schöne Version

imageInput.c

@@ -33,7 +33,6 @@ GrayScaleImageSeries *readImages(const char *path)
         return NULL;
     }
     //liest die Anzahl der Bilder aus
-    series->count = 0;
     fread(&series->count, sizeof(unsigned short),1, data);
     series->images = malloc(series->count * sizeof(GrayScaleImage));
     if (series->images == NULL){

imageInput.h

@@ -12,7 +12,7 @@ typedef struct
 
 typedef struct
 {
-    GrayScaleImage *images;
+    GrayScaleImage *images; //in sich verschachtelte Struktur
     unsigned char *labels;
     unsigned int count;
 } GrayScaleImageSeries;

imageInputTests.c

@@ -123,8 +123,7 @@ void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) 
+void tearDown(void) {
-{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 

matrix.c

@@ -2,22 +2,20 @@
 #include <string.h>
 #include "matrix.h"
 
-// TODO Matrix-Funktionen implementieren
-
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix m = {NULL, 0, 0};
 
     if (rows == 0 || cols == 0)
-        return matrix;   //gibt leere Matrix zurück
+        return m;
 
-    matrix.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
+    m.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
-    if (matrix.buffer == NULL) //auf verfügbaren Speicherplatz prüfen
+    if (m.buffer == NULL)
-        return matrix;
+        return m;
 
-    matrix.rows = rows;  
+    m.rows = rows;
-    matrix.cols = cols;
+    m.cols = cols;
-    return matrix;   //Matrix zurückgeben
+    return m;
 }
 
 void clearMatrix(Matrix *matrix)
@@ -25,18 +23,18 @@ void clearMatrix(Matrix *matrix)
 
     if (matrix != NULL)
     {
-        free(matrix->buffer);   //Speicherplatz bereinigen
+        free(matrix->buffer);
-        matrix->buffer = NULL;  //Werte auf 0 setzen
+        matrix->buffer = NULL;
         matrix->rows = 0;
         matrix->cols = 0;
     }
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+// Matrix matrix zu Matrix *matrix, empfehlung
 {
-    if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL) //Prüft ob Zugriff möglich
+    if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL)
         matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
-        //schreibt 2D element in 1D Liste: Element_Reihe*Matrix_Spalten + Element_Spalte
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
@@ -47,10 +45,11 @@ MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int co
 }
 
 // TODO: Funktionen implementieren
+
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    //  immer Probe, gleiche Zeilen der Matrizen
+    //  check, equal rows
-    // "Elementweise Addition": Probe, ob matrix gleiche größe hat
+    // "Elementweise Addition": test, if two matrix has exact size
     if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols)
     {
         Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
@@ -65,7 +64,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
         }
         return result_add;
     }
-    // "Broadcasting": matrix1 hat 1 Spalte
+    // "Broadcasting": matrix1 has 1 collum
     if (matrix1.rows == matrix2.rows && matrix1.cols == 1)
     {
         Matrix result_add = createMatrix(matrix1.rows, matrix2.cols);
@@ -79,7 +78,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
         }
         return result_add;
     }
-    // "Broadcasting": matrix2 hat 1 Spalte
+    // "Broadcasting": matrix2 has 1 collum
     if (matrix1.rows == matrix2.rows && matrix2.cols == 1)
     {
         Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
@@ -99,14 +98,13 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
+    // Needed: rows/Zeilen, collums/Spalten
     MatrixType buffer_add;
-    
+    // Probe ob Spalten1 = Zeilen2
-    if (!matrix1.buffer || !matrix2.buffer) // Probe ob leere Matrize vorliegt 
+    if (matrix1.cols != matrix2.rows)
-        return createMatrix(0, 0);
-    if (matrix1.cols != matrix2.rows) // Probe ob Spalten1 = Zeilen2
         return createMatrix(0, 0);
 
-    Matrix result_mul = createMatrix(matrix1.rows, matrix2.cols);
+    Matrix result_mul = createMatrix(matrix1.rows, matrix2.cols); // ""
 
     for (unsigned int index = 0; index < matrix1.rows; index++)
     {
@@ -115,9 +113,11 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
             buffer_add = 0;
             for (unsigned int skalar = 0; skalar < matrix1.cols; skalar++)
             {
+                // buffer_add += matrix1[index][skalar]*matrix2[skalar][shift];
                 buffer_add += getMatrixAt(matrix1, index, skalar) * getMatrixAt(matrix2, skalar, shift);
             }
-            setMatrixAt(buffer_add, result_mul, index, shift);
+            // matrix_mul[index][shift] = buffer_add;
+            setMatrixAt(buffer_add, result_mul, index, shift); // result als Pointer, also mit &result
         }
     }
     return result_mul;

matrix.h

@@ -7,7 +7,7 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 typedef struct {
-    MatrixType *buffer;
+    MatrixType *matrix;
     unsigned int rows;
     unsigned int cols;
 }Matrix;

neuralNetworkTests.c

@@ -5,49 +5,10 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    FILE *file = fopen(path, "wb");
+    // TODO
-    if (!file)
-        return;
-
-    const char *tag = "__info2_neural_network_file_format__";
-    fwrite(tag, 1, strlen(tag), file);
-
-    // Überprüfung, ob es Layer gibt
-    if (nn.numberOfLayers == 0)
-    {
-        fclose(file);
-        return;
-    }
-
-    // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
-    int input = nn.layers[0].weights.cols;
-    int output = nn.layers[0].weights.rows;
-
-    fwrite(&input, sizeof(int), 1, file);
-    fwrite(&output, sizeof(int), 1, file);
-
-    // Schreibe die Layer-Daten
-    for (int i = 0; i < nn.numberOfLayers; i++)
-    {
-        const Layer *layer = &nn.layers[i];
-        int out = layer->weights.rows;
-        int in = layer->weights.cols;
-
-        fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
-
-        fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
-
-        if (i + 1 < nn.numberOfLayers)
-        {
-            int nextOut = nn.layers[i + 1].weights.rows;
-            fwrite(&nextOut, sizeof(int), 1, file);
-        }
-    }
-    fclose(file);
-
-   
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -244,8 +205,8 @@ void test_predictReturnsCorrectLabels(void)
     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},
+    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
-                      {.weights = weights2, .biases = biases2, .activation = someActivation},
+                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
                       {.weights=weights3, .biases=biases3, .activation=someActivation}};
     NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
     unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
@@ -255,13 +216,11 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void)
+void setUp(void) {
-{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void)
+void tearDown(void) {
-{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }