Solved remeining 2 Neural Tests

matrix.c

@@ -3,21 +3,18 @@
 #include <string.h>
 #include "matrix.h"
 
-// TODO Matrix-Funktionen implementieren
-
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
     if (rows != 0 && cols != 0)
-	{
+    {
         Matrix matrix;
         matrix.rows = rows;
         matrix.cols = cols;
-        matrix.buffer = (float*) calloc(rows * cols, sizeof(float)); //belegt den speicherplatz mit calloc -> mit 0
+        matrix.buffer = (MatrixType*) calloc((size_t)rows * cols, sizeof(MatrixType));
         return matrix;
     }
-	else
+    else
-	{    //Bei einer "falschen" Matrix eine leere zurückgeben, ohne speicher zu belegen
+    {
-        printf("Nullgroesse der Matrix!!!\n");
         Matrix matrix;
         matrix.rows = 0;
         matrix.cols = 0;
@@ -28,87 +25,105 @@ Matrix createMatrix(unsigned int rows, unsigned int cols)
 
 void clearMatrix(Matrix *matrix)
 {
-	// Sicherheits-Check für den übergebenen Zeiger
+    if (matrix != NULL)
-	if (matrix != NULL)
+    {
-	{
+        if (matrix->buffer != NULL) {
-		// **WICHTIGE KORREKTUR:** Puffer-Check vor free()
+            free(matrix->buffer);
-		if (matrix->buffer != NULL) {
+        }
-			free(matrix->buffer);
-		}
 
-		matrix->buffer = NULL; // Zeiger auf NULL setzen
+        matrix->buffer = NULL;
-		matrix->rows = 0;
+        matrix->rows = 0;
-		matrix->cols = 0;
+        matrix->cols = 0;
-	}
+    }
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-	matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+    matrix.buffer[(size_t)rowIdx * matrix.cols + colIdx] = value;
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-	if(rowIdx < matrix.rows && colIdx < matrix.cols){
+    if(rowIdx < matrix.rows && colIdx < matrix.cols){
-    	return matrix.buffer[rowIdx * matrix.cols + colIdx]; //ACHTUNG! rowIdx und colIDX sind in Array position gedacht! matrix.cols ist normal gedacht!
+        return matrix.buffer[(size_t)rowIdx * matrix.cols + colIdx];
-	}else{
+    }else{
-		return 0;
+        return 0;
-	}
+    }
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-	//Überprüfen, ob die Matrizen die gleichen Dimensionen haben
+    // Case A: same shape -> elementwise add
-		//wenn nicht muss die matrix "rows/cols=0 und buffer = NULL" leer zurückgegeben werden
+    if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols)
+    {
+        Matrix result = createMatrix(matrix1.rows, matrix1.cols);
+        if (result.buffer == NULL) return result;
 
-	if (matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols)
+        size_t n = (size_t)result.rows * result.cols;
-	{
+        for (size_t i = 0; i < n; i++)
-		Matrix result = {0}; // Struktur auf 0/NULL initialisieren
+        {
-		result.rows = 0;
+            result.buffer[i] = matrix1.buffer[i] + matrix2.buffer[i];
-		result.cols = 0;
+        }
-		result.buffer = NULL;
+        return result;
-		return result;
+    }
-	}
 
-	else
+    // Case B: matrix1 has shape (rows x cols) and matrix2 is (rows x 1) -> broadcast second across columns
-	{
+    if (matrix1.rows == matrix2.rows && matrix2.cols == 1 && matrix1.cols > 1)
-		// **WICHTIGE KORREKTUR:** Speicher für das Ergebnis reservieren
+    {
-		Matrix result = createMatrix(matrix1.rows, matrix1.cols);
+        Matrix result = createMatrix(matrix1.rows, matrix1.cols);
+        if (result.buffer == NULL) return result;
 
-		// Prüfen, ob Speicherreservierung erfolgreich war
+        for (unsigned int r = 0; r < matrix1.rows; r++)
-		if (result.buffer == NULL) {
+        {
-			return result; // Gibt Null-Matrix zurück, falls malloc fehlschlug
+            MatrixType b = matrix2.buffer[(size_t)r * matrix2.cols + 0];
-		}
+            for (unsigned int c = 0; c < matrix1.cols; c++)
+            {
+                result.buffer[(size_t)r * result.cols + c] = matrix1.buffer[(size_t)r * matrix1.cols + c] + b;
+            }
+        }
+        return result;
+    }
 
