#region License Information /* HeuristicLab * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Optimization; using HeuristicLab.Parameters; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] public abstract class DataAnalysisProblemData : ParameterizedNamedItem, IDataAnalysisProblemData { private const string DatasetParameterName = "Dataset"; private const string InputVariablesParameterName = "Input variables"; private const string TrainingSamplesStartParameterName = "Training partition start"; private const string TrainingSamplesEndParameterName = "Training partition end"; private const string TestSamplesStartParameterName = "Test partition start"; private const string TestSamplesEndParameterName = "Test partition end"; #region default data // y = x^4 + x^3 + x^2 + x private static double[,] kozaF1 = new double[,] { {2.017885919, -1.449165046}, {1.30060506, -1.344523885}, {1.147134798, -1.317989331}, {0.877182504, -1.266142284}, {0.852562452, -1.261020794}, {0.431095788, -1.158793317}, {0.112586002, -1.050908405}, {0.04594507, -1.021989402}, {0.042572879, -1.020438113}, {-0.074027291, -0.959859562}, {-0.109178553, -0.938094706}, {-0.259721109, -0.803635355}, {-0.272991057, -0.387519561}, {-0.161978191, -0.193611001}, {-0.102489983, -0.114215349}, {-0.01469968, -0.014918985}, {-0.008863365, -0.008942626}, {0.026751057, 0.026054094}, {0.166922436, 0.14309643}, {0.176953808, 0.1504144}, {0.190233418, 0.159916534}, {0.199800708, 0.166635331}, {0.261502822, 0.207600348}, {0.30182879, 0.232370249}, {0.83763905, 0.468046718} }; #endregion #region parameter properties public IValueParameter DatasetParameter { get { return (IValueParameter)Parameters[DatasetParameterName]; } } public IValueParameter> InputVariablesParameter { get { return (IValueParameter>)Parameters[InputVariablesParameterName]; } } public IValueParameter TrainingSamplesStartParameter { get { return (IValueParameter)Parameters[TrainingSamplesStartParameterName]; } } public IValueParameter TrainingSamplesEndParameter { get { return (IValueParameter)Parameters[TrainingSamplesEndParameterName]; } } public IValueParameter TestSamplesStartParameter { get { return (IValueParameter)Parameters[TestSamplesStartParameterName]; } } public IValueParameter TestSamplesEndParameter { get { return (IValueParameter)Parameters[TestSamplesEndParameterName]; } } #endregion #region propeties public Dataset Dataset { get { return DatasetParameter.Value; } } public IEnumerable InputVariables { get { return InputVariablesParameter.Value.CheckedItems.Select(i => i.Value.Value); } } public int TrainingSamplesStart { get { return TrainingSamplesStartParameter.Value.Value; } set { if (value != TrainingSamplesStart) { TrainingSamplesStartParameter.Value.Value = value; } } } public int TrainingSamplesEnd { get { return TrainingSamplesEndParameter.Value.Value; } set { if (value != TrainingSamplesEnd) { TrainingSamplesEndParameter.Value.Value = value; } } } public int TestSamplesStart { get { return TestSamplesStartParameter.Value.Value; } set { if (value != TestSamplesStart) { TestSamplesStartParameter.Value.Value = value; } } } public int TestSamplesEnd { get { return TestSamplesEndParameter.Value.Value; } set { if (value != TestSamplesEnd) { TestSamplesEndParameter.Value.Value = value; } } } public event EventHandler Changed; #endregion protected DataAnalysisProblemData(DataAnalysisProblemData original, Cloner cloner) : base(original, cloner) { RegisterEventHandlers(); } [StorableConstructor] protected DataAnalysisProblemData(bool deserializing) : base(deserializing) { } public DataAnalysisProblemData() : base() { List variableNames = new List() { "x", "f(x)" }; Dataset kozaF1Dataset = new Dataset(variableNames, kozaF1); kozaF1Dataset.Name = "Fourth-order Polynomial Function Benchmark Dataset"; kozaF1Dataset.Description = "f(x) = x^4 + x^3 + x^2 + x^1"; CheckedItemList inputVariablesList = new CheckedItemList(); StringValue x = new StringValue(variableNames[0]); StringValue fx = new StringValue(variableNames[1]); inputVariablesList.Add(x, true); inputVariablesList.Add(fx, false); Parameters.Add(new ValueParameter(DatasetParameterName, kozaF1Dataset)); Parameters.Add(new ValueParameter>(InputVariablesParameterName, inputVariablesList)); Parameters.Add(new ValueParameter(TrainingSamplesStartParameterName, new IntValue(0))); Parameters.Add(new ValueParameter(TrainingSamplesEndParameterName, new IntValue(kozaF1Dataset.Rows))); Parameters.Add(new ValueParameter(TestSamplesStartParameterName, new IntValue(kozaF1Dataset.Rows))); Parameters.Add(new ValueParameter(TestSamplesEndParameterName, new IntValue(kozaF1Dataset.Rows))); RegisterEventHandlers(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); } #region changed event propagation private void RegisterEventHandlers() { DatasetParameter.Value.Reset += new EventHandler(DatasetChanged); DatasetParameter.Value.ColumnsChanged += new EventHandler(DatasetChanged); DatasetParameter.Value.RowsChanged += new EventHandler(DatasetChanged); InputVariablesParameter.Value.CheckedItemsChanged += new Collections.CollectionItemsChangedEventHandler>(InputVariables_CheckedItemsChanged); InputVariablesParameter.Value.CollectionReset += new Collections.CollectionItemsChangedEventHandler>(InputVariables_CollectionReset); InputVariablesParameter.Value.ItemsAdded += new Collections.CollectionItemsChangedEventHandler>(InputVariables_ItemsAdded); InputVariablesParameter.Value.ItemsMoved += new Collections.CollectionItemsChangedEventHandler>(InputVariables_ItemsMoved); InputVariablesParameter.Value.ItemsRemoved += new Collections.CollectionItemsChangedEventHandler>(InputVariables_ItemsRemoved); InputVariablesParameter.Value.ItemsReplaced += new Collections.CollectionItemsChangedEventHandler>(InputVariables_ItemsReplaced); TrainingSamplesStartParameter.ValueChanged += new EventHandler(TrainingSamplesStartParameter_ValueChanged); TrainingSamplesEndParameter.ValueChanged += new EventHandler(TrainingSamplesEndParameter_ValueChanged); TestSamplesStartParameter.ValueChanged += new EventHandler(TestSamplesStartParameter_ValueChanged); TestSamplesEndParameter.ValueChanged += new EventHandler(TestSamplesEndParameter_ValueChanged); TrainingSamplesStartParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); TrainingSamplesEndParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); TestSamplesStartParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); TestSamplesEndParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); } private void TestSamplesEndParameter_ValueChanged(object sender, EventArgs e) { TestSamplesEndParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); } private void TestSamplesStartParameter_ValueChanged(object sender, EventArgs e) { TestSamplesStartParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); } private void TrainingSamplesEndParameter_ValueChanged(object sender, EventArgs e) { TrainingSamplesEndParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); } private void TrainingSamplesStartParameter_ValueChanged(object sender, EventArgs e) { TrainingSamplesStartParameter.Value.ValueChanged += new EventHandler(Partitions_ValueChanged); } private void Partitions_ValueChanged(object sender, EventArgs e) { OnProblemChanged(); } private void InputVariables_ItemsReplaced(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void InputVariables_ItemsRemoved(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void InputVariables_ItemsMoved(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void InputVariables_ItemsAdded(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void InputVariables_CollectionReset(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void InputVariables_CheckedItemsChanged(object sender, Collections.CollectionItemsChangedEventArgs> e) { OnProblemChanged(); } private void DatasetChanged(object sender, EventArgs e) { OnProblemChanged(); } protected void OnProblemChanged() { var listeners = Changed; if (listeners != null) listeners(this, EventArgs.Empty); } #endregion } }