatrixAt(matrix1, i, j);
+        int valueM2 = getMatrixAt(matrix2, i, j);
+        int sum = valueM1 + valueM2;
+        setMatrixAt(sum, result, i, j);
+      }
+    }
+    return result;
+  } else if (rowsEqual == 1 && (cols1 == 1 || cols2 == 1)) {
+    if (cols1 == 1) { // broadcasting von vektor 1 zu matrix 1, add
+      Matrix newMatrix = broadCastCols(matrix1, cols2);
+      // add
+      Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
+      if (result.buffer == NULL) {
+        return (Matrix){0, 0, NULL};
+      }
+      for (int i = 0; i < rows1; i++) {
+        for (int j = 0; j < cols2; j++) {
+          int valueM1 = getMatrixAt(newMatrix, i, j);
+          int valueM2 = getMatrixAt(matrix2, i, j);
+          int sum = valueM1 + valueM2;
+          setMatrixAt(sum, result, i, j);
+        }
+      }
+      clearMatrix(&newMatrix);
+      return result;
+    } else {
+      Matrix newMatrix2 = broadCastCols(matrix2, cols1);
+      // add
+      Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
+      if (result.buffer == NULL) {
+        return (Matrix){0, 0, NULL};
+      }
+      for (int i = 0; i < rows1; i++) {
+        for (int j = 0; j < cols1; j++) {
+          int valueM1 = getMatrixAt(matrix1, i, j);
+          int valueM2 = getMatrixAt(newMatrix2, i, j);
+          int sum = valueM1 + valueM2;
+          setMatrixAt(sum, result, i, j);
+        }
+      }
+
+      return result;
+    }
+  }
+
+  else if ((rows1 == 1 || rows2 == 1) && colsEqual == 1) {
+    if (rows1 == 1) {
+      Matrix newMatrix = broadCastRows(matrix1, rows2);
+      // add
+      Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
+      if (result.buffer == NULL) {
+        return (Matrix){0, 0, NULL};
+      }
+      for (int i = 0; i < rows2; i++) {
+        for (int j = 0; j < cols1; j++) {
+          int valueM1 = getMatrixAt(newMatrix, i, j);
+          int valueM2 = getMatrixAt(matrix2, i, j);
+          int sum = valueM1 + valueM2;
+          setMatrixAt(sum, result, i, j);
+        }
+      }
+      return result;
+    } else {
+      Matrix newMatrix2 = broadCastRows(matrix2, rows1);
+      // add
+      Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
+      if (result.buffer == NULL) {
+        return (Matrix){0, 0, NULL};
+      }
+      for (int i = 0; i < rows1; i++) {
+        for (int j = 0; j < cols1; j++) {
+          int valueM1 = getMatrixAt(matrix1, i, j);
+          int valueM2 = getMatrixAt(newMatrix2, i, j);
+          int sum = valueM1 + valueM2;
+          setMatrixAt(sum, result, i, j);
+        }
+      }
+      clearMatrix(&newMatrix2);
+      return result;
+    }
+  } else {
+    // kein add möglich
+    Matrix errorMatrix = {0, 0, NULL};
+    return errorMatrix;
+  }
+  return result;
+}
+Matrix multiply(const Matrix matrix1, const Matrix matrix2) {
+  // Spalten1 müssen gleich zeilen2 sein! dann multiplizieren
+  if (matrix1.cols == matrix2.rows) {
+    Matrix multMatrix = createMatrix(matrix1.rows, matrix2.cols);
+    // durch neue matrix iterieren
+    for (int r = 0; r < matrix1.rows; r++) {
+      for (int c = 0; c < matrix2.cols; c++) {
+        MatrixType sum = 0.0;
+        // skalarprodukte berechnen, k damit die ganze zeile mal die ganze
+        // spalte genommen wird quasi
+        for (int k = 0; k < matrix1.cols; k++) {
+          // sum+=
+          // matrix1.buffer[r*matrix1.cols+k]*matrix2.buffer[k*matrix2.cols+c];
+          sum += getMatrixAt(matrix1, r, k) * getMatrixAt(matrix2, k, c);
+        }
+        // Ergebnisse in neue matrix speichern
+        setMatrixAt(sum, multMatrix, r, c);
+      }
+    }
+    return multMatrix;
+  }
+  // sonst fehler, kein multiply möglich
+  else {
+    Matrix errorMatrix = {0, 0, NULL};
+    return errorMatrix;
+  }
+}

