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scATAC-seq/src/ClusteringNew/SequenceLayer.hpp
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SequenceLayer.hpp
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#ifndef SEQUENCELAYER_HPP
#define SEQUENCELAYER_HPP
#include <DataLayer.hpp>
#include <iostream>
#include <matrices.hpp>
class SequenceLayer : public DataLayer
{ public:
/*!
* \brief Constructs an object with the
* given data and an empty (0 values)
* model.
* \param data the data.
* \param n_class the number of classes
* of the model.
* \param n_shift the number of shift
* states of the model.
* \param flip whether flipping is allowed.
*/
SequenceLayer(const matrix2d_i& data,
size_t n_class,
size_t n_shift,
bool flip) ;
/*!
* \brief Construct an object with the
* given data and model.
* \param data the data. The sequences
* should be stored as integer values :
* A:0, C:1, G:2, T:3, else:5.
* \param the model.
* \param flip whether flipping is allowed.
*/
SequenceLayer(const matrix2d_i& data,
const matrix3d_d& model,
bool flip) ;
/*!
* \brief Construct an object with the
* given data and model.
* \param data the data. The sequences
* will be transformed into integer values :
* A:0, C:1, G:2, T:3, else:5.
* \param the model.
* \param flip whether flipping is allowed.
*/
SequenceLayer(const matrix2d_c& data,
const matrix3d_d& model,
bool flip) ;
/*!
* Destructor
*/
virtual ~SequenceLayer() override ;
/*!
* \brief Sets the model values randomly.
*/
virtual void seed_model_randomly() ;
/*!
* \brief Sets the model values by
* sampling rows in the data and
* assigning them as initial model
* values.
*/
virtual void seed_model_sampling() ;
/*!
* \brief Sets the model values by
* using the first n_class rows in data.
*/
virtual void seed_model_toy() ;
/*!
* \brief Computes the log likelihood of the data
* given the current model parameters.
* \param logliklihood a matrix to store the
* results. It should have the following dimensions :
* 1st : same as the data number of row
* 2nd : same as the model number of classes
* 3rd : same as the number of shifts
* 4th : same as the number of flip states
* \param loglikelihood_max a vector containing the
* max value for each row of loglikelihood.
* Its length should be equal to the data row number.
* \throw std::invalid_argument if the dimensions are
* incorrect.
*/
virtual void compute_loglikelihoods(matrix4d_d& loglikelihood,
vector_d& loglikelihood_max) const override ;
/*!
* \brief Updates the model given the posterior
* probabilities (the probabilities of each row
* in the data to be assigned to each class,
* for each shift and flip state).
* \param posterior_prob the data assignment probabilities to
* the different classes.
*/
virtual void update_model(const matrix4d_d& posterior_prob) override ;
} ;
#endif // SEQUENCELAYER_HPP
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