private void ExecuteStartTraining(bool canEvaluate) { ((MainWindowVM)Parent).Save(); var config = GetTestConfiguration(); ((MainWindowVM)Parent).ExpressionsVisibility = Visibility.Hidden; try { if (_defaultChooser == null) { _defaultChooser = new DefaultExpressionChooser(DateTimeProvider, DataAccess); _flashCardsAlgorithmChooser = new FlashCardsExpressionsChooser(DateTimeProvider, DataAccess); _defaultLearningAlgorithm = new DefaultLearningAlgorithm(DateTimeProvider); _flashCardsLearningAlgorithm = new FlashCardsLearningAlgorithm(); } WindowService.ShowDialog( new TestVM( canEvaluate, config, FlashCardsAlgorithm ? _flashCardsAlgorithmChooser : _defaultChooser, FlashCardsAlgorithm ? _flashCardsLearningAlgorithm : _defaultLearningAlgorithm)); } catch (Exception ex) { WindowService.ShowError(ex); } ((MainWindowVM)Parent).ExpressionsVisibility = Visibility.Visible; ((MainWindowVM)Parent).RebuildList(); }
public TestVM(bool canEvaluate, TestConfiguration config, IExpressionsChooser chooser, ILearningAlgorithm algorithm) { config.NotNull("config"); chooser.NotNull("chooser"); algorithm.NotNull("algorithm"); CanEvaluate = canEvaluate; Config = config; Algorithm = algorithm; Expressions = chooser.SelectExpressions(Config); ExpressionsView = CollectionViewSource.GetDefaultView(Expressions); }
/// <summary> /// Initializes a new instance of the <see cref="MultiLayerNetwork{T}" /> class. /// </summary> /// <param name="learningAlgorithm">The learning algorithm.</param> /// <param name="minWeight">The minimum weight.</param> /// <param name="maxWeight">The maximum weight.</param> /// <param name="weightProbabilities">The weight probability distribution.</param> /// <param name="maxInputActivations">The maximum number of activated input units.</param> /// <param name="activationThreshold">The total 'signal' required for a unit to become active.</param> /// <param name="classes">The number of distinct classes.</param> /// <param name="unitsPerClass">The number of output units per class.</param> /// <param name="units">The number of units in each layer, excluding the output layer.</param> public MultiLayerNetwork( ILearningAlgorithm learningAlgorithm, int minWeight, int maxWeight, double[] weightProbabilities, int maxInputActivations, int activationThreshold, int classes, int unitsPerClass, params int[] units) : base( learningAlgorithm, minWeight, maxWeight, weightProbabilities, 1.0, // Synapse success probability maxInputActivations, activationThreshold, classes, unitsPerClass, units) { }
/// <summary> /// Initializes the agent /// </summary> /// <param name="environment">The environment that the agent is added to</param> public void Initialize(TEnvironment environment) { this.environment = environment; this.learner = this.createLearnerFn(environment); }
/// <summary> /// Initializes the agent /// </summary> /// <param name="environment">The environment that the agent is added to</param> public virtual void Initialize(TEnvironment environment) { this.environment = environment; this.learner = this.CreateLearner(); }