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Java LinearClassifierFactory類代碼示例

本文整理匯總了Java中edu.stanford.nlp.classify.LinearClassifierFactory類的典型用法代碼示例。如果您正苦於以下問題：Java LinearClassifierFactory類的具體用法？Java LinearClassifierFactory怎麽用？Java LinearClassifierFactory使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。


LinearClassifierFactory類屬於edu.stanford.nlp.classify包，在下文中一共展示了LinearClassifierFactory類的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: trainRVF

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
public LinearClassifier trainRVF(List<HashMap<String, Double>> list_feature2values,
		List<String> list_labels) {
	List<Datum<String, String>> trainingData = new ArrayList<Datum<String, String>>();
	for (int i = 0; i < list_feature2values.size(); i++) {
		HashMap<String, Double> feature2values = list_feature2values.get(i);
		String label = list_labels.get(i);
		Datum<String, String> d = new RVFDatum(Counters.fromMap(feature2values), label);
		trainingData.add(d);
	}
	// Build a classifier factory
	LinearClassifierFactory<String, String> factory = new LinearClassifierFactory<String, String>();
	factory.setSigma(3);
	factory.setEpsilon(15);
	factory.useQuasiNewton();
	factory.setVerbose(true);
	LinearClassifier<String, String> classifier = factory.trainClassifier(trainingData);
	// {
	// ArrayList<String> temp = new ArrayList<String>();
	// temp.add("NS=" + GREEN);
	// System.out.println(classifier.scoreOf(new BasicDatum<String,
	// String>(temp, BROKEN), BROKEN));
	// }

	this.classifier = classifier;
	return classifier;
}
開發者ID:zhangcongle，項目名稱:NewsSpikeRe，代碼行數:27，代碼來源:StanfordRegression.java

示例2: adaptMaxEnt

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
private void adaptMaxEnt(Dataset<String, String> adapt) {
  if (classifier instanceof LinearClassifier) {
    // So far the adaptation is only done on Gaussian Prior. Haven't checked how it'll work on other kinds of priors. -pichuan
    int prior = LogPrior.LogPriorType.QUADRATIC.ordinal();
    if (flags.useHuber) {
      throw new UnsupportedOperationException();
    } else if (flags.useQuartic) {
      throw new UnsupportedOperationException();
    }

    LinearClassifierFactory<String, String> lcf = new LinearClassifierFactory<String, String>(flags.tolerance, flags.useSum, prior, flags.adaptSigma, flags.epsilon, flags.QNsize);
    ((LinearClassifier<String, String>)classifier).adaptWeights(adapt,lcf);
  } else {
    throw new UnsupportedOperationException();
  }
}
開發者ID:paulirwin，項目名稱:Stanford.NER.Net，代碼行數:17，代碼來源:CMMClassifier.java

示例3: trainSemiSup

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
private void trainSemiSup(Dataset<String, String> data, Dataset<String, String> biasedData, double[][] confusionMatrix) {
  int prior = LogPrior.LogPriorType.QUADRATIC.ordinal();
  if (flags.useHuber) {
    prior = LogPrior.LogPriorType.HUBER.ordinal();
  } else if (flags.useQuartic) {
    prior = LogPrior.LogPriorType.QUARTIC.ordinal();
  }

  LinearClassifierFactory<String, String> lcf;
  lcf = new LinearClassifierFactory<String, String>(flags.tolerance, flags.useSum, prior, flags.sigma, flags.epsilon, flags.QNsize);
  if (flags.useQN) {
    lcf.useQuasiNewton();
  } else{
    lcf.useConjugateGradientAscent();
  }

  this.classifier = (LinearClassifier<String, String>) lcf.trainClassifierSemiSup(data, biasedData, confusionMatrix, null);
}
開發者ID:paulirwin，項目名稱:Stanford.NER.Net，代碼行數:19，代碼來源:CMMClassifier.java

示例4: trainBasic

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
public LinearClassifier trainBasic(
		List<List<String>> list_features, List<String> list_labels) {
	List<Datum<String, String>> trainingData = new ArrayList<Datum<String, String>>();
	for (int i = 0; i < list_features.size(); i++) {
		List<String> features = list_features.get(i);
		String label = list_labels.get(i);
		Datum<String, String> d = new BasicDatum<String, String>(features, label);
		trainingData.add(d);
	}
	// Build a classifier factory
	LinearClassifierFactory<String, String> factory = new LinearClassifierFactory<String, String>();
	// factory.setTol(tol);
	// factory.setSigma(1);
	// factory.setEpsilon(0.01);
	// factory.useQuasiNewton();
	factory.setVerbose(true);
	LinearClassifier<String, String> classifier = factory.trainClassifier(trainingData);
	// {
	// ArrayList<String> temp = new ArrayList<String>();
	// temp.add("NS=" + GREEN);
	// System.out.println(classifier.scoreOf(new BasicDatum<String,
	// String>(temp, BROKEN), BROKEN));
	// }

	this.classifier = classifier;
	return classifier;
}
開發者ID:zhangcongle，項目名稱:NewsSpikeRe，代碼行數:28，代碼來源:StanfordRegression.java

