public void BuildOutputEquilateral(DataNormalization norm, IInputField coverType)
{
var outType = new OutputEquilateral();
outType.AddItem(coverType, 1);
outType.AddItem(coverType, 2);
outType.AddItem(coverType, 3);
outType.AddItem(coverType, 4);
outType.AddItem(coverType, 5);
outType.AddItem(coverType, 6);
outType.AddItem(coverType, 7);
norm.AddOutputField(outType, true);
}
public IActionResult DistribuicaoFuncoes(DistribuicaoFuncoesVM distribuicaoFuncoesVM)
{
List <int> listaAnos = new FiliadosFuncionariosBO(_connectionStrings.DefaultConnection).GetAllDataCargosDisponiveis();
distribuicaoFuncoesVM.selecaoAno = new SelectList(listaAnos);
List <FiliadosFuncionariosDTO> listaRegioesFuncoes = new FiliadosFuncionariosBO(_connectionStrings.DefaultConnection).GetDistribuicaoFuncoes(new FiliadosFuncionariosDTO()
{
Ano = distribuicaoFuncoesVM.anoSelecionado
});
distribuicaoFuncoesVM.arrayDados = DataNormalization.NormalizeDistribuicaoFuncoes(listaRegioesFuncoes);
return(View(distribuicaoFuncoesVM));
}
public JsonResult GetDistribuicaoFuncoes(int ano)
{
List <FiliadosFuncionariosDTO> listaRegioesFuncoes = new FiliadosFuncionariosBO(_connectionStrings.DefaultConnection).GetDistribuicaoFuncoes(new FiliadosFuncionariosDTO()
{
Ano = ano
});
if (listaRegioesFuncoes.Count > 0)
{
return(Json(DataNormalization.NormalizeDistribuicaoFuncoes(listaRegioesFuncoes)));
}
else
{
return(Json(null));
}
}
public void EvaluateNetwork()
{
BasicNetwork network = LoadNetwork();
DataNormalization norm = LoadNormalization();
var csv = new ReadCSV(_config.EvaluateFile.ToString(), false, ',');
var input = new double[norm.InputFields.Count];
var eqField = (OutputEquilateral)norm.FindOutputField(
typeof(OutputEquilateral), 0);
int correct = 0;
int total = 0;
while (csv.Next())
{
total++;
for (int i = 0; i < input.Length; i++)
{
input[i] = csv.GetDouble(i);
}
IMLData inputData = norm.BuildForNetworkInput(input);
IMLData output = network.Compute(inputData);
int coverTypeActual = DetermineTreeType(eqField, output);
int coverTypeIdeal = (int)csv.GetDouble(54) - 1;
KeepScore(coverTypeActual, coverTypeIdeal);
if (coverTypeActual == coverTypeIdeal)
{
correct++;
}
}
Console.WriteLine(@"Total cases:" + total);
Console.WriteLine(@"Correct cases:" + correct);
double percent = correct / (double)total;
Console.WriteLine(@"Correct percent:"
+ Format.FormatPercentWhole(percent));
for (int i = 0; i < 7; i++)
{
double p = (_treeCorrect[i] / (double)_treeCount[i]);
Console.WriteLine(@"Tree Type #" + i + @" - Correct/total: "
+ _treeCorrect[i] + @"/" + _treeCount[i] + @"("
+ Format.FormatPercentWhole(p) + @")");
}
}
/// <summary>
/// Loads a normalization from the specified directory and file.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <returns>a datanormalization object</returns>
public static DataNormalization LoadNormalization(string directory, string file)
{
DataNormalization norm = null;
FileInfo networkFile = FileUtil.CombinePath(new FileInfo(@directory), @file);
if (networkFile.Exists)
{
norm = (DataNormalization)SerializeObject.Load(networkFile.FullName);
}
if (norm == null)
{
Console.WriteLine(@"Can't find normalization resource: "
+ directory + file);
return(null);
}
return(norm);
}
public DataNormalization LoadNormalization()
{
DataNormalization norm = null;
if (_config.NormalizeFile.Exists)
{
norm = (DataNormalization)SerializeObject.Load(_config.NormalizeFile.ToString());
}
if (norm == null)
{
Console.WriteLine(@"Can't find normalization resource: "
+ _config.NormalizeFile);
return(null);
}
return(norm);
}
public static void Generate(ForestConfig config, bool useOneOf)
{
var generate = new GenerateData(config);
generate.Step1();
generate.Step2();
DataNormalization norm = generate.Step3(useOneOf);
// save the normalize object
SerializeObject.Save(config.NormalizeFile.ToString(), norm);
