tensor.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#ifndef GUM_TENSOR_H
#define GUM_TENSOR_H
 
#include <functional>
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/multidim/implementations/multiDimDecorator.h>
 
namespace gum {
  // ==========================================================================
  // ===                          TENSOR                                 ===
  // ==========================================================================

  template < typename GUM_SCALAR >
  class Tensor final: public MultiDimDecorator< GUM_SCALAR > {
    public:
    static Tensor< GUM_SCALAR > deterministicTensor(const DiscreteVariable& var, Idx value);
    static Tensor< GUM_SCALAR > deterministicTensor(const DiscreteVariable& var,
                                                    const std::string&      label);
    static Tensor< GUM_SCALAR > uniformTensor(const DiscreteVariable& var);
    // =========================================================================
    // =========================================================================

    Tensor();

    Tensor(const std::vector< const DiscreteVariable* >& vars);

    explicit Tensor(MultiDimImplementation< GUM_SCALAR >* aContent);

    Tensor(MultiDimImplementation< GUM_SCALAR >*  aContent,
           const MultiDimContainer< GUM_SCALAR >& src);
    Tensor(const Tensor< GUM_SCALAR >& src);
    Tensor< GUM_SCALAR >& operator=(const Tensor< GUM_SCALAR >& src);

    Tensor(Tensor< GUM_SCALAR >&& from);
    Tensor< GUM_SCALAR >& operator=(Tensor< GUM_SCALAR >&& src);
 

    ~Tensor() final;

    // ========================================================================
    // ========================================================================
 
    Tensor< GUM_SCALAR >* newFactory() const final;

    // ========================================================================
    // ========================================================================

    const Tensor< GUM_SCALAR >& random() const;

    const Tensor< GUM_SCALAR >& randomDistribution() const;

    const Tensor< GUM_SCALAR >& randomCPT() const;

    const Tensor< GUM_SCALAR >& noising(GUM_SCALAR alpha) const;

    Tensor< GUM_SCALAR > sumOut(const gum::VariableSet& del_vars) const;

    Tensor< GUM_SCALAR > sumIn(const gum::VariableSet& kept_vars) const;

    Tensor< GUM_SCALAR > prodOut(const gum::VariableSet& del_vars) const;

    Tensor< GUM_SCALAR > prodIn(const gum::VariableSet& kept_vars) const;

    Tensor< GUM_SCALAR > minOut(const gum::VariableSet& del_vars) const;

    Tensor< GUM_SCALAR > minIn(const gum::VariableSet& kept_vars) const;

    Tensor< GUM_SCALAR > maxOut(const gum::VariableSet& del_vars) const;

    Tensor< GUM_SCALAR > maxIn(const gum::VariableSet& kept_vars) const;

    Tensor< GUM_SCALAR > isNonZeroMap() const;

    GUM_SCALAR sum() const;
    GUM_SCALAR product() const;
    GUM_SCALAR max() const;
    GUM_SCALAR min() const;
    GUM_SCALAR maxNonOne() const;
    GUM_SCALAR minNonZero() const;

    Set< Instantiation > findAll(GUM_SCALAR v) const;

    std::pair< Set< Instantiation >, GUM_SCALAR > argmax() const;

    std::pair< Set< Instantiation >, GUM_SCALAR > argmin() const;

    GUM_SCALAR expectedValue(std::function< GUM_SCALAR(const gum::Instantiation&) >) const;

    GUM_SCALAR entropy() const;

    Tensor< GUM_SCALAR > reorganize(const std::vector< const DiscreteVariable* >& vars) const;

    Tensor< GUM_SCALAR > reorganize(const std::vector< std::string >& vars) const;

    Tensor< GUM_SCALAR > extract(const Instantiation& inst) const;

    Tensor< GUM_SCALAR > putFirst(const DiscreteVariable* var) const;

    Tensor< GUM_SCALAR > putFirst(const std::string& varname) const;

    const Tensor< GUM_SCALAR >& fillWith(const Tensor< GUM_SCALAR >& src) const;

    const Tensor< GUM_SCALAR >& fillWith(const Tensor< GUM_SCALAR >&       src,
                                         const std::vector< std::string >& mapSrc) const;

    const Tensor< GUM_SCALAR >& fillWith(const std::vector< GUM_SCALAR >& data) const;

    const Tensor< GUM_SCALAR >& fillWith(const GUM_SCALAR& val) const;
 

    const Tensor< GUM_SCALAR >& abs() const;

    const Tensor< GUM_SCALAR >& sq() const;

    const Tensor< GUM_SCALAR >& log2() const;

    const Tensor< GUM_SCALAR >& sgn() const;

