The example shows how one should classify countries according to the assigned set of characteristics, using a Kohonen network. Data about countries is implemented by the Country class.
To execution the example, add links to the NN and Collections system assemblies.
Class Country: Object
Public Name: String;
Public GDP: Double;
Public Population: Double;
Public PopulationGrowthRate: Double;
Public AreaLand: Double;
Public AreaWater: Double;
Public IrrigatedLand: Double;
Public Constructor Create(_Name: String; _GDP: Double; _Population: Double; _PopulationGrowthRate: Double; _AreaLand: Double; _AreaWater: Double; _IrrigatedLand: Double);
Begin
Name := _Name;
GDP := _GDP;
Population := _Population;
PopulationGrowthRate := _PopulationGrowthRate;
AreaLand := _AreaLand;
AreaWater := _AreaWater;
IrrigatedLand := _IrrigatedLand;
End Constructor Create;
Public Constructor CreateEmpty;
Begin
End Constructor CreateEmpty;
End Class Country;
Function Max(D1, D2: Double): Double;
Begin
If D1 > D2 Then
Return D1;
End If;
Return D2;
End Function Max;
// Method for initialization of the network
Sub NetworkInitialize(Network: NeuralNetwork);
Var
numberOfClusters: Integer =3;
numberOfNeurons: array[0..0] Of Integer;
Begin
numberOfNeurons[0] := numberOfClusters;
Network.CreateNetworkEx(1, 6, numberOfNeurons, 0.5, 3);
Network.InitSynapsesConvex;
End Sub NetworkInitialize;
Sub Main;
Var
network: NeuralNetwork;
countries : ArrayList;
normalizedCountries: ArrayList;
i, epoch: Integer;
normalizedCountry, currCountry: Country;
inputs : array[0..5] Of Double;
alfa : Double;
learnRate : Double;
learnRadius: Integer;
sampleClass: Integer;
Begin
// Assign initial values
countries := New ArrayList.Create;
countries.Add(New Country.Create("Algeria", 3.9, 31193917, 1.74, 2381740, 0, 5550));
countries.Add(New Country.Create("Argentina", -3, 36955182, 1.16, 2736690, 30200, 17000));
countries.Add(New Country.Create("Brazil", 0.8, 172860370, 0.94, 8456510, 55455, 28000));
countries.Add(New Country.Create("Canada", 3.6, 31281092, 1.02, 9220970, 755170, 7100));
countries.Add(New Country.Create("China", 7, 1261832482, 0.9, 9326410, 270550, 498720));
countries.Add(New Country.Create("India", 5.5, 1014003817, 1.58, 2973190, 314400, 480000));
countries.Add(New Country.Create("Indonesia", 0, 224784210, 1.63, 1826440, 93000, 45970));
countries.Add(New Country.Create("Iran", 1, 65619636, 0.83, 1636000, 12000, 94000));
countries.Add(New Country.Create("Kazakhstan", 1.7, 16733227, -0.05, 2669800, 47500, 22000));
countries.Add(New Country.Create("Libya", 2, 5115450, 2.42, 1759540, 0, 4700));
countries.Add(New Country.Create("Mexico", 3.7, 100349766, 1.53, 1923040, 49510, 61000));
countries.Add(New Country.Create("Mongolia", 3.5, 2650952, 1.54, 1565000, 0, 800));
countries.Add(New Country.Create("Niger", 2, 10075511, 2.75, 1266700, 300, 660));
countries.Add(New Country.Create("Peru", 2.4, 27012899, 1.75, 1280000, 5220, 12800));
countries.Add(New Country.Create("Russia", 3.2, 146001176, -0.38, 16995800, 79400, 40000));
countries.Add(New Country.Create("Saudi Arabia", 1.6, 22023506, 3.28, 1960582, 0, 4350));
countries.Add(New Country.Create("Sudan", 3, 35079814, 2.84, 2376000, 129810, 19460));
countries.Add(New Country.Create("United States", 4.1, 275562673, 0.91, 9158960, 470131, 207000));
normalizedCountry := New Country.CreateEmpty;
For i := 0 To countries.Count - 1 Do
currCountry := countries.Item(i) As Country;
normalizedCountry.GDP := Max(normalizedCountry.GDP, currCountry.GDP);
normalizedCountry.Population := Max(normalizedCountry.Population, currCountry.Population);
normalizedCountry.PopulationGrowthRate := Max(normalizedCountry.PopulationGrowthRate, currCountry.PopulationGrowthRate);
normalizedCountry.AreaLand := Max(normalizedCountry.AreaLand, currCountry.AreaLand);
normalizedCountry.AreaWater := Max(normalizedCountry.AreaWater, currCountry.AreaWater);
normalizedCountry.IrrigatedLand := Max(normalizedCountry.IrrigatedLand, currCountry.IrrigatedLand);
End For;
normalizedCountries := New ArrayList.Create;
For i := 0 To countries.Count - 1 Do
currCountry := countries.Item(i) As Country;
normalizedCountries.Add(New Country.Create(currCountry.Name, currCountry.GDP / normalizedCountry.GDP, currCountry.Population / normalizedCountry.Population,
currCountry.PopulationGrowthRate / normalizedCountry.PopulationGrowthRate, currCountry.AreaLand / normalizedCountry.AreaLand,
currCountry.AreaWater / normalizedCountry.AreaWater, currCountry.IrrigatedLand / normalizedCountry.IrrigatedLand));
End For;
// Create a neural network
network := New NeuralNetwork.Create;
// Initialize network
NetworkInitialize(network);
// Learn network
For epoch := 1 To 300 Do
For i := 0 To normalizedCountries.Count - 1 Do
currCountry := normalizedCountries.Item(i) As Country;
inputs[0] := currCountry.GDP;
inputs[1] := currCountry.Population;
inputs[2] := currCountry.PopulationGrowthRate;
inputs[3] := currCountry.AreaLand;
inputs[4] := currCountry.AreaWater;
inputs[5] := currCountry.IrrigatedLand;
alfa := epoch / 100;
network.SetInputValuesConvex(inputs, alfa);
learnRate := 0.6 * ((300 - epoch) / 300);
learnRadius := (3 * ((300 - epoch) / 350)) As Integer;
network.SetLearnRadius(learnRadius);
network.SetLearnRate(learnRate);
network.PropagateSOFM;
network.LearnSOFM;
End For;
End For;
// Test input values
For i := 0 To normalizedCountries.Count - 1 Do
currCountry := normalizedCountries.Item(i) As Country;
inputs[0] := currCountry.GDP;
inputs[1] := currCountry.Population;
inputs[2] := currCountry.PopulationGrowthRate;
inputs[3] := currCountry.AreaLand;
inputs[4] := currCountry.AreaWater;
inputs[5] := currCountry.IrrigatedLand;
alfa := 1;
network.SetInputValuesConvex(inputs, alfa);
network.PropagateSOFM;
sampleClass := network.GetClosestNeuron;
Debug.WriteLine(CurrCountry.Name + ". Class: " + sampleClass.ToString);
End For;
// Delete network
network.DeleteNetwork;
End Sub Main;
After executing the example each country, which data is tested, is related to one of the three classes. Results are displayed in the console window.
See also: