用到的C++的相关知识-----未完待续

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前言

只是为方便学习，不做其他用途

一、vector函数的使用

有关的文章

1. C++ vector的用法（整理）
2. C++中vector的用法详解

1.1 构造向量

	//vector():创建一个空vectorvector<int> v1 = vector<int>();                         //v1 = []//vector(int nSize):创建一个vector,元素个数为nSizevector<int> v2 = vector<int>(3);                        //v2 = [0, 0, 0]//vector(int nSize,const t& t): 创建一个vector，元素个数为nSize,且值均为tvector<int> v3 = vector<int>(3, 10);                    //v3 = [10, 10, 10]//vector(const vector&):                                复制构造函数vector<int> v4 = vector<int>(v3);                       //v4 = [10, 10, 10]//vector(begin,end):  复制[begin,end)区间内另一个数组的元素到vector中vector<int> v5 = vector<int>(v4.begin(), v4.end() - 1); //v5 = [10, 10]vector<vector<int>> v6 = vector<vector<int>>(3, vector<int>(3););    //v6 = [[0, 0, 0][0, 0, 0][0, 0, 0]]

二、常用函数

2.1 矩阵输出函数

// 输出矩阵的各个值
void Print(MatrixXd K)
{for (int j = 0; j < K.rows(); j++){for (int i = 0; i < K.cols(); i++){cout << K(j, i) << "  ";}cout << endl;}
}

2.2 向量输出函数

#include <vector>
// 输出向量的各个值
void Print_Vector(vector<double> U)
{for (int i = 0; i < U.size(); i++){cout << " U_ " << i << " = " << U[i] << endl;}
}

2.3 矩阵的使用

eigen库和matlab中对应命令

// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle
#include<iostream>
#include <gismo.h>
#include <Eigen/Dense>
using namespace Eigen;
using namespace gismo;
using namespace std;
#include <vector>int main()
{gsMatrix<double, 3, 3> A;               // Fixed rows and cols. Same as Matrix3d.Matrix<double, 3, Dynamic> B;         // Fixed rows, dynamic cols.Matrix<double, Dynamic, Dynamic> C;   // Full dynamic. Same as MatrixXd.Matrix<double, 3, 3, RowMajor> E;     // Row major; default is column-major.Matrix3f P, Q, R;                     // 3x3 float matrix.Vector3f x, y, z;                     // 3x1 float matrix.RowVector3f a, b, c;                  // 1x3 float matrix.VectorXd v;                           // Dynamic column vector of doublesdouble s;// Basic usage// Eigen          // Matlab           // commentsx.size()          // length(x)        // vector sizeC.rows()          // size(C,1)        // number of rowsC.cols()          // size(C,2)        // number of columnsx(i)              // x(i+1)           // Matlab is 1-basedC(i, j)            // C(i+1,j+1)       //A.resize(4, 4);   // Runtime error if assertions are on.B.resize(4, 9);   // Runtime error if assertions are on.A.resize(3, 3);   // Ok; size didn't change.B.resize(3, 9);   // Ok; only dynamic cols changed.A << 1, 2, 3,     // Initialize A. The elements can also be4, 5, 6,     // matrices, which are stacked along cols7, 8, 9;     // and then the rows are stacked.B << A, A, A;     // B is three horizontally stacked A's.A.fill(10);       // Fill A with all 10's.// Eigen                            // MatlabMatrixXd::Identity(rows, cols)       // eye(rows,cols)C.setIdentity(rows, cols)            // C = eye(rows,cols)MatrixXd::Zero(rows, cols)           // zeros(rows,cols)C.setZero(rows, cols)                // C = ones(rows,cols)MatrixXd::Ones(rows, cols)           // ones(rows,cols)C.setOnes(rows, cols)                // C = ones(rows,cols)MatrixXd::Random(rows, cols)         // rand(rows,cols)*2-1        // MatrixXd::Random returns uniform random numbers in (-1, 1).C.setRandom(rows, cols)              // C = rand(rows,cols)*2-1VectorXd::LinSpaced(size, low, high)   // linspace(low,high,size)'v.setLinSpaced(size, low, high)        // v = linspace(low,high,size)'// Matrix slicing and blocks. All expressions listed here are read/write.// Templated size versions are faster. Note that Matlab is 1-based (a size N// vector is x(1)...x(N)).// Eigen                           // Matlabx.head(n)                          // x(1:n)x.head<n>()                        // x(1:n)x.tail(n)                          // x(end - n + 1: end)x.tail<n>()                        // x(end - n + 1: end)x.segment(i, n)                    // x(i+1 : i+n)x.segment<n>(i)                    // x(i+1 : i+n)P.block(i, j, rows, cols)          // P(i+1 : i+rows, j+1 : j+cols)P.block<rows, cols>(i, j)          // P(i+1 : i+rows, j+1 : j+cols)P.row(i)                           // P(i+1, :)P.col(j)                           // P(:, j+1)P.leftCols<cols>()                 // P(:, 1:cols)P.leftCols(cols)                   // P(:, 1:cols)P.middleCols<cols>(j)              // P(:, j+1:j+cols)P.middleCols(j, cols)              // P(:, j+1:j+cols)P.rightCols<cols>()                // P(:, end-cols+1:end)P.rightCols(cols)                  // P(:, end-cols+1:end)P.topRows<rows>()                  // P(1:rows, :)P.topRows(rows)                    // P(1:rows, :)P.middleRows<rows>(i)              // P(i+1:i+rows, :)P.middleRows(i, rows)              // P(i+1:i+rows, :)P.bottomRows<rows>()               // P(end-rows+1:end, :)P.bottomRows(rows)                 // P(end-rows+1:end, :)P.topLeftCorner(rows, cols)        // P(1:rows, 1:cols)P.topRightCorner(rows, cols)       // P(1:rows, end-cols+1:end)P.bottomLeftCorner(rows, cols)     // P(end-rows+1:end, 1:cols)P.bottomRightCorner(rows, cols)    // P(end-rows+1:end, end-cols+1:end)P.topLeftCorner<rows, cols>()       // P(1:rows, 1:cols)P.topRightCorner<rows, cols>()      // P(1:rows, end-cols+1:end)P.bottomLeftCorner<rows, cols>()    // P(end-rows+1:end, 1:cols)P.bottomRightCorner<rows, cols>()   // P(end-rows+1:end, end-cols+1:end)// Of particular note is Eigen's swap function which is highly optimized.// Eigen                           // MatlabR.row(i) = P.col(j);               // R(i, :) = P(:, i)R.col(j1).swap(mat1.col(j2));      // R(:, [j1 j2]) = R(:, [j2, j1])// Views, transpose, etc; all read-write except for .adjoint().// Eigen                           // MatlabR.adjoint()                        // R'R.transpose()                      // R.' or conj(R')R.diagonal()                       // diag(R)x.asDiagonal()                     // diag(x)R.transpose().colwise().reverse(); // rot90(R)R.conjugate()                      // conj(R)// All the same as Matlab, but matlab doesn't have *= style operators.// Matrix-vector.  Matrix-matrix.   Matrix-scalar.y = M * x;          R = P * Q;        R = P * s;a = b * M;          R = P - Q;      R = s * P;a *= M;            R = P + Q;      R = P / s;R *= Q;          R = s * P;R += Q;          R *= s;R -= Q;          R /= s;// Vectorized operations on each element independently// Eigen                  // MatlabR = P.cwiseProduct(Q);    // R = P .* QR = P.array() * s.array();// R = P .* sR = P.cwiseQuotient(Q);   // R = P ./ QR = P.array() / Q.array();// R = P ./ QR = P.array() + s.array();// R = P + sR = P.array() - s.array();// R = P - sR.array() += s;           // R = R + sR.array() -= s;           // R = R - sR.array() < Q.array();    // R < QR.array() <= Q.array();   // R <= QR.cwiseInverse();         // 1 ./ PR.array().inverse();      // 1 ./ PR.array().sin()           // sin(P)R.array().cos()           // cos(P)R.array().pow(s)          // P .^ sR.array().square()        // P .^ 2R.array().cube()          // P .^ 3R.cwiseSqrt()             // sqrt(P)R.array().sqrt()          // sqrt(P)R.array().exp()           // exp(P)R.array().log()           // log(P)R.cwiseMax(P)             // max(R, P)R.array().max(P.array())  // max(R, P)R.cwiseMin(P)             // min(R, P)R.array().min(P.array())  // min(R, P)R.cwiseAbs()              // abs(P)R.array().abs()           // abs(P)R.cwiseAbs2()             // abs(P.^2)R.array().abs2()          // abs(P.^2)(R.array() < s).select(P, Q);  // (R < s ? P : Q)// Reductions.int r, c;// Eigen                  // MatlabR.minCoeff()              // min(R(:))R.maxCoeff()              // max(R(:))s = R.minCoeff(&r, &c)    // [s, i] = min(R(:)); [r, c] = ind2sub(size(R), i);s = R.maxCoeff(&r, &c)    // [s, i] = max(R(:)); [r, c] = ind2sub(size(R), i);R.sum()                   // sum(R(:))R.colwise().sum()         // sum(R)R.rowwise().sum()         // sum(R, 2) or sum(R')'R.prod()                  // prod(R(:))R.colwise().prod()        // prod(R)R.rowwise().prod()        // prod(R, 2) or prod(R')'R.trace()                 // trace(R)R.all()                   // all(R(:))R.colwise().all()         // all(R)R.rowwise().all()         // all(R, 2)R.any()                   // any(R(:))R.colwise().any()         // any(R)R.rowwise().any()         // any(R, 2)// Dot products, norms, etc.// Eigen                  // Matlabx.norm()                  // norm(x).    Note that norm(R) doesn't work in Eigen.x.squaredNorm()           // dot(x, x)   Note the equivalence is not true for complexx.dot(y)                  // dot(x, y)x.cross(y)                // cross(x, y) Requires #include <Eigen/Geometry>// Eigen                           // MatlabA.cast<double>();                  // double(A)A.cast<float>();                   // single(A)A.cast<int>();                     // int32(A)A.real();                          // real(A)A.imag();                          // imag(A)// if the original type equals destination type, no work is done// Note that for most operations Eigen requires all operands to have the same type:MatrixXf F = MatrixXf::Zero(3, 3);A += F;                // illegal in Eigen. In Matlab A = A+F is allowedA += F.cast<double>(); // F converted to double and then added (generally, conversion happens on-the-fly)// Eigen can map existing memory into Eigen matrices.float array[3];Vector3f::Map(array).fill(10);            // create a temporary Map over array and sets entries to 10int data[4] = { 1, 2, 3, 4 };Matrix2i mat2x2(data);                    // copies data into mat2x2Matrix2i::Map(data) = 2 * mat2x2;           // overwrite elements of data with 2*mat2x2MatrixXi::Map(data, 2, 2) += mat2x2;      // adds mat2x2 to elements of data (alternative syntax if size is not know at compile time)// Solve Ax = b. Result stored in x. Matlab: x = A \ b.x = A.ldlt().solve(b));  // A sym. p.s.d.    #include <Eigen/Cholesky>x = A.llt().solve(b));  // A sym. p.d.      #include <Eigen/Cholesky>x = A.lu().solve(b));  // Stable and fast. #include <Eigen/LU>x = A.qr().solve(b));  // No pivoting.     #include <Eigen/QR>x = A.svd().solve(b));  // Stable, slowest. #include <Eigen/SVD>// .ldlt() -> .matrixL() and .matrixD()// .llt()  -> .matrixL()// .lu()   -> .matrixL() and .matrixU()// .qr()   -> .matrixQ() and .matrixR()// .svd()  -> .matrixU(), .singularValues(), and .matrixV()// Eigenvalue problems// Eigen                          // MatlabA.eigenvalues();                  // eig(A);EigenSolver<Matrix3d> eig(A);     // [vec val] = eig(A)eig.eigenvalues();                // diag(val)eig.eigenvectors();               // vec// For self-adjoint matrices use SelfAdjointEigenSolver<>
}

