private void classify()
{
if (layer2Symbolize == null)
{
return;
}
IFeatureClass featureClass = layer2Symbolize.FeatureClass;
ITable pTable = (ITable)featureClass;
ITableHistogram pTableHistogram = new BasicTableHistogramClass();
IBasicHistogram pHistogram = (IBasicHistogram)pTableHistogram;
pTableHistogram.Field = strRendererField;
if (strNormalizeField.ToLower() != "none")
{
pTableHistogram.NormField = strNormalizeField;
}
pTableHistogram.Table = pTable;
object dataFrequency;
object dataValues;
pHistogram.GetHistogram(out dataValues, out dataFrequency);
//下面是分级方法，用于根据获得的值计算得出符合要求的数据
//根据条件计算出Classes和ClassesCount，numDesiredClasses为预定的分级数目
IClassifyGEN pClassify = new NaturalBreaksClass();
switch (strClassifyMethod)
{
case "等间隔分类":
pClassify = new EqualIntervalClass();
break;
//case "预定义间隔分类":
// pClassify = new DefinedIntervalClass();
// break;
case "分位数分类":
pClassify = new QuantileClass();
break;
case "自然裂点分类":
pClassify = new NaturalBreaksClass();
break;
case "标准差分类":
pClassify = new StandardDeviationClass();
break;
case "几何间隔分类":
pClassify = new GeometricalIntervalClass();
break;
default:
break;
}
int numDesiredClasses = gClassCount;
pClassify.Classify(dataValues, dataFrequency, ref numDesiredClasses);
gClassbreaks = (double[])pClassify.ClassBreaks;
}
private void classify()
{
if (layer2Symbolize == null)
{
return;
}
IFeatureClass featureClass = layer2Symbolize.FeatureClass;
ITable pTable = featureClass as ITable;
ITableHistogram pTableHistogram = new BasicTableHistogramClass();
IBasicHistogram pHistogram = pTableHistogram as IBasicHistogram;
pTableHistogram.Field = strRendererField;
if (strNormalizeField.ToLower() != "none")
{
pTableHistogram.NormField = strNormalizeField;
}
pTableHistogram.Table = pTable;
object dataFrequency;
object dataValues;
pHistogram.GetHistogram(out dataValues, out dataFrequency);
IClassifyGEN pClassify = new NaturalBreaksClass();
switch (strClassifyMethod)
{
case "Equal Interval":
pClassify = new EqualIntervalClass();
break;
case "Quantile":
pClassify = new QuantileClass();
break;
case "Natural Breaks":
pClassify = new NaturalBreaksClass();
break;
case "Geometrical Interval":
pClassify = new GeometricalIntervalClass();
break;
default:
break;
}
int numDesiredClass = gClassCount;
pClassify.Classify(dataValues, dataFrequency, ref numDesiredClass);
gClassbreaks = (double[])pClassify.ClassBreaks;
}
private void classify()
{
if (layer2Symbolize == null) return;
IFeatureClass featureClass = layer2Symbolize.FeatureClass;
ITable pTable = (ITable)featureClass;
ITableHistogram pTableHistogram = new BasicTableHistogramClass();
IBasicHistogram pHistogram = (IBasicHistogram)pTableHistogram;
pTableHistogram.Field = strRendererField;
if (strNormalizeField.ToLower() != "none")
pTableHistogram.NormField = strNormalizeField;
pTableHistogram.Table = pTable;
object dataFrequency;
object dataValues;
pHistogram.GetHistogram(out dataValues, out dataFrequency);
//下面是分级方法，用于根据获得的值计算得出符合要求的数据
//根据条件计算出Classes和ClassesCount，numDesiredClasses为预定的分级数目
IClassifyGEN pClassify = new NaturalBreaksClass();
switch (strClassifyMethod)
{
case "等间隔分类":
pClassify = new EqualIntervalClass();
break;
//case "预定义间隔分类":
// pClassify = new DefinedIntervalClass();
// break;
case "分位数分类":
pClassify = new QuantileClass();
break;
case "自然裂点分类":
pClassify = new NaturalBreaksClass();
break;
//case "标准差分类":
// pClassify = new StandardDeviationClass();
// break;
case "几何间隔分类":
pClassify = new GeometricalIntervalClass();
break;
default:
break;
}
int numDesiredClasses = gClassCount;
pClassify.Classify(dataValues, dataFrequency, ref numDesiredClasses);
gClassbreaks = (double[])pClassify.ClassBreaks;
}
