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Lara Nabozny15-Jan-09 13:05
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Vikram A Punathambekar15-Jan-09 18:02
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Lara Nabozny16-Jan-09 4:31
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Question[REQ: Help] Neural Network Pin
MavKilla15-Jan-09 12:38
MavKilla15-Jan-09 12:38 
Hi,

I'm currently sparetime coding on a neural network analyzer of Kismet (Wlan) data.
Project language is c# (.net 2-3.5)
As NN I'm using NeuronDotNet-3 or encog-1.1.0

Current state:
* Read data from logfiles
* Sort data into Lists<points> devided by track and BSSID
* work in progress: Analyse input data and complete field calculation with a FeedForward BackPropagation NNetwork
* ToDo: display calculated data, calculated center of field

As sampled input data has a lot of errors, the NN is my current problem. Is there anybody to contribute to this project? It will be licensed under LGPL or something similar.

If anybody is interested, please write a short mail with the title "Neural Network KisLyzer" to:
dhg8641 /\a_t/& g(m)x [dot.t] net

Source is maintained on a public subversion server and my be moved to sourceforge, if projekt is progressing.

Backgrund: Mathematical analysis works in free space or with a lot of calculation power and exact placement of walls and fences.
If these parts are not given, the analysis has to follow other rules. A neural network seems to produce the best fit.

If there are some interested people, I'll publish a webpage with the current source and something like a dev-blog.

current code version of pre-alpha NN-code (NeuronDotNet-3):
        public List<clstypedatapoint> CalcSigmoidSignal(List<clstypedatapoint> sampleData, List<clstypedatapoint> requestPoints)
        {
            updateLogDelegate("Initializing samples");
            updateProgressMDelegate(10);
            List<clstypedatapoint> calcData = new List<clstypedatapoint>();
            foreach (ClsTypeDataPoint p in sampleData)
            {
                ClsTypeDataPoint pt = p;
                pt.Signal = (p.Signal - 128) / 256;
                calcData.Add(pt);
            }



            updateLogDelegate("Initializing neural network.");
            updateProgressMDelegate(20);

            LinearLayer inputLayer = new LinearLayer(2);
            SigmoidLayer hiddenLayer1 = new SigmoidLayer(this.neuronCount);
            SigmoidLayer hiddenLayer2 = new SigmoidLayer(this.neuronCount);
            SigmoidLayer outputLayer = new SigmoidLayer(1);

            BackpropagationConnector bp1 =  new BackpropagationConnector(inputLayer, hiddenLayer1);
            BackpropagationConnector bp3 = new BackpropagationConnector(hiddenLayer1, hiddenLayer2);
            BackpropagationConnector bp2 = new BackpropagationConnector(hiddenLayer2, outputLayer);
            bp1.Momentum = momentum;
            bp2.Momentum = momentum;

            BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
            
            network.SetLearningRate(learningRate);
            outputLayer.Initializer = new ZeroFunction();
            hiddenLayer1.Initializer = new RandomFunction();
            network.Initialize();

            int progress = 0;
            network.EndEpochEvent += new TrainingEpochEventHandler(delegate(object senderNetwork, TrainingEpochEventArgs args)
            {
                progress++;
                if (progress % 1 == 0)
                {
                    updateProgressTDelegate(((int)((progress) * 100) / trainingCycles)%100);
                    updateLogDelegate("SquaredError: " + network.MeanSquaredError);
                }
            });

            updateLogDelegate("Adding training data.");
            updateProgressMDelegate(30);

            TrainingSet trainingSet = new TrainingSet(2, 1);
            foreach (ClsTypeDataPoint p in calcData)
            {
                trainingSet.Add(new TrainingSample(new double[] { p.Longitude, p.Latitude }, new double[] { (p.Signal)}));
            }

            updateLogDelegate("Training network.");
            updateProgressMDelegate(50);

            network.Learn(trainingSet, trainingCycles);

            network.StopLearning();

            updateProgressTDelegate(0);
            updateLogDelegate("Calculating field.");
            updateProgressMDelegate(90);

            int reqCnt = requestPoints.Count;
            int cnt = 0;
            foreach (ClsTypeDataPoint p in requestPoints)
            {
                cnt++;
                if ((cnt % 100) == 0)
                    updateProgressTDelegate((int)(100 * (cnt) / reqCnt));
                double[] ret; // = new double[];
                ret = network.Run(new double[] { p.Longitude, p.Latitude });
                p.Signal = ret[0];
            }
            updateProgressTDelegate(0);
            updateProgressMDelegate(0);
            return requestPoints;

        }

</clstypedatapoint></clstypedatapoint></clstypedatapoint></clstypedatapoint></clstypedatapoint>

Questionhow can made student form application in C#.net and login form?please send me coding and tutorial Pin
mohammedali200615-Jan-09 11:28
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AnswerRe: how can made student form application in C#.net and login form?please send me coding and tutorial Pin
User 665815-Jan-09 11:53
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Generalwhich books is best? Pin
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GeneralRe: how can made student form application in C#.net and login form?please send me coding and tutorial Pin
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AnswerRe: how can made student form application in C#.net and login form?please send me coding and tutorial Pin
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QuestionPassing information from a service to a client via .NET Remoting Pin
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