forked from freudenreichan/info2Praktikum-NeuronalesNetz
106 lines
2.4 KiB
C
106 lines
2.4 KiB
C
#include <stdlib.h>
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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 erzeugen
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Matrix createMatrix(unsigned int rows, unsigned int cols)
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{
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Matrix matrix;
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matrix.rows = rows;
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matrix.cols = cols;
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if (rows == 0 || cols == 0) {
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matrix.data = NULL;
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return matrix;
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}
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matrix.data = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
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if (matrix.data == NULL)
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{
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matrix.rows = 0;
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matrix.cols = 0;
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}
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return matrix;
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}
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// Matrix Speicher freigeben
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void clearMatrix(Matrix *matrix)
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{
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if (matrix->data != NULL)
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{
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free(matrix->data);
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matrix->data = NULL;
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}
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matrix->rows = 0;
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matrix->cols = 0;
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}
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// Wert setzen
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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.data[rowIdx * matrix.cols + colIdx] = value;
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}
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}
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// Wert auslesen
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MatrixType getMatrixAt(const 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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return matrix.data[rowIdx * matrix.cols + colIdx];
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}
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return 0; // Fallback
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}
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// Matrizen addieren
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Matrix add(const Matrix m1, const Matrix m2)
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{
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if (m1.rows != m2.rows || m1.cols != m2.cols)
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{
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return createMatrix(0, 0); // Dimension passt nicht
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}
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Matrix result = createMatrix(m1.rows, m1.cols);
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if (result.data == NULL) return result;
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for (unsigned int r = 0; r < m1.rows; r++)
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{
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for (unsigned int c = 0; c < m1.cols; c++)
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{
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result.data[r * m1.cols + c] =
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getMatrixAt(m1, r, c) + getMatrixAt(m2, r, c);
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}
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}
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return result;
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}
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// Matrizen multiplizieren
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Matrix multiply(const Matrix m1, const Matrix m2)
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{
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if (m1.cols != m2.rows)
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{
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return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
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}
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Matrix result = createMatrix(m1.rows, m2.cols);
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if (result.data == NULL) return result;
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for (unsigned int r = 0; r < m1.rows; r++)
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{
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for (unsigned int c = 0; c < m2.cols; c++)
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{
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MatrixType sum = 0;
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for (unsigned int k = 0; k < m1.cols; k++)
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{
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sum += getMatrixAt(m1, r, k) * getMatrixAt(m2, k, c);
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}
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result.data[r * m2.cols + c] = sum;
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}
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}
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return result;
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} |