#ifdef MATRIX_H_ // since the .cpp file has to be included by the .hpp file this // will evaluate to true #include "Matrix.hpp" #include #include #include #include #include template Matrix::Matrix(float value) { this->Fill(value); } template Matrix::Matrix(const std::array &array) { this->setMatrixToArray(array); } template template Matrix::Matrix(Args... args) { constexpr uint16_t arraySize{static_cast(rows) * static_cast(columns)}; std::initializer_list initList{static_cast(args)...}; // choose whichever buffer size is smaller for the copy length uint32_t minSize = std::min(arraySize, static_cast(initList.size())); memcpy(this->matrix.begin(), initList.begin(), minSize * sizeof(float)); } template void Matrix::Identity() { this->Fill(0); for (uint8_t idx{0}; idx < rows; idx++) { this->matrix[idx * columns + idx] = 1; } } template Matrix::Matrix(const Matrix &other) { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { this->matrix[row_idx * columns + column_idx] = other.Get(row_idx, column_idx); } } } template void Matrix::setMatrixToArray( const std::array &array) { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { uint16_t array_idx = static_cast(row_idx) * static_cast(columns) + static_cast(column_idx); if (array_idx < array.size()) { this->matrix[row_idx * columns + column_idx] = array[array_idx]; } else { this->matrix[row_idx * columns + column_idx] = 0; } } } } template Matrix & Matrix::Add(const Matrix &other, Matrix &result) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) + other.Get(row_idx, column_idx); } } return result; } template Matrix & Matrix::Sub(const Matrix &other, Matrix &result) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) - other.Get(row_idx, column_idx); } } return result; } template template Matrix & Matrix::Mult(const Matrix &other, Matrix &result) const { // allocate some buffers for all of our dot products Matrix<1, columns> this_row; Matrix other_column; for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { // get our row this->GetRow(row_idx, this_row); for (uint8_t column_idx{0}; column_idx < other_columns; column_idx++) { // get the other matrix'ss column other.GetColumn(column_idx, other_column); // the result's index is equal to the dot product of these two vectors result[row_idx][column_idx] = Matrix::DotProduct(this_row, other_column.Transpose()); } } return result; } template Matrix & Matrix::Mult(float scalar, Matrix &result) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) * scalar; } } return result; } template Matrix Matrix::Invert() const { // since all matrix sizes have to be statically specified at compile time we // can do this static_assert(rows == columns, "Your matrix isn't square and can't be inverted"); Matrix result{}; // unfortunately we can't calculate this at compile time so we'll just reurn // zeros float determinant{this->Det()}; if (determinant == 0) { // you can't invert a matrix with a negative determinant result.Fill(0); return result; } // TODO: This algorithm is really inneficient because of the matrix of minors. // We should make a different algorithm how to calculate the inverse: // https://www.mathsisfun.com/algebra/matrix-inverse-minors-cofactors-adjugate.html // calculate the matrix of minors Matrix minors{}; this->MatrixOfMinors(minors); // now adjugate the matrix and save it in our output minors.adjugate(result); // scale the result by 1/determinant and we have our answer result = result * (1 / determinant); // result.Mult(1 / determinant, result); return result; } template Matrix Matrix::Transpose() const { Matrix result{}; for (uint8_t column_idx{0}; column_idx < rows; column_idx++) { for (uint8_t row_idx{0}; row_idx < columns; row_idx++) { result[row_idx][column_idx] = this->Get(column_idx, row_idx); } } return result; } // explicitly define the determinant for a 2x2 matrix because it is definitely // the fastest way to calculate a 2x2 matrix determinant // template <> // inline float Matrix<0, 0>::Det() const { return 1e+6; } template <> inline float Matrix<1, 1>::Det() const { return this->matrix[0]; } template <> inline float Matrix<2, 2>::Det() const { return this->matrix[0] * this->matrix[3] - this->matrix[1] * this->matrix[2]; } template float Matrix::Det() const { static_assert(rows == columns, "You can't take the determinant of a non-square matrix."); Matrix MinorMatrix{}; float determinant{0}; for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { // for odd indices the sign is negative float sign = (column_idx % 2 == 0) ? 