Merge branch 'Krisp2'

.gitignore

@@ -2,3 +2,6 @@ mnist
 runTests
 *.o
 *.exe
+.vscode/c_cpp_properties.json
+.vscode/launch.json
+.vscode/settings.json

.vscode/c_cpp_properties.json

@@ -0,0 +1,18 @@
+{
+  "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

@@ -0,0 +1,24 @@
+{
+  "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

@@ -1,3 +1,59 @@
 {
-    "makefile.configureOnOpen": false
+  "C_Cpp_Runner.cCompilerPath": "gcc",
+  "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

@@ -28,6 +28,7 @@ static int readMeta(FILE *file, unsigned short *count, unsigned short *width,
     return 0;
   if (fread(height, sizeof(unsigned short), 1, file) != 1)
     return 0;
+
   return 1;
 }
 
@@ -74,9 +75,11 @@ GrayScaleImageSeries *readImages(const char *path) {
 
   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) {

makefile

@@ -63,3 +63,4 @@ ifeq ($(OS),Windows_NT)
 else
 	rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
 endif
+ 

matrix.c

@@ -3,35 +3,22 @@
 #include <stdlib.h>
 #include <string.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 matrix;
-  Matrix errorMatrix = {0, 0, NULL};
-  if (rows == 0 || cols == 0) {
 
+Matrix createMatrix(unsigned int rows, unsigned int cols) {
+  if (cols == 0 || rows == 0) {
+    Matrix errorMatrix = {0, 0, NULL};
     return errorMatrix;
   }
-  matrix.rows = rows;
+  MatrixType *buffer =
-  matrix.cols = cols;
+      malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc
-
+                                                // liefert Zeiger auf Speicher
-  matrix.buffer = malloc(rows * cols * sizeof(MatrixType));
+  Matrix newMatrix = {rows, cols, buffer};      // neue Matrix nach struct
-  if (matrix.buffer == NULL) {
+  return newMatrix;
-    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) {
 
@@ -47,13 +34,12 @@ void setMatrixAt(const MatrixType value, Matrix matrix,
                  const unsigned int rowIdx, // Kopie der Matrix wird übergeben
                  const unsigned int colIdx) {
 
-  if (rowIdx >= matrix.rows || colIdx >= matrix.cols ||
+  if (rowIdx >= matrix.rows || colIdx >= matrix.cols) {
-      matrix.buffer == NULL) { // Speichergröße nicht überschreiten
+    // Speichergröße nicht überschreiten
     return;
   }
-
+  matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
-  matrix.buffer[rowIdx * matrix.cols + colIdx] =
+  // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
-      value; // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
   // innerhalb der Zeile
 }
 MatrixType getMatrixAt(const Matrix matrix,
@@ -68,187 +54,131 @@ MatrixType getMatrixAt(const Matrix matrix,
 
   return value;
 }
-
+Matrix broadcastingCols(const Matrix matrix, const unsigned int cols) {
-Matrix broadCastCols(const Matrix matrix, const unsigned int rows,
+  Matrix copy1 = createMatrix(matrix.rows, cols);
-                     const unsigned int cols) {
+  for (int r = 0; r < matrix.rows; r++) {
-
+    MatrixType valueMatrix1 = getMatrixAt(matrix, r, 0);
-  Matrix copy = createMatrix(
-      rows, cols); // Matrix 1 Kopie erstellen mit Dimensionen von Matrix2
-  for (int r = 0; r < rows; r++) {
-
-    MatrixType value = getMatrixAt(matrix, r, 0);
     for (int c = 0; c < cols; c++) {
-
+      setMatrixAt(valueMatrix1, copy1, r, c);
-      setMatrixAt(value, copy, r, c);
     }
   }
-
+  return copy1;
-  return copy;
 }
-Matrix broadCastRows(const Matrix matrix, const unsigned int rows,
+Matrix broadcastingRows(const Matrix matrix, const unsigned int rows) {
-                     const unsigned int cols) {
+  Matrix copy1 = createMatrix(rows, matrix.cols);
-
+  for (int c = 0; c < matrix.cols; c++) {
-  Matrix copy = createMatrix(rows, cols);
+    MatrixType valueMatrix1 = getMatrixAt(matrix, 0, c);
-
-  for (int c = 0; c < cols; c++) {
-    MatrixType value = getMatrixAt(matrix, 0, c);
-
     for (int r = 0; r < rows; r++) {
-
+      setMatrixAt(valueMatrix1, copy1, r, c);
-      setMatrixAt(value, copy, r, c);
     }
   }
-
+  return copy1;
-  return copy;
 }
-
 Matrix add(const Matrix matrix1, const Matrix matrix2) {
 
-  const unsigned int rows1 = matrix1.rows;
+  // Ergebnismatrix
-  const unsigned int rows2 = matrix2.rows;
+  Matrix result;
-  const unsigned int cols1 = matrix1.cols;
+  const int cols1 = matrix1.cols;
-  const unsigned int cols2 = matrix2.cols;
+  const int rows1 = matrix1.rows;
+  const int cols2 = matrix2.cols;
+  const int rows2 = matrix2.rows;
 
-  const int rowsEqual = ((rows1 == rows2) ? 1 : 0);
+  const int rowsEqual = (matrix1.rows == matrix2.rows) ? 1 : 0;
+  const int colsEqual = (matrix1.cols == matrix2.cols) ? 1 : 0;
 
-  const int colsEqual = ((cols1 == cols2) ? 1 : 0);
+  // Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender
-
+  // Matrix
-  if (rowsEqual && colsEqual) // addieren
+  if (rowsEqual == 1 && colsEqual == 1) {
-
+    Matrix result = createMatrix(matrix1.rows, matrix1.cols);
-  {
-    Matrix result = createMatrix(rows1, cols1); // Speicher reservieren
     if (result.buffer == NULL) {
       return (Matrix){0, 0, NULL};
     }
-
+    for (int i = 0; i < rows1; i++) {
-    for (int i = 0; i < (rows1 * cols1); i++) { // addieren
+      for (int j = 0; j < cols1; j++) {
-
+        int valueM1 = getMatrixAt(matrix1, i, j);
-      result.buffer[i] =
+        int valueM2 = getMatrixAt(matrix2, i, j);
-          (matrix1.buffer[i] +
+        int sum = valueM1 + valueM2;
-           matrix2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
+        setMatrixAt(sum, result, i, j);
-                               // Startadresse + (i * sizeof(MatrixType))
       }
-
-    return result; // zurückgeben
     }
-
-  else if (rowsEqual && !colsEqual) {
-
-    if (cols1 == 1) {
-
-      Matrix result = createMatrix(rows2, cols2);
-      if (result.buffer == NULL) {
-        return (Matrix){0, 0, NULL};
-      }
-
-      Matrix copy1 = broadCastCols(matrix1, rows2, cols2);
-      if (!copy1.buffer) {
-        clearMatrix(&result);
-        return (Matrix){0, 0, NULL};
-      }
-
-      for (unsigned int i = 0; i < rows2 * cols2; i++) {
-        result.buffer[i] = copy1.buffer[i] + matrix2.buffer[i];
-      }
-
-      /* freigeben, weil nicht mehr benötigt */
-      clearMatrix(&copy1);
     return result;
-
+  } else if (rowsEqual == 1 && (cols1 == 1 || cols2 == 1)) {
-      // add und return
+    if (cols1 == 1) { // broadcasting von vektor 1 zu matrix 1, add
-
+      Matrix newMatrix = broadcastingCols(matrix1, cols2);
-    } else if (cols2 == 1) {
+      // add
-
+      Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
-      Matrix result = createMatrix(rows1, cols1);
       if (result.buffer == NULL) {
-        Matrix error = {0, 0, NULL};
+        return (Matrix){0, 0, NULL};
-        return error;
+      }
+      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);
+        }
+      }
+      return result;
+    } else {
+      Matrix newMatrix2 = broadcastingCols(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);
         }
-
-      // Matrix2 hat nur eine Spalte -> horizontal broadcasten
-      Matrix copy2 = broadCastCols(matrix2, rows1, cols1);
-
-      for (unsigned int i = 0; i < rows1 * cols1; i++) {
-        result.buffer[i] = matrix1.buffer[i] + copy2.buffer[i];
       }
-
-      // Optional: Speicher von copy2 freigeben
-      clearMatrix(&copy2);
-
       return result;
     }
-
-    else {
-
-      printf("Fehlermeldung"); // vielleicht Fehlermeldung ändern zu
-                               // Programmabbruch
-      Matrix error = {0, 0, NULL};
-      return error;
   }
 