示例5: trainMaxEnt

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
private void trainMaxEnt(Dataset<String, String> train) {
  int prior = LogPrior.LogPriorType.QUADRATIC.ordinal();
  if (flags.useHuber) {
    prior = LogPrior.LogPriorType.HUBER.ordinal();
  } else if (flags.useQuartic) {
    prior = LogPrior.LogPriorType.QUARTIC.ordinal();
  }

  LinearClassifier<String, String> lc;
  if (flags.useNB) {
    lc = new NBLinearClassifierFactory<String, String>(flags.sigma).trainClassifier(train);
  } else {
    LinearClassifierFactory<String, String> lcf = new LinearClassifierFactory<String, String>(flags.tolerance, flags.useSum, prior, flags.sigma, flags.epsilon, flags.QNsize);
    if (flags.useQN) {
      lcf.useQuasiNewton(flags.useRobustQN);
    } else if(flags.useStochasticQN) {
      lcf.useStochasticQN(flags.initialGain,flags.stochasticBatchSize);
    } else if(flags.useSMD) {
      lcf.useStochasticMetaDescent(flags.initialGain, flags.stochasticBatchSize,flags.stochasticMethod,flags.SGDPasses);
    } else if(flags.useSGD) {
      lcf.useStochasticGradientDescent(flags.gainSGD,flags.stochasticBatchSize);
    } else if(flags.useSGDtoQN) {
      lcf.useStochasticGradientDescentToQuasiNewton(flags.initialGain, flags.stochasticBatchSize,
                                     flags.SGDPasses, flags.QNPasses, flags.SGD2QNhessSamples,
                                     flags.QNsize, flags.outputIterationsToFile);
    } else if(flags.useHybrid) {
      lcf.useHybridMinimizer(flags.initialGain, flags.stochasticBatchSize ,flags.stochasticMethod ,flags.hybridCutoffIteration );
    } else {
      lcf.useConjugateGradientAscent();
    }
    lc = lcf.trainClassifier(train);
  }
  this.classifier = lc;
}
開發者ID:paulirwin，項目名稱:Stanford.NER.Net，代碼行數:35，代碼來源:CMMClassifier.java

示例6: retrain

import edu.stanford.nlp.classify.LinearClassifierFactory; //導入依賴的package包/類
/**
 * @param featureLabels retrain docs
 * @param featureIndex featureIndex of original dataset (used in training)
 * @param labelIndex labelIndex of original dataset (used in training)
 */
public void retrain(ObjectBank<List<IN>> featureLabels, Index<String> featureIndex, Index<String> labelIndex) {
  int fs = featureIndex.size(); // old dim
  int ls = labelIndex.size();   // old dim

  Dataset<String, String> adapt = getDataset(featureLabels, featureIndex, labelIndex);

  int prior = LogPrior.LogPriorType.QUADRATIC.ordinal();
  LinearClassifier<String, String> lc = (LinearClassifier<String, String>) classifier;
  LinearClassifierFactory<String, String> lcf = new LinearClassifierFactory<String, String>(flags.tolerance, flags.useSum, prior, flags.sigma, flags.epsilon, flags.QNsize);

  double[][] weights = lc.weights();  // old dim
  Index<String> newF = adapt.featureIndex;
  Index<String> newL = adapt.labelIndex;
  int newFS = newF.size();
  int newLS = newL.size();
  double[] x = new double[newFS*newLS]; // new dim
  //System.err.println("old  ["+fs+"]"+"["+ls+"]");
  //System.err.println("new  ["+newFS+"]"+"["+newLS+"]");
  //System.err.println("new  ["+newFS*newLS+"]");
  for (int i = 0; i < fs; i++) {
    for (int j = 0; j < ls; j++) {
      String f = featureIndex.get(i);
      String l = labelIndex.get(j);
      int newi = newF.indexOf(f)*newLS+newL.indexOf(l);
      x[newi] = weights[i][j];
      //if (newi == 144745*2) {
      //System.err.println("What??"+i+"\t"+j);
      //}
    }
  }
  //System.err.println("x[144745*2]"+x[144745*2]);
  weights = lcf.trainWeights(adapt, x);
  //System.err.println("x[144745*2]"+x[144745*2]);
  //System.err.println("weights[144745]"+"[0]="+weights[144745][0]);

  lc.setWeights(weights);
  /*
  int delme = 0;
  if (true) {
    for (double[] dd : weights) {
      delme++;
      for (double d : dd) {
      }
    }
  }
  System.err.println(weights[delme-1][0]);
  System.err.println("size of weights: "+delme);
  */
}
開發者ID:paulirwin，項目名稱:Stanford.NER.Net，代碼行數:55，代碼來源:CMMClassifier.java


注：本文中的edu.stanford.nlp.classify.LinearClassifierFactory類示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。