// create and save the neural network
BasicNetwork network =
EncogUtility.SimpleFeedForward(norm.GetNetworkInputLayerSize(),
config.HiddenCount, 0, norm.GetNetworkOutputLayerSize(), true);
EncogDirectoryPersistence.SaveObject(config.TrainedNetworkFile, network);
}
public void Copy(FileInfo source, FileInfo target, int start, int stop, int size)
{
var inputField = new IInputField[55];
var norm = new DataNormalization {
Report = this, Storage = new NormalizationStorageCSV(target.ToString())
};
for (int i = 0; i < 55; i++)
{
inputField[i] = new InputFieldCSV(true, source.ToString(), i);
norm.AddInputField(inputField[i]);
IOutputField outputField = new OutputFieldDirect(inputField[i]);
norm.AddOutputField(outputField);
}
// load only the part we actually want, i.e. training or eval
var segregator2 = new IndexSampleSegregator(start, stop, size);
norm.AddSegregator(segregator2);
norm.Process();
}
public void Narrow(FileInfo source, FileInfo target, int field, int count)
{
var inputField = new IInputField[55];
var norm = new DataNormalization {
Report = this, Storage = new NormalizationStorageCSV(target.ToString())
};
for (int i = 0; i < 55; i++)
{
inputField[i] = new InputFieldCSV(true, source.ToString(), i);
norm.AddInputField(inputField[i]);
IOutputField outputField = new OutputFieldDirect(inputField[i]);
norm.AddOutputField(outputField);
}
var segregator = new IntegerBalanceSegregator(inputField[field], count);
norm.AddSegregator(segregator);
norm.Process();
Console.WriteLine(@"Samples per tree type:");
Console.WriteLine(segregator.DumpCounts());
}
public void Execute(IExampleInterface app)
{
string inputFile = "C:\\jth\\iris.csv";
DataNormalization normalize = new DataNormalization();
IInputField a, b, c, d;
normalize.AddInputField(a = new InputFieldCSV(true, inputFile, "sepal_l"));
normalize.AddInputField(b = new InputFieldCSV(true, inputFile, "sepal_w"));
normalize.AddInputField(c = new InputFieldCSV(true, inputFile, "petal_l"));
normalize.AddInputField(d = new InputFieldCSV(true, inputFile, "petal_w"));
normalize.AddInputField(new InputFieldCSV(false, inputFile, "species"));
normalize.AddOutputField(new OutputFieldRangeMapped(a));
normalize.AddOutputField(new OutputFieldRangeMapped(b));
normalize.AddOutputField(new OutputFieldRangeMapped(c));
normalize.AddOutputField(new OutputFieldRangeMapped(d));
//normalize.AddOutputField(new OutputOneOf(1,0));
NormalizationStorageMLDataSet store = new NormalizationStorageMLDataSet(4, 0);
normalize.Storage = store;
normalize.Report = new ConsoleStatusReportable();
normalize.Process(true);
Console.WriteLine(store.DataSet.Count);
}
public void Execute(IExampleInterface app)
{
string inputFile = @"C:\Development\AI\machinelearning\encog-dotnet-core-master\encog-core-test\Resources\iris.csv";
DataNormalization normalize = new DataNormalization();
IInputField a, b, c, d;
normalize.AddInputField(a = new InputFieldCSV(true, inputFile, "sepal_l"));
normalize.AddInputField(b = new InputFieldCSV(true, inputFile, "sepal_w"));
normalize.AddInputField(c = new InputFieldCSV(true, inputFile, "petal_l"));
normalize.AddInputField(d = new InputFieldCSV(true, inputFile, "petal_w"));
normalize.AddInputField(new InputFieldCSV(false, inputFile, "species"));
normalize.AddOutputField(new OutputFieldRangeMapped(a));
normalize.AddOutputField(new OutputFieldRangeMapped(b));
normalize.AddOutputField(new OutputFieldRangeMapped(c));
normalize.AddOutputField(new OutputFieldRangeMapped(d));
//normalize.AddOutputField(new OutputOneOf(1,0));
NormalizationStorageMLDataSet store = new NormalizationStorageMLDataSet(4, 0);
normalize.Storage = store;
normalize.Report = new ConsoleStatusReportable();
normalize.Process(true);
Console.WriteLine(store.DataSet.Count + " Datasets validated");
}
/// <summary>
/// Init the object.