-		// Addition der beiden Matrizen
+    // Case C: matrix1 is (rows x 1) and matrix2 is (rows x cols) -> broadcast first across columns
-		for (unsigned int i = 0; i < result.rows * result.cols; i++)
+    if (matrix2.rows == matrix1.rows && matrix1.cols == 1 && matrix2.cols > 1)
-		{
+    {
-			// Achtung: Wenn Sie die Matrizen nicht per const Pointer übergeben,
+        Matrix result = createMatrix(matrix2.rows, matrix2.cols);
-			// müssen Sie wissen, dass die Daten nicht temporär sind (hier ok, da lokale Kopien).
+        if (result.buffer == NULL) return result;
-			result.buffer[i] = matrix1.buffer[i] + matrix2.buffer[i];
-		}
-
-		return result;
-	}
 
+        for (unsigned int r = 0; r < matrix2.rows; r++)
+        {
+            MatrixType b = matrix1.buffer[(size_t)r * matrix1.cols + 0];
+            for (unsigned int c = 0; c < matrix2.cols; c++)
+            {
+                result.buffer[(size_t)r * result.cols + c] = matrix2.buffer[(size_t)r * matrix2.cols + c] + b;
+            }
+        }
+        return result;
+    }
 
+    // unsupported shapes -> return empty matrix
+    Matrix result = {0};
+    result.rows = 0;
+    result.cols = 0;
+    result.buffer = NULL;
+    return result;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-	//Spalten matrix 1 muss mit Reihen Matrix 2 übereinstimmen
+    if (matrix1.cols != matrix2.rows)
-	if (matrix1.cols != matrix2.rows)
+    {
-	{
+        Matrix result;
-		Matrix result;
         result.rows = 0;
         result.cols = 0;
         result.buffer = NULL;
         return result;
-	}
+    }
-
+    else
-	else
+    {
-	{
         Matrix result = createMatrix(matrix1.rows, matrix2.cols);
+        if (result.buffer == NULL) return result;
+
         for (unsigned int i = 0; i < result.rows; i++)
         {
             for (unsigned int j = 0; j < result.cols; j++)
@@ -122,6 +137,5 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
             }
         }
         return result;
-
+    }
-	}
+}
-}

neuralNetwork.c

@@ -2,39 +2,41 @@
 #include <stdio.h>
 #include <math.h>
 #include <string.h>
+#include <stdint.h>
 #include "neuralNetwork.h"
 
-#define BUFFER_SIZE 100
+#define BUFFER_SIZE 200
 #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));
+        double *colSums = (double *)calloc((size_t)matrix->cols, sizeof(double));
+        if(colSums == NULL) return;
 
-        if(colSums != NULL)
+        for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
         {
-            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
+            for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
             {
-                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
+                MatrixType expValue = (MatrixType)exp(getMatrixAt(*matrix, rowIdx, colIdx));
-                {
+                setMatrixAt(expValue, *matrix, rowIdx, colIdx);
-                    MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
+                colSums[colIdx] += expValue;
-                    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);
         }
+
+        for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
+        {
+            double s = colSums[colIdx];
+            if(s == 0.0) s = 1.0;
+            for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
+            {
+                MatrixType normalizedValue = (MatrixType)(getMatrixAt(*matrix, rowIdx, colIdx) / s);
+                setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
+            }
+        }
+
+        free(colSums);
     }
 }
 
@@ -46,39 +48,53 @@ static void relu(Matrix *matrix)
     }
 }
 
+/* Prüft den Dateikopf. Liefert 1 bei Erfolg, 0 bei Fehler. */
 static int checkFileHeader(FILE *file)
 {
-    int isValid = 0;
+    if(file == NULL) return 0;
-    int fileHeaderLen = strlen(FILE_HEADER_STRING);
-    char buffer[BUFFER_SIZE] = {0};
 
-    if(BUFFER_SIZE-1 < fileHeaderLen)
+    size_t headerLen = strlen(FILE_HEADER_STRING);
-        fileHeaderLen = BUFFER_SIZE-1;
+    if(headerLen == 0 || headerLen >= BUFFER_SIZE) return 0;
 
