cc.mallet.types.InstanceList.getInstanceWeight()方法的使用及代码示例
本文整理了Java中cc.mallet.types.InstanceList.getInstanceWeight()方法的一些代码示例，展示了InstanceList.getInstanceWeight()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。InstanceList.getInstanceWeight()方法的具体详情如下：
包路径：cc.mallet.types.InstanceList
类名称：InstanceList
方法名：getInstanceWeight
InstanceList.getInstanceWeight介绍
暂无
代码示例
代码示例来源：origin: de.julielab/jcore-mallet-2.0.9
/**
* Returns an <code>InstanceList</code> of the same size, where the instances come from the
* random sampling (with replacement) of this list using the instance weights.
* The new instances all have their weights set to one.
*/
// added by Gary - ghuang@cs.umass.edu
@Deprecated
// Move to InstanceListUtils
public InstanceList sampleWithInstanceWeights(java.util.Random r)
{
double[] weights = new double[size()];
for (int i = 0; i < weights.length; i++)
weights[i] = getInstanceWeight(i);
return sampleWithWeights(r, weights);
}代码示例来源：origin: cc.mallet/mallet
/**
* Returns an <code>InstanceList</code> of the same size, where the instances come from the
* random sampling (with replacement) of this list using the instance weights.
* The new instances all have their weights set to one.
*/
// added by Gary - ghuang@cs.umass.edu
@Deprecated
// Move to InstanceListUtils
public InstanceList sampleWithInstanceWeights(java.util.Random r)
{
double[] weights = new double[size()];
for (int i = 0; i < weights.length; i++)
weights[i] = getInstanceWeight(i);
return sampleWithWeights(r, weights);
}代码示例来源：origin: com.github.steveash.mallet/mallet
/**
* Returns an <code>InstanceList</code> of the same size, where the instances come from the
* random sampling (with replacement) of this list using the instance weights.
* The new instances all have their weights set to one.
*/
// added by Gary - ghuang@cs.umass.edu
@Deprecated
// Move to InstanceListUtils
public InstanceList sampleWithInstanceWeights(java.util.Random r)
{
double[] weights = new double[size()];
for (int i = 0; i < weights.length; i++)
weights[i] = getInstanceWeight(i);
return sampleWithWeights(r, weights);
}代码示例来源：origin: com.github.steveash.mallet/mallet
@Test
public void setWeights() {
InstanceList instances = new InstanceList(new Noop());
Instance instance1 = new Instance("test", null, null, null);
Instance instance2 = new Instance("test", null, null, null);
Instance instance3 = new Instance("test", null, null, null);
instances.add(instance1, 10.0);
instances.add(instance2);
assertEquals("#1", instances.getInstanceWeight(0), 10.0, 0.0);
assertEquals("#2", instances.getInstanceWeight(instance1), 10.0, 0.0);
assertEquals("#3", instances.getInstanceWeight(1), 1.0, 0.0);
assertEquals("#4", instances.getInstanceWeight(instance2), 1.0, 0.0);
// Reset an existing weight
instances.setInstanceWeight(0, 1.0);
assertEquals("#5", instances.getInstanceWeight(0), 1.0, 0.0);
// Reset an existing default (and therefore not represented) weight
instances.setInstanceWeight(1, 5.0);
assertEquals("#6", instances.getInstanceWeight(1), 5.0, 0.0);
}代码示例来源：origin: cc.mallet/mallet
@Test
public void setWeights() {
InstanceList instances = new InstanceList(new Noop());
Instance instance1 = new Instance("test", null, null, null);
Instance instance2 = new Instance("test", null, null, null);
Instance instance3 = new Instance("test", null, null, null);
instances.add(instance1, 10.0);
instances.add(instance2);
assertEquals("#1", instances.getInstanceWeight(0), 10.0, 0.0);
assertEquals("#2", instances.getInstanceWeight(instance1), 10.0, 0.0);
assertEquals("#3", instances.getInstanceWeight(1), 1.0, 0.0);
assertEquals("#4", instances.getInstanceWeight(instance2), 1.0, 0.0);
// Reset an existing weight
instances.setInstanceWeight(0, 1.0);
assertEquals("#5", instances.getInstanceWeight(0), 1.0, 0.0);
// Reset an existing default (and therefore not represented) weight
instances.setInstanceWeight(1, 5.0);
assertEquals("#6", instances.getInstanceWeight(1), 5.0, 0.0);
}代码示例来源：origin: de.julielab/jcore-mallet-2.0.9
public NaiveBayes trainIncremental (InstanceList trainingInstancesToAdd)
{
// Initialize and check instance variables as necessary...
setup(trainingInstancesToAdd, null);
// Incrementally add the counts of this new training data
for (Instance instance : trainingInstancesToAdd)
incorporateOneInstance(instance, trainingInstancesToAdd.getInstanceWeight(instance));
// Estimate multinomials, and return a new naive Bayes classifier.
