# Integration¶

**Table of contents**- Integration
- What to do with the Data

# 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

Updated by Federico Vera almost 3 years ago · 5 revisions

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