Test commit

matrix.c

@@ -131,14 +131,4 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
             }
     }
     return result;
-}
-void writeMatrix(Matrix matrix, FILE *file)//Added for neuralNetworkTests
-{
-	for(int i = 0; i < matrix.rows; i++)
-	{
-		for(int j = 0; j < matrix.cols; j++)
-		{
-			fprintf(file, (char*)(matrix.buffer + (j+i)*sizeof(MatrixType)));
-		}
-	}
 }

matrix.h

@@ -21,7 +21,6 @@ void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
-void writeMatrix(Matrix matrix, FILE *file);//Added for neuralNetworkTests
 
 
 #endif

neuralNetwork.c

@@ -155,7 +155,6 @@ NeuralNetwork loadModel(const char *path)
 
                 model.layers[model.numberOfLayers] = layer;
                 model.numberOfLayers++;
-
                 inputDimension = outputDimension;
                 outputDimension = readDimension(file);
             }
@@ -170,7 +169,7 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix matrix = {0, 0, NULL}; // TODO changed this line to fit our functionality
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -4,20 +4,34 @@
 #include <math.h>
 #include "unity.h"
 #include "neuralNetwork.h"
-#include "matrix.h"
 
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-	FILE *testDatei = fopen(path, "w");
-	fprintf(testDatei, "__info2_neural_network_file_format__");
-	for(int i = 0; i < nn.numberOfLayers; i++)
-	{
-		fprintf(testDatei, "\n");
-		writeMatrix(nn.layers[i].weights, testDatei);
-		writeMatrix(nn.layers[i].biases, testDatei);
+    // TODO
+	FILE* file = fopen(path, "wb");
+	if(file == NULL) {
+		printf("Failed to open file");
+		return;
 	}
-	fclose(testDatei);
+	printf("\nLayers in pNNF: %d\n", nn.numberOfLayers);
+	const char* header = "__info2_neural_network_file_format__";
+	fwrite(header, sizeof(const char), strlen(header), file);
+
+	for (int i = 0; i < nn.numberOfLayers; i++) {
+
+        fwrite(&(nn.layers[i].weights.cols), sizeof(unsigned int), 1, file);
+        fwrite(&(nn.layers[i].weights.rows), sizeof(unsigned int), 1, file);
+    }
+
+	for(int i = 0; i < nn.numberOfLayers; i++) {
+		//write everything to do with weights
+		fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
+
+		//write everything to do with biases
+		fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
+	}
+	fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -34,6 +48,7 @@ void test_loadModelReturnsCorrectNumberOfLayers(void)
     Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
 
     NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
+	printf("\nexpectedNetLayers: %d", expectedNet.numberOfLayers);
     NeuralNetwork netUnderTest;
 
     prepareNeuralNetworkFile(path, expectedNet);