matrix.h

@@ -6,14 +6,23 @@
 typedef float MatrixType;
 
 // TODO Matrixtyp definieren
+typedef struct {
+  unsigned int rows;
+  unsigned int cols;
+  MatrixType *buffer;
 
+} Matrix;
 
-Matrix createMatrix(unsigned int rows, unsigned int cols);
+Matrix createMatrix(const unsigned int rows, const unsigned int cols);
 void clearMatrix(Matrix *matrix);
-void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
+void setMatrixAt(const MatrixType value, Matrix matrix,
+                 const unsigned int rowIdx, const unsigned int colIdx);
+MatrixType getMatrixAt(const Matrix matrix, const unsigned int rowIdx,
+                       const unsigned int colIdx);
+
+Matrix broadCastCols(const Matrix matrix, const unsigned int cols);
+Matrix broadCastRows(const Matrix matrix, const unsigned int rows);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
 
-
 #endif

neuralN_readFiles

@@ -0,0 +1,29 @@
+Inhalte: Dynamische Speicherverwaltung, Strukturen, Dateien lesen.
+
+Ziel: Die Bilder aus mnist_test.info 2 auslesen
+
+Struktur für einlesen des Strings am Anfang der Datei:
+	int AnzahlBilder
+	int breiteBilder
+	int LaengeBilder
+
+Struktur für Bilder:
+	unsinged int array Breite * Höhe
+	unsigned int Klasse (Label 0 - 9)
+
+
+Speicher für Bilder dynamisch allokieren
+
+GrayScaleImageSeries:
+	datei einlesen
+	header String aus der Datei lesen
+	mit header String den benötigten Speicher freigeben
+	in den Speicher die Datei einschreiben (mit Hilfsfunktion)
+
+Hilfsfunktion (saveFile)
+	gehe zum Anfang des Strings
+	speicher alles der Reihe nach ein
+
+clearSeries:
+	pointer der be malloc kommt nehemen
+	free()

neuralNetwork.c

@@ -1,268 +1,235 @@
-#include <stdlib.h>
-#include <stdio.h>
-#include <math.h>
-#include <string.h>
 #include "neuralNetwork.h"
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
 
 #define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_neural_network_file_format__"
 
-static void softmax(Matrix *matrix)
-{
-    if(matrix->cols > 0)
-    {
-        double *colSums = (double *)calloc(matrix->cols, sizeof(double));
+static void softmax(Matrix *matrix) {
+  if (matrix->cols > 0) {
+    double *colSums = (double *)calloc(matrix->cols, sizeof(double));
 
-        if(colSums != NULL)
-        {
-            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
-            {
-                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
-                {
-                    MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
-                    setMatrixAt(expValue, *matrix, rowIdx, colIdx);
-                    colSums[colIdx] += expValue;
-                }
-            }
-
-            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
-            {
-                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
-                {
-                    MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
-                    setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
-                }
-            }
-            free(colSums);
+    if (colSums != NULL) {
+      for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
+        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
+          MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
+          setMatrixAt(expValue, *matrix, rowIdx, colIdx);
+          colSums[colIdx] += expValue;
         }
-    }
-}
+      }
 
