Integration¶

• Índice de contenidos
• Integration
• What to do with the Data
  • Evaluating from Java
  • Evaluating from CLI
  • GNU Octave
    • Exporting to Octave format

What to do with the Data¶

Now that you have successfully trained your MLP you might want to integrate it with other things, or whatever.

Evaluating from Java¶

First of all you'll need to grab a copy of libai and add it to your classpath.

Then here's a sample code that can be used as a guide:

import libai.nn.supervised.MLP;
import libai.common.Matrix;

...
//The rest of your code
...

public double f(double x) {
    MLP mlp = MLP.open("weights.dat"); //<- the weights you want to use
    Matrix m = new Matrix(1, 1);       //<- we only use single neuron inputs
    m.position(0, 0, x);               //<- set the value in the matrix
    return mlp.simulate(m).position(0, 0); //<- we only use single neuron output
}

Evaluating from CLI¶

$ java -jar mrft-VERSION.jar FILENAME VALUES

So for instance if you train the MLP with cos(x), it should output:

$ java -jar mrft-VERSION.jar weights.dat 0 1.0

You can also use the -csv or -tsv flags, so the output will be:

$ java -jar mrft-VERSION.jar weights.dat -csv 0 0.0, 1.0

The output will always be via sdt::out so you can use something like tee to create a file

$ java -jar mrft-VERSION.jar -csv FILENAME VALUES | tee OUT.csv

GNU Octave¶

The current version of libai supports exporting matrices as Octave-Level-1 binary matrices, but it still doesn't do that for MLP, so if someone actually want's to collaborate with some code, or wait a bit till I have some spare time.

Exporting to Octave format¶

$ java -jar mrft-VERSION.jar -octave weights.dat