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📄 featurecounts.java

📁 常用机器学习算法,java编写源代码,内含常用分类算法,包括说明文档
💻 JAVA
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/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).   http://www.cs.umass.edu/~mccallum/mallet   This software is provided under the terms of the Common Public License,   version 1.0, as published by http://www.opensource.org.  For further   information, see the file `LICENSE' included with this distribution. *//**	 The number of instances in which each feature occurs.	 Note that we aren't attending to the feature's value, and MALLET doesn't currently	 have any support at all for categorical features.   @author Andrew McCallum <a href="mailto:mccallum@cs.umass.edu">mccallum@cs.umass.edu</a> */package edu.umass.cs.mallet.base.types;public class FeatureCounts extends RankedFeatureVector{	// increment by 1 for each instance that has the feature, ignoring the feature's value	static boolean countInstances = true;		private static double[] calcFeatureCounts (InstanceList ilist)	{		int numInstances = ilist.size();		int numClasses = ilist.getTargetAlphabet().size();		int numFeatures = ilist.getDataAlphabet().size();		double[] counts = new double[numFeatures];		double count;		for (int i = 0; i < ilist.size(); i++) {			Instance inst = ilist.getInstance(i);			if (!(inst.getData() instanceof FeatureVector))				throw new IllegalArgumentException ("Currently only handles FeatureVector data");			FeatureVector fv = (FeatureVector) inst.getData ();			if (ilist.getInstanceWeight(i) == 0)				continue;			for (int j = 0; j < fv.numLocations(); j++) {				if (countInstances)					counts[fv.indexAtLocation(j)] += 1;				else					counts[fv.indexAtLocation(j)] += fv.valueAtLocation(j);			}							}		return counts;	}	public FeatureCounts (InstanceList ilist)	{		super (ilist.getDataAlphabet(), calcFeatureCounts (ilist));	}	public FeatureCounts (Alphabet vocab, double[] counts)	{		super (vocab, counts);	}	public static class Factory implements RankedFeatureVector.Factory	{		public Factory ()		{		}				public RankedFeatureVector newRankedFeatureVector (InstanceList ilist)		{			return new FeatureCounts (ilist);		}	}	}

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