-static void relu(Matrix *matrix)
-{
-    for(int i = 0; i < matrix->rows * matrix->cols; i++)
-    {
-        matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
-    }
-}
-
-static int checkFileHeader(FILE *file)
-{
-    int isValid = 0;
-    int fileHeaderLen = strlen(FILE_HEADER_STRING);
-    char buffer[BUFFER_SIZE] = {0};
-
-    if(BUFFER_SIZE-1 < fileHeaderLen)
-        fileHeaderLen = BUFFER_SIZE-1;
-
-    if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
-        isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
-
-    return isValid;
-}
-
-static unsigned int readDimension(FILE *file)
-{
-    int dimension = 0;
-
-    if(fread(&dimension, sizeof(int), 1, file) != 1)
-        dimension = 0;
-    
-    return dimension;
-}
-
-static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
-{
-    Matrix matrix = createMatrix(rows, cols);
-    
-    if(matrix.buffer != NULL)
-    {
-        if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
-            clearMatrix(&matrix);
-    }
-
-    return matrix;
-}
-
-static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension)
-{
-    Layer layer;
-    layer.weights = readMatrix(file, outputDimension, inputDimension);
-    layer.biases = readMatrix(file, outputDimension, 1);
-
-    return layer;
-}
-
-static int isEmptyLayer(const Layer layer)
-{
-    return layer.biases.cols == 0 || layer.biases.rows == 0 || layer.biases.buffer == NULL || layer.weights.rows == 0 || layer.weights.cols == 0 || layer.weights.buffer == NULL;
-}
-
-static void clearLayer(Layer *layer)
-{
-    if(layer != NULL)
-    {
-        clearMatrix(&layer->weights);
-        clearMatrix(&layer->biases);
-        layer->activation = NULL;
-    }
-}
-
-static void assignActivations(NeuralNetwork model)
-{
-    for(int i = 0; i < (int)model.numberOfLayers-1; i++)
-    {
-        model.layers[i].activation = relu;
-    }
-
-    if(model.numberOfLayers > 0)
-        model.layers[model.numberOfLayers-1].activation = softmax;
-}
-
-NeuralNetwork loadModel(const char *path)
-{
-    NeuralNetwork model = {NULL, 0};
-    FILE *file = fopen(path, "rb");
-
-    if(file != NULL)
-    {
-        if(checkFileHeader(file))
-        {
-            unsigned int inputDimension = readDimension(file); 
-            unsigned int outputDimension = readDimension(file);
-
-            while(inputDimension > 0 && outputDimension > 0)
-            {
-                Layer layer = readLayer(file, inputDimension, outputDimension);
-                Layer *layerBuffer = NULL;
-
-                if(isEmptyLayer(layer))
-                {
-                    clearLayer(&layer);
-                    clearModel(&model);
-                    break;
-                }
-                
-                layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
-
-                if(layerBuffer != NULL)
-                    model.layers = layerBuffer;
-                else
-                {
-                    clearModel(&model);
-                    break;
-                }
-
-                model.layers[model.numberOfLayers] = layer;
-                model.numberOfLayers++;
-
-                inputDimension = outputDimension;
-                outputDimension = readDimension(file);
-            }
+      for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
+        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
+          MatrixType normalizedValue =
+              getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
+          setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
         }
-        fclose(file);
-
-        assignActivations(model);
+      }
+      free(colSums);
     }
-
-    return model;
+  }
 }
 
-static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
-{
-    Matrix matrix = {NULL, 0, 0};
+static void relu(Matrix *matrix) {
+  for (int i = 0; i < matrix->rows * matrix->cols; i++) {
+    matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
+  }
+}
 
-    if(count > 0 && images != NULL)
-    {
-        matrix = createMatrix(images[0].height * images[0].width, count);
+static int checkFileHeader(FILE *file) {
+  int isValid = 0;
+  int fileHeaderLen = strlen(FILE_HEADER_STRING);
+  char buffer[BUFFER_SIZE] = {0};
 
