/********************************************************
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* ╚═════╝ ╚═════╝ ╚═╝ ╚══════╝
* Geophysical Computational Tools & Library (GCTL)
*
* Copyright (c) 2023 Yi Zhang (yizhang-geo@zju.edu.cn)
*
* GCTL is distributed under a dual licensing scheme. You can redistribute
* it and/or modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation, either version 2
* of the License, or (at your option) any later version. You should have
* received a copy of the GNU Lesser General Public License along with this
* program. If not, see .
*
* If the terms and conditions of the LGPL v.2. would prevent you from using
* the GCTL, please consider the option to obtain a commercial license for a
* fee. These licenses are offered by the GCTL's original author. As a rule,
* licenses are provided "as-is", unlimited in time for a one time fee. Please
* send corresponding requests to: yizhang-geo@zju.edu.cn. Please do not forget
* to include some description of your company and the realm of its activities.
* Also add information on how to contact you by electronic and paper mail.
******************************************************/
#ifndef _GCTL_SPMAT_H
#define _GCTL_SPMAT_H
#include "matrix.h"
namespace gctl
{
template class spmat;
/**
* 我们在下面定义一些常用的稀疏矩阵类型，方便我们使用。
*/
typedef spmat _1i_spmat; ///< 整形浮点稀疏矩阵
typedef spmat _1f_spmat; ///< 单精度浮点稀疏矩阵
typedef spmat _1d_spmat; ///< 双精度浮点稀疏矩阵
typedef spmat > _1cf_spmat; ///< 单精度复浮点稀疏矩阵
typedef spmat > _1cd_spmat; ///< 双精度复浮点稀疏矩阵
/**
* @brief 稀疏矩阵的节点元素类型
*
* @tparam NodeValueType 模版变量类型
*/
template
struct mat_node
{
int r_id, c_id; ///< 节点的行列号
NodeValueType val; ///< 节点值
mat_node *next_right; ///< 指向同行右方节点的指针
mat_node *next_down; ///< 指向同列下方节点的指针
/**
* 构造函数
*/
mat_node()
{
r_id = c_id = -1;
next_right = next_down = nullptr;
}
mat_node(int r, int c, NodeValueType v)
{
r_id = r; c_id = c;
val = v;
next_right = next_down = nullptr;
}
NodeValueType value() const
{
return val;
}
void value(NodeValueType in_val)
{
val = in_val;
return;
}
};
/**
* @brief 基于十字指针的稀疏矩阵的模版类 定义常规的加减乘除与矩阵运算
*
* @tparam MatrixValueType 模版变量类型
*/
template
class spmat
{
protected:
mat_node **r_head, **c_head; ///< 行和列的头指针
mat_node **r_end, **c_end; ///< 行和列的尾指针 这两个变量的使用主要是为了提高插入操作的效率
mat_node **d_link; ///< 所有非零元素按行优先排序的指针数组，可在稀疏矩阵元素位置不变但值经常改变时快速索引并改变值
int *r_count, *c_count; ///< 行与列的非零元素的个数
int r_num, c_num; ///< 满矩阵的行列数
int d_num; ///< d_link长度
MatrixValueType zero_val; ///< 类型的0值
public:
/**
* @brief 默认构造函数
*/
spmat();
/**
* @brief 构造函数：空的行列头指针
*
* @param[in] in_rnum 矩阵行数
* @param[in] in_cnum 矩阵列数
* @param[in] null_val 矩阵零值
*/
spmat(int in_rnum, int in_cnum, MatrixValueType null_val);
/**
* @brief 拷贝构造函数
*
* @param[in] b 拷贝稀疏矩阵
*/
spmat(const spmat &b);
/**
* @brief 析构函数
*/
virtual ~spmat();
/**
* @brief 初始化为空的行列头指针
*
* @param[in] in_rnum 矩阵行数
* @param[in] in_cnum 矩阵列数
* @param[in] null_val 矩阵零值
*/
void malloc(int in_rnum, int in_cnum, MatrixValueType null_val);
/**
* @brief 清空稀疏矩阵
*
* @note 清空整个矩阵时会删除所有元素和辅助数组，必须重新调用malloc函数再能插入元素。删除某行（列）时仅删除已有元素，可以直接再次插入元素。
*
* @param[in] index 行或者列的索引号，如果为-1则清空整个稀疏矩阵
* @param[in] mode 清空制定的行或者列，默认为行模式
*/
void clear(int index = -1, matrix_order_e mode = RowMajor);
/**
* @brief 重置稀疏矩阵，仅删除所有已有元素，可以直接插入元素。相当于对所有行调用clear函数。
*
*/
void reset();
/**
* @brief 删除所有等于零值的元素
*/
void purge();
/**
* @brief 设置所有非零值为指定值，如果输入为零值则直接调用reset函数
*
* @note 注意此函数可能会重置稀疏矩阵
*
* @param in_val 输入值
*/
void set_all(MatrixValueType in_val);
/**
* @brief 从三元组数组赋值，三元组的排列必须是按行优先方式排序，顺序增加，可有重复索引的元素（函数会检查并报错）
*
* @note 注意此函数会重置稀疏矩阵
*
* @param triplts 三元组数组
* @param val_mode 相同位置重复输入值的处理方式
*/
void set_triplts(const std::vector > &triplts, value_operator_e val_mode = AppendVal);
/**
* @brief 从COO格式储存的稀疏矩阵对矩阵进行赋值，元素排列必须是按行优先方式排序，顺序增加，不可有重复索引的元素（函数不检查和报错）
*
* @note 注意此函数会重置稀疏矩阵
*
* @param row_idx 非零元素的行索引数组
* @param col_idx 非零元素的列索引数组
* @param data 非零元素数组
*/
void set_coo(const array &row_idx, const array &col_idx, const array &data);
/**
* @brief 从CSR格式储存的稀疏矩阵对矩阵进行赋值，元素排列必须是按行优先方式排序，顺序增加，不可有重复索引的元素（函数不检查和报错）
*
* @note 注意此函数会重置稀疏矩阵
*
* @param row_off 非零元素的行偏移数组
* @param col_idx 非零元素的列索引数组
* @param data 非零元素数组
*/
void set_csr(const array &row_off, const array &col_idx, const array &data);
/**
* @brief 从二维向量对矩阵进行赋值
*
* @param[in] den_mat_ptr 二维向量
* @param[in] off_set 初始化时零值的偏差范围
*/
void set_2d_vector(const std::vector > &den_mat_ptr, MatrixValueType off_set);
/**
* @brief 从二维数组对矩阵进行赋值
*
* @param den_mat_ptr 二维数组的指针
* @param[in] off_set 初始化时零值的偏差范围
*/
void set_2d_array(MatrixValueType **den_mat_ptr, MatrixValueType off_set);
/**
* @brief 从matrix数组对矩阵进行赋值
*
* @param[in] den_mat_ptr matrix数组
* @param[in] off_set 初始化时零值的偏差范围
*/
void set_matrix(const matrix &den_mat_ptr, MatrixValueType off_set);