-    if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
+    char buffer[BUFFER_SIZE];
-        isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
+    if(fseek(file, 0, SEEK_SET) != 0) return 0;
+    if(fread(buffer, sizeof(char), headerLen, file) != headerLen) return 0;
+    if(memcmp(buffer, FILE_HEADER_STRING, headerLen) != 0) return 0;
 
-    return isValid;
+    return 1;
 }
 
+/* Liest eine Dimension (wie vom Test-Writer mit sizeof(int) geschrieben) und prüft Plausibilität.
+   Liefert 0 bei Lesefehler oder wenn der gelesene Wert offensichtlich nicht in die verbleibende Dateigröße passt.
+*/
 static unsigned int readDimension(FILE *file)
 {
-    int dimension = 0;
+    if (file == NULL) return 0;
 
-    if(fread(&dimension, sizeof(int), 1, file) != 1)
+    int value = 0;
-        dimension = 0;
+    if (fread(&value, sizeof(int), 1, file) != 1) {
+        return 0;
+    }
 
-    return dimension;
+    if (value < 0) return 0;
+    return (unsigned int)value;
 }
 
+
+/* Liest eine Matrix rows x cols; wenn Einlese-Fehler: leere Matrix zurückgeben */
 static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
 {
     Matrix matrix = createMatrix(rows, cols);
+    if(matrix.buffer == NULL) return matrix;
 
-    if(matrix.buffer != NULL)
+    size_t toRead = (size_t)rows * cols;
+    if(toRead > 0)
     {
-        if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
+        size_t readCount = fread(matrix.buffer, sizeof(MatrixType), toRead, file);
+        if(readCount != toRead)
+        {
             clearMatrix(&matrix);
+        }
     }
 
     return matrix;
@@ -87,91 +103,113 @@ static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
 static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension)
 {
     Layer layer;
+    layer.activation = NULL;
     layer.weights = readMatrix(file, outputDimension, inputDimension);
-    layer.biases = readMatrix(file, outputDimension, 1);
+    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;
+    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)
+    if(layer == NULL) return;
-    {
+    clearMatrix(&layer->weights);
-        clearMatrix(&layer->weights);
+    clearMatrix(&layer->biases);
-        clearMatrix(&layer->biases);
+    layer->activation = NULL;
-        layer->activation = NULL;
-    }
 }
 
 static void assignActivations(NeuralNetwork model)
 {
-    for(int i = 0; i < (int)model.numberOfLayers-1; i++)
+    if(model.numberOfLayers == 0) return;
-    {
+    for(int i = 0; i < (int)model.numberOfLayers - 1; i++)
         model.layers[i].activation = relu;
-    }
+    model.layers[model.numberOfLayers - 1].activation = softmax;
-
-    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) return model;
 
-    if(file != NULL)
+    if(fseek(file, 0, SEEK_SET) != 0)
     {
-        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);
-            }
-        }
         fclose(file);
-
+        return model;
-        assignActivations(model);
     }
 