-        if(matrix.buffer != NULL)
-        {
-            for(int i = 0; i < count; i++)
-            {
-                for(int j = 0; j < images[i].width * images[i].height; j++)
-                {
-                    setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
-                }
-            }
+  if (BUFFER_SIZE - 1 < fileHeaderLen)
+    fileHeaderLen = BUFFER_SIZE - 1;
+
+  if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
+    isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
+
+  return isValid;
+}
+
+static unsigned int readDimension(FILE *file) {
+  int dimension = 0;
+
+  if (fread(&dimension, sizeof(int), 1, file) != 1)
+    dimension = 0;
+
+  return dimension;
+}
+
+static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) {
+  Matrix matrix = createMatrix(rows, cols);
+
+  if (matrix.buffer != NULL) {
+    if (fread(matrix.buffer, sizeof(MatrixType), rows * cols, file) !=
+        rows * cols)
+      clearMatrix(&matrix);
+  }
+
+  return matrix;
+}
+
+static Layer readLayer(FILE *file, unsigned int inputDimension,
+                       unsigned int outputDimension) {
+  Layer layer;
+  layer.weights = readMatrix(file, outputDimension, inputDimension);
+  layer.biases = readMatrix(file, outputDimension, 1);
+
+  return layer;
+}
+
+static int isEmptyLayer(const Layer layer) {
+  return layer.biases.cols == 0 || layer.biases.rows == 0 ||
+         layer.biases.buffer == NULL || layer.weights.rows == 0 ||
+         layer.weights.cols == 0 || layer.weights.buffer == NULL;
+}
+
+static void clearLayer(Layer *layer) {
+  if (layer != NULL) {
+    clearMatrix(&layer->weights);
+    clearMatrix(&layer->biases);
+    layer->activation = NULL;
+  }
+}
+
+static void assignActivations(NeuralNetwork model) {
+  for (int i = 0; i < (int)model.numberOfLayers - 1; i++) {
+    model.layers[i].activation = relu;
+  }
+
+  if (model.numberOfLayers > 0)
+    model.layers[model.numberOfLayers - 1].activation = softmax;
+}
+
+NeuralNetwork loadModel(const char *path) {
+  NeuralNetwork model = {NULL, 0};
+  FILE *file = fopen(path, "rb");
+
+  if (file != NULL) {
+    if (checkFileHeader(file)) {
+      unsigned int inputDimension = readDimension(file);
+      unsigned int outputDimension = readDimension(file);
+
+      while (inputDimension > 0 && outputDimension > 0) {
+        Layer layer = readLayer(file, inputDimension, outputDimension);
+        Layer *layerBuffer = NULL;
+
+        if (isEmptyLayer(layer)) {
+          clearLayer(&layer);
+          clearModel(&model);
+          break;
         }
-    }
 
-    return matrix;
-}
+        layerBuffer = (Layer *)realloc(
+            model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
-static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
-{
-    Matrix result = inputBatch;
-
-    if(result.buffer != NULL)
-    {
-        for(int i = 0; i < model.numberOfLayers; i++)
-        {
-            Matrix biasResult;
-            Matrix weightResult;
-            
-            weightResult = multiply(model.layers[i].weights, result);
-            clearMatrix(&result);
-            biasResult = add(model.layers[i].biases, weightResult);
-            clearMatrix(&weightResult);
-
-            if(model.layers[i].activation != NULL)
-                model.layers[i].activation(&biasResult);
-            result = biasResult;
+        if (layerBuffer != NULL)
+          model.layers = layerBuffer;
+        else {
+          clearModel(&model);
+          break;
         }
-    }
 
-    return result;
+        model.layers[model.numberOfLayers] = layer;
+        model.numberOfLayers++;
+
+        inputDimension = outputDimension;
+        outputDimension = readDimension(file);
+      }
+    }
+    fclose(file);
+
+    assignActivations(model);
+  }
+
+  return model;
 }
 
