forked from freudenreichan/info2Praktikum-NeuronalesNetz
matrix fehler behoben
This commit is contained in:
parent
a031bb0b7a
commit
6c26652744
119
matrix.c
119
matrix.c
@ -1,8 +1,8 @@
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include "matrix.h"
|
||||
#include <stdio.h>
|
||||
|
||||
// TODO Matrix-Funktionen implementieren
|
||||
|
||||
// Matrix erzeugen
|
||||
Matrix createMatrix(unsigned int rows, unsigned int cols)
|
||||
@ -12,28 +12,32 @@ Matrix createMatrix(unsigned int rows, unsigned int cols)
|
||||
matrix.rows = 0;
|
||||
matrix.cols = 0;
|
||||
|
||||
if (rows == 0 || cols == 0)
|
||||
return matrix; // leere Matrix
|
||||
|
||||
matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
|
||||
if (!matrix.buffer)
|
||||
return matrix; // Speicher konnte nicht reserviert werden
|
||||
|
||||
matrix.rows = rows;
|
||||
matrix.cols = cols;
|
||||
|
||||
// Initialisiere alle Werte auf UNDEFINED_MATRIX_VALUE
|
||||
for (unsigned int i = 0; i < rows * cols; i++)
|
||||
matrix.buffer[i] = UNDEFINED_MATRIX_VALUE;
|
||||
|
||||
// Wenn die Dimensionen gültig sind, Speicher reservieren
|
||||
if (rows > 0 && cols > 0)
|
||||
{
|
||||
matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
|
||||
if (matrix.buffer != NULL)
|
||||
{
|
||||
matrix.rows = rows;
|
||||
matrix.cols = cols;
|
||||
}
|
||||
}
|
||||
return matrix;
|
||||
}
|
||||
|
||||
// Matrix Speicher freigeben
|
||||
void clearMatrix(Matrix *matrix)
|
||||
{
|
||||
if (matrix->buffer != NULL)
|
||||
{
|
||||
if (!matrix) return;
|
||||
|
||||
if (matrix->buffer)
|
||||
free(matrix->buffer);
|
||||
matrix->buffer = NULL;
|
||||
}
|
||||
|
||||
matrix->buffer = NULL;
|
||||
matrix->rows = 0;
|
||||
matrix->cols = 0;
|
||||
}
|
||||
@ -41,67 +45,84 @@ void clearMatrix(Matrix *matrix)
|
||||
// Wert setzen
|
||||
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
|
||||
{
|
||||
if (rowIdx < matrix.rows && colIdx < matrix.cols)
|
||||
{
|
||||
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
|
||||
}
|
||||
if (!matrix.buffer) return;
|
||||
if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return;
|
||||
|
||||
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
|
||||
}
|
||||
|
||||
// Wert auslesen
|
||||
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
|
||||
{
|
||||
if (rowIdx < matrix.rows && colIdx < matrix.cols)
|
||||
{
|
||||
return matrix.buffer[rowIdx * matrix.cols + colIdx];
|
||||
}
|
||||
return 0; // Fallback
|
||||
}
|
||||
if (!matrix.buffer) return UNDEFINED_MATRIX_VALUE;
|
||||
if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return UNDEFINED_MATRIX_VALUE;
|
||||
|
||||
return matrix.buffer[rowIdx * matrix.cols + colIdx];
|
||||
}
|
||||
// Matrizen addieren
|
||||
Matrix add(const Matrix m1, const Matrix m2)
|
||||
{
|
||||
if (m1.rows != m2.rows || m1.cols != m2.cols)
|
||||
if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
|
||||
|
||||
// gleiche Dimension
|
||||
if (m1.rows == m2.rows && m1.cols == m2.cols)
|
||||
{
|
||||
return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
|
||||
Matrix result = createMatrix(m1.rows, m1.cols);
|
||||
if (!result.buffer) return result;
|
||||
|
||||
for (unsigned int r = 0; r < m1.rows; r++)
|
||||
for (unsigned int c = 0; c < m1.cols; c++)
|
||||
result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r * m2.cols + c];
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
Matrix result = createMatrix(m1.rows, m1.cols);
|
||||
if (result.buffer == NULL) return result;
|
||||
|
||||
for (unsigned int r = 0; r < m1.rows; r++)
|
||||
// Matrix2 ist ein Spaltenvektor
|
||||
if (m1.rows == m2.rows && m2.cols == 1)
|
||||
{
|
||||
for (unsigned int c = 0; c < m1.cols; c++)
|
||||
{
|
||||
result.buffer[r * m1.cols + c] =
|
||||
getMatrixAt(m1, r, c) + getMatrixAt(m2, r, c);
|
||||
}
|
||||
Matrix result = createMatrix(m1.rows, m1.cols);
|
||||
if (!result.buffer) return result;
|
||||
|
||||
for (unsigned int r = 0; r < m1.rows; r++)
|
||||
for (unsigned int c = 0; c < m1.cols; c++)
|
||||
result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r];
|
||||
|
||||
return result;
|
||||
}
|
||||
return result;
|
||||
|
||||
// Matrix1 ist ein Spaltenvektor
|
||||
if (m1.rows == m2.rows && m1.cols == 1)
|
||||
{
|
||||
Matrix result = createMatrix(m2.rows, m2.cols);
|
||||
if (!result.buffer) return result;
|
||||
|
||||
for (unsigned int r = 0; r < m2.rows; r++)
|
||||
for (unsigned int c = 0; c < m2.cols; c++)
|
||||
result.buffer[r * result.cols + c] = m1.buffer[r] + m2.buffer[r * m2.cols + c];
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// passt nicht
|
||||
return createMatrix(0,0);
|
||||
}
|
||||
|
||||
// Matrizen multiplizieren
|
||||
Matrix multiply(const Matrix m1, const Matrix m2)
|
||||
{
|
||||
if (m1.cols != m2.rows)
|
||||
{
|
||||
return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
|
||||
}
|
||||
if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
|
||||
if (m1.cols != m2.rows) return createMatrix(0,0);
|
||||
|
||||
Matrix result = createMatrix(m1.rows, m2.cols);
|
||||
if (result.buffer == NULL) return result;
|
||||
if (!result.buffer) return result;
|
||||
|
||||
for (unsigned int r = 0; r < m1.rows; r++)
|
||||
{
|
||||
for (unsigned int c = 0; c < m2.cols; c++)
|
||||
{
|
||||
MatrixType sum = 0;
|
||||
for (unsigned int k = 0; k < m1.cols; k++)
|
||||
{
|
||||
sum += getMatrixAt(m1, r, k) * getMatrixAt(m2, k, c);
|
||||
}
|
||||
result.buffer[r * m2.cols + c] = sum;
|
||||
sum += m1.buffer[r * m1.cols + k] * m2.buffer[k * m2.cols + c];
|
||||
result.buffer[r * result.cols + c] = sum;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
@ -197,7 +197,7 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
|
||||
|
||||
if(result.buffer != NULL)
|
||||
{
|
||||
for(int i = 0; i < model.numberOfLayers; i++)
|
||||
for(int i = 0; i < model.numberOfLayers; i++)
|
||||
{
|
||||
Matrix biasResult;
|
||||
Matrix weightResult;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user