/*!
  \file
  \brief Common DataSources.
  \author Vaclav Smidl.

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/

#ifndef DATASOURCE_H
#define DATASOURCE_H


#include "../base/bdmbase.h"
#include "../stat/exp_family.h"
#include "../base/user_info.h"

namespace bdm {
/*!
* \brief Memory storage of off-line data column-wise

The data are stored in an internal matrix \c Data . Each column of Data corresponds to one discrete time observation \f$t\f$. Access to this matrix is via indices \c rowid and \c delays.

The data can be loaded from a file.
*/
class MemDS : public DS {
protected:
	//! internal matrix of data
	mat Data;
	//! active column in the Data matrix
	int time;
	//!  vector of rows that are presented in Dt
	ivec rowid;
	//! vector of delays that are presented in Dt
	ivec delays;

public:
	void getdata ( vec &dt );
	void getdata ( vec &dt, const ivec &indeces );
	void set_rvs ( RV &drv, RV &urv );
	void write ( vec &ut ) {
		it_error ( "MemDS::write is not supported" );
	}
	void write ( vec &ut, ivec &indices ) {
		it_error ( "MemDS::write is not supported" );
	}
	void step();
	//!Default constructor
	MemDS () {};
	MemDS ( mat &Dat, ivec &rowid0, ivec &delays0 );
};

/*! Pseudovirtual class for reading data from files

*/
class FileDS: public MemDS {

public:
	void getdata ( vec &dt ) {
		it_assert_debug ( dt.length() == Data.rows(), "" );
		dt = Data.get_col ( time );
	};
	void getdata ( vec &dt, const ivec &indeces ) {
		it_assert_debug ( dt.length() == indeces.length(), "" );
		vec tmp ( indeces.length() );
		tmp = Data.get_col ( time );
		dt = tmp ( indeces );
	};
	//! returns number of data in the file;
	int ndat() {
		return Data.cols();
	}
	//! no sense to log this type
	void log_add ( logger &L ) {};
	//! no sense to log this type
	void logit ( logger &L ) {};
};

/*!
* \brief Read Data Matrix from an IT file

The constructor creates an internal matrix \c Data from an IT++ file. The file is binary and can be made using the IT++ library or the Matlab/Octave function itsave. NB: the data are stored columnwise, i.e. each column contains the data for time \f$t\f$!

*/
class ITppFileDS: public FileDS {

public:
	ITppFileDS ( const string &fname, const string &varname ) : FileDS() {
		it_file it ( fname );
		it << Name ( varname );
		it >> Data;
		time = 0;
		//rowid and delays are ignored
	};

	ITppFileDS () : FileDS() {
	};

	void from_setting ( const Setting &set );

	// TODO dodelat void to_setting( Setting &set ) const;

};

UIREGISTER ( ITppFileDS );
SHAREDPTR ( ITppFileDS );

/*!
* \brief CSV file data storage
The constructor creates \c Data matrix from the records in a CSV file \c fname. The orientation can be of two types:
1. \c BY_COL which is default - the data are stored in columns; one column per time \f$t\f$, one row per data item.
2. \c BY_ROW if the data are stored the classical CSV style. Then each column stores the values for data item, for ex. \f$[y_{t} y_{t-1} ...]\f$, one row for each discrete time instant.

*/
class CsvFileDS: public FileDS {

public:
	//! Constructor - create DS from a CSV file.
	CsvFileDS ( const string& fname, const string& orientation = "BY_COL" );
};



/*!
\brief Generator of ARX data

*/
class ArxDS : public DS {
protected:
	//! Rv of the regressor
	RV Rrv;
	//! History, ordered as \f$[y_t, u_t, y_{t-1 }, u_{t-1}, \ldots]\f$
	vec H;
	//! (future) input
	vec U;
	//! temporary variable for regressor
	vec rgr;
	//! data link: H -> rgr
	datalink rgrlnk;
	//! model of Y - linear Gaussian
	mlnorm<chmat> model;
	//! options
	bool opt_L_theta;
	//! loggers
	int L_theta;
	int L_R;
	int dt_size;
public:
	void getdata ( vec &dt ) {
		//it_assert_debug ( dt.length() ==Drv.count(),"ArxDS" );
		dt = H;
	};
	void getdata ( vec &dt, const ivec &indices ) {
		it_assert_debug ( dt.length() == indices.length(), "ArxDS" );
		dt = H ( indices );
	};
	void write ( vec &ut ) {
		//it_assert_debug ( ut.length() ==Urv.count(),"ArxDS" );
		U = ut;
	};
	void write ( vec &ut, const ivec &indices ) {
		it_assert_debug ( ut.length() == indices.length(), "ArxDS" );
		set_subvector ( U, indices, ut );
	};
	void step();
	//!Default constructor
	ArxDS ( ) {};
	//! Set parameters of the internal model, H is maximum time delay
	void set_parameters ( const mat &Th0, const vec mu0, const chmat &sqR0 ) {
		model.set_parameters ( Th0, mu0, sqR0 );
	};
	//! Set
	void set_drv ( const RV &yrv, const RV &urv, const RV &rrv ) {
		Rrv = rrv;
		Urv = urv;
		dt_size = yrv._dsize() + urv._dsize();