-unsigned char *argmax(const Matrix matrix)
-{
-    unsigned char *maxIdx = NULL;
+static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
+                                               unsigned int count) {
+  Matrix matrix = {0, 0, NULL}; // falsch herum
 
-    if(matrix.rows > 0 && matrix.cols > 0)
-    {
-        maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
+  if (count > 0 && images != NULL) {
+    matrix = createMatrix(images[0].height * images[0].width, count);
 
-        if(maxIdx != NULL)
-        {
-            for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
-            {
-                maxIdx[colIdx] = 0;
-
-                for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
-                {
-                    if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
-                        maxIdx[colIdx] = rowIdx;
-                }
-            }
+    if (matrix.buffer != NULL) {
+      for (int i = 0; i < count; i++) {
+        for (int j = 0; j < images[i].width * images[i].height; j++) {
+          setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
         }
+      }
     }
+  }
 
-    return maxIdx;
+  return matrix;
 }
 
-unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
-{
-    Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
-    Matrix outputBatch = forward(model, inputBatch);
+static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
+  Matrix result = inputBatch;
 
-    unsigned char *result = argmax(outputBatch);
-    
-    clearMatrix(&outputBatch);
-    
-    return result;
+  if (result.buffer != NULL) {
+    for (int i = 0; i < model.numberOfLayers; i++) {
+      Matrix biasResult;
+      Matrix weightResult;
+
+      weightResult = multiply(model.layers[i].weights, result);
+      clearMatrix(&result);
+      biasResult = add(model.layers[i].biases, weightResult);
+      clearMatrix(&weightResult);
+
+      if (model.layers[i].activation != NULL)
+        model.layers[i].activation(&biasResult);
+      result = biasResult;
+    }
+  }
+
+  return result;
 }
 
-void clearModel(NeuralNetwork *model)
-{
-    if(model != NULL)
-    {
-        for(int i = 0; i < model->numberOfLayers; i++)
-        {
-            clearLayer(&model->layers[i]);
+unsigned char *argmax(const Matrix matrix) {
+  unsigned char *maxIdx = NULL;
+
+  if (matrix.rows > 0 && matrix.cols > 0) {
+    maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
+
+    if (maxIdx != NULL) {
+      for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
+        maxIdx[colIdx] = 0;
+
+        for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) {
+          if (getMatrixAt(matrix, rowIdx, colIdx) >
+              getMatrixAt(matrix, maxIdx[colIdx], colIdx))
+            maxIdx[colIdx] = rowIdx;
         }
-        model->layers = NULL;
-        model->numberOfLayers = 0;
+      }
     }
+  }
+
+  return maxIdx;
+}
+
+unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
+                       unsigned int numberOfImages) {
+  Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
+  Matrix outputBatch = forward(model, inputBatch);
+
+  unsigned char *result = argmax(outputBatch);
+
+  clearMatrix(&outputBatch);
+
+  return result;
+}
+
+void clearModel(NeuralNetwork *model) {
+  if (model != NULL) {
+    for (int i = 0; i < model->numberOfLayers; i++) {
+      clearLayer(&model->layers[i]);
+    }
+    model->layers = NULL;
+    model->numberOfLayers = 0;
+  }
 }

neuralNetworkTests.c

@@ -1,242 +1,334 @@
+#include "neuralNetwork.h"
+#include "unity.h"
+#include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include <math.h>
-#include "unity.h"
-#include "neuralNetwork.h"
 
-
-static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
+/*typedef struct
 {
-    // TODO
-}
+    Matrix weights;
+    Matrix biases;
+    ActivationFunctionType activation;
+} Layer;
 
-void test_loadModelReturnsCorrectNumberOfLayers(void)
+typedef struct
 {
-    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 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 biases2 = {.buffer=buffer4, .rows=2, .cols=1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
+    Layer *layers;
+    unsigned int numberOfLayers;
+} NeuralNetwork;*/
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
-    NeuralNetwork netUnderTest;
+/*Layer: Ebene im neuronalen Netzwerk, besteht aus mehreren Neuronen
+Input-Layer: Eingabedatei
+Hidden-Layer: verarbeiten die Daten
+Output-Layer: Ergebnis
 
-    prepareNeuralNetworkFile(path, expectedNet);
 
-    netUnderTest = loadModel(path);
-    remove(path);
+Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
 
-    TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
-    clearModel(&netUnderTest);
-}
+Dimension: Form der Matrizen für einen Layer*/
 
-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};
-    MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+/* Gewichtsmatrix der Layer:
+ */
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
-    NeuralNetwork netUnderTest;
+// speichert NeuralNetwork nn in binäre Datei->später kann es wieder geöffnet
+// werden
+static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
+  FILE *fptr = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen
+  if (fptr == NULL)
+    return; // file konnte nicht geöffnet werden
 
-    prepareNeuralNetworkFile(path, expectedNet);
+  // Header ist Erkennungsstring am Anfang der Datei, loadmodel erkennt
+  // Dateiformat
+  const char header[] = "__info2_neural_network_file_format__"; // header string
+  fwrite(header, sizeof(char), strlen(header),
+         fptr); // der header wird am Anfang der Datei platziert
 
-    netUnderTest = loadModel(path);
-    remove(path);
+  // Wenn es keine Layer gibt, 0 eintragen, LoadModel erkennt, dass Datei leer
+  // ist
+  if (nn.numberOfLayers == 0) {
+    int zero = 0;
+    fwrite(&zero, sizeof(int), 1, fptr);
+    fclose(fptr);
+    return;
+  }
 
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
-    clearModel(&netUnderTest);
-}
+  // Layer 0, inputDimension: Anzahl Input-Neuronen, outputDimension: Anzahl
+  // Output-Neuronen wird in Datei eingefügt
+  int inputDim = (int)nn.layers[0].weights.cols;
+  int outputDim = (int)nn.layers[0].weights.rows;
+  fwrite(&inputDim, sizeof(int), 1, fptr);
+  fwrite(&outputDim, sizeof(int), 1, fptr);
 
-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};
-    MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+  /* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
+     x 1). Zwischen Layern wird nur die nächste outputDimension (int)
+     geschrieben. */
+  for (int i = 0; i < nn.numberOfLayers; i++) {
+    Layer layer = nn.layers[i]; // kürzer, durch alle layer iterieren
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
-    NeuralNetwork netUnderTest;
+    int wrows = (int)layer.weights.rows;
+    int wcols = (int)layer.weights.cols;
+    int wcount = wrows * wcols; // Anzahl Gewichtseinträge
+    int bcount =
+        layer.biases.rows * layer.biases.cols; // Anzahl der Bias-Einträge
 
-    prepareNeuralNetworkFile(path, expectedNet);
+    /* Gewichte */
+    if (wcount > 0 && layer.weights.buffer != NULL) {
+      fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
+    } // Gewichte werden als Matrix gespeichert
 
-    netUnderTest = loadModel(path);
-    remove(path);
+    /* Biases */
+    if (bcount > 0 && layer.biases.buffer != NULL) {
+      fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, fptr);
+    } // Biases werden als Vektor gespeichert
 
