当前位置: 首页>>代码示例>>C#>>正文


C# Instances.enumerateAttributes方法代码示例

本文整理汇总了C#中weka.core.Instances.enumerateAttributes方法的典型用法代码示例。如果您正苦于以下问题:C# Instances.enumerateAttributes方法的具体用法?C# Instances.enumerateAttributes怎么用?C# Instances.enumerateAttributes使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在weka.core.Instances的用法示例。


在下文中一共展示了Instances.enumerateAttributes方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的C#代码示例。

示例1: selectModel

		/// <summary> Selects C4.5-type split for the given dataset.</summary>
		public override ClassifierSplitModel selectModel(Instances data)
		{
			
			double minResult;
			//double currentResult;
			BinC45Split[] currentModel;
			BinC45Split bestModel = null;
			NoSplit noSplitModel = null;
			double averageInfoGain = 0;
			int validModels = 0;
			bool multiVal = true;
			Distribution checkDistribution;
			double sumOfWeights;
			int i;
			
			try
			{
				
				// Check if all Instances belong to one class or if not
				// enough Instances to split.
				checkDistribution = new Distribution(data);
				noSplitModel = new NoSplit(checkDistribution);
				if (Utils.sm(checkDistribution.total(), 2 * m_minNoObj) || Utils.eq(checkDistribution.total(), checkDistribution.perClass(checkDistribution.maxClass())))
					return noSplitModel;
				
				// Check if all attributes are nominal and have a 
				// lot of values.
				System.Collections.IEnumerator enu = data.enumerateAttributes();
				//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
				while (enu.MoveNext())
				{
					//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
                    weka.core.Attribute attribute = (weka.core.Attribute)enu.Current;
					if ((attribute.Numeric) || (Utils.sm((double) attribute.numValues(), (0.3 * (double) m_allData.numInstances()))))
					{
						multiVal = false;
						break;
					}
				}
				currentModel = new BinC45Split[data.numAttributes()];
				sumOfWeights = data.sumOfWeights();
				
				// For each attribute.
				for (i = 0; i < data.numAttributes(); i++)
				{
					
					// Apart from class attribute.
					if (i != (data).classIndex())
					{
						
						// Get models for current attribute.
						currentModel[i] = new BinC45Split(i, m_minNoObj, sumOfWeights);
						currentModel[i].buildClassifier(data);
						
						// Check if useful split for current attribute
						// exists and check for enumerated attributes with 
						// a lot of values.
						if (currentModel[i].checkModel())
							if ((data.attribute(i).Numeric) || (multiVal || Utils.sm((double) data.attribute(i).numValues(), (0.3 * (double) m_allData.numInstances()))))
							{
								averageInfoGain = averageInfoGain + currentModel[i].infoGain();
								validModels++;
							}
					}
					else
						currentModel[i] = null;
				}
				
				// Check if any useful split was found.
				if (validModels == 0)
					return noSplitModel;
				averageInfoGain = averageInfoGain / (double) validModels;
				
				// Find "best" attribute to split on.
				minResult = 0;
				for (i = 0; i < data.numAttributes(); i++)
				{
					if ((i != (data).classIndex()) && (currentModel[i].checkModel()))
					// Use 1E-3 here to get a closer approximation to the original
					// implementation.
						if ((currentModel[i].infoGain() >= (averageInfoGain - 1e-3)) && Utils.gr(currentModel[i].gainRatio(), minResult))
						{
							bestModel = currentModel[i];
							minResult = currentModel[i].gainRatio();
						}
				}
				
				// Check if useful split was found.
				if (Utils.eq(minResult, 0))
					return noSplitModel;
				
				// Add all Instances with unknown values for the corresponding
				// attribute to the distribution for the model, so that
				// the complete distribution is stored with the model. 
				bestModel.distribution().addInstWithUnknown(data, bestModel.attIndex());
				
				// Set the split point analogue to C45 if attribute numeric.
				bestModel.SplitPoint = m_allData;
				return bestModel;
//.........这里部分代码省略.........
开发者ID:intille,项目名称:mitessoftware,代码行数:101,代码来源:BinC45ModelSelection.cs


注:本文中的weka.core.Instances.enumerateAttributes方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。