2.4

：

三、new的用法

参考文章 c++中new的作用、C++如何让函数返回数组

 //可以在new后面直接赋值int* p = new int(3);//也可以单独赋值//*p = 3;//如果不想使用指针，可以定义一个变量，在new之前用“*”表示new出来的内容int q = *new int;q = 1;cout << q << endl;

3.1 内存的四种分区

栈区（stack）： 编译器自动分配和释放的，主要存储的是函数的参数值，局部变量等值。发生函数调用时就为函数运行时用到的数据分配内存，函数调用结束后就将之前分配的内存全部销毁。所以局部变量、参数只在当前函数中有效，不能传递到函数外部。栈内存的大小和编译器有关，编译器会为栈内存指定一个最大值，在 VC/VS 下，默认是 1M。

堆区（heap）： 动态分配。一般由程序员分配和释放（动态内存申请malloc与释放free），需要手动free。否则会一直存在，若程序员不释放，程序结束时可能由操作系统回收。

全局区（静态区）（static）： 静态分配。全局变量和静态变量的存储是放在一块的，该区域在程序结束后由操作系统释放。

代码区：： 通常用来存放程序执行代码（包含类成员函数和全局函数及其他函数代码），这部分区域的大小在程序运行前就已经确定，也有可能包含一些只读的常数变量，例如字符串变量。

3.2 new的作用

用法示例：

int *a = new int[5];
class A {...}   //声明一个类 A
A *obj = new A();  //使用 new 创建对象
delete []a;
delete obj;

3.3

：

3.4

：

四、

4.1

：

4.2

：

4.3

：

4.4

：

总结

：

二维数