/// </summary>
/// <param name="normalization">The normalization object that owns this range.</param>
public void Init(DataNormalization normalization)
{
this.normalization = normalization;
}
/// <summary>
/// Init the object.
/// </summary>
/// <param name="normalization">The normalization object that owns this range.</param>
public void Init(DataNormalization normalization)
{
_normalization = normalization;
}
public DataNormalization Step3(bool useOneOf)
{
Console.WriteLine(@"Step 3: Normalize training data");
IInputField inputElevation;
IInputField inputAspect;
IInputField inputSlope;
IInputField hWater;
IInputField vWater;
IInputField roadway;
IInputField shade9;
IInputField shade12;
IInputField shade3;
IInputField firepoint;
var wilderness = new IInputField[4];
var soilType = new IInputField[40];
IInputField coverType;
var norm = new DataNormalization
{
Report = this,
Storage = new NormalizationStorageCSV(_config.NormalizedDataFile.ToString())
};
norm.AddInputField(inputElevation = new InputFieldCSV(true, _config.BalanceFile.ToString(), 0));
norm.AddInputField(inputAspect = new InputFieldCSV(true, _config.BalanceFile.ToString(), 1));
norm.AddInputField(inputSlope = new InputFieldCSV(true, _config.BalanceFile.ToString(), 2));
norm.AddInputField(hWater = new InputFieldCSV(true, _config.BalanceFile.ToString(), 3));
norm.AddInputField(vWater = new InputFieldCSV(true, _config.BalanceFile.ToString(), 4));
norm.AddInputField(roadway = new InputFieldCSV(true, _config.BalanceFile.ToString(), 5));
norm.AddInputField(shade9 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 6));
norm.AddInputField(shade12 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 7));
norm.AddInputField(shade3 = new InputFieldCSV(true, _config.BalanceFile.ToString(), 8));
norm.AddInputField(firepoint = new InputFieldCSV(true, _config.BalanceFile.ToString(), 9));
for (int i = 0; i < 4; i++)
{
norm.AddInputField(wilderness[i] = new InputFieldCSV(true, _config.BalanceFile.ToString(), 10 + i));
}
for (int i = 0; i < 40; i++)
{
norm.AddInputField(soilType[i] = new InputFieldCSV(true, _config.BalanceFile.ToString(), 14 + i));
}
norm.AddInputField(coverType = new InputFieldCSV(false, _config.BalanceFile.ToString(), 54));
norm.AddOutputField(new OutputFieldRangeMapped(inputElevation));
norm.AddOutputField(new OutputFieldRangeMapped(inputAspect));
norm.AddOutputField(new OutputFieldRangeMapped(inputSlope));
norm.AddOutputField(new OutputFieldRangeMapped(hWater));
norm.AddOutputField(new OutputFieldRangeMapped(vWater));
norm.AddOutputField(new OutputFieldRangeMapped(roadway));
norm.AddOutputField(new OutputFieldRangeMapped(shade9));
norm.AddOutputField(new OutputFieldRangeMapped(shade12));
norm.AddOutputField(new OutputFieldRangeMapped(shade3));
norm.AddOutputField(new OutputFieldRangeMapped(firepoint));
for (int i = 0; i < 40; i++)
{
norm.AddOutputField(new OutputFieldDirect(soilType[i]));
}
if (useOneOf)
{
BuildOutputOneOf(norm, coverType);
}
else
{
BuildOutputEquilateral(norm, coverType);
}
norm.Process();
return(norm);
}
/// <summary>
/// Saves a normalization to the specified folder with the specified name.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <param name="normTosave">The norm tosave.</param>
public static void SaveNormalization(string directory, string file, DataNormalization normTosave)
{
SerializeObject.Save(directory + file, normTosave);
}
