| C | |
| CascadeActivationFunctions | |
| CascadeActivationFunctionsCount | |
| CascadeActivationSteepnesses | |
| CascadeActivationSteepnessesCount | |
| CascadeCandidateChangeFraction | |
| CascadeCandidateGroupsCount | |
| CascadeCandidateLimit | |
| CascadeCandidatesCount | |
| CascadeCandidateStagnationEpochs | |
| CascadeMaxCandEpochs | |
| CascadeMaxOutEpochs | |
| CascadeOutputChangeFraction | |
| CascadeOutputStagnationEpochs | |
| CascadetrainOnData | |
| CascadetrainOnFile | |
| CascadeWeightMultiplier | |
| ClearScalingParams | |
| Connection | |
| ConnectionArray, FANNCSharp. | |
| ConnectionRate | |
| Connections | |
| Count | |
| CreateTrainFromCallback | |
| Current, FANNCSharp. |
The cascade activation functions array is an array of the different activation functions used by the candidates.
public ActivationFunction[] CascadeActivationFunctions { get, set }
The cascade activation functions array is an array of the different activation functions used by the candidates.
public ActivationFunction[] CascadeActivationFunctions { get, set }
The number of activation functions in the CascadeActivationFunctions array.
public uint CascadeActivationFunctionsCount { get }
The number of activation functions in the CascadeActivationFunctions array.
public uint CascadeActivationFunctionsCount { get }
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
public double[] CascadeActivationSteepnesses { get, set }
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
public float[] CascadeActivationSteepnesses { get, set }
The number of activation steepnesses in the CascadeActivationFunctions array.
public uint CascadeActivationSteepnessesCount { get }
The number of activation steepnesses in the CascadeActivationFunctions array.
public uint CascadeActivationSteepnessesCount { get }
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeCandidateStagnationEpochs during training of the candidate neurons, in order for the training not to stagnate.
public float CascadeCandidateChangeFraction { get, set }
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeCandidateStagnationEpochs during training of the candidate neurons, in order for the training not to stagnate.
public float CascadeCandidateChangeFraction { get, set }
The number of candidate groups is the number of groups of identical candidates which will be used during training.
public uint CascadeCandidateGroupsCount { get, set }
The number of candidate groups is the number of groups of identical candidates which will be used during training.
public uint CascadeCandidateGroupsCount { get, set }
The candidate limit is a limit for how much the candidate neuron may be trained.
public double CascadeCandidateLimit { get, set }
The candidate limit is a limit for how much the candidate neuron may be trained.
public float CascadeCandidateLimit { get, set }
The number of candidates used during training (calculated by multiplying CascadeActivationFunctionsCount, CascadeActivationSteepnessesCount and CascadeCandidateGroupsCount).
public uint CascadeCandidatesCount { get }
The number of candidates used during training (calculated by multiplying CascadeActivationFunctionsCount, CascadeActivationSteepnessesCount and CascadeCandidateGroupsCount).
public uint CascadeCandidatesCount { get }
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeCandidateChangeFraction.
public uint CascadeCandidateStagnationEpochs { get, set }
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeCandidateChangeFraction.
public uint CascadeCandidateStagnationEpochs { get, set }
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
public uint CascadeMaxCandEpochs { get, set }
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
public uint CascadeMaxCandEpochs { get, set }
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
public uint CascadeMaxOutEpochs { get, set }
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
public uint CascadeMaxOutEpochs { get, set }
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeOutputStagnationEpochs during training of the output connections, in order for the training not to stagnate.
public float CascadeOutputChangeFraction { get, set }
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the MSE value should change within CascadeOutputStagnationEpochs during training of the output connections, in order for the training not to stagnate.
public float CascadeOutputChangeFraction { get, set }
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeOutputChangeFraction.
public uint CascadeOutputStagnationEpochs { get, set }
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of CascadeOutputChangeFraction.
public uint CascadeOutputStagnationEpochs { get, set }
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
public void CascadetrainOnData( TrainingData data, uint maxNeurons, uint neuronsBetweenReports, float desiredError )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
public void CascadetrainOnData( TrainingData data, uint maxNeurons, uint neuronsBetweenReports, float desiredError )
Does the same as CascadetrainOnData, but reads the training data directly from a file.
public void CascadetrainOnFile( string filename, uint maxNeurons, uint neuronsBetweenReports, float desiredError )
Does the same as CascadetrainOnData, but reads the training data directly from a file.
public void CascadetrainOnFile( string filename, uint maxNeurons, uint neuronsBetweenReports, float desiredError )
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
public double CascadeWeightMultiplier { get, set }
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
public float CascadeWeightMultiplier { get, set }
Clears scaling parameters.
public bool ClearScalingParams()
Clears scaling parameters.
public bool ClearScalingParams()
Creates a connection with the specified parameters Parameters: fromNeuron - Unique number used to identify source neuron toNeuron - Unique number used to identify destination neuron weight - The numerical value of the weight Example:
public Connection( uint fromNeuron, uint toNeuron, double weight )
Creates a connection with the specified parameters Parameters: fromNeuron - Unique number used to identify source neuron toNeuron - Unique number used to identify destination neuron weight - The numerical value of the weight Example:
public Connection( uint fromNeuron, uint toNeuron, int weight )
Creates a connection with the specified parameters Parameters: fromNeuron - Unique number used to identify source neuron toNeuron - Unique number used to identify destination neuron weight - The numerical value of the weight Example:
public Connection( uint fromNeuron, uint toNeuron, float weight )
Get the connections in the network.
public Connection[] ConnectionArray { get }
Get the connection rate used when the network was created
public float ConnectionRate { get }
Get the connection rate used when the network was created
public float ConnectionRate { get }
Get the connection rate used when the network was created
public float ConnectionRate { get }
Get the connections in the network.
public Connection[] Connections { get }
Get the connections in the network.
public Connection[] Connections { get }
The number of elements in the accessor.
int Count { get }
Creates the training data from a user supplied function.
public void CreateTrainFromCallback( uint dataCount, uint inputCount, uint outputCount, DataCreateCallback callback )
Creates the training data from a user supplied function.
public void CreateTrainFromCallback( uint dataCount, uint inputCount, uint outputCount, DataCreateCallback callback )
Creates the training data from a user supplied function.
public void CreateTrainFromCallback( uint dataCount, uint inputCount, uint outputCount, DataCreateCallback callback )
Returns the item in the collection currently being referenced by the internal reference
public T Current { get }