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.
Imports Prognoz.Platform.Interop.NN;
Imports System.Collections.Generic;
Class Country
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;
Public Override Function ToString(): string;
Begin
Return
"Name=" + Name +
" GDP=" + GDP.ToString() +
" Population=" + Population.ToString() +
" PopulationGrowthRate=" + PopulationGrowthRate.ToString() +
" AreaLand=" + AreaLand.ToString() +
" AreaWater=" + AreaWater.ToString() +
" IrrigatedLand=" + IrrigatedLand.ToString();
End Function ToString;
End Class Country;
// Method for initialization of the network
Friend Sub NetworkInitialize(Neuro: NeuralNetwork);
Var
numberOfClusters: integer = 3;
numberOfNeurons: array[] Of integer = New integer[1];
arr: System.Array;
Begin
numberOfNeurons[0] := numberOfClusters;
arr := numberOfNeurons;
Neuro.CreateNetworkEx(1, 6, Var arr, 0.5, 3);
Neuro.InitSynapsesConvex();
End Sub NetworkInitialize;
// Method for the assignment of the initial values
Friend Function GenerateCountries(): List < Country >;
Var
Countries: List < Country >;
Begin
Countries := New List < Country > (18);
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));
Return Countries;
End Function GenerateCountries;
Friend Function GetMaxCountry(Countries: List < Country >): Country;
Var
Result, Cur: Country;
Begin
Result := New Country.CreateEmpty();
For Each Cur In Countries Do
Result.GDP := System.Math.Max(Result.GDP, Cur.GDP);
Result.Population := System.Math.Max(Result.Population, Cur.Population);
Result.PopulationGrowthRate := System.Math.Max(Result.PopulationGrowthRate, Cur.PopulationGrowthRate);
Result.AreaLand := System.Math.Max(Result.AreaLand, Cur.AreaLand);
Result.AreaWater := System.Math.Max(Result.AreaWater, Cur.AreaWater);
Result.IrrigatedLand := System.Math.Max(Result.IrrigatedLand, Cur.IrrigatedLand);
End For;
Return Result;
End Function GetMaxCountry;
Friend Function NormailizeCountries(Countries: List < Country >): List < Country >;
Var
Result: List < Country >;
MaxCountry: Country = GetMaxCountry(Countries);
Cur, Normalized: Country;
Begin
Result := New List < Country > (18);
For Each Cur In Countries Do
Normalized := New Country.Create(Cur.Name, Cur.GDP / MaxCountry.GDP, Cur.Population / MaxCountry.Population,
Cur.PopulationGrowthRate / MaxCountry.PopulationGrowthRate, Cur.AreaLand / MaxCountry.AreaLand,
Cur.AreaWater / MaxCountry.AreaWater, Cur.IrrigatedLand / MaxCountry.IrrigatedLand);
Result.Add(Normalized);
End For;
Return Result;
End Function NormailizeCountries;
// Method to learn the network
Friend Sub TeachNeuro(Neuro: NeuralNetwork; Countries: List < Country >);
Var
Epoch: integer;
Alfa: double;
Cur: Country;
Inputs: array[0..5] Of double = New double[0..5];
arr: System.Array;
learnRate: Double;
learnRadius: Integer;
Begin
For Epoch := 1 To 300 Do
For Each Cur In Countries Do
inputs[0] := Cur.GDP;
inputs[1] := Cur.Population;
inputs[2] := Cur.PopulationGrowthRate;
inputs[3] := Cur.AreaLand;
inputs[4] := Cur.AreaWater;
inputs[5] := Cur.IrrigatedLand;
Alfa := epoch / 100.0;
arr := Inputs;
Neuro.SetInputValuesConvex(Var arr, Alfa);
learnRate := 0.6 * ((300 - epoch) / 300.0);
learnRadius := System.Convert.ToInt32(System.Math.Round(3 * ((300 - epoch) / 350.0)));
Neuro.SetLearnRadius(learnRadius);
Neuro.SetLearnRate(learnRate);
Neuro.PropagateSOFM();
Neuro.LearnSOFM();
End For;
End For;
End Sub TeachNeuro;
// Method to classify values
Friend Function IdentifyCountry(Neuro: NeuralNetwork; Country: Country): integer;
Var
Alfa: double;
Inputs: array[0..5] Of double = New double[0..5];
arr: System.Array;
Begin
inputs[0] := Country.GDP;
inputs[1] := Country.Population;
inputs[2] := Country.PopulationGrowthRate;
inputs[3] := Country.AreaLand;
inputs[4] := Country.AreaWater;
inputs[5] := Country.IrrigatedLand;
Alfa := 1;
arr := Inputs;
Neuro.SetInputValuesConvex(Var arr, Alfa);
Neuro.PropagateSOFM();
Return Neuro.GetClosestNeuron();
End Function IdentifyCountry;
Friend Sub TipaMain();
Var
countries: List < Country >;
Neuro: NeuralNetwork;
Country: Country;
Begin
System.Diagnostics.Debug.WriteLine("===<BEGIN>===");
// Create a network
Neuro := New NeuralNetwork();
// Initialize network
NetworkInitialize(Neuro);
countries := NormailizeCountries(GenerateCountries());
// Learn network
TeachNeuro(Neuro, countries);
// Test input values
For Each Country In countries Do
System.Diagnostics.Debug.WriteLine(Country.Name + ". Class: " + IdentifyCountry(Neuro, Country).ToString());
End For;
System.Diagnostics.Debug.WriteLine("===< END >===");
// Delete network
Neuro.DeleteNetwork();
End Sub TipaMain;
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: