angepasst

matrix.c

@@ -6,30 +6,119 @@
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    
+    Matrix matrix = {NULL, 0, 0};
+
+    if (rows == 0 || cols == 0)
+        return matrix;   //gibt leere Matrix zurück
+
+    matrix.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
+    if (matrix.buffer == NULL) //auf verfügbaren Speicherplatz prüfen
+        return matrix;
+
+    matrix.rows = rows;  
+    matrix.cols = cols;
+    return matrix;   //Matrix zurückgeben
 }
 
 void clearMatrix(Matrix *matrix)
 {
-    
+
+    if (matrix != NULL)
+    {
+        free(matrix->buffer);   //Speicherplatz bereinigen
+        matrix->buffer = NULL;  //Werte auf 0 setzen
+        matrix->rows = 0;
+        matrix->cols = 0;
+    }
 }
 
-void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx) 
 {
-    
+    if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL) //Prüft ob Zugriff möglich
+        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)
 {
-    
+    if (rowIdx >= matrix.rows || colIdx >= matrix.cols || matrix.buffer == NULL)
+        return 0; // Sicherheitscheck
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
 
+// TODO: Funktionen implementieren
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    
+    //  immer Probe, gleiche Zeilen der Matrizen
+    // "Elementweise Addition": Probe, ob matrix gleiche größe hat
+    if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols)
+    {
+        Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
+        for (int r = 0; r < matrix1.rows; r++)
+        {
+            for (int c = 0; c < matrix1.cols; c++)
+            {
+                // first version: matrix_add[r][c] = matrix1[r][c] + matrix2[r][c]
+                MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, c);
+                setMatrixAt(sum, result_add, r, c);
+            }
+        }
+        return result_add;
+    }
+    // "Broadcasting": matrix1 hat 1 Spalte
+    if (matrix1.rows == matrix2.rows && matrix1.cols == 1)
+    {
+        Matrix result_add = createMatrix(matrix1.rows, matrix2.cols);
+        for (int r = 0; r < matrix1.rows; r++)
+        {
+            for (int c = 0; c < matrix2.cols; c++)
+            {
+                MatrixType sum = getMatrixAt(matrix2, r, c) + getMatrixAt(matrix1, r, 0);
+                setMatrixAt(sum, result_add, r, c);
+            }
+        }
+        return result_add;
+    }
+    // "Broadcasting": matrix2 hat 1 Spalte
+    if (matrix1.rows == matrix2.rows && matrix2.cols == 1)
+    {
+        Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
+        for (int r = 0; r < matrix1.rows; r++)
+        {
+            for (int c = 0; c < matrix1.cols; c++)
+            {
+                MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, 0);
+                setMatrixAt(sum, result_add, r, c);
+            }
+        }
+        return result_add;
+    }
+
+    return createMatrix(0, 0);
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
+    MatrixType buffer_add;
     
+    if (!matrix1.buffer || !matrix2.buffer) // Probe ob leere Matrize vorliegt 
+        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);
+
+    for (unsigned int index = 0; index < matrix1.rows; index++)
+    {
+        for (unsigned int shift = 0; shift < matrix2.cols; shift++)
+        {
+            buffer_add = 0;
+            for (unsigned int skalar = 0; skalar < matrix1.cols; skalar++)
+            {
+                buffer_add += getMatrixAt(matrix1, index, skalar) * getMatrixAt(matrix2, skalar, shift);
+            }
+            setMatrixAt(buffer_add, result_mul, index, shift);
+        }
+    }
+    return result_mul;
 }

matrix.h

@@ -6,6 +6,11 @@
 typedef float MatrixType;
 
 // TODO Matrixtyp definieren
+typedef struct {
+    MatrixType *buffer;
+    unsigned int rows;
+    unsigned int cols;
+} Matrix;
 
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);

neuralNetworkTests.c

@@ -5,10 +5,74 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    /*
+    typedef struct
+    {
+        Matrix weights;
+        Matrix biases;
+        ActivationFunctionType activation;
+    } Layer;
+
+    typedef struct
+    {
+        Layer *layers;
+        unsigned int numberOfLayers;
+    } NeuralNetwork;
+
+    */
+    FILE *file = fopen(path, "wb");
+    if (!file)
+        return;
+
+    //---------------------------------------------------------------------------
+    const char *tag = "__info2_neural_network_file_format__";
+    fwrite(tag, 1, strlen(tag), file);
+
+    // Schreibe die Anzahl der Layer
+    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);
+
+    // Debuging-Ausgabe
+    printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+    {
+        Layer layer = nn.layers[i];
+        printf("Layer %u: weights (%u x %u), biases (%u x %u)\n",
+               i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
+    }
+    //---------------------------------------------------------------------------
+
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -16,15 +80,15 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
     const char *path = "some__nn_test_file.info2";
     MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
     MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2};
-    Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3};
+    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}};
+    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=2};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -40,12 +104,12 @@ void test_loadModelReturnsCorrectWeightDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -63,12 +127,12 @@ void test_loadModelReturnsCorrectBiasDimensions(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -86,12 +150,12 @@ void test_loadModelReturnsCorrectWeights(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -111,12 +175,12 @@ void test_loadModelReturnsCorrectBiases(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -138,7 +202,7 @@ void test_loadModelFailsOnWrongFileTag(void)
     NeuralNetwork netUnderTest;
     FILE *file = fopen(path, "wb");
 
-    if(file != NULL)
+    if (file != NULL)
     {
         const char *fileTag = "info2_neural_network_file_format";
 
@@ -159,12 +223,12 @@ void test_clearModelSetsMembersToNull(void)
 {
     const char *path = "some__nn_test_file.info2";
     MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
     MatrixType biasBuffer[] = {7, 8, 9};
-    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
-    Layer layers[] = {{.weights=weights, .biases=biases}};
+    Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
+    Layer layers[] = {{.weights = weights, .biases = biases}};
 
-    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);
@@ -181,7 +245,7 @@ void test_clearModelSetsMembersToNull(void)
 
 static void someActivation(Matrix *matrix)
 {
-    for(int i = 0; i < matrix->rows * matrix->cols; i++)
+    for (int i = 0; i < matrix->rows * matrix->cols; i++)
     {
         matrix->buffer[i] = fabs(matrix->buffer[i]);
     }
@@ -192,23 +256,23 @@ void test_predictReturnsCorrectLabels(void)
     const unsigned char expectedLabels[] = {4, 2};
     GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
     GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
-    GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
+    GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}};
     MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
     MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
     MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
-    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
-    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
-    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
+    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};
+    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]));
@@ -216,11 +280,13 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }