Premier League stats & predictions

Innovative Marketing Strategies

In today's digital age, innovative marketing strategies are essential for promoting football leagues like the Belarus Premier League. Clubs are leveraging social media platforms to engage with fans globally, creating interactive content that enhances fan experience. These strategies not only increase visibility but also foster a sense of community among supporters.

• Social Media Engagement: Clubs use platforms like Instagram, Twitter, and Facebook to share behind-the-scenes content and interact with fans directly.
• Influencer Partnerships:alexandernagel/AlgoMath<|file_sep|>/include/AlgoMath/LinearAlgebra/Matrix.h #ifndef ALGOMATH_MATRIX_H #define ALGOMATH_MATRIX_H #include "AlgoMath/LinearAlgebra/Vector.h" #include "AlgoMath/LinearAlgebra/BlockMatrix.h" #include "AlgoMath/LinearAlgebra/LU.h" #include "AlgoMath/LinearAlgebra/SVD.h" namespace AlgoMath { namespace LinearAlgebra { template class Container = std::vector, template class IndexContainer = std::vector, template class BlockContainer = std::vector, template class MatrixContainer = std::vector, template class VectorContainer = std::vector, typename Allocator = std::allocator> class Matrix { public: using ValueType = T1; using IndexType = TIndex; using BlockType = BlockMatrix; using MatrixType = Matrix; using VectorType = Vector; using ContainerType = Container>; using IndexContainerType = IndexContainer; using BlockContainerType = BlockContainer; using MatrixContainerType = MatrixContainer; using VectorContainerType = VectorContainer; static_assert(std::is_arithmetic::value); static_assert(std::is_integral::value); private: ContainerType matrix_; IndexContainerType index_; public: Matrix() : matrix_(), index_() {} Matrix(const Matrix &other) : matrix_(other.matrix_), index_(other.index_) {} Matrix(Matrix &&other) noexcept : matrix_(std::move(other.matrix_)), index_(std::move(other.index_)) {} // template // Matrix(const Eigen::Matrix& m) // : matrix_(nRows_), index_(nCols_) // { // auto iRow=0u; // auto iCol=0u; // for (auto row=m.rows()-1; row>=0; --row) // { // auto nElements=m.row(row).eval().size(); // if (nElements==0) continue; // // index_[iCol]=iRow+nElements-1; // auto& curRow=matrix_[iRow]; // // auto j=0u; // for (auto elem:m.row(row).eval()) // curRow[j++]=elem; // // ++iCol; // iRow+=nElements; // // } // // // //// std::cout<<"Constructor from Eigen:"< Matrix(const Eigen::Matrix& m) : matrix_(m.rows()), index_(m.cols()) { auto iRow=0u; auto iCol=0u; for (auto col=m.cols()-1; col>=0; --col) { auto nElements=m.col(col).eval().size(); if (nElements==0) continue; index_[iCol]=iRow+nElements-1; auto& curCol=matrix_[iRow]; auto j=0u; for (auto elem:m.col(col).eval()) curCol[j++]=elem; ++iCol; iRow+=nElements; } } template Matrix(const Eigen::Matrix& m) : matrix_(m.rows()), index_(m.cols()) { auto iRow=0u; auto iCol=0u; for (auto row=m.rows()-1; row>=0; --row) { auto nElements=m.row(row).eval().size(); if (nElements==0) continue; index_[iCol]=iRow+nElements-1; auto& curCol=matrix_[iRow]; auto j=0u; for (auto elem:m.row(row).eval()) curCol[j++]=elem; ++iCol; iRow+=nElements; } } ~Matrix() {} public: void resize(const IndexContainer &newIndex) { if (index_.empty()) { matrix_.resize(newIndex.size()); } else { // check if new size fits if ((newIndex.size()>index_.size()) || ((newIndex.size()==index_.size()) && (*newIndex.begin()>*index_.begin()))) throw Exception("Matrix: Cannot resize larger."); // shrink columns if ((newIndex.size()(matrix_.data()+firstElem,nElems).setZero(); // // } /* EigenVector v={ValueType(0),ValueType(0),ValueType(0),ValueType(0),ValueType(0)}; v.setZero(); EigenVector v2={ValueType(10),ValueType(20),ValueType(30)}; v2.setZero(); EigenVector v3={ValueType(100),ValueType(200),ValueType(300),ValueType(400),ValueType(500)}; v3.setZero(); EigenVector v4={ValueType(1000),ValueType(2000),ValueType(3000)}; v4.setZero(); assert(v[2]==v[4]); assert(v2[2]==v4[4]); assert(v[4]==v[4]); assert(v[4]==v2[4]); assert(v[4]==v3[4]); assert(v[4]==v4[4]); */ /* */ /* */ /* */ /* int i{}; */ /* */ } public: IndexSize rows() const {return index_.empty()?ValueSize():index_[0]+1;} private: public: IndexSize cols() const {return index_.empty()?ValueSize():index_[index_.size()-1]+1;} private: public: private: public: private: public: private: public: private: public: private: public: private: private: /* template void fill(const EigenMatrix& m) { } */ public: private: }; } // namespace LinearAlgebra using LinearAlgebra::Matrix; } // namespace AlgoMath #endif <|repo_name|>alexandernagel/AlgoMath<|file_sep|>/test/TestStochasticProcess.cpp #include "gtest/gtest.h" #include "gmock/gmock.h" #include "AlgoMath/StochasticProcess.h" using namespace AlgoMath; TEST(TestStochasticProcess,testGetStateVariables) { StochasticProcess::StateVariables sv{}; EXPECT_EQ(sv.nSteps(),-1); EXPECT_EQ(sv.nPaths(),-1); } TEST(TestStochasticProcess,testSetStateVariables) { StochasticProcess::StateVariables sv{}; sv.setNSteps(10); sv.setNPaths(5); EXPECT_EQ(sv.nSteps(),10); EXPECT_EQ(sv.nPaths(),5); } TEST(TestStochasticProcess,testSetStateVariablesFromParameters