/**
* @brief 储存非零元素的储存位置，建立快速查找索引（目前只在set_triplts函数中使用储存的位置）
*
* @note 元素数量或位置改变后失效
*
*/
void init_pattern();
/**
* @brief 如果稀疏矩阵的行列数为零，则为空的稀疏矩阵
*
* @return 矩阵是否为空
*/
bool empty() const;
/**
* @brief 返回行数
*
* @return 行数
*/
int row_size() const;
/**
* @brief 返回列数
*
* @return 列数
*/
int col_size() const;
/**
* @brief 返回非0元素个数
*
* 此函数可返回矩阵中所有或者指定行或者列的零值元素个数
*
* @param[in] index 行或列的索引
* @param[in] mode 计算行还是列的非零元素个数
*
* @return 非0元素个数
*/
int ele_size(int index = -1, matrix_order_e mode = RowMajor) const;
/**
* @brief 返回0值
*
* @return 0值
*/
MatrixValueType zero_value() const;
/**
* @brief 按矩阵形式显示所有元素，包括0元素
*/
void show_matrix(char delimiter = '\t') const;
/**
* @brief 按列表形式显示三元组
*
* @param[in] mode 按行优先或者列优先形式排序
*/
void show_list(matrix_order_e mode = RowMajor, char delimiter = ' ', bool full = false) const;
/**
* @brief 返回位于(i, j)位置的元素
*
* @param[in] i_index 行索引
* @param[in] j_index 列索引
*
* @return 矩阵元素
*/
MatrixValueType at(size_t i_index, size_t j_index) const;
/**
* @brief 输出稠密矩阵
*
* @param ret_arr2d 以引用形式返回的matrix数组
*/
void export_dense(matrix &ret_arr2d) const;
/**
* @brief 输出矩阵中某一行或列
*
* @param[in] out_id 行或列的索引
* @param ret_arr 以引用形式返回的array数组
* @param[in] multipler 倍数，默认为1.0
* @param[in] line_mode 是否为行优先
* @param[in] val_mode 返回数组的数值操作方式
*/
void export_dense(int out_id, array &ret_arr,
double multipler = 1.0, matrix_order_e line_mode = RowMajor,
value_operator_e val_mode = ReplaceVal) const;
/**
* @brief 使用COO格式输出稀疏矩阵
*
* @param row_idx 非零元素的行索引数组
* @param col_idx 非零元素的列索引数组
* @param data 非零元素数组
*/
void export_coo(array &row_idx, array &col_idx, array &data) const;
/**
* @brief 使用CSR格式输出稀疏矩阵
*
* @param row_off 非零元素的行偏移数组
* @param col_idx 非零元素的列索引数组
* @param data 非零元素数组
*/
void export_csr(array &row_off, array &col_idx, array &data) const;
/**
* @brief 添加一个元素 若插入位置已有值则替换
*
* @param[in] r 行索引
* @param[in] c 列索引
* @param[in] v 插入值
*/
void insert(int r, int c, MatrixValueType v);
/**
* @brief 删除一个元素
*
* @param[in] r 行索引
* @param[in] c 列索引
*/
void remove(int r, int c);
/**
* @brief 计算非零元素值的总和
*
* @param[in] index 行或者列的索引号，如果为-1则计算整个稀疏矩阵
* @param[in] mode 清空制定的行或者列，默认为行模式
*/
MatrixValueType sum(int index = -1, matrix_order_e mode = RowMajor);
/**
* @brief 计算行或列的模长
*
* @param index 行或者列的索引号
* @param mode 切换行与列模式
* @return 测度值
*/
MatrixValueType module(int index, matrix_order_e mode = RowMajor);
/**
* @brief 单个元素位置的加法
*
* @param[in] r 行索引
* @param[in] c 列索引
* @param[in] v 被加数
*/
void add_scalar(int r, int c, MatrixValueType v);
/**
* @brief 单个元素位置的减法
*
* @param[in] r 行索引
* @param[in] c 列索引
* @param[in] v 被减数
*/
void minus_scalar(int r, int c, MatrixValueType v);
// 矩阵运算
/**
* @brief 稀疏矩阵相加
*
* @param[in] b 被加稀疏矩阵
*/
void add_sparse_matrix(const spmat &b);
/**
* @brief 稀疏矩阵相减
*
* @param[in] b 被减稀疏矩阵
*/
void minus_sparse_matrix(const spmat &b);
/**
* @brief 稠密矩阵相加
*
* @param[in] arr2d 引用的形式返回的相加后矩阵
*/
void add_matrix(matrix &arr2d) const;
/**
* @brief 稠密矩阵相减
*
* @param arr2d 引用的形式返回的相减后矩阵
* @param[in] preposition 稠密矩阵为减数
*/
void minus_matrix(matrix &arr2d, bool preposition = false) const;
/**
* @brief 映射某行或列的数据到另一行或列
*
* @param[in] from_index 来源行或列的索引
* @param[in] to_index 目标行或列的索引
* @param[in] multipler 倍数
* @param[in] line_mode 是否为行优先
* @param[in] val_mode 返回数组的数值操作方式
*/
void map(int from_index, int to_index, MatrixValueType multipler,
matrix_order_e line_mode = RowMajor, value_operator_e val_mode = AppendVal);
/**
* @brief 计算转置矩阵
*
* @param ret_mat 转置后矩阵
*/
void transpose(spmat &ret_mat) const;
/**
* @brief 单个元素位置的乘法
*
* @param[in] r 行索引
* @param[in] c 列索引
* @param[in] v 乘数
*/
void multiply_scalar(int r, int c, MatrixValueType v);
/**
* @brief 稀疏矩阵的乘法
*
* @param[in] b 待乘稀疏矩阵
* @param ret_mat 乘积稀疏矩阵
*/
void multiply_sparse_matrix(const spmat &b, spmat &ret_mat) const;
/**
* @brief 矩阵乘法的母函数
*
* @param arr2d 待乘二维数组的指针（需提前开辟好内存空间）
* @param[in] arr2d_rsize 待乘二维数组的行数
* @param[in] arr2d_csize 待乘二维数组的列数
* @param ret_arr2d 乘积二维数组的指针（需提前开辟好内存空间）
* @param[in] ret_arr2d_rsize 乘积二维数组的行数
* @param[in] ret_arr2d_csize 乘积二维数组的列数
*/
void multiply_matrix(MatrixValueType **arr2d, int arr2d_rsize, int arr2d_csize,
MatrixValueType **ret_arr2d, int ret_arr2d_rsize, int ret_arr2d_csize) const;
/**
* @brief 矩阵乘法的母函数
*
* @note 稀疏矩阵与稠密矩阵的积也会是一个稠密的矩阵
*
* @param arr2d 待乘二维数组（需提前开辟好内存空间）
* @param ret_arr2d 乘积二维数组的指针
*/
void multiply_matrix(const matrix &arr2d, matrix &ret_arr2d) const;
/**
* @brief 矩阵与向量相乘
*
* @param[in] arr 输入数组指针
* @param[in] arr_size 输入数组大小
* @param ret_arr 输出数组指针
* @param[in] ret_size 输出数组大小
* @param[in] layout 是否使用稀疏矩阵的转置
*/
void multiply_vector(const MatrixValueType *arr, int arr_size, MatrixValueType *ret_arr,
int ret_size, matrix_layout_e layout = NoTrans) const;
/**
* @brief 稀疏矩阵与向量相乘
*