// Note that, unlike MaxEnt, NaiveBayes is immutable, so we create a new one each time.
classifier = new NaiveBayes (instancePipe, pe.estimate(), estimateFeatureMultinomials());
return classifier;
}代码示例来源：origin: cc.mallet/mallet
public NaiveBayes trainIncremental (InstanceList trainingInstancesToAdd)
{
// Initialize and check instance variables as necessary...
setup(trainingInstancesToAdd, null);
// Incrementally add the counts of this new training data
for (Instance instance : trainingInstancesToAdd)
incorporateOneInstance(instance, trainingInstancesToAdd.getInstanceWeight(instance));
// Estimate multinomials, and return a new naive Bayes classifier.
// Note that, unlike MaxEnt, NaiveBayes is immutable, so we create a new one each time.
classifier = new NaiveBayes (instancePipe, pe.estimate(), estimateFeatureMultinomials());
return classifier;
}代码示例来源：origin: com.github.steveash.mallet/mallet
public NaiveBayes trainIncremental (InstanceList trainingInstancesToAdd)
{
// Initialize and check instance variables as necessary...
setup(trainingInstancesToAdd, null);
// Incrementally add the counts of this new training data
for (Instance instance : trainingInstancesToAdd)
incorporateOneInstance(instance, trainingInstancesToAdd.getInstanceWeight(instance));
// Estimate multinomials, and return a new naive Bayes classifier.
// Note that, unlike MaxEnt, NaiveBayes is immutable, so we create a new one each time.
classifier = new NaiveBayes (instancePipe, pe.estimate(), estimateFeatureMultinomials());
return classifier;
}代码示例来源：origin: cc.mallet/mallet
public void split (FeatureSelection fs)
{
if (ilist == null)
throw new IllegalStateException ("Frozen. Cannot split.");
InstanceList ilist0 = new InstanceList (ilist.getPipe());
InstanceList ilist1 = new InstanceList (ilist.getPipe());
for (int i = 0; i < ilist.size(); i++) {
Instance instance = ilist.get(i);
FeatureVector fv = (FeatureVector) instance.getData ();
// xxx What test should this be? What to do with negative values?
// Whatever is decided here should also go in InfoGain.calcInfoGains()
if (fv.value (featureIndex) != 0) {
//System.out.println ("list1 add "+instance.getUri()+" weight="+ilist.getInstanceWeight(i));
ilist1.add (instance, ilist.getInstanceWeight(i));
} else {
//System.out.println ("list0 add "+instance.getUri()+" weight="+ilist.getInstanceWeight(i));
ilist0.add (instance, ilist.getInstanceWeight(i));
}
}
logger.info("child0="+ilist0.size()+" child1="+ilist1.size());
child0 = new Node (ilist0, this, fs);
child1 = new Node (ilist1, this, fs);
}代码示例来源：origin: cc.mallet/mallet
/**
* Set the constraints by running forward-backward with the <i>output label
* sequence provided</i>, thus restricting it to only those paths that agree with
* the label sequence.
*/
protected void gatherConstraints(InstanceList ilist) {
logger.info("Gathering constraints...");
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
Transducer.Incrementor incrementor =
instanceWeight == 1.0 ? constraints.new Incrementor()
: constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
constraints.assertNotNaNOrInfinite();
}代码示例来源：origin: com.github.steveash.mallet/mallet
/**
* Set the constraints by running forward-backward with the <i>output label
* sequence provided</i>, thus restricting it to only those paths that agree with
* the label sequence.
*/
protected void gatherConstraints(InstanceList ilist) {
logger.info("Gathering constraints...");
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
Transducer.Incrementor incrementor =
instanceWeight == 1.0 ? constraints.new Incrementor()
: constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
constraints.assertNotNaNOrInfinite();
}代码示例来源：origin: de.julielab/jcore-mallet-2.0.9
/**
* Set the constraints by running forward-backward with the <i>output label
* sequence provided</i>, thus restricting it to only those paths that agree with
* the label sequence.
