Archives / 2015 / January
  • Encog Training

    Hello everybody,

    suppose you have read two of my previous notes about network creating and basic input into neural network and now you have huge desire to make training on neural network with Encog. You are on the right way. One of the options which you have to try is the following:

    var train = new Backpropagation(_network, trainSet);double error;



    train.Iteration(); error = train.Error; } while (error > 0.01);

    Backpropogation is one of the trainings algorithms. Other training algorithms of Encog are: LMA, Similated annealing, quick propogation, Mahatan update rule, scaled conjugate rule and other, which I didn't yet tried. 

    In mentioned code train is Backpropogation … more

  • Encog create simple network

    Hello everybody,

    today I want to share how to create simple neural network in Encog. It's very simple process:

    var network = BasicNetwork();

    Each neural network have layer. 

    Example of layer creating: 

    network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 5));

    The first paramether of BasicLayer is activation function, which is in our case ActivationSigmoid,

    The second paramether is Bias neuron. True means that layer will have bias layer also. 

    The third paramether represents number of neurons in layer. 

    If you think that creating network is enough for training, you are wrong. As a lot of staff in our world, in Encog you need to call FinalizeStructure. It looks like … more

  • Backpropogation Encog

    Here is Backpropogation algorithm declaration of Encog:

    var train = new Backpropogation(network, trainingSet, learningRate, momentum);

    Today I discovered for myself purpose of momentum paramether. 


    Here we have error function with global minimum and three local minimums. In order to jump out of local minima and run into global minima, neural network can take into account previous modification of weights. Momentum is coeficient, which manages which part of previous iteration take into account. If it is 1, then previous result will be taken into account completely. If it is 0, then previous update will be ignored. more

  • Encog compute


    Some other generalizations of how to use Encog.

    For getting result of network you can use Compute method:

    var output = network.Compute(input);

    If we want to get result of bigger number of items, we can use following construction

    foreach(var item in trainingSet)


    var output = network.Compute(item.Input);

    } more

  • How to add menu to button in Acumatica

    Hello everybody,

    today I want to share trick which I call convert Acumatica button into menu.

    Lets say in graph APBillManager created button in the following way:

    public PXAction<APBill> Report;

    If you want to convert it to menu with one item you can do the following:

    public APBillManager()




    public PXAction<PRPayroll> bankStatementReport;


    [PXUIField(DisplayName = "Bank Statement")]

    protected void BankStatementReport()


    } more

  • Encog BasicMLDataSet

    Hello everybody,

    today I want to share few words about my learning of Encog.

    Let's say you have array of 15 doubles:

    double []s = new double[15];

    Then for simple case you can use BasciMLData class:

    IMLData data = new BasicMLData(s); 

    Now data can be used to feed data to any neural network. 

    Next point to consider is inputting bigger values.

    Suppose you want to have input as xor:


    double [][] xorInput =


    new []{0.0, 0.0},

    new []{1.0, 0.0},

    new []{0.0, 1.0},

    new []{1.0, 1.0}


    // output

    double [][] xorIdeal =


    new []{0.0},

    new []{1.0},

    new []{1.0},

    new []{0.0}


    var trainSet = new BasicMLDataSet(xorInput, xorIdeal);

    Now you can use … more

  • Normalization and scaling in neural networks

    Hello everybody.

    I'm passing coursera course about neural networks.

    Today I discovered for myself reason why normalization and scaling in neural networks provides faster learning. Everything is related with error surface and optimization. If to put simply 

    the task of neural network is to find a global minimub at error surface. Algorithms of study of neural networks gradually move at error surface in order to finally find global minima of error

    surface. Going to global minima in the circle will go faster then going to global minima in some ellipse or other kind of error surface.

    Suppose we have training for neural network with two samples:

    101,101 - > 2

    101, 99 - > 0

    Then error … more

  • Enable disable button of grid or PXToolBarButton, which depends from value of column in Acumatica

    Hello everybody.

    My next notice is about following case. 

    Suppose you have from PR301000, which has grid with id "grid" with button calculate. Also grid has column, which is bounded to column caculated, and you need the following:

    If in selected row field "Calculated" is true, then disable button Calculate. If in selected row field "Calculated" is unchecked, then enable button calculate. 

    In order to implement this following should be implemented:

    1. In grid at page pr301000:

            <ActionBar ActionsText="True">


                    <px:PXToolBarButton Text="Calculate" DependOnGrid="grid" StateColumn="Calculated">

       <AutoCallBack Command="Calculate" Target="ds" > … more

  • Maintenance pages in Acumatica

    Hello everybody.

    Here goes some my notes of Maintenance pages. 

    First convention is that maintenance pages has number start of 20. For example pr203000.aspx means that it is maintenance page for pr, and I make this conclusion on basis that numbers start from 20.

    As usually they are placed under manage group at sitemap and used for input of helper data, not the main. more

  • Create graph instance

    Hello everybody,

    today I want to notice how to create graph. There are two ways:



    If you want to get extention class, from base class, you can use following function:

    GraphInstance.GetExtension<ExtentionClass>(); more