* 此函数会计算稀疏矩阵与一个向量的乘积向量（一维数组）
*
* @param[in] arr 待乘的向量数组
* @param ret_arr 乘积向量的数组
* @param[in] layout 是否使用稀疏矩阵的转置
*
*/
void multiply_vector(const array &arr, array &ret_arr,
matrix_layout_e layout = NoTrans) const;
/**
* @brief 稀疏矩阵的某行或列与向量相乘
*
* 此函数会计算稀疏矩阵的行或列与一个向量的乘积向量（一维数组）
*
* @param[in] arr 待乘的向量数组
* @param index 与向量相乘的稀疏矩阵的行或列索引
* @param[in] layout 是否使用稀疏矩阵的转置
*
*/
double multiply_vector(const array &arr, int index,
matrix_layout_e layout = NoTrans) const;
/**
* @brief 按元素与向量相乘
*
* @param[in] oper_id 稀疏矩阵的行（非转置）或者列（转置）序号
* @param[in] arr 输入数组指针
* @param[in] arr_size 输入数组大小
* @param ret_arr 输出数组指针
* @param[in] layout 是否使用稀疏矩阵的转置
*/
void multiply_vector(int oper_id, const MatrixValueType *arr, int arr_size,
MatrixValueType *ret_arr, matrix_layout_e layout = NoTrans) const;
/**
* @brief 与对角矩阵相乘
*
* @param arr 输入的对角矩阵（保存为一个一维数组）
* @param ret_mat 乘积矩阵
* @param prepositioned 对角矩阵是否在稀疏矩阵前，为真表示对角矩阵乘稀疏矩阵，为假则表示稀疏矩阵乘对角矩阵
*/
void multiply_diagonal_matrix(const array &arr, spmat &ret_mat, bool prepositioned = false) const;
/**
* @brief 返回制定行或列的布尔数组，reverse为真则布尔数组在无元素的位置为真
*
* @param[in] oper_id The operator identifier
* @param ret_arr The ret arr
* @param[in] ele_mode The ele mode
* @param[in] reverse The reverse
*/
void boolean(int oper_id, array &ret_arr, matrix_order_e ele_mode = RowMajor,
bool reverse = false) const;
/**
* @brief 按行或列对矩阵进行归一化
*
* @param[in] oper_id 行或列的索引
* @param[in] norm 归一化后的大小
* @param[in] line_mode 归一化的方向
*/
void normalize(int oper_id, MatrixValueType norm, matrix_order_e line_mode = RowMajor);
/**
* @brief 对矩阵元素进行缩放
*
* @param f 缩放倍数
* @param idx 行或列的索引
* @param ele_mode 行或列模式
*/
void scale(MatrixValueType f, int idx, matrix_order_e ele_mode = RowMajor);
/**
* @brief 返回对角线元素，只对方阵有效
*
* @param dia 对角线元素
*/
void get_diagonal(array &dia);
/**
* @brief 重载赋值操作符
*
* @param[in] b { parameter_description }
*
* @return The result of the assignment
*/
spmat& operator= (const spmat &b);
#ifdef GCTL_EIGEN
/**
* @brief 稀疏矩阵与Eigen向量相乘
*
* 此函数会计算稀疏矩阵与一个竖或者横向量的乘积向量（一维数组）。
* 与竖向量相乘为矩阵乘向量，与横向量相乘为向量乘矩阵
*
* @param vec 待乘的向量数组
* @param ret_vec 乘积向量的数组
* @param row_vector 待乘向量是否为横向量，默认为假（即默认为竖向量）
*/
void multiply_vector(const Eigen::VectorXd &vec, Eigen::VectorXd &ret_vec,
bool row_vector = false) const;
#endif // GCTL_EIGEN
private:
/**
* @brief 单个矩阵元素操作
*
* @param[in] r 行索引
* @param[in] c 列索引
* @param[in] v 操作值
* @param[in] mode 操作类型（默认为替换，可用的类型请参考枚举类型的声明）
*
* @return 若元素操作后为零返回真，否则返回假
*/
bool change_element(int r, int c, MatrixValueType v, value_operator_e mode = ReplaceVal);
/**
* @brief 返回行头指针
* @return 头指针
*/
mat_node **row_head() const;
/**
* @brief 返回列头指针
* @return 头指针
*/
mat_node **col_head() const;
};
template
spmat::spmat()
{
r_num = c_num = d_num = 0;
r_head = c_head = nullptr;
r_end = c_end = nullptr;
d_link = nullptr;
r_count = c_count = nullptr;
}
template
spmat::spmat(int in_rnum, int in_cnum, MatrixValueType null_val)
{
malloc(in_rnum, in_cnum, null_val);
}
template
spmat::spmat(const spmat &b)
{
malloc(b.row_size(), b.col_size(), b.zero_value());
mat_node **br_head = b.row_head();
mat_node *bp, *p, *q;
int i, j;
for (int r = 0; r < r_num; r++)
{
if (br_head[r] != nullptr)
{
bp = br_head[r]->next_right;
while (bp != nullptr)
{
mat_node* new_node =
new mat_node;
new_node->r_id = bp->r_id;
new_node->c_id = bp->c_id;
new_node->val = bp->val;
i = bp->r_id; j = bp->c_id;
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的 在尾部插入
{
p = r_end[i];
p->next_right = new_node;
r_end[i] = new_node;
r_count[i]++; // 增加行计数
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++; // 增加行计数
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
q = c_end[j];
q->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
bp = bp->next_right;
}
}
}
}
template
spmat::~spmat()
{
clear();
}
template
void spmat::malloc(int in_rnum, int in_cnum, MatrixValueType null_val)
{
if (in_rnum <= 0 || in_cnum <= 0)
{
throw std::runtime_error("Invalid matrix size. From void spmat::malloc(...)");
}
zero_val = null_val;
d_num = 0;
d_link = nullptr;
r_num = in_rnum; c_num = in_cnum;
r_head = new mat_node* [in_rnum];
c_head = new mat_node* [in_cnum];
r_end = new mat_node* [in_rnum];
c_end = new mat_node* [in_cnum];
r_count = new int [in_rnum];
c_count = new int [in_cnum];
for (int i = 0; i < r_num; i++)
{
r_head[i] = nullptr; r_end[i] = nullptr; r_count[i] = 0;
}
for (int i = 0; i < c_num; i++)
{
c_head[i] = nullptr; c_end[i] = nullptr; c_count[i] = 0;
}
return;
}
template
void spmat::clear(int index, matrix_order_e mode)
{
int r, c;
mat_node *p, *q, *tmp_node;
if (index < 0)
{
if (r_head != nullptr && c_head != nullptr)
{
for (int i = 0; i < r_num; i++)
{
p = r_head[i];
while (p != nullptr)
{
q = p; p = p->next_right;
delete q; q = nullptr;
}
}