*/
protected void gatherConstraints(InstanceList ilist) {
logger.info("Gathering constraints...");
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
Transducer.Incrementor incrementor =
instanceWeight == 1.0 ? constraints.new Incrementor()
: constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
constraints.assertNotNaNOrInfinite();
}代码示例来源：origin: de.julielab/jcore-mallet-2.0.9
public LabelVector targetLabelDistribution ()
{
if (this.size() == 0) return null;
if (!(get(0).getTarget() instanceof Labeling))
throw new IllegalStateException ("Target is not a labeling.");
double[] counts = new double[getTargetAlphabet().size()];
for (int i = 0; i < this.size(); i++) {
Instance instance = get(i);
Labeling l = (Labeling) instance.getTarget();
l.addTo (counts, getInstanceWeight(i));
}
return new LabelVector ((LabelAlphabet)getTargetAlphabet(), counts);
}代码示例来源：origin: com.github.steveash.mallet/mallet
public LabelVector targetLabelDistribution ()
{
if (this.size() == 0) return null;
if (!(get(0).getTarget() instanceof Labeling))
throw new IllegalStateException ("Target is not a labeling.");
double[] counts = new double[getTargetAlphabet().size()];
for (int i = 0; i < this.size(); i++) {
Instance instance = get(i);
Labeling l = (Labeling) instance.getTarget();
l.addTo (counts, getInstanceWeight(i));
}
return new LabelVector ((LabelAlphabet)getTargetAlphabet(), counts);
}代码示例来源：origin: cc.mallet/mallet
public LabelVector targetLabelDistribution ()
{
if (this.size() == 0) return null;
if (!(get(0).getTarget() instanceof Labeling))
throw new IllegalStateException ("Target is not a labeling.");
double[] counts = new double[getTargetAlphabet().size()];
for (int i = 0; i < this.size(); i++) {
Instance instance = get(i);
Labeling l = (Labeling) instance.getTarget();
l.addTo (counts, getInstanceWeight(i));
}
return new LabelVector ((LabelAlphabet)getTargetAlphabet(), counts);
}代码示例来源：origin: com.github.steveash.jg2p/jg2p-core
private static RankedFeatureVector countFeatures(InstanceList ilist, boolean countInstances) {
int numFeatures = ilist.getDataAlphabet().size();
double[] counts = new double[numFeatures];
for (int i = 0; i < ilist.size(); i++) {
Instance inst = ilist.get(i);
if (ilist.getInstanceWeight(i) == 0) {
continue;
}
Object data = inst.getData();
if (data instanceof FeatureVectorSequence) {
FeatureVectorSequence fvs = (FeatureVectorSequence) data;
for (int j = 0; j < fvs.size(); j++) {
countVector(counts, fvs.get(j), countInstances);
}
} else {
throw new IllegalArgumentException("Currently only handles FeatureVectorSequence data");
}
}
return new RankedFeatureVector(ilist.getDataAlphabet(), counts);
}代码示例来源：origin: cc.mallet/mallet
protected void gatherConstraints (InstanceList ilist)
{
// Set the constraints by running forward-backward with the *output
// label sequence provided*, thus restricting it to only those
// paths that agree with the label sequence.
// Zero the constraints[]
// Reset constraints[] to zero before we fill them again
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
//System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size());
Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
// System.out.println ("testing Value and Gradient");
// TestOptimizable.testValueAndGradientCurrentParameters (this);
}代码示例来源：origin: de.julielab/jcore-mallet-2.0.9
protected void gatherConstraints (InstanceList ilist)
{
// Set the constraints by running forward-backward with the *output
// label sequence provided*, thus restricting it to only those
// paths that agree with the label sequence.
// Zero the constraints[]
// Reset constraints[] to zero before we fill them again
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
//System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size());
Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
// System.out.println ("testing Value and Gradient");
// TestOptimizable.testValueAndGradientCurrentParameters (this);
}代码示例来源：origin: com.github.steveash.mallet/mallet
protected void gatherConstraints (InstanceList ilist)
{
// Set the constraints by running forward-backward with the *output
// label sequence provided*, thus restricting it to only those
// paths that agree with the label sequence.
// Zero the constraints[]
// Reset constraints[] to zero before we fill them again
assert (constraints.structureMatches(crf.parameters));
constraints.zero();
for (Instance instance : ilist) {
FeatureVectorSequence input = (FeatureVectorSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
double instanceWeight = ilist.getInstanceWeight(instance);
//System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size());
Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight);
new SumLatticeDefault (this.crf, input, output, incrementor);
}
// System.out.println ("testing Value and Gradient");
// TestOptimizable.testValueAndGradientCurrentParameters (this);
}代码示例来源：origin: cc.mallet/mallet
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.get(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;
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