Fit the time dependence of a three state hidden Markov model. More...

#include <ThreeNaMarkov.hpp>

Inheritance diagram for Unfit::Examples::ThreeNaMarkov:

Public Member Functions
	ThreeNaMarkov (const std::vector< double > &open_prob, double dt)

std::vector< double >	operator() (const std::vector< double > &param)

Public Member Functions inherited from Unfit::GenericCostFunction
virtual	~GenericCostFunction ()

Private Attributes
const std::vector< double >	open_prob_

const double	dt_

Detailed Description

Fit the time dependence of a three state hidden Markov model.

This is an example of fitting a hidden Markov model as can be used to describe an ion channel. There are three states which leads to three ordinary differential equations (ODEs) and four rate constants to fit. The states are labelled C = closed, O = open, and I = inactive. The initial conditions for the states are C = 1.0, O & I = 0.0. The three ODEs are:

dO/dt = kco*C + kio*I - (koc+koi)*O dC/dt = koc*O - kco*C dI/dt = koi*O - kio*I

The goal is to find the values of the k's (rate constants) that best fit the data, which is in the form of O vs time. In terms of the model, kco = param[0], koc = param[1], koi = param[2], kio = param[3]. These ODEs are integrated with the forward Euler method.

Constructor & Destructor Documentation

◆ ThreeNaMarkov()

Unfit::Examples::ThreeNaMarkov::ThreeNaMarkov	(	const std::vector< double > &	open_prob,
		double	dt
	)

inline

Create the cost function. Here the experimental data must be passed in, and cannot be changed (if you want to, just create another cost function object). Here the experimental data is a vector containing the value of the open probabilities, and the time step used in the data collection.

Intended use : ThreeNaMarkov cost_func(open_prob, dt);

Parameters

open_prob	A vector of experimental open probabilities
dt	The experimental time step

Member Function Documentation

◆ operator()()

std::vector<double> Unfit::Examples::ThreeNaMarkov::operator() ( const std::vector< double > & param )

inlinevirtual

Calculate the linear distance (residuals) between our model and the data. This method encapsulates the model, and expects the current estimates of the unknown parameters as an input. See the class documentation for details about the model.

Intended use : residuals = cost_func(param)

Parameters

param A vector containing the current estimates of the parameters we are trying to fit

Returns: A vector containing the residuals

Implements Unfit::GenericCostFunction.

Member Data Documentation

◆ dt_

const double Unfit::Examples::ThreeNaMarkov::dt_

private

A vector to store the time step, dt

◆ open_prob_

const std::vector<double> Unfit::Examples::ThreeNaMarkov::open_prob_

private

A vector to store the experimental open probabilities

The documentation for this class was generated from the following file:

examples/ThreeNaMarkov.hpp

Public Member Functions

Private Attributes