    Tensor< GUM_SCALAR > new_abs() const;

    Tensor< GUM_SCALAR > new_sq() const;

    Tensor< GUM_SCALAR > new_log2() const;

    Tensor< GUM_SCALAR > new_sgn() const;
 

    const Tensor< GUM_SCALAR >& normalize() const;

    GUM_SCALAR KL(const Tensor< GUM_SCALAR >& p) const;

    const Tensor< GUM_SCALAR >& normalizeAsCPT(const Idx& varId = 0) const;

    const Tensor< GUM_SCALAR >& scale(GUM_SCALAR v) const;

    const Tensor< GUM_SCALAR >& translate(GUM_SCALAR v) const;

    const Tensor< GUM_SCALAR >& inverse() const;

    [[nodiscard]] Idx draw() const;

    Size memoryFootprint() const;

 
    // ========================================================================
    // ========================================================================
    Tensor< GUM_SCALAR >  operator+(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR >  operator+(const GUM_SCALAR& v) const;
    Tensor< GUM_SCALAR >  operator-(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR >  operator-(const GUM_SCALAR& v) const;
    Tensor< GUM_SCALAR >  operator*(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR >  operator*(const GUM_SCALAR& v) const;
    Tensor< GUM_SCALAR >  operator/(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR >  operator/(const GUM_SCALAR& v) const;
    Tensor< GUM_SCALAR >& operator+=(const Tensor< GUM_SCALAR >& r);
    Tensor< GUM_SCALAR >& operator+=(const GUM_SCALAR& v);
    Tensor< GUM_SCALAR >& operator*=(const Tensor< GUM_SCALAR >& r);
    Tensor< GUM_SCALAR >& operator*=(const GUM_SCALAR& v);
    Tensor< GUM_SCALAR >& operator-=(const Tensor< GUM_SCALAR >& r);
    Tensor< GUM_SCALAR >& operator-=(const GUM_SCALAR& v);
    Tensor< GUM_SCALAR >& operator/=(const Tensor< GUM_SCALAR >& r);
    Tensor< GUM_SCALAR >& operator/=(const GUM_SCALAR& v);
 
    // these operations are only defined for boolean-like Tensor (evidence/likelihood)
    [[nodiscard]] bool   isEvidence() const;
    Tensor< GUM_SCALAR > operator|(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR > operator&(const Tensor< GUM_SCALAR >& p2) const;
    Tensor< GUM_SCALAR > operator~() const;
 
    bool operator==(const Tensor< GUM_SCALAR >& r) const;
 
    Tensor< GUM_SCALAR >& operator<<(const DiscreteVariable& v);
 
    [[nodiscard]] std::string toString() const final;

    static Tensor< GUM_SCALAR > evEq(const DiscreteVariable& v, double val);
    static Tensor< GUM_SCALAR > evIn(const DiscreteVariable& v, double val1, double val2);
    static Tensor< GUM_SCALAR > evGt(const DiscreteVariable& v, double val);
    static Tensor< GUM_SCALAR > evLt(const DiscreteVariable& v, double val);
 
    protected:
    [[nodiscard]] gum::VariableSet complementVars_(const gum::VariableSet& del_vars) const;
  };
 
#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
  extern template class Tensor< double >;
#endif
 
  template < typename GUM_SCALAR >
  inline Tensor< GUM_SCALAR > log2(const Tensor< GUM_SCALAR >& arg) {
    return arg.new_log2();
  }
 
  template < typename GUM_SCALAR >
  inline Tensor< GUM_SCALAR > abs(const Tensor< GUM_SCALAR >& arg) {
    return arg.new_abs();
  }
 
  template < typename GUM_SCALAR >
  inline Tensor< GUM_SCALAR > sq(const Tensor< GUM_SCALAR >& arg) {
    return arg.new_sq();
  }
} /* namespace gum */
 
#include <agrum/base/multidim/tensor_tpl.h>
#endif /* GUM_TENSOR_H */