MLP Real Function Trainer

# Integration

# 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`](https://github.com/kronenthaler/libai) and add it to your classpath.

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

~~~Java

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.