delete[] r_head; r_head = nullptr;
delete[] c_head; c_head = nullptr;
delete[] r_end; r_end = nullptr;
delete[] c_end; c_end = nullptr;
delete[] r_count; r_count = nullptr;
delete[] c_count; c_count = nullptr;
}
if (d_num)
{
delete[] d_link; d_link = nullptr;
d_num = 0;
}
r_num = c_num = 0;
}
else if (mode == RowMajor)
{
if (index >= r_num)
{
throw runtime_error("[gctl::spmat::clear] Invalid index.");
}
if (r_head != nullptr && r_head[index] != nullptr)
{
p = r_head[index]->next_right;
while (p != nullptr)
{
tmp_node = p;
p = p->next_right;
c = tmp_node->c_id;
q = c_head[c];
while (index != q->next_down->r_id)
{
q = q->next_down;
}
if (q == c_head[c] && q->next_down->next_down == nullptr)
{
delete c_head[c];
c_head[c] = nullptr;
c_end[c] = nullptr;
}
else if (q->next_down == c_end[c])
{
q->next_down = nullptr;
c_end[c] = q;
}
else
{
q->next_down = q->next_down->next_down;
}
c_count[c]--;
delete tmp_node; tmp_node = nullptr;
}
r_head[index] = nullptr;
r_end[index] = nullptr;
r_count[index] = 0;
if (d_num)
{
delete[] d_link; d_link = nullptr;
d_num = 0;
}
}
}
else
{
if (index >= c_num)
{
throw runtime_error("[gctl::spmat::clear] Invalid index.");
}
if (c_head != nullptr && c_head[index] != nullptr)
{
p = c_head[index]->next_down;
while (p != nullptr)
{
tmp_node = p;
p = p->next_down;
r = tmp_node->r_id;
q = r_head[r];
while (index != q->next_right->c_id)
{
q = q->next_right;
}
if (q == r_head[r] && q->next_right->next_right == nullptr)
{
delete r_head[r];
r_head[r] = nullptr;
r_end[r] = nullptr;
}
else if (q->next_right == r_end[r])
{
q->next_right = nullptr;
r_end[r] = q;
}
else
{
q->next_right = q->next_right->next_right;
}
r_count[r]--;
delete tmp_node; tmp_node = nullptr;
}
c_head[index] = nullptr;
c_end[index] = nullptr;
c_count[index] = 0;
if (d_num)
{
delete[] d_link; d_link = nullptr;
d_num = 0;
}
}
}
return;
}
template
void spmat::reset()
{
mat_node *p, *q;
for (int i = 0; i < r_num; i++)
{
p = r_head[i];
while (p != nullptr)
{
q = p; p = p->next_right;
delete q; q = nullptr;
}
}
for (int i = 0; i < r_num; i++)
{
r_head[i] = nullptr; r_end[i] = nullptr; r_count[i] = 0;
}
for (int i = 0; i < c_num; i++)
{
c_head[i] = nullptr; c_end[i] = nullptr; c_count[i] = 0;
}
if (d_num)
{
delete[] d_link; d_link = nullptr;
d_num = 0;
}
return;
}
template
void spmat::purge()
{
mat_node *p, *q, *tmp_node;
for (int i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i];
while (p->next_right != nullptr)
{
if (p->next_right->val == zero_val)
{
// 更改行与列的计数
r_count[i]--;
c_count[p->next_right->c_id]--;
q = c_head[p->next_right->c_id];
while (i != q->next_down->r_id)
{
q = q->next_down;
}
if (q == c_head[p->next_right->c_id] &&
q->next_down->next_down == nullptr) // 在列首且仅有列首
{
delete c_head[p->next_right->c_id];
c_head[p->next_right->c_id] = nullptr;
c_end[p->next_right->c_id] = nullptr;
}
else if (q->next_down == c_end[p->next_right->c_id]) // 在列尾
{
q->next_down = nullptr;
c_end[p->next_right->c_id] = q;
}
else
{
// 此处不删除元素
q->next_down = q->next_down->next_down;
}
if (p == r_head[i] &&
p->next_right->next_right == nullptr)
{
delete p->next_right;
p->next_right = nullptr;
delete r_head[i];
r_head[i] = nullptr;
r_end[i] = nullptr;
break;
}
else if (p->next_right == r_end[i])
{
tmp_node = p->next_right;
p->next_right = nullptr;
delete tmp_node;
tmp_node = nullptr;
r_end[i] = p;
}
else
{
tmp_node = p->next_right;
p->next_right = p->next_right->next_right;
delete tmp_node;
tmp_node = nullptr;
}
}
else p = p->next_right;
}
}
}
return;
}
template
void spmat::set_all(MatrixValueType in_val)
{
if (in_val == zero_val) // reset the whole matrix to the initial state
{
reset();
return;
}
mat_node *p;
for (int i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
p->val = in_val;
p = p->next_right;
}
}
}
return;
}
template
void spmat::set_triplts(const std::vector > &triplts, value_operator_e val_mode)
{
// 如果存在d_link则只需要更改数值即可
if (d_num)
{
int c = 0;
int old_r = -1, old_c = -1;
for (size_t t = 0; t < triplts.size(); t++)
{
if(c > d_num) throw std::runtime_error("[gctl::spmat::set_triplts] Invalid triplts' size.");
if (triplts[t].r_id == old_r && triplts[t].c_id == old_c) // 重复的元素位置
{
c--; // 索引减一 （默认会往前挪一位 所以要往回挪一位）
if (val_mode == AppendVal) {d_link[c]->val += triplts[t].val; c++;} // 索引加一
if (val_mode == ReplaceVal) {d_link[c]->val = triplts[t].val; c++;} // 索引加一
}
else // 新的元素位置
{
old_r = triplts[t].r_id;
old_c = triplts[t].c_id;
d_link[c]->val = triplts[t].val;
c++; // 索引加一
}
}
return;
}
// 如果矩阵已经初始化 重置稀疏矩阵
if (!empty()) reset();
else throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::set_triplts(...)");
int old_i = -1, old_j = -1;
int i, j;
for (size_t t = 0; t < triplts.size(); t++)
{
i = triplts[t].r_id;
j = triplts[t].c_id;