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows, netUnderTest.layers[0].biases.rows);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols, netUnderTest.layers[0].biases.cols);
-    clearModel(&netUnderTest);
-}
-
-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};
-    MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
-
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
-    NeuralNetwork netUnderTest;
-
-    prepareNeuralNetworkFile(path, expectedNet);
-
-    netUnderTest = loadModel(path);
-    remove(path);
-
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
-    int n = netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
-    TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer, netUnderTest.layers[0].weights.buffer, n);
-    clearModel(&netUnderTest);
-}
-
-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};
-    MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
-
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
-    NeuralNetwork netUnderTest;
-
-    prepareNeuralNetworkFile(path, expectedNet);
-
-    netUnderTest = loadModel(path);
-    remove(path);
-
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
-    int n = netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
-    TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer, netUnderTest.layers[0].biases.buffer, n);
-    clearModel(&netUnderTest);
-}
-
-void test_loadModelFailsOnWrongFileTag(void)
-{
-    const char *path = "some_nn_test_file.info2";
-    NeuralNetwork netUnderTest;
-    FILE *file = fopen(path, "wb");
-
-    if(file != NULL)
-    {
-        const char *fileTag = "info2_neural_network_file_format";
-
-        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
-
-        fclose(file);
+    /* outputDimensionen der nächsten Layer */
+    if (i + 1 < nn.numberOfLayers) {
+      int nextOutput = (int)nn.layers[i + 1].weights.rows;
+      fwrite(&nextOutput, sizeof(int), 1, fptr);
+    } else {
+      // loadModel erkennt 0 als Ende der Datei
+      int zero = 0;
+      fwrite(&zero, sizeof(int), 1, fptr);
     }
+  }
 
-    netUnderTest = loadModel(path);
-
-    remove(path);
-
-    TEST_ASSERT_NULL(netUnderTest.layers);
-    TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+  fclose(fptr); // Datei schließen
 }
 
-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};
-    MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+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 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 biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
+  Layer layers[] = {{.weights = weights1, .biases = biases1},
+                    {.weights = weights2, .biases = biases2}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
-    NeuralNetwork netUnderTest;
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
+  NeuralNetwork netUnderTest;
 
-    prepareNeuralNetworkFile(path, expectedNet);
+  prepareNeuralNetworkFile(path, expectedNet);
 
-    netUnderTest = loadModel(path);
-    remove(path);
+  netUnderTest = loadModel(path);
+  remove(path);
 
-    TEST_ASSERT_NOT_NULL(netUnderTest.layers);
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-    clearModel(&netUnderTest);
-    TEST_ASSERT_NULL(netUnderTest.layers);
-    TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+  TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers,
+                        netUnderTest.numberOfLayers);
+  clearModel(&netUnderTest);
 }
 
-static void someActivation(Matrix *matrix)
-{
-    for(int i = 0; i < matrix->rows * matrix->cols; i++)
-    {
-        matrix->buffer[i] = fabs(matrix->buffer[i]);
-    }
+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};
+  MatrixType biasBuffer[] = {7, 8, 9};
+  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+  Layer layers[] = {{.weights = weights, .biases = biases}};
+
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+  NeuralNetwork netUnderTest;
+
+  prepareNeuralNetworkFile(path, expectedNet);
+
+  netUnderTest = loadModel(path);
+  remove(path);
+
+  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
+                        netUnderTest.layers[0].weights.rows);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
+                        netUnderTest.layers[0].weights.cols);
+  clearModel(&netUnderTest);
 }
 
-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}};
-    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 weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
-    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 biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
-    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
-                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
-    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]));
-    TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
-    free(predictedLabels);
+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};
+  MatrixType biasBuffer[] = {7, 8, 9};
+  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+  Layer layers[] = {{.weights = weights, .biases = biases}};
+
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+  NeuralNetwork netUnderTest;
+
+  prepareNeuralNetworkFile(path, expectedNet);
+
+  netUnderTest = loadModel(path);
+  remove(path);
+
+  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows,
+                        netUnderTest.layers[0].biases.rows);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols,
+                        netUnderTest.layers[0].biases.cols);
+  clearModel(&netUnderTest);
+}
+
+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};
+  MatrixType biasBuffer[] = {7, 8, 9};
+  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+  Layer layers[] = {{.weights = weights, .biases = biases}};
+
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+  NeuralNetwork netUnderTest;
+
+  prepareNeuralNetworkFile(path, expectedNet);
+
+  netUnderTest = loadModel(path);
+  remove(path);
+
+  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
+                        netUnderTest.layers[0].weights.rows);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
+                        netUnderTest.layers[0].weights.cols);
+  int n =
+      netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
+  TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer,
+                              netUnderTest.layers[0].weights.buffer, n);
+  clearModel(&netUnderTest);
+}
+
+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};
+  MatrixType biasBuffer[] = {7, 8, 9};
+  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+  Layer layers[] = {{.weights = weights, .biases = biases}};
+
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+  NeuralNetwork netUnderTest;
+
+  prepareNeuralNetworkFile(path, expectedNet);
+
+  netUnderTest = loadModel(path);
+  remove(path);
+
+  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
+                        netUnderTest.layers[0].weights.rows);
+  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
+                        netUnderTest.layers[0].weights.cols);
+  int n =
+      netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
+  TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer,
+                              netUnderTest.layers[0].biases.buffer, n);
+  clearModel(&netUnderTest);
+}
+
+void test_loadModelFailsOnWrongFileTag(void) {
+  const char *path = "some_nn_test_file.info2";
+  NeuralNetwork netUnderTest;
+  FILE *file = fopen(path, "wb");
+
+  if (file != NULL) {
+    const char *fileTag = "info2_neural_network_file_format";
+
+    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+    fclose(file);
+  }
+
+  netUnderTest = loadModel(path);
+
+  remove(path);
+
+  TEST_ASSERT_NULL(netUnderTest.layers);
+  TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+}
+
+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};
+  MatrixType biasBuffer[] = {7, 8, 9};
+  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+  Layer layers[] = {{.weights = weights, .biases = biases}};
+
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
+  NeuralNetwork netUnderTest;
+
+  prepareNeuralNetworkFile(path, expectedNet);
+
+  netUnderTest = loadModel(path);
+  remove(path);
+
+  TEST_ASSERT_NOT_NULL(netUnderTest.layers);
+  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  clearModel(&netUnderTest);
+  TEST_ASSERT_NULL(netUnderTest.layers);
+  TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+}
+
+static void someActivation(Matrix *matrix) {
+  for (int i = 0; i < matrix->rows * matrix->cols; i++) {
+    matrix->buffer[i] = fabs(matrix->buffer[i]);
+  }
+}
+
+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}};
+  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 weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
+  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 biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
+  Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
+  Layer layers[] = {
+      {.weights = weights1, .biases = biases1, .activation = someActivation},
+      {.weights = weights2, .biases = biases2, .activation = someActivation},
+      {.weights = weights3, .biases = biases3, .activation = someActivation}};
+  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]));
+  TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
+  free(predictedLabels);
 }
 
 void setUp(void) {
-    // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
+  // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
 void tearDown(void) {
-    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+  // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
-int main()
-{
-    UNITY_BEGIN();
+int main() {
+  UNITY_BEGIN();
 
-    printf("\n============================\nNeural network tests\n============================\n");
-    RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
-    RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
-    RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
-    RUN_TEST(test_loadModelReturnsCorrectWeights);
-    RUN_TEST(test_loadModelReturnsCorrectBiases);
-    RUN_TEST(test_loadModelFailsOnWrongFileTag);
-    RUN_TEST(test_clearModelSetsMembersToNull);
-    RUN_TEST(test_predictReturnsCorrectLabels);
+  printf("\n============================\nNeural network "
+         "tests\n============================\n");
+  RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
+  RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
+  RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
+  RUN_TEST(test_loadModelReturnsCorrectWeights);
+  RUN_TEST(test_loadModelReturnsCorrectBiases);
+  RUN_TEST(test_loadModelFailsOnWrongFileTag);
+  RUN_TEST(test_clearModelSetsMembersToNull);
+  RUN_TEST(test_predictReturnsCorrectLabels);
 
-    return UNITY_END();
+  return UNITY_END();
 }