`dplbnDE`

:
An R package for discriminative parameter learning of bayesian networks
by Differential EvolutionImplements Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given BN structure encodes assumptions about conditional independencies among the attributes and will result in error if they do not hold in the data. Such an error includes the classification dimension. The main goal of Discriminative learning is minimize this type of error.

Make sure you have at least version 3.2.0 of R. You can get the
current development version of `dplbnDE`

from Github:

```
# install.packages('devtools')
::install_github('alexplatasl/dplbnDE') devtools
```

Load a data set and learn parameters of a bayesian network with custom structure or one learned by naive bayes, tree augmented naive Bayes using Chow-Liuâ€™s algorithm or Hill-climbing.

```
library(dplbnDE)
data(car)
<- DEbest(NP=30, G=25, data = car, class.name = names(car)[7], crossover = "bin",
run.DEbest mutation.pairs = 1, structure = "tan", F = 0.5, CR = 0.55,
edgelist = NULL, verbose = 5)
#Gen: 5 CLL= -1911.61 NP= 30
#Gen: 10 CLL= -1532.554 NP= 30
#Gen: 15 CLL= -1392.074 NP= 30
#Gen: 20 CLL= -1252.369 NP= 30
#Gen: 25 CLL= -1181.117 NP= 30
run.DEbest#Number of evaluations: 780
#Final population size: 30
#
#Summary results of fitness in final population:
#
#Best CLL: -1181.117
#Worst CLL: -1251.721
#Median: -1218.063
#Std. Dev.: 17.96752
#plot(run.DEbest)
```

To learn parameters of a custom structure, load a matrix of sizes edges x 2. Where columns represents direction (from-to) of edges. Like the following matrix:

```
my_structure# from to
#[1,] "class" "buying"
#[2,] "class" "maint"
#[3,] "class" "doors"
#[4,] "class" "persons"
#[5,] "class" "lug_boot"
#[6,] "class" "safety"
#[7,] "maint" "buying"
#[8,] "lug_boot" "safety"
= lshade(NP=5, G=25, data = car, class.name = names(car)[7], c = 0.1,
run.shade pB=0.05, edgelist = my_structure, verbose = 5)
#Gen: 5 CLL= -1616.161 NP= 24
#Gen: 10 CLL= -1229.425 NP= 20
#Gen: 15 CLL= -1161.089 NP= 17
#Gen: 20 CLL= -1076.062 NP= 14
#Gen: 25 CLL= -1022.326 NP= 12
run.shade#Number of evaluations: 519
#Final population size: 12
#
#Summary results of fitness in final population:
#
#Best CLL: -1022.326
#Worst CLL: -1132.318
#Median: -1081.651
#Std. Dev.: 39.99802
#plot(run.shade)
```

After the learning process, returned bayesian networks can be
analyzed with `bnclassify`

package.