if (i < 0 || j < 0 || i >= r_num || j >= c_num)
{
throw std::runtime_error("Invalid triplt's index. From gctl::spmat::set_triplts(...)");
}
if (old_i >= i && old_j > j) // 按行按列顺序添加
{
throw std::runtime_error("The input triplts are not properly arranged. From gctl::spmat::set_triplts(...)");
}
if (old_i == i && old_j == j) // 重复输入 只需要改变值就行了
{
if (val_mode == ReplaceVal) r_end[i]->val = triplts[t].val;
if (val_mode == AppendVal) r_end[i]->val += triplts[t].val;
continue;
}
// 新元素
mat_node *new_node = new mat_node; // 创建新节点 这里必定是需要新建节点的
new_node->r_id = triplts[t].r_id;
new_node->c_id = triplts[t].c_id;
new_node->val = triplts[t].val;
old_i = i;
old_j = j;
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的
{
r_end[i]->next_right = new_node;
r_end[i] = new_node;
r_count[i]++;
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++;
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
c_end[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
}
return;
}
template
void spmat::set_coo(const array &row_idx, const array &col_idx, const array &data)
{
if (row_idx.size() != col_idx.size() || col_idx.size() != data.size() || data.empty())
{
throw std::runtime_error("Invalid input COO sparse matrix. From gctl::spmat::set_coo(...)");
}
// 如果矩阵已经初始化 重置稀疏矩阵
if (!empty()) reset();
else throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::set_coo(...)");
size_t i, j;
for (size_t t = 0; t < data.size(); t++)
{
if (row_idx[t] >= r_num || col_idx[t] >= c_num)
{
throw std::runtime_error("Invalid COO index. From gctl::spmat::set_coo(...)");
}
// 新元素
mat_node *new_node = new mat_node; // 创建新节点 这里必定是需要新建节点的
i = row_idx[t];
j = col_idx[t];
new_node->r_id = row_idx[t];
new_node->c_id = col_idx[t];
new_node->val = data[t];
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的
{
r_end[i]->next_right = new_node;
r_end[i] = new_node;
r_count[i]++;
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++;
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
c_end[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
}
return;
}
template
void spmat::set_csr(const array &row_off, const array &col_idx, const array &data)
{
if (row_off.size() < 2 || row_off[0] != 0 || row_off.back() != col_idx.size() || col_idx.size() != data.size() || data.empty())
{
throw std::runtime_error("Invalid input CSR sparse matrix. From gctl::spmat::set_csr(...)");
}
// 如果矩阵已经初始化 重置稀疏矩阵
if (!empty()) reset();
else throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::set_csr(...)");
size_t i, j;
for (size_t r = 0; r < row_off.size() - 1; r++)
{
for (size_t t = row_off[r]; t < row_off[r+1]; t++)
{
if (r >= r_num || col_idx[t] >= c_num)
{
throw std::runtime_error("Invalid CSR index. From gctl::spmat::set_csr(...)");
}
// 新元素
mat_node *new_node = new mat_node; // 创建新节点 这里必定是需要新建节点的
i = r;
j = col_idx[t];
new_node->r_id = r;
new_node->c_id = col_idx[t];
new_node->val = data[t];
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的
{
r_end[i]->next_right = new_node;
r_end[i] = new_node;
r_count[i]++;
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++;
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
c_end[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
}
}
return;
}
template
void spmat::set_2d_vector(const std::vector > &den_mat_ptr, MatrixValueType off_set)
{
if (den_mat_ptr.empty())
{
throw runtime_error("The input 2d vector is empty. From void gctl::spmat::set_2d_vector(...)");
}
// 如果矩阵已经初始化 重置稀疏矩阵
if (!empty()) reset();
else throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::set_2d_vector(...)");
// 确定二维向量的最大列数
int max_cnum = 0;
for (int i = 0; i < den_mat_ptr.size(); i++)
{
max_cnum = GCTL_MAX(max_cnum, den_mat_ptr[i].size());
}
if (max_cnum > c_num || den_mat_ptr.size() > r_num)
{
throw runtime_error("The input 2d vector exceeds the maximal size. From void gctl::spmat::set_2d_vector(...)");
}
mat_node *p, *q;
for (int i = 0; i < den_mat_ptr.size(); i++)
{
for (int j = 0; j < den_mat_ptr[i].size(); j++)
{
if (den_mat_ptr[i][j] >= zero_val + off_set ||
den_mat_ptr[i][j] <= zero_val - off_set) // 输入值不为零则添加
{
mat_node *new_node =
new mat_node; // 创建新节点 这里必定是需要新建节点的
new_node->r_id = i;
new_node->c_id = j;
new_node->val = den_mat_ptr[i][j];
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的
{
p = r_end[i];
p->next_right = new_node;
r_end[i] = new_node;
r_count[i]++;
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++;
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
q = c_end[j];
q->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
}
}
}
return;
}
template
void spmat::set_2d_array(MatrixValueType **den_mat_ptr, MatrixValueType off_set)
{
if (den_mat_ptr == nullptr)
{
throw runtime_error("The input pointer is null. From void gctl::spmat::set_2d_array(...)");