+    if(!checkFileHeader(file))
+    {
+        fclose(file);
+        return model;
+    }
+
+    /* Lese erstes Dimensions-Paar (input, output) */
+    unsigned int inputDimension = readDimension(file);
+    unsigned int outputDimension = readDimension(file);
+
+    fprintf(stderr, "[loadModel] first dims: input=%u output=%u\n", inputDimension, outputDimension);
+
+    while (inputDimension > 0 && outputDimension > 0)
+    {
+        Layer layer = readLayer(file, inputDimension, outputDimension);
+
+        if (isEmptyLayer(layer))
+        {
+            clearLayer(&layer);
+            clearModel(&model);
+            break;
+        }
+
+        Layer *tmp = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
+        if (tmp == NULL)
+        {
+            clearLayer(&layer);
+            clearModel(&model);
+            break;
+        }
+        model.layers = tmp;
+        model.layers[model.numberOfLayers] = layer;
+        model.numberOfLayers++;
+
+        fprintf(stderr, "[loadModel] loaded layer %d: weights %u x %u, biases %u x %u\n",
+                model.numberOfLayers,
+                layer.weights.rows, layer.weights.cols,
+                layer.biases.rows, layer.biases.cols);
+
+        /* Lese das nächste Dimensions-Paar (writer schreibt für jede Schicht ein Paar) */
+        unsigned int nextInput = readDimension(file);
+        unsigned int nextOutput = readDimension(file);
+
+        fprintf(stderr, "[loadModel] next raw dims read: nextInput=%u nextOutput=%u\n", nextInput, nextOutput);
+
+        /* Wenn nächstes Paar (0,0) -> Ende */
+        if (nextInput == 0 || nextOutput == 0)
+        {
+            inputDimension = 0;
+            outputDimension = 0;
+            break;
+        }
+
+        /* Setze für die nächste Iteration */
+        inputDimension = nextInput;
+        outputDimension = nextOutput;
+
+        fprintf(stderr, "[loadModel] next dims: input=%u output=%u\n", inputDimension, outputDimension);
+    }
+
+    fclose(file);
+    assignActivations(model);
     return model;
 }
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-       //Matrix matrix = {NULL, 0, 0};
-    // Explizite Initialisierung verwenden, um die Feldreihenfolge in matrix.h zu umgehen:
     Matrix matrix;
     matrix.buffer = NULL;
     matrix.rows = 0;
@@ -180,15 +218,12 @@ static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], un
     if(count > 0 && images != NULL)
     {
         matrix = createMatrix(images[0].height * images[0].width, count);
-
         if(matrix.buffer != NULL)
         {
-            for(int i = 0; i < count; i++)
+            for(unsigned int i = 0; i < count; i++)
             {
-                for(int j = 0; j < images[i].width * images[i].height; j++)
+                for(unsigned int j = 0; j < images[i].width * images[i].height; j++)
-                {
                     setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
-                }
             }
         }
     }
@@ -199,23 +234,20 @@ static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], un
 static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
 {
     Matrix result = inputBatch;
+    if(result.buffer == NULL) return result;
 
-    if(result.buffer != NULL)
+    for(int i = 0; i < model.numberOfLayers; i++)
     {
-        for(int i = 0; i < model.numberOfLayers; i++)
+        Matrix weightResult = multiply(model.layers[i].weights, result);
-        {
+        clearMatrix(&result);
-            Matrix biasResult;
-            Matrix weightResult;
 
-            weightResult = multiply(model.layers[i].weights, result);
+        Matrix biasResult = add(weightResult, model.layers[i].biases);
-            clearMatrix(&result);
+        clearMatrix(&weightResult);
-            biasResult = add(model.layers[i].biases, weightResult);
-            clearMatrix(&weightResult);
 
-            if(model.layers[i].activation != NULL)
+        if(model.layers[i].activation != NULL)
-                model.layers[i].activation(&biasResult);
+            model.layers[i].activation(&biasResult);
-            result = biasResult;
+
-        }
+        result = biasResult;
     }
 
     return result;
@@ -223,27 +255,21 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
 
 unsigned char *argmax(const Matrix matrix)
 {
-    unsigned char *maxIdx = NULL;
+    if(matrix.rows == 0 || matrix.cols == 0) return NULL;
 
-    if(matrix.rows > 0 && matrix.cols > 0)
+    unsigned char *maxIdx = (unsigned char *)malloc((size_t)matrix.cols * sizeof(unsigned char));
+    if(maxIdx == NULL) return NULL;
+
+    for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
     {
-        maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
+        int best = 0;
-
+        for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
-        if(maxIdx != NULL)
         {
-            for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
+            if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, best, colIdx))
-            {
+                best = rowIdx;
-                maxIdx[colIdx] = 0;
-
-                for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
-                {
-                    if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
-                        maxIdx[colIdx] = rowIdx;
-                }
-            }
         }
+        maxIdx[colIdx] = (unsigned char)best;
     }
-
     return maxIdx;
 }
 
@@ -251,23 +277,17 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
 {
     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)
+    if(model == NULL) return;
-    {
+    for(int i = 0; i < model->numberOfLayers; i++)
-        for(int i = 0; i < model->numberOfLayers; i++)
+        clearLayer(&model->layers[i]);
-        {
+    free(model->layers);
-            clearLayer(&model->layers[i]);
+    model->layers = NULL;
-        }
+    model->numberOfLayers = 0;
-        model->layers = NULL;
+}
-        model->numberOfLayers = 0;
-    }
-}