matrix.c aenderungen

.gitignore

@@ -2,6 +2,9 @@ mnist
 runTests
 *.o
 *.exe
-.vscode/c_cpp_properties.json
+.vscode/settings.json
 .vscode/launch.json
 .vscode/settings.json
+.vscode/settings.json
+runImageInputTests
+testFile.info2

.vscode/c_cpp_properties.json

@@ -1,18 +0,0 @@
-{
-  "configurations": [
-    {
-      "name": "windows-gcc-x64",
-      "includePath": [
-        "${workspaceFolder}/**"
-      ],
-      "compilerPath": "C:/ProgramData/mingw64/mingw64/bin/gcc.exe",
-      "cStandard": "${default}",
-      "cppStandard": "${default}",
-      "intelliSenseMode": "windows-gcc-x64",
-      "compilerArgs": [
-        ""
-      ]
-    }
-  ],
-  "version": 4
-}

.vscode/launch.json

@@ -1,24 +0,0 @@
-{
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "name": "C/C++ Runner: Debug Session",
-      "type": "cppdbg",
-      "request": "launch",
-      "args": [],
-      "stopAtEntry": false,
-      "externalConsole": true,
-      "cwd": "c:/Users/Max-R/I2Pr/repoKachelto/I2-Pr_neuronalesNetz/info2Praktikum-NeuronalesNetz",
-      "program": "c:/Users/Max-R/I2Pr/repoKachelto/I2-Pr_neuronalesNetz/info2Praktikum-NeuronalesNetz/build/Debug/outDebug",
-      "MIMode": "gdb",
-      "miDebuggerPath": "gdb",
-      "setupCommands": [
-        {
-          "description": "Enable pretty-printing for gdb",
-          "text": "-enable-pretty-printing",
-          "ignoreFailures": true
-        }
-      ]
-    }
-  ]
-}

.vscode/settings.json

@@ -1,59 +1,3 @@
 {
-  "C_Cpp_Runner.cCompilerPath": "gcc",
+    "makefile.configureOnOpen": false
-  "C_Cpp_Runner.cppCompilerPath": "g++",
-  "C_Cpp_Runner.debuggerPath": "gdb",
-  "C_Cpp_Runner.cStandard": "",
-  "C_Cpp_Runner.cppStandard": "",
-  "C_Cpp_Runner.msvcBatchPath": "C:/Program Files/Microsoft Visual Studio/VR_NR/Community/VC/Auxiliary/Build/vcvarsall.bat",
-  "C_Cpp_Runner.useMsvc": false,
-  "C_Cpp_Runner.warnings": [
-    "-Wall",
-    "-Wextra",
-    "-Wpedantic",
-    "-Wshadow",
-    "-Wformat=2",
-    "-Wcast-align",
-    "-Wconversion",
-    "-Wsign-conversion",
-    "-Wnull-dereference"
-  ],
-  "C_Cpp_Runner.msvcWarnings": [
-    "/W4",
-    "/permissive-",
-    "/w14242",
-    "/w14287",
-    "/w14296",
-    "/w14311",
-    "/w14826",
-    "/w44062",
-    "/w44242",
-    "/w14905",
-    "/w14906",
-    "/w14263",
-    "/w44265",
-    "/w14928"
-  ],
-  "C_Cpp_Runner.enableWarnings": true,
-  "C_Cpp_Runner.warningsAsError": false,
-  "C_Cpp_Runner.compilerArgs": [],
-  "C_Cpp_Runner.linkerArgs": [],
-  "C_Cpp_Runner.includePaths": [],
-  "C_Cpp_Runner.includeSearch": [
-    "*",
-    "**/*"
-  ],
-  "C_Cpp_Runner.excludeSearch": [
-    "**/build",
-    "**/build/**",
-    "**/.*",
-    "**/.*/**",
-    "**/.vscode",
-    "**/.vscode/**"
-  ],
-  "C_Cpp_Runner.useAddressSanitizer": false,
-  "C_Cpp_Runner.useUndefinedSanitizer": false,
-  "C_Cpp_Runner.useLeakSanitizer": false,
-  "C_Cpp_Runner.showCompilationTime": false,
-  "C_Cpp_Runner.useLinkTimeOptimization": false,
-  "C_Cpp_Runner.msvcSecureNoWarnings": false
 }

imageInput.c

@@ -4,6 +4,8 @@
 #include <string.h>
 
 #define FILE_HEADER_STRING "__info2_image_file_format__"
+// define BUFFER 100
+// 10x10 pixel
 
 /* ----------------------------------------------------------
    1. Header prüfen
@@ -40,14 +42,14 @@ static int readSingleImage(FILE *file, GrayScaleImage *img,
   img->width = width;
   img->height = height;
 
-  size_t numPixels = (size_t)width * (size_t)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;
+    img->buffer = NULL; // fehler bei ungültiger eingabe
     return 0;
   }
   return 1;

imageInputTests.c

@@ -126,19 +126,25 @@ void test_readImagesFailsOnWrongFileTag(void) {
   remove(path);
 }
 
-void test_read_GrayScale_Pixel(void) {
+// Test
-  GrayScaleImageSeries *series = NULL;
+
+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);
+  prepareImageFile(path, 8, 8, 1,
+                   1); // Höhe x Breite in Pixel, Anzahl Bilder und Kategorie
   series = readImages(path);
 
-  TEST_ASSERT_NOT_NULL(series);
+  TEST_ASSERT_NOT_NULL(series);             // Speicher reservieren
-  TEST_ASSERT_NOT_NULL(series->images);
+  TEST_ASSERT_NOT_NULL(series->images);     // Inhalt ist da
-  TEST_ASSERT_EQUAL_UINT(1, series->count);
+  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]);
+    TEST_ASSERT_EQUAL_UINT8(
+        (GrayScalePixelType)i,
+        series->images[0].buffer[i]); // alle Pixelwerte prüfen
   }
 
   clearSeries(series);

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

@@ -9,7 +9,7 @@
     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;
@@ -42,9 +42,10 @@ void setMatrixAt(const MatrixType value, Matrix matrix,
   // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
   // innerhalb der Zeile
 }
-MatrixType getMatrixAt(const Matrix matrix,
+MatrixType
-                       unsigned int rowIdx, // Kopie der Matrix wird übergeben
+getMatrixAt(const Matrix matrix,
-                       unsigned int colIdx) {
+            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;
@@ -54,7 +55,7 @@ MatrixType getMatrixAt(const Matrix matrix,
 
   return value;
 }
-Matrix broadcastingCols(const Matrix matrix, const unsigned int cols) {
+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);
@@ -64,7 +65,7 @@ Matrix broadcastingCols(const Matrix matrix, const unsigned int cols) {
   }
   return copy1;
 }
-Matrix broadcastingRows(const Matrix matrix, const unsigned int rows) {
+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);
@@ -104,7 +105,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
     return result;
   } else if (rowsEqual == 1 && (cols1 == 1 || cols2 == 1)) {
     if (cols1 == 1) { // broadcasting von vektor 1 zu matrix 1, add
-      Matrix newMatrix = broadcastingCols(matrix1, cols2);
+      Matrix newMatrix = broadCastCols(matrix1, cols2);
       // add
       Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
       if (result.buffer == NULL) {
@@ -118,9 +119,10 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
           setMatrixAt(sum, result, i, j);
         }
       }
+      clearMatrix(&newMatrix);
       return result;
     } else {
-      Matrix newMatrix2 = broadcastingCols(matrix2, cols1);
+      Matrix newMatrix2 = broadCastCols(matrix2, cols1);
       // add
       Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
       if (result.buffer == NULL) {
@@ -134,13 +136,14 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
           setMatrixAt(sum, result, i, j);
         }
       }
+
       return result;
     }
   }
 
