private void ClassificationMethodCombo_SelectionChanged(object sender, SelectionChangedEventArgs e)
{
ClassificationMethod method = (ClassificationMethod)(sender as ComboBox).SelectedItem;
IntervalCombo.IsEnabled = (method == ClassificationMethod.StandardDeviation) ? true : false;
BreakCountTb.IsEnabled = (method == ClassificationMethod.StandardDeviation) ? false : true;
}
/// <summary>
/// Applies the Classify colorizer to the basic raster layer.
/// </summary>
/// <param name="basicRasterLayer">The basic raster layer is either a raster or image service layer, or the image sub-layer of the mosaic layer.</param>
/// <returns></returns>
public static async Task SetToClassifyColorizer(BasicRasterLayer basicRasterLayer)
{
// Defines values for parameters in colorizer definition.
string fieldName = "Value";
ClassificationMethod classificationMethod = ClassificationMethod.NaturalBreaks;
int numberofClasses = 7;
string colorRampStyle = "ArcGIS Colors";
string colorRampName = "Aspect";
await QueuedTask.Run(async() =>
{
// Gets a color ramp from a style.
IList <ColorRampStyleItem> rampList = GetColorRampsFromStyleAsync(Project.Current, colorRampStyle, colorRampName);
CIMColorRamp colorRamp = rampList[0].ColorRamp;
// Creates a new Classify Colorizer Definition using defined parameters.
ClassifyColorizerDefinition classifyColorizerDef = new ClassifyColorizerDefinition(fieldName, numberofClasses, classificationMethod, colorRamp);
// Creates a new Classify colorizer using the colorizer definition created above.
CIMRasterClassifyColorizer newColorizer = await basicRasterLayer.CreateColorizerAsync(classifyColorizerDef) as CIMRasterClassifyColorizer;
// Sets the newly created colorizer on the layer.
basicRasterLayer.SetColorizer(newColorizer);
});
}
public override RegressionModel Train(BaseVector[] x, int[] nominal, double[] y, Parameters param, int ntheads, Action <double> reportProgress)
{
x = ClassificationMethod.ToOneHotEncoding(x, nominal);
int k = param.GetParam <int>("Number of neighbours").Value;
IDistance distance = Distances.GetDistanceFunction(param);
return(new KnnRegressionModel(x, y, k, distance));
}
public ClassificationWithRankingMultiSizes(ClassificationMethod classifier,
ClassificationFeatureRankingMethod ranker, double reductionFactor, int maxFeatures, Parameters classifierParam,
Parameters rankerParam)
{
this.classifier = classifier;
this.ranker = ranker;
this.reductionFactor = reductionFactor;
this.maxFeatures = maxFeatures;
this.classifierParam = classifierParam;
this.rankerParam = rankerParam;
}
public ClassificationWithRanking(ClassificationMethod classifier, ClassificationFeatureRankingMethod ranker,
int nfeatures, Parameters classifierParam, Parameters rankerParam, bool groupWiseSelection, int[] groupWiseNfeatures)
{
this.classifier = classifier;
this.ranker = ranker;
this.nfeatures = nfeatures;
this.classifierParam = classifierParam;
this.rankerParam = rankerParam;
this.groupWiseSelection = groupWiseSelection;
this.groupWiseNfeatures = groupWiseNfeatures;
}
public ClassificationWithRanking(ClassificationMethod classifier, ClassificationFeatureRankingMethod ranker,
int nfeatures, Parameters classifierParam, Parameters rankerParam, bool groupWiseSelection, int[] groupWiseNfeatures)
{
this.classifier = classifier;
this.ranker = ranker;
this.nfeatures = nfeatures;
this.classifierParam = classifierParam;
this.rankerParam = rankerParam;
this.groupWiseSelection = groupWiseSelection;
this.groupWiseNfeatures = groupWiseNfeatures;
}
public ClassificationWithRankingMultiSizes(ClassificationMethod classifier,
ClassificationFeatureRankingMethod ranker, double reductionFactor, int maxFeatures, Parameters classifierParam,
Parameters rankerParam)
{
this.classifier = classifier;
this.ranker = ranker;
this.reductionFactor = reductionFactor;
this.maxFeatures = maxFeatures;
this.classifierParam = classifierParam;
this.rankerParam = rankerParam;
}
public override RegressionModel Train(BaseVector[] x, int[] nominal, double[] y, Parameters param, int nthreads, Action <double> reportProgress)
{
x = ClassificationMethod.ToOneHotEncoding(x, nominal);
ParameterWithSubParams <int> kernelParam = param.GetParamWithSubParams <int>("Kernel");
SvmParameter sp = new SvmParameter {
kernelFunction = KernelFunctions.GetKernelFunction(kernelParam.Value, kernelParam.GetSubParameters()),
svmType = SvmType.EpsilonSvr,
c = param.GetParam <double>("C").Value
};
SvmModel model = SvmMain.SvmTrain(new SvmProblem(x, y), sp);
return(new SvmRegressionModel(model));
}
private void PageListBox_SelectionChanged(object sender, SelectionChangedEventArgs e)
{
if (sender != null)
{
((ChildPage)(((ListBox)sender).Parent)).IsOpen = false;
if (((ListBox)sender).Tag != null)
{
if (((ListBox)sender).Tag.ToString().Equals("ClassificationMethod"))
{
ClassificationMethod method = (ClassificationMethod)(sender as ListBox).SelectedItem;
IntervalButton.IsEnabled = (method == ClassificationMethod.StandardDeviation) ? true : false;
BreakCountTB.IsEnabled = (method == ClassificationMethod.StandardDeviation) ? false : true;
}
else if (((ListBox)sender).Tag.ToString().Equals("NormalizationType"))
{
NormalizationType normType = (NormalizationType)(sender as ListBox).SelectedItem;
NormalizationFieldButton.IsEnabled = (normType == NormalizationType.Field) ? true : false;
}
}
}
}
public override RegressionModel Train(BaseVector[] x, int[] nominal, double[] y, Parameters param, int nthreads, Action <double> reportProgress)
{
x = ClassificationMethod.ToOneHotEncoding(x, nominal);
throw new System.NotImplementedException();
}
