brainnet.neuralframework.xml

neural networks applications

<?xml version="1.0"?>
<doc>
  <assembly>
    <name>BrainNet.NeuralFramework</name>
    <version>1.0.2346.43108</version>
    <fullname>BrainNet.NeuralFramework, Version=1.0.2346.43108, Culture=neutral, PublicKeyToken=null</fullname>
  </assembly>
  <members>
    <member name="T:BrainNet.NeuralFramework.BackPropNetworkFactory">
      <summary> A Factory for creating a backward propagation neural network</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNetworkFactory.CreateNetwork(System.Collections.ArrayList)">
      <summary> Method to create a network. The input is a list of long values that represent the number 
of neurons in each layer</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNetworkFactory.CreateNetwork(System.Int64,System.Int64)">
      <summary> Build a simple back prop neural network</summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.NeuronStrategyException">
      <summary> Exception that may be thrown by NeuronStrategy class </summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.BackPropNeuronStrategy">
      <summary> A backward propagation neuron strategy. This is a concrete implementation of INeuronStrategy </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNeuronStrategy.FindDelta(System.Single,System.Single)">
      <summary> Implementation of Find Delta </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNeuronStrategy.Activation(System.Single)">
      <summary> Implementation of activation function </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNeuronStrategy.FindNetValue(BrainNet.NeuralFramework.NeuronConnections,System.Single)">
      <summary> Implementation of finding net value </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNeuronStrategy.FindNewBias(System.Single,System.Single)">
      <summary> Implementation of Finding new bias value</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.BackPropNeuronStrategy.UpdateWeights(BrainNet.NeuralFramework.NeuronConnections@,System.Single)">
      <summary> Calculating the new weight values</summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.ImageProcessingHelper">
      <summary> The class provides some basic image processing methods 
and conversions required for neural network image processing  </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.ImageToMonochrome(System.Drawing.Image)">
      <summary> Convert a color image to monochrome </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.ArrayListFromImage(System.Drawing.Image)">
      <summary> Converts an image of any size to a pattern that 
can be feeded to the network.  </summary>
 
<remarks> The product of width and height should be equal 
to the number of neurons in input or output layer. Basically 
only the monochrome version of image is converted to pattern </remarks></member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.DrawImage(System.Windows.Forms.PictureBox,System.Windows.Forms.PictureBox,System.Boolean)">
      <summary> Shrinks an image in one picture box to another, using AntiAliasing </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.DrawImage(System.Drawing.Bitmap,System.Windows.Forms.PictureBox,System.Boolean)">
      <summary> Shrinks a bitmap and draw it to a picture box, using AntiAliasing </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.ShrinkImage(System.Drawing.Bitmap,System.Int32,System.Int32,System.Boolean)">
      <summary> Shrinks a bitmap and returns the shrinked version </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.ImageProcessingHelper.ImageFromArraylist(System.Collections.ArrayList,System.Int32,System.Int32)">
      <summary> Create an image from a given pattern </summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.INetworkFactory">
      <summary> An interface for a neural network factory </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INetworkFactory.CreateNetwork(System.Int64,System.Int64)">
      <summary> Create a neural Network. </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.TrainNetwork(BrainNet.NeuralFramework.TrainingData)">
      <summary>Method to train a network </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.ConnectNeurons(BrainNet.NeuralFramework.INeuron,BrainNet.NeuralFramework.INeuron,System.Single)">
      <summary>This function can be used for connecting two neurons together </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.ConnectNeurons(BrainNet.NeuralFramework.INeuron,BrainNet.NeuralFramework.INeuron)">
      <summary>This function can be used for connecting two neurons together with random weight </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.ConnectLayers(BrainNet.NeuralFramework.NeuronLayer,BrainNet.NeuralFramework.NeuronLayer)">
      <summary>This function can be used for connecting neurons in two layers together with random weights </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.ConnectLayers">
      <summary>This function can be used for connecting all layers together </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.RunNetwork(System.Collections.ArrayList)">
      <summary>This function may be used for running the network </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuralNetwork.GetOutput">
      <summary>This function may be used to obtain the output list </summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuralNetwork.InputLayer">
      <summary>Gets the first (input) layer</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuralNetwork.OutputLayer">
      <summary>Gets the last (output) layer</summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.INeuron">
      <summary>The interface for defining a neuron </summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.BiasValue">
      <summary> Gets the current bias this neuron</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.OutputValue">
      <summary> Gets the current output this neuron</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.DeltaValue">
      <summary> Gets the current delta value this neuron</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.ForwardConnections">
      <summary> Gets a list of neurons to which this neuron is connected</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.Inputs">
      <summary> Gets a list of neurons connected to this neuron</summary>
    </member>
    <member name="P:BrainNet.NeuralFramework.INeuron.Strategy">
      <summary> Gets or sets the strategy of this neuron</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuron.UpdateOutput">
      <summary> Method to update the output of a neuron</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuron.UpdateDelta(System.Single)">
      <summary> Method to find new delta value</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuron.UpdateFreeParams">
      <summary> Method to update free parameters</summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.INeuronStrategy">
      <summary>The interface for defining the strategy of a neuron </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuronStrategy.FindDelta(System.Single,System.Single)">
      <summary>Function to find the delta or error rate of this INeuron </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuronStrategy.Activation(System.Single)">
      <summary>Activation Function, or ThreshHold function</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuronStrategy.FindNetValue(BrainNet.NeuralFramework.NeuronConnections,System.Single)">
      <summary>Summation Function for finding the net value</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuronStrategy.FindNewBias(System.Single,System.Single)">
      <summary>Function for calculating new bias</summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.INeuronStrategy.UpdateWeights(BrainNet.NeuralFramework.NeuronConnections@,System.Single)">
      <summary>Function for updating weights</summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.NotInitializedException">
      <summary> Exception thrown when a function is called with out intializing 
the helper </summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.NetworkHelperException">
      <summary> Exception thrown when a helper error occurs </summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.NetworkHelper">
      <summary> The class is to help you to initialize,train and run the network. It maintains 
a list of training data elements. </summary>
    </member>
    <member name="E:BrainNet.NeuralFramework.NetworkHelper.TrainingProgress">
      <summary> This event will be raised after each training </summary>
    </member>
    <member name="T:BrainNet.NeuralFramework.NetworkHelper.TrainingProgressEventHandler">
      <summary> This event will be raised after each training </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.#ctor(BrainNet.NeuralFramework.INeuralNetwork)">
      <summary> Initialize with an existing neural network </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.#ctor">
      <summary> Default constructor </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.Initialize(BrainNet.NeuralFramework.INeuralNetwork)">
      <summary> Re-Initialize this network manager with a network </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(BrainNet.NeuralFramework.TrainingData)">
      <summary> This function takes a training data object  </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Collections.ArrayList,System.Collections.ArrayList)">
      <summary> This function takes a list of Single values as input and output </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.String,System.String)">
      <summary> This function takes a string pattern consists of 1s and 0s 
 as input and output </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Int64,System.Int64)">
      <summary> This function takes numbers as inputs and outputs, convert it to binary strig 
pattern, and add it to the training queue. Eg. 2 will be converted to 10 </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Drawing.Image,System.Drawing.Image)">
      <summary> This function takes images as inputs and outputs. The images will be 
resized based on the number of inputs and outputs </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Drawing.Image,System.Collections.ArrayList)">
      <summary> This function takes an image as input and arraylist as output </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Drawing.Image,System.String)">
      <summary> This function takes an image as input and a pattern as output </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.AddTrainingData(System.Drawing.Image,System.Int64)">
      <summary> This function takes an image as input and a value as output </summary>
    </member>
    <member name="M:BrainNet.NeuralFramework.NetworkHelper.Train(System.Int64,System.Boolean)">
      <summary> This function trains the network using the training data queue the 
 specified number of rounds </summary>
    </member>