Particle

a vector with the names of the nodes in t_0

ordering

size

vector that defines the random velocity initialization probabilities

v_probs

score

dataset to evaluate the fitness of the particle

parameter that varies the effect of the inertia

in_cte

parameter that varies the effect of the global best

gb_cte

gb_ps

parameter that varies the effect of the local best

lb_cte

vector that defines the range of random variation of gb_cte and lb_cte

r_probs

Constructor of the 'Particle' class
Evaluate the score of the particle's position
Evaluate the score of the particle's position.
Updates the local best if the new one is better.
Update the position of the particle with the velocity
Update the position of the particle given the constants after calculating
the new velocity

Learning and inference over dynamic Bayesian networks of arbitrary
Markovian order. Extends some of the functionality offered by the 'bnlearn'
package to learn the networks from data and perform exact inference.
It offers three structure learning algorithms for dynamic Bayesian networks
and the possibility to perform forecasts of arbitrary length. A tool for
visualizing the structure of the net is also provided via the 'visNetwork'
package.

David Quesada

dbnR

Dynamic Bayesian Network Learning and Inference

Gabriel Valverde

Particle function

<dl class="dl-horizontal">
<dt><code>ps</code></dt><dd>position of the particle</dd></dl><dt><code>cl</code></dt><dd>velocity of the particle</dd><dt><code>lb</code></dt><dd>local best score obtained</dd><dt><code>lb_ps</code></dt><dd>local best position found</dd><dt><code>score</code></dt><dd>bnlearn score function used</dd>

Particle: R6 class that defines a Particle in the PSO algorithm

Description

Arguments

Value

Fields

Details