		RV drv = concat ( yrv, urv );
		Drv = drv;
		int td = rrv.mint();
		H.set_size ( drv._dsize() * ( -td + 1 ) );
		U.set_size ( Urv._dsize() );
		for ( int i = -1; i >= td; i-- ) {
			drv.t ( -1 );
			Drv.add ( drv ); //shift u1
		}
		rgrlnk.set_connection ( rrv, Drv );

		dtsize = Drv._dsize();
		utsize = Urv._dsize();
	}
	//! set options from a string
	void set_options ( const string &s ) {
		opt_L_theta = ( s.find ( "L_theta" ) != string::npos );
	};
	virtual void log_add ( logger &L ) {
		//DS::log_add ( L ); too long!!
		L_dt = L.add ( Drv ( 0, dt_size ), "" );
		L_ut = L.add ( Urv, "" );

		mat &A = model._A();
		mat R = model._R();
		if ( opt_L_theta ) {
			L_theta = L.add ( RV ( "{th }", vec_1 ( A.rows() * A.cols() ) ), "t" );
		}
		if ( opt_L_theta ) {
			L_R = L.add ( RV ( "{R }", vec_1 ( R.rows() * R.cols() ) ), "r" );
		}
	}
	virtual void logit ( logger &L ) {
		//DS::logit ( L );
		L.logit ( L_dt, H.left ( dt_size ) );
		L.logit ( L_ut, U );

		mat &A = model._A();
		mat R = model._R();
		if ( opt_L_theta ) {
			L.logit ( L_theta, vec ( A._data(), A.rows() *A.cols() ) );
		};
		if ( opt_L_theta ) {
			L.logit ( L_R, vec ( R._data(), R.rows() *R.rows() ) );
		};
	}

	// TODO dokumentace - aktualizovat
	/*! UI for ArxDS using factorized description!

	The ArxDS is constructed from a structure with fields:
	\code
	system = {
		type = "ArxDS";
		// description of y variables
		y = {type="rv"; names=["y", "u"];};
		// description of u variable
		u = {type="rv"; names=[];}
		// description of regressor
		rgr = {type="rv";
			names = ["y","y","y","u"];
			times = [-1, -2, -3, -1];
		}

		// theta
		theta = [0.8, -0.3, 0.4, 1.0,
				 0.0, 0.0, 0.0, 0.0];
		// offset (optional)
		offset = [0.0, 0.0];
		//variance
		r = [0.1, 0.0,
			 0.0, 1.0];
		//options: L_theta = log value of theta,
		opt = "L_theta";
	};
	\endcode

	Result is ARX data source offering with full history as Drv.
	*/
	void from_setting ( const Setting &set );

	// TODO dodelat void to_setting( Setting &set ) const;
};

UIREGISTER ( ArxDS );
SHAREDPTR ( ArxDS );

class stateDS : public DS {
private:
	//!conditional pdf of the state evolution \f$ f(x_t|x_{t-1}) \f$
	shared_ptr<mpdf> IM;

	//!conditional pdf of the observations \f$ f(d_t|x_t) \f$
	shared_ptr<mpdf> OM;

protected:
	//! result storage
	vec dt;
	//! state storage
	vec xt;
	//! input storage
	vec ut;
	//! Logger
	int L_xt;

public:
	void getdata ( vec &dt0 ) {
		dt0 = dt;
	}
	void getdata ( vec &dt0, const ivec &indeces ) {
		dt0 = dt ( indeces );
	}

	stateDS ( const shared_ptr<mpdf> &IM0, const shared_ptr<mpdf> &OM0, int usize ) : IM ( IM0 ), OM ( OM0 ),
		dt ( OM0->dimension() ), xt ( IM0->dimension() ),
		ut ( usize ), L_xt(0) { }

	stateDS() : L_xt(0) { }

	virtual void step() {
		xt = IM->samplecond ( concat ( xt, ut ) );
		dt = OM->samplecond ( concat ( xt, ut ) );
	}

	virtual void log_add ( logger &L ) {
		DS::log_add ( L );
		L_xt = L.add ( IM->_rv(), "true" );
	}
	virtual void logit ( logger &L ) {
		DS::logit ( L );
		L.logit ( L_xt, xt );
	}

	/*! UI for stateDS

	The DS is constructed from a structure with fields:
	\code
	system = {
		type = "stateDS";
		//Internal model
		IM = { type = "mpdf"; //<-- valid offspring! e.g. "mlnorm"
			rv = { //description of x_t
				names=["name1",...];
				sizes=[2,1]; // optional default=[1,1...];
				times=[0,0]; // optional default=[0,0...];
				}
			rvu= { //description of  u_t
				//optional default=empty
				}

			// remaining fields depending on the chosen type
			};
		//Observation model
		OM = { type = "mpdf-offspring";
			rv = {}; //description of d_t
			rvu = {type="internal", path="system.IM.rvu"}; //description of u_t

			//remaining fields
		}
	};
	\endcode
	*/
	void from_setting ( const Setting &set );

	// TODO dodelat void to_setting( Setting &set ) const;

};

UIREGISTER ( stateDS );
SHAREDPTR ( stateDS );

}; //namespace

#endif // DS_H