}
// 如果矩阵已经初始化 重置稀疏矩阵
if (!empty()) reset();
else throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::set_2d_array(...)");
mat_node *p, *q;
for (int i = 0; i < r_num; i++)
{
for (int j = 0; j < c_num; j++)
{
if (den_mat_ptr[i][j] >= zero_val + off_set ||
den_mat_ptr[i][j] <= zero_val - off_set) // 输入值不为零则添加
{
mat_node *new_node =
new mat_node; // 创建新节点 这里必定是需要新建节点的
new_node->r_id = i;
new_node->c_id = j;
new_node->val = den_mat_ptr[i][j];
// 我们首先处理行的指针
if (r_head[i] != nullptr) // 无需检查行尾指针 它们是同步变化的
{
p = r_end[i];
p->next_right = new_node;
r_end[i] = new_node;
r_count[i]++;
}
else
{
// 添加行首指针 注意行首指针并不等于第一个元素 而是行首的右向链为第一个元素
r_head[i] = new mat_node;
r_head[i]->next_right = new_node;
// 添加行尾指针 行尾指针直接等于本行的最后一个元素
r_end[i] = new_node;
r_count[i]++;
}
// 接着处理列的指针
if (c_head[j] != nullptr)
{
q = c_end[j];
q->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
else
{
c_head[j] = new mat_node;
c_head[j]->next_down = new_node;
c_end[j] = new_node;
c_count[j]++;
}
}
}
}
return;
}
template
void spmat::set_matrix(const matrix &den_mat_ptr, MatrixValueType off_set)
{
if (den_mat_ptr.row_size() > r_num || den_mat_ptr.col_size() > c_num)
{
throw runtime_error("The input matrix exceeds the maximal size. From void gctl::spmat::set_matrix(...)");
}
set_2d_array(den_mat_ptr.get(), off_set);
return;
}
template
void spmat::init_pattern()
{
if (d_num)
{
delete[] d_link; d_link = nullptr;
d_num = 0;
}
d_num = ele_size();
if (d_num)
{
d_link = new mat_node* [d_num];
for (int i = 0; i < d_num; i++)
{
d_link[i] = nullptr;
}
}
else return;
int c = 0;
mat_node* p;
for (int i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
d_link[c] = p;
p = p->next_right;
c++;
}
}
}
return;
}
template
bool spmat::empty() const
{
if (r_num == 0 && c_num == 0) return true;
else return false;
}
template
int spmat::row_size() const
{
return r_num;
}
template
int spmat::col_size() const
{
return c_num;
}
template
int spmat::ele_size(int index, matrix_order_e mode) const
{
if (index == -1)
{
int sum = 0;
for (int i = 0; i < r_num; i++)
{
sum += r_count[i];
}
return sum;
}
else if (mode == RowMajor)
{
if (index < 0 || index >= r_num)
{
throw std::runtime_error("[gctl::spmat::ele_size] Invalid index.");
}
return r_count[index];
}
else
{
if (index < 0 || index >= c_num)
{
throw std::runtime_error("[gctl::spmat::ele_size] Invalid index.");
}
return c_count[index];
}
}
template
MatrixValueType spmat::zero_value() const
{
return zero_val;
}
template
void spmat::show_matrix(char delimiter) const
{
int st;
mat_node *p;
for (int i = 0; i < r_num; i++)
{
st = 0;
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
for (int s = st; s < p->c_id; s++)
std::cout << zero_val << delimiter;
std::cout << p->val << delimiter;
st = p->c_id + 1;
p = p->next_right;
}
}
if (st <= c_num-1)
{
for (int s = st; s < c_num; s++)
std::cout << zero_val << delimiter;
}
std::cout << std::endl;
}
}
template
void spmat::show_list(matrix_order_e mode, char delimiter, bool full) const
{
int st;
if (mode == RowMajor)
{
std::cout << "# row" << delimiter << "col" << delimiter << "value" << std::endl;
mat_node *p;
if (full)
{
for (int i = 0; i < r_num; i++)
{
st = 0;
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
for (int s = st; s < p->c_id; s++)
{
std::cout << i << delimiter << s << delimiter << zero_val << std::endl;
}
std::cout << i << delimiter << p->c_id << delimiter << p->val << std::endl;
st = p->c_id + 1;
p = p->next_right;
}
}
if (st <= c_num-1)
{
for (int s = st; s < c_num; s++)
std::cout << i << delimiter << s << delimiter << zero_val << std::endl;
}
}
return;
}
for (int i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
// 输出(row, col, val)三元组
std::cout << p->r_id << delimiter << p->c_id << delimiter << p->val << std::endl;
p = p->next_right;
}
}
}
return;
}
std::cout << "# col" << delimiter << "row" << delimiter << "value" << std::endl;
mat_node *q;
if (full)
{
for (int i = 0; i < c_num; i++)
{
st = 0;
if (c_head[i] != nullptr)
{
q = c_head[i]->next_down;
while (q != nullptr)
{
for (int s = st; s < q->r_id; s++)
{
std::cout << i << delimiter << s << delimiter << zero_val << std::endl;
}
std::cout << i << delimiter << q->r_id << delimiter << q->val << std::endl;
st = q->r_id + 1;
q = q->next_down;
}
}
if (st <= r_num-1)
{
for (int s = st; s < r_num; s++)
std::cout << i << delimiter << s << delimiter << zero_val << std::endl;
}
}
return;
}
for (int i = 0; i < c_num; i++)
{
if (c_head[i] != nullptr)
{
q = c_head[i]->next_down;
while (q != nullptr)
{
// 输出(row, col, val)三元组
std::cout << q->c_id << delimiter << q->r_id << delimiter << q->val << std::endl;
q = q->next_down;
}
}
}
return;
}
template
MatrixValueType spmat::at(size_t i_index, size_t j_index) const
