generated from freudenreichan/info2Praktikum-NeuronalesNetz
feat(matrix): implement matrix functions
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@@ -2,34 +2,161 @@
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#include <string.h>
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#include "matrix.h"
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// TODO Matrix-Funktionen implementieren
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Matrix createMatrix(unsigned int rows, unsigned int cols)
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{
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Matrix matrix = {NULL, 0, 0}; //Initialwerte, da unsicher ob allocierung funktioniert
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if(rows > 0 && cols > 0)
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{
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MatrixType *buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
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if(buffer != NULL)
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{
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matrix.buffer = buffer;
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matrix.rows = rows;
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matrix.cols = cols;
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}
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}
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return matrix;
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}
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void clearMatrix(Matrix *matrix)
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{
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if(matrix != NULL)
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{
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free(matrix->buffer);
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matrix->buffer = NULL;
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matrix->rows = 0;
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matrix->cols = 0;
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}
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}
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void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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if(rowIdx < matrix.rows && colIdx < matrix.cols)
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{
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matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
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}
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}
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MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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MatrixType value = UNDEFINED_MATRIX_VALUE;
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if(rowIdx < matrix.rows && colIdx < matrix.cols)
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{
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value = matrix.buffer[rowIdx * matrix.cols + colIdx];
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}
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return value;
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}
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static int isValidMatrix(const Matrix matrix)
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{
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return matrix.buffer != NULL && matrix.rows > 0 && matrix.cols > 0;
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}
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static int hasSameDimensions(const Matrix matrix1, const Matrix matrix2)
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{
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return matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols;
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}
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static int isColumnVector(const Matrix matrix)
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{
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return matrix.cols == 1;
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}
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static Matrix addElementWise(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = createMatrix(matrix1.rows, matrix1.cols);
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if(isValidMatrix(result))
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{
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for(unsigned int i = 0; i < matrix1.rows * matrix1.cols; i++)
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{
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result.buffer[i] = matrix1.buffer[i] + matrix2.buffer[i];
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}
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}
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return result;
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}
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static Matrix addBroadcast(const Matrix matrix, const Matrix vector)
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{
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Matrix result = createMatrix(matrix.rows, matrix.cols);
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if(isValidMatrix(result))
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{
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for(unsigned int rowIdx = 0; rowIdx < matrix.rows; rowIdx++)
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{
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for(unsigned int colIdx = 0; colIdx < matrix.cols; colIdx++)
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{
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unsigned int index = rowIdx * matrix.cols + colIdx;
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result.buffer[index] = matrix.buffer[index] + getMatrixAt(vector, rowIdx, 0);
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}
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}
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}
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return result;
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}
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Matrix add(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = {NULL, 0, 0}; //leere Matrix als Fehlerfall
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if(isValidMatrix(matrix1) && isValidMatrix(matrix2))
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{
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//normal add
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if(hasSameDimensions(matrix1, matrix2))
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{
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result = addElementWise(matrix1, matrix2);
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}
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//add Broadcast matrix2 = vektor and matrix1 = matrix
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else if(isColumnVector(matrix2) && matrix1.rows == matrix2.rows)
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{
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result = addBroadcast(matrix1, matrix2);
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}
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//add Broadcast matrix1 = vektor and matrix2 = matrix
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else if(isColumnVector(matrix1) && matrix1.rows == matrix2.rows)
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{
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result = addBroadcast(matrix2, matrix1);
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}
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}
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return result;
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}
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/* (Bedingung: matrix1.cols == matrix2.rows)
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* | 1 2 3 | | 7 8 | | 1*7+2*9+3*11 1*8+2*10+3*12 | | 58 64 |
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* | 4 5 6 | * | 9 10 | = | 4*7+5*9+6*11 4*8+5*10+6*12 | = | 139 154 |
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* | 11 12 |
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*/
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Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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{
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Matrix result = {NULL, 0, 0}; //leere Matrix als Fehlerfall
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if(isValidMatrix(matrix1) && isValidMatrix(matrix2) && matrix1.cols == matrix2.rows)
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{
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result = createMatrix(matrix1.rows, matrix2.cols);
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if(isValidMatrix(result))
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{
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for(unsigned int rowIdx = 0; rowIdx < matrix1.rows; rowIdx++)
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{
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for(unsigned int colIdx = 0; colIdx < matrix2.cols; colIdx++)
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{
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MatrixType sum = 0;
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for(unsigned int k = 0; k < matrix1.cols; k++)
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{
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sum += getMatrixAt(matrix1, rowIdx, k) * getMatrixAt(matrix2, k, colIdx);
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}
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setMatrixAt(sum, result, rowIdx, colIdx);
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}
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}
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}
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}
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return result;
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}
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+7
-2
@@ -3,9 +3,14 @@
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#define UNDEFINED_MATRIX_VALUE 0
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typedef float MatrixType;
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typedef float MatrixType; //sets the datatype of the matrix
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// TODO Matrixtyp definieren
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typedef struct
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{
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MatrixType *buffer;
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unsigned int rows;
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unsigned int cols;
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} Matrix;
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Matrix createMatrix(unsigned int rows, unsigned int cols);
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