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DataLayer.cpp
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Tue, May 14, 05:40

DataLayer.cpp
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#include <DataLayer.hpp>
#include <stdexcept> // std::invalid_argument
#include <cmath> // log()
#include <matrices.hpp>
DataLayer::DataLayer()
{}
DataLayer::DataLayer(const matrix2d_i& data,
size_t n_class,
size_t n_shift,
bool flip)
:data(data),
flip(flip),
n_row(data.size()),
n_col(data[0].size()),
n_class(n_class),
l_model(n_col - n_shift + 1),
n_shift(n_shift),
n_flip(flip + 1)
{ // models cannot be initialise here
// as the number of categories depend
// on the exact class
}
DataLayer::DataLayer(const matrix2d_i& data,
const matrix3d_d& model,
bool flip)
: data(data),
model(model),
flip(flip),
n_row(data.size()),
n_col(data[0].size()),
n_class(model.size()),
l_model(model[0].size()),
n_category(model[0][0].size()),
n_shift(n_col - l_model + 1),
n_flip(flip + 1)
{ // check if model is not too long
if(this->n_col < this->l_model)
{ char msg[4096] ;
sprintf(msg,
"Error! model is longer than data : %zu / %zu",
this->l_model, this->n_col) ;
throw std::invalid_argument(msg) ;
}
this->n_shift = this->n_col - this->l_model + 1 ;
}
DataLayer::~DataLayer()
{}
matrix3d_d DataLayer::get_model() const
{ return this->model ; }
void DataLayer::check_loglikelihood_dim(const matrix4d_d& loglikelihood) const
{ if(loglikelihood.size() != this->n_row)
{ char msg[4096] ;
sprintf(msg,
"Error! loglikelihood matrix 1st dimension is not "
"equal to data row number : %zu / %zu",
loglikelihood.size(), this->n_row) ;
throw std::invalid_argument(msg) ;
}
else if(loglikelihood[0].size() != this->n_class)
{ char msg[4096] ;
sprintf(msg,
"Error! loglikelihood matrix 2nd dimension is not "
"equal to model class number : %zu / %zu",
loglikelihood[0].size(), this->n_class) ;
throw std::invalid_argument(msg) ;
}
else if(loglikelihood[0][0].size() != this->n_shift)
{ char msg[4096] ;
sprintf(msg,
"Error! loglikelihood matrix 3rd dimension is not "
"equal to model shift state number : %zu / %zu",
loglikelihood[0][0].size(), this->n_shift) ;
throw std::invalid_argument(msg) ;
}
else if(loglikelihood[0][0][0].size() != this->n_flip)
{ char msg[4096] ;
sprintf(msg,
"Error! loglikelihood matrix 4th dimension is not "
"equal to model flip state number : %zu / %zu",
loglikelihood[0][0][0].size(), this->n_flip) ;
throw std::invalid_argument(msg) ;
}
}
void DataLayer::check_loglikelihood_max_dim(const vector_d& loglikelihood_max) const
{ if(loglikelihood_max.size() != this->n_row)
{ char msg[4096] ;
sprintf(msg,
"Error! loglikelihood_max length is not "
"equal to data row number : %zu / %zu",
loglikelihood_max.size(), this->n_flip) ;
throw std::invalid_argument(msg) ;
}
}
void DataLayer::check_posterior_prob_dim(const matrix4d_d& posterior_prob) const
{ if(posterior_prob.size() != this->n_row)
{ char msg[4096] ;
sprintf(msg,
"Error! posterior_prob matrix 1st dimension is not "
"equal to data row number : %zu / %zu",
posterior_prob.size(), this->n_row) ;
throw std::invalid_argument(msg) ;
}
else if(posterior_prob[0].size() != this->n_class)
{ char msg[4096] ;
sprintf(msg,
"Error! posterior_prob matrix 2nd dimension is not "
"equal to model class number : %zu / %zu",
posterior_prob[0].size(), this->n_class) ;
throw std::invalid_argument(msg) ;
}
else if(posterior_prob[0][0].size() != this->n_shift)
{ char msg[4096] ;
sprintf(msg,
"Error! posterior_prob matrix 3rd dimension is not "
"equal to model shift state number : %zu / %zu",
posterior_prob[0][0].size(), this->n_shift) ;
throw std::invalid_argument(msg) ;
}
else if(posterior_prob[0][0][0].size() != this->n_flip)
{ char msg[4096] ;
sprintf(msg,
"Error! posterior_prob matrix 4th dimension is not "
"equal to model flip state number : %zu / %zu",
posterior_prob[0][0][0].size(), this->n_flip) ;
throw std::invalid_argument(msg) ;
}
}
const double DataLayer::p_min = 1e-100 ;
const double DataLayer::p_min_log = log(DataLayer::p_min) ;

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