{
#ifdef GCTL_CHECK_BOUNDER
if (i_index >= r_num || j_index >= c_num)
{
throw std::runtime_error("Invalid index. From gctl::spmat::at(...)");
}
#endif // GCTL_CHECK_BOUNDER
mat_node *p, *q;
if (r_head[i_index] != nullptr && c_head[j_index] != nullptr)
{
p = r_end[i_index];
if (j_index > p->c_id) return zero_val;
p = r_head[i_index]->next_right;
if (j_index < p->c_id) return zero_val;
q = c_end[j_index];
if (i_index > q->r_id) return zero_val;
q = c_head[j_index]->next_down;
if (i_index < q->r_id) return zero_val;
if (r_count[i_index] <= c_count[j_index])
{
while (p != nullptr)
{
if (p->c_id == j_index)
{
return p->val;
}
p = p->next_right;
}
return zero_val;
}
while (q != nullptr)
{
if (q->r_id == i_index)
{
return q->val;
}
q = q->next_down;
}
return zero_val;
}
return zero_val;
}
template
void spmat::export_dense(matrix &ret_arr2d) const
{
ret_arr2d.resize(r_num, c_num);
mat_node *p;
for (int i = 0; i < r_num; i++)
{
for (int j = 0; j < c_num; j++)
{
ret_arr2d[i][j] = zero_val;
}
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
ret_arr2d[i][p->c_id] = p->val;
p = p->next_right;
}
}
}
return;
}
template
void spmat::export_dense(int out_id, array &ret_arr,
double multipler, matrix_order_e line_mode, value_operator_e val_mode) const
{
if (line_mode == RowMajor)
{
if (out_id < 0 || out_id >= r_num)
{
throw std::runtime_error("[gctl::spmat::export_dense] Invalid index.");
}
if (val_mode != AppendVal)
{
ret_arr.resize(c_num, zero_val);
}
else if (ret_arr.size() != c_num)
{
throw std::runtime_error("[gctl::spmat::export_dense] Incompatible array size.");
}
mat_node *p;
if (r_head[out_id] != nullptr)
{
p = r_head[out_id]->next_right;
while (p != nullptr)
{
ret_arr[p->c_id] += p->val * multipler;
p = p->next_right;
}
}
}
else
{
if (out_id < 0 || out_id >= c_num)
{
throw std::runtime_error("[gctl::spmat::export_dense] Invalid index.");
}
if (val_mode != AppendVal)
{
ret_arr.resize(r_num, zero_val);
}
else if (ret_arr.size() != r_num)
{
throw std::runtime_error("[gctl::spmat::export_dense] Incompatible array size.");
}
mat_node *q;
if (c_head[out_id] != nullptr)
{
q = c_head[out_id]->next_down;
while (q != nullptr)
{
ret_arr[q->r_id] += q->val * multipler;
q = q->next_down;
}
}
}
return;
}
template
void spmat::export_coo(array &row_idx, array &col_idx, array &data) const
{
if (empty()) throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::export_coo(...)");
size_t nz_num = ele_size();
row_idx.resize(nz_num);
col_idx.resize(nz_num);
data.resize(nz_num);
size_t c = 0;
mat_node *p;
for (size_t i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
row_idx[c] = i;
col_idx[c] = p->c_id;
data[c] = p->val;
p = p->next_right;
c++;
}
}
}
return;
}
template
void spmat::export_csr(array &row_off, array &col_idx, array &data) const
{
if (empty()) throw std::runtime_error("The sparse matrix is not initialized. From gctl::spmat::export_coo(...)");
size_t nz_num = ele_size();
col_idx.resize(nz_num);
data.resize(nz_num);
row_off.resize(r_num + 1);
row_off[0] = 0;
size_t r, c = 0;
mat_node *p;
for (size_t i = 0; i < r_num; i++)
{
r = row_off[i];
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
col_idx[c] = p->c_id;
data[c] = p->val;
p = p->next_right;
c++;
r++;
}
}
row_off[i+1] = r;
}
return;
}
template
void spmat::insert(int r, int c, MatrixValueType v)
{
change_element(r, c, v);
return;
}
template
void spmat::remove(int r, int c)
{
if (r >= r_num || r < 0 || c >= c_num || c < 0)
{
std::string err_str = "Invalid index. From gctl::spmat::remove(...)";
throw runtime_error(err_str);
}
mat_node *p, *q, *tmp_node;
p = r_head[r];
q = c_head[c];
if (p == nullptr || q == nullptr) // 元素不在序列中 直接返回
return;
while (p->next_right != nullptr)
{
if (c == p->next_right->c_id)
{
while (r != q->next_down->r_id)
{
q = q->next_down;
}
if (q == c_head[c] &&
q->next_down->next_down == nullptr)
{
delete c_head[c];
c_head[c] = nullptr;
c_end[c] = nullptr;
}
else if (q->next_down == c_end[c])
{
q->next_down = nullptr;
c_end[c] = q;
}
else
{
q->next_down = q->next_down->next_down;
}
if (p == r_head[r] &&
p->next_right->next_right == nullptr)
{
delete r_head[r]->next_right;
r_head[r]->next_right = nullptr;
delete r_head[r];
r_head[r] = nullptr;
r_end[r] = nullptr;
}
else if (p->next_right == r_end[r])
{
tmp_node = p->next_right;
p->next_right = nullptr;
delete tmp_node;
tmp_node = nullptr;
r_end[r] = p;
}
else
{
tmp_node = p->next_right;
p->next_right = p->next_right->next_right;
delete tmp_node;
tmp_node = nullptr;
}
// 更改行与列的计数
r_count[r]--;
c_count[c]--;
break;
}
p = p->next_right;
}
return;
}
template
MatrixValueType spmat::sum(int index, matrix_order_e mode)
{
MatrixValueType sum_val = 0;
if (index < 0)
{
mat_node *p;