1 : -1; determinant += sign * this->matrix[column_idx] * this->MinorMatrix(MinorMatrix, 0, column_idx).Det(); } return determinant; } template Matrix & Matrix::ElementMultiply(const Matrix &other, Matrix &result) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) * other.Get(row_idx, column_idx); } } return result; } template Matrix & Matrix::ElementDivide(const Matrix &other, Matrix &result) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) / other.Get(row_idx, column_idx); } } return result; } template float Matrix::Get(uint8_t row_index, uint8_t column_index) const { if (row_index > rows - 1 || column_index > columns - 1) { return 1e+10; // TODO: We should throw something here instead of failing // quietly } return this->matrix[row_index * columns + column_index]; } template Matrix<1, columns> & Matrix::GetRow(uint8_t row_index, Matrix<1, columns> &row) const { memcpy(&(row[0]), this->matrix.begin() + row_index * columns, columns * sizeof(float)); return row; } template Matrix & Matrix::GetColumn(uint8_t column_index, Matrix &column) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { column[row_idx][0] = this->Get(row_idx, column_index); } return column; } template void Matrix::ToString(std::string &stringBuffer) const { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { stringBuffer += "|"; for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { stringBuffer += std::to_string(this->matrix[row_idx * columns + column_idx]); if (column_idx != columns - 1) { stringBuffer += "\t"; } } stringBuffer += "|\n"; } } template std::array & Matrix::operator[](uint8_t row_index) { if (row_index > rows - 1) { // TODO: We should throw something here instead of failing quietly. row_index = 0; } // cursed reinterpret_cast that will help us fake having a nested array when // we really don't return *reinterpret_cast *>( &(this->matrix[row_index * columns])); } template Matrix & Matrix::operator=(const Matrix &other) { memcpy(this->matrix.begin(), other.matrix.begin(), rows * columns * sizeof(float)); // return a reference to ourselves so you can chain together these functions return *this; } template Matrix Matrix::operator+(const Matrix &other) const { Matrix buffer{}; this->Add(other, buffer); return buffer; } template Matrix Matrix::operator-(const Matrix &other) const { Matrix buffer{}; this->Sub(other, buffer); return buffer; } template template Matrix Matrix::operator*( const Matrix &other) const { Matrix buffer{}; this->Mult(other, buffer); return buffer; } template Matrix Matrix::operator*(float scalar) const { Matrix buffer{}; this->Mult(scalar, buffer); return buffer; } template template float Matrix::DotProduct(const Matrix<1, vector_size> &vec1, const Matrix<1, vector_size> &vec2) { float sum{0}; for (uint8_t i{0}; i < vector_size; i++) { sum += vec1.Get(0, i) * vec2.Get(0, i); } return sum; } template template float Matrix::DotProduct(const Matrix &vec1, const Matrix &vec2) { float sum{0}; for (uint8_t i{0}; i < vector_size; i++) { sum += vec1.Get(i, 0) * vec2.Get(i, 0); } return sum; } template void Matrix::Fill(float value) { for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { this->matrix[row_idx * columns + column_idx] = value; } } } template Matrix & Matrix::MatrixOfMinors(Matrix &result) const { Matrix MinorMatrix{}; for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { this->MinorMatrix(MinorMatrix, row_idx, column_idx); result[row_idx][column_idx] = MinorMatrix.Det(); } } return result; } template Matrix & Matrix::MinorMatrix(Matrix &result, uint8_t row_idx, uint8_t column_idx) const { std::array subArray{}; uint16_t array_idx{0}; for (uint8_t row_iter{0}; row_iter < rows; row_iter++) { if (row_iter == row_idx) { continue; } for (uint8_t column_iter{0}; column_iter < columns; column_iter++) { if (column_iter == column_idx) { continue; } subArray[array_idx] = this->Get(row_iter, column_iter); array_idx++; } } result = Matrix{subArray}; return result; } template Matrix & Matrix::adjugate(Matrix &result) const { for (uint8_t row_iter{0}; row_iter < rows; row_iter++) { for (uint8_t column_iter{0}; column_iter < columns; column_iter++) { float sign = ((row_iter + 1) % 2) == 0 ? -1 : 1; sign *= ((column_iter + 1) % 2) == 0 ? -1 : 1; result[column_iter][row_iter] = this->Get(row_iter, column_iter) * sign; } } return result; } template Matrix & Matrix::Normalize(Matrix &result) const { float sum{0}; for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { float val{this->Get(row_idx, column_idx)}; sum += val * val; } } if (sum == 0) { // this wouldn't do anything anyways result.Fill(1e+6); return result; } sum = sqrt(sum); for (uint8_t row_idx{0}; row_idx < rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < columns; column_idx++) { result[row_idx][column_idx] = this->Get(row_idx, column_idx) / sum; } } return result; } template template Matrix Matrix::SubMatrix() const { // static assert that sub_rows + row_offset <= rows // static assert that sub_columns + column_offset <= columns static_assert(sub_rows + row_offset <= rows, "The submatrix you're trying to get is out of bounds (rows)"); static_assert( sub_columns + column_offset <= columns, "The submatrix you're trying to get is out of bounds (columns)"); Matrix buffer{}; for (uint8_t row_idx{0}; row_idx < sub_rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < sub_columns; column_idx++) { buffer[row_idx][column_idx] = this->Get(row_idx + row_offset, column_idx + column_offset); } } return buffer; } template template void Matrix::SetSubMatrix( const Matrix &sub_matrix) { static_assert(sub_rows + row_offset <= rows, "The submatrix you're trying to set is out of bounds (rows)"); static_assert( sub_columns + column_offset <= columns, "The submatrix you're trying to set is out of bounds (columns)"); for (uint8_t row_idx{0}; row_idx < sub_rows; row_idx++) { for (uint8_t column_idx{0}; column_idx < sub_columns; column_idx++) { this->matrix[(row_idx + row_offset) * columns + column_idx + column_offset] = sub_matrix.Get(row_idx, column_idx); } } } // QR decomposition: decomposes this matrix A into Q and R // Assumes square matrix template void Matrix::QRDecomposition(Matrix &Q, Matrix &R) const { static_assert(columns <= rows, "QR decomposition requires columns <= rows"); // Gram-Schmidt orthogonalization Matrix a_col, u, e, proj; Matrix q_col; Q.Fill(0); R.Fill(0); for (uint8_t k = 0; k < columns; ++k) { this->GetColumn(k, a_col); u = a_col; for (uint8_t j = 0; j < k; ++j) { Q.GetColumn(j, q_col); float r_jk = Matrix::DotProduct(q_col, a_col); R[j][k] = r_jk; // proj = r_jk * q_j proj = q_col * r_jk; u = u - proj; } float norm = sqrt(Matrix::DotProduct(u, u)); if (norm == 0) { norm = 1e-12f; // avoid div by zero } for (uint8_t i = 0; i < rows; ++i) { Q[i][k] = u[i][0] / norm; } R[k][k] = norm; } } template void Matrix::EigenQR(Matrix &eigenVectors, Matrix &eigenValues, uint32_t maxIterations, float tolerance) const { static_assert(rows > 1, "Matrix size must be > 1 for QR iteration"); Matrix Ak = *this; // Copy original matrix Matrix QQ{}; QQ.Identity(); for (uint32_t iter = 0; iter < maxIterations; ++iter) { Matrix Q, R; Ak.QRDecomposition(Q, R); Ak = R * Q; QQ = QQ * Q; // Check convergence: off-diagonal norm float offDiagSum = 0.0f; for (uint32_t row = 1; row < rows; row++) { for (uint32_t column = 0; column < row; column++) { offDiagSum += fabs(Ak[row][column]); } } if (offDiagSum < tolerance) { break; } } // Diagonal elements are the eigenvalues for (uint8_t i = 0; i < rows; i++) { eigenValues[i][0] = Ak[i][i]; } eigenVectors = QQ; } #endif // MATRIX_H_