-  }
+  else if ((rows1 == 1 || rows2 == 1) && colsEqual == 1) {
-
-  else if (!rowsEqual && colsEqual) {
-
     if (rows1 == 1) {
-
+      Matrix newMatrix = broadcastingRows(matrix1, rows2);
-      Matrix result = createMatrix(rows2, cols2);
+      // add
+      Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
       if (result.buffer == NULL) {
         return (Matrix){0, 0, NULL};
       }
-
+      for (int i = 0; i < rows2; i++) {
-      Matrix copy1 = broadCastRows(matrix1, rows2, cols2);
+        for (int j = 0; j < cols1; j++) {
-
+          int valueM1 = getMatrixAt(newMatrix, i, j);
-      for (int i = 0; i < (rows2 * cols2); i++) { // addieren
+          int valueM2 = getMatrixAt(matrix2, i, j);
-
+          int sum = valueM1 + valueM2;
-        result.buffer[i] =
+          setMatrixAt(sum, result, i, j);
-            (copy1.buffer[i] +
+        }
-             matrix2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                                 // Startadresse + (i * sizeof(MatrixType))
       }
       return result;
-
+    } else {
-      // add und return
+      Matrix newMatrix2 = broadcastingRows(matrix2, rows1);
-
+      // add
-    } else if (rows2 == 1) {
+      Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
-
-      Matrix result = createMatrix(rows1, cols1);
       if (result.buffer == NULL) {
         return (Matrix){0, 0, NULL};
       }
-
+      for (int i = 0; i < rows1; i++) {
-      Matrix copy2 = broadCastRows(matrix2, rows1, cols1);
+        for (int j = 0; j < cols1; j++) {
-
+          int valueM1 = getMatrixAt(matrix1, i, j);
-      // add und return
+          int valueM2 = getMatrixAt(newMatrix2, i, j);
-
+          int sum = valueM1 + valueM2;
-      for (int i = 0; i < (rows1 * cols1); i++) { // addieren
+          setMatrixAt(sum, result, i, j);
-
+        }
-        result.buffer[i] =
-            (matrix1.buffer[i] +
-             copy2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                               // Startadresse + (i * sizeof(MatrixType))
       }
       return result;
-
     }
-
+  } else {
-    else {
+    // kein add möglich
-
+    Matrix errorMatrix = {0, 0, NULL};
-      printf("Fehlermeldung"); // vielleicht Fehlermeldung ändern zu
+    return errorMatrix;
-                               // Programmabbruch
-      Matrix error = {0, 0, NULL};
-      return error;
   }
+  return result;
 }
-
-  else {
-    printf("Fehlermeldung"); // vielleicht Fehlermeldung ändern zu
-                             // Programmabbruch
-    Matrix error = {0, 0, NULL};
-    return error;
-  }
-}
-
 Matrix multiply(const Matrix matrix1, const Matrix matrix2) {
   // Spalten1 müssen gleich zeilen2 sein! dann multiplizieren
   if (matrix1.cols == matrix2.rows) {

neuralNetworkTests.c

@@ -30,20 +30,21 @@ Dimension: Form der Matrizen für einen Layer*/
 
 // speichert NeuralNetwork nn in binäre Datei->erzeugt Dateiformat
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
-  FILE *f = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen
+  FILE *fptr = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen
-  if (f == NULL)
+  if (fptr == NULL)
-    return;
+    return; // file konnte nicht geöffnet werden
 
   // Header ist Erkennungsstring am Anfang der Datei, loadmodel erkennt
   // Dateiformat
-  const char header[] = "__info2_neural_network_file_format__";
+  const char header[] =
-  fwrite(header, sizeof(char), strlen(header), f);
+      "__info2_neural_network_file_format__"; // header vor jedem Layer
+  fwrite(header, sizeof(char), strlen(header), fptr);
 
   // Wenn es keine Layer gibt, 0 eintragen, LoadModel gibt 0 zurück
   if (nn.numberOfLayers == 0) {
     int zero = 0;
-    fwrite(&zero, sizeof(int), 1, f);
+    fwrite(&zero, sizeof(int), 1, fptr);
-    fclose(f);
+    fclose(fptr);
     return;
   }
 
@@ -51,8 +52,8 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
   // Output-Neuronen
   int inputDim = (int)nn.layers[0].weights.cols;
   int outputDim = (int)nn.layers[0].weights.rows;
-  fwrite(&inputDim, sizeof(int), 1, f);
+  fwrite(&inputDim, sizeof(int), 1, fptr);
-  fwrite(&outputDim, sizeof(int), 1, f);
+  fwrite(&outputDim, sizeof(int), 1, fptr);
 
   /* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
      x 1). Zwischen Layern wird nur die nächste outputDimension (int)
@@ -68,28 +69,28 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
 
     /* Gewichte (MatrixType binär) */
     if (wcount > 0 && layer.weights.buffer != NULL) {
-      fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, f);
+      fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
     }
 
     /* Biases (MatrixType binär) */
     if (bcount > 0 && layer.biases.buffer != NULL) {
-      fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, f);
+      fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, fptr);
     }
 
     /* Für die nächste Layer: falls vorhanden, schreibe deren outputDimension */
     if (i + 1 < nn.numberOfLayers) {
       int nextOutput = (int)nn.layers[i + 1].weights.rows;
-      fwrite(&nextOutput, sizeof(int), 1, f);
+      fwrite(&nextOutput, sizeof(int), 1, fptr);
     } else {
       /* Letzte Layer: wir können das Ende signalisieren, indem wir ein 0
          schreiben. loadModel liest dann outputDimension = 0 und beendet die
          Schleife. */
       int zero = 0;
-      fwrite(&zero, sizeof(int), 1, f);
+      fwrite(&zero, sizeof(int), 1, fptr);
     }
   }
 
-  fclose(f);
+  fclose(fptr);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void) {