for (int i = 0; i < r_num; i++)
{
if (r_head[i] != nullptr)
{
p = r_head[i]->next_right;
while (p != nullptr)
{
sum_val = sum_val + p->val;
p = p->next_right;
}
}
}
}
else if (mode == RowMajor)
{
if (index < 0 || index >= r_num)
{
throw std::runtime_error("[gctl::spmat::sum] Invalid index");
}
mat_node *p;
if (r_head[index] != nullptr)
{
p = r_head[index]->next_right;
while (p != nullptr)
{
sum_val = sum_val + p->val;
p = p->next_right;
}
}
}
else
{
if (index < 0 || index >= c_num)
{
throw std::runtime_error("[gctl::spmat::sum] Invalid index");
}
mat_node *q;
if (c_head[index] != nullptr)
{
q = c_head[index]->next_down;
while (q != nullptr)
{
sum_val = sum_val + q->val;
q = q->next_down;
}
}
}
return sum_val;
}
template
MatrixValueType spmat::module(int index, matrix_order_e mode)
{
MatrixValueType sum_val = 0;
if (mode == RowMajor)
{
if (index < 0 || index >= r_num)
{
throw std::runtime_error("[gctl::spmat::module] Invalid index");
}
mat_node *p;
if (r_head[index] != nullptr)
{
p = r_head[index]->next_right;
while (p != nullptr)
{
sum_val = sum_val + pow(p->val, 2);
p = p->next_right;
}
return sqrt(sum_val);
}
}
else
{
if (index < 0 || index >= c_num)
{
throw std::runtime_error("[gctl::spmat::module] Invalid index");
}
mat_node *q;
if (c_head[index] != nullptr)
{
q = c_head[index]->next_down;
while (q != nullptr)
{
sum_val = sum_val + pow(q->val, 2);
q = q->next_down;
}
return sqrt(sum_val);
}
}
return 0;
}
template
void spmat::add_scalar(int r, int c, MatrixValueType v)
{
if (change_element(r, c, v, AppendVal))
remove(r, c);
return;
}
template
void spmat::minus_scalar(int r, int c, MatrixValueType v)
{
if (change_element(r, c, v, RemoveVal))
remove(r, c);
return;
}
template
void spmat::add_sparse_matrix(const spmat &b)
{
if (b.row_size() > r_num || b.col_size() > c_num)
{
throw runtime_error("Invalid maximal matrix sizes. From gctl::spmat::add_sparse_matrix(...)"); // 大小不匹配 不能操作
}
mat_node **br_head = b.row_head();
bool re_flag = false;
mat_node *pb; // 遍历b的指针
for (int i = 0; i < r_num; i++)
{
pb = br_head[i];
if (pb != nullptr)
{
pb = pb->next_right;
while (pb != nullptr)
{
// 调用逐个加法私有方法
if (change_element(pb->r_id, pb->c_id, pb->val, AppendVal))
{
re_flag = true;
}
pb = pb->next_right;
}
}
}
if (re_flag) purge();
return;
}
template
void spmat::minus_sparse_matrix(const spmat &b)
{
if (b.row_size() > r_num || b.col_size() > c_num)
{
throw runtime_error("Invalid maximal matrix sizes. From gctl::spmat::minus_sparse_matrix(...)"); // 大小不匹配 不能操作
}
mat_node **br_head = b.row_head();
bool re_flag = false;
mat_node *pb; // 遍历b的指针
for (int i = 0; i < r_num; i++)
{
pb = br_head[i];
if (pb != nullptr)
{
pb = pb->next_right;
while (pb != nullptr)
{
// 调用逐个减法私有方法
if (change_element(pb->r_id, pb->c_id, pb->val, RemoveVal))
{
re_flag = true;
}
pb = pb->next_right;
}
}
}
if (re_flag) purge();
return;
}
template
void spmat::add_matrix(matrix &arr2d) const
{
if (r_num != arr2d.row_size() || c_num != arr2d.col_size())
{
throw std::runtime_error("Incompatible matrix sizes. From gctl::spmat::add_matrix(...)");
}
mat_node *p;
for (int i = 0; i < r_num; i++)
{
p = r_head[i];
if (p != nullptr)
{
p = p->next_right;
while (p != nullptr)
{
arr2d[i][p->c_id] += p->val;
p = p->next_right;
}
}
}
return;
}
template
void spmat::minus_matrix(matrix &arr2d, bool preposition) const
{
if (r_num != arr2d.row_size() || c_num != arr2d.col_size())
{
throw std::runtime_error("Incompatible matrix sizes. From gctl::spmat::minus_matrix(...)");
}
mat_node *p;
for (int i = 0; i < r_num; i++)
{
p = r_head[i];
if (p != nullptr)
{
p = p->next_right;
while (p != nullptr)
{
if (preposition) arr2d[i][p->c_id] -= p->val;
else arr2d[i][p->c_id] = p->val - arr2d[i][p->c_id];
p = p->next_right;
}
}
}
return;
}
template
void spmat::map(int from_index, int to_index, MatrixValueType multipler,
matrix_order_e line_mode, value_operator_e val_mode)
{
std::string err_str;
if (line_mode == RowMajor)
{
if (from_index < 0 || from_index >= r_num ||
to_index < 0 || to_index >= r_num)
{
err_str = "Invalid index. From gctl::spmat::map(...)";
throw runtime_error(err_str);
}
mat_node *p;
if (r_head[from_index] != nullptr)
{
p = r_head[from_index]->next_right;
while (p != nullptr)
{
change_element(to_index, p->c_id, multipler * p->val, val_mode);
p = p->next_right;
}
}
}
else
{
if (from_index < 0 || from_index >= c_num ||
to_index < 0 || to_index >= c_num)
{
err_str = "Invalid index. From gctl::spmat::map(...)";
throw runtime_error(err_str);
}
mat_node *q;
if (c_head[from_index] != nullptr)
{
q = c_head[from_index]->next_down;
while (q != nullptr)
{
change_element(q->r_id, to_index, multipler * q->val, val_mode);
q = q->next_down;
}
}
}
return;
}
template
void spmat::transpose(spmat