   else if ((rows1 == 1 || rows2 == 1) && colsEqual == 1) {
     if (rows1 == 1) {
-      Matrix newMatrix = broadcastingRows(matrix1, rows2);
+      Matrix newMatrix = broadCastRows(matrix1, rows2);
       // add
       Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
       if (result.buffer == NULL) {
@@ -156,7 +159,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
       }
       return result;
     } else {
-      Matrix newMatrix2 = broadcastingRows(matrix2, rows1);
+      Matrix newMatrix2 = broadCastRows(matrix2, rows1);
       // add
       Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
       if (result.buffer == NULL) {
@@ -170,6 +173,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
           setMatrixAt(sum, result, i, j);
         }
       }
+      clearMatrix(&newMatrix2);
       return result;
     }
   } else {

matrix.h

@@ -13,17 +13,15 @@ typedef struct {
 
 } 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,
+void setMatrixAt(const MatrixType value, Matrix matrix,
-                 unsigned int colIdx);
+                 const unsigned int rowIdx, const unsigned int colIdx);
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx,
+MatrixType getMatrixAt(const Matrix matrix, const unsigned int rowIdx,
-                       unsigned int colIdx);
+                       const unsigned int colIdx);
 
-Matrix broadCastCols(const Matrix matrix, const unsigned int rows,
+Matrix broadCastCols(const Matrix matrix, const unsigned int cols);
-                     const unsigned int cols);
+Matrix broadCastRows(const Matrix matrix, const unsigned int rows);
-Matrix broadCastRows(const Matrix matrix, const unsigned int rows,
-                     const unsigned int cols);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
 

neuralNetworkTests.c

@@ -28,7 +28,11 @@ Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
 
 Dimension: Form der Matrizen für einen Layer*/
 
-// speichert NeuralNetwork nn in binäre Datei->erzeugt Dateiformat
+/* Gewichtsmatrix der Layer:
+ */
+
+// 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)
@@ -36,11 +40,12 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
 
   // Header ist Erkennungsstring am Anfang der Datei, loadmodel erkennt
   // Dateiformat
-  const char header[] =
+  const char header[] = "__info2_neural_network_file_format__"; // header string
-      "__info2_neural_network_file_format__"; // header vor jedem Layer
+  fwrite(header, sizeof(char), strlen(header),
-  fwrite(header, sizeof(char), strlen(header), fptr);
+         fptr); // der header wird am Anfang der Datei platziert
 
-  // Wenn es keine Layer gibt, 0 eintragen, LoadModel gibt 0 zurück
+  // 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);
@@ -49,7 +54,7 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
   }
 
   // Layer 0, inputDimension: Anzahl Input-Neuronen, outputDimension: Anzahl
-  // Output-Neuronen
+  // 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);
@@ -59,38 +64,36 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
      x 1). Zwischen Layern wird nur die nächste outputDimension (int)
      geschrieben. */
   for (int i = 0; i < nn.numberOfLayers; i++) {
-    Layer layer = nn.layers[i];
+    Layer layer = nn.layers[i]; // kürzer, durch alle layer iterieren
 
     int wrows = (int)layer.weights.rows;
     int wcols = (int)layer.weights.cols;
-    int wcount = wrows * wcols;
+    int wcount = wrows * wcols; // Anzahl Gewichtseinträge
     int bcount =
-        layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */
+        layer.biases.rows * layer.biases.cols; // Anzahl der Bias-Einträge
 
-    /* Gewichte (MatrixType binär) */
+    /* Gewichte */
     if (wcount > 0 && layer.weights.buffer != NULL) {
       fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
-    }
+    } // Gewichte werden als Matrix gespeichert
 
-    /* Biases (MatrixType binär) */
+    /* Biases */
     if (bcount > 0 && layer.biases.buffer != NULL) {
       fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, fptr);
-    }
+    } // Biases werden als Vektor gespeichert
 
-    /* Für die nächste Layer: falls vorhanden, schreibe deren outputDimension */
+    /* 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 {
-      /* Letzte Layer: wir können das Ende signalisieren, indem wir ein 0
+      // loadModel erkennt 0 als Ende der Datei
-         schreiben. loadModel liest dann outputDimension = 0 und beendet die
-         Schleife. */
       int zero = 0;
       fwrite(&zero, sizeof(int), 1, fptr);
     }
   }
 
-  fclose(fptr);
+  fclose(fptr); // Datei schließen
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void) {