本文整理汇总了Java中weka.classifiers.bayes.NaiveBayesUpdateable类的典型用法代码示例。如果您正苦于以下问题:Java NaiveBayesUpdateable类的具体用法?Java NaiveBayesUpdateable怎么用?Java NaiveBayesUpdateable使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
NaiveBayesUpdateable类属于weka.classifiers.bayes包,在下文中一共展示了NaiveBayesUpdateable类的11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,您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: getClassifierClassName
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Get classifier's class name by a short name
* */
public static String getClassifierClassName(String classifierName) {
String className = "";
switch (classifierName) {
case "SGD":
className = SGD.class.toString();
break;
case "SGDText":
className = SGDText.class.toString();
break;
case "J48":
className = J48.class.toString();
break;
case "PART":
className = PART.class.toString();
break;
case "NaiveBayes":
className = NaiveBayes.class.toString();
break;
case "NBUpdateable":
className = NaiveBayesUpdateable.class.toString();
break;
case "AdaBoostM1":
className = AdaBoostM1.class.toString();
break;
case "LogitBoost":
className = LogitBoost.class.toString();
break;
case "Bagging":
className = Bagging.class.toString();
break;
case "Stacking":
className = Stacking.class.toString();
break;
case "AdditiveRegression":
className = AdditiveRegression.class.toString();
break;
case "Apriori":
className = Apriori.class.toString();
break;
default:
className = SGD.class.toString();
}
className = className.substring(6);
return className;
}
开发者ID:Eyasics,项目名称:recon,代码行数:49,代码来源:Util.java
示例2: crossValidate
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Utility method for fast 5-fold cross validation of a naive bayes
* model
*
* @param fullModel a <code>NaiveBayesUpdateable</code> value
* @param trainingSet an <code>Instances</code> value
* @param r a <code>Random</code> value
* @return a <code>double</code> value
* @exception Exception if an error occurs
*/
public static double crossValidate(NaiveBayesUpdateable fullModel,
Instances trainingSet,
Random r) throws Exception {
// make some copies for fast evaluation of 5-fold xval
Classifier [] copies = AbstractClassifier.makeCopies(fullModel, 5);
Evaluation eval = new Evaluation(trainingSet);
// make some splits
for (int j = 0; j < 5; j++) {
Instances test = trainingSet.testCV(5, j);
// unlearn these test instances
for (int k = 0; k < test.numInstances(); k++) {
test.instance(k).setWeight(-test.instance(k).weight());
((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));
// reset the weight back to its original value
test.instance(k).setWeight(-test.instance(k).weight());
}
eval.evaluateModel(copies[j], test);
}
return eval.incorrect();
}
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:31,代码来源:NBTreeNoSplit.java
示例3: crossValidate
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Utility method for fast 5-fold cross validation of a naive bayes
* model
*
* @param fullModel a <code>NaiveBayesUpdateable</code> value
* @param trainingSet an <code>Instances</code> value
* @param r a <code>Random</code> value
* @return a <code>double</code> value
* @exception Exception if an error occurs
*/
public static double crossValidate(NaiveBayesUpdateable fullModel,
Instances trainingSet,
Random r) throws Exception {
// make some copies for fast evaluation of 5-fold xval
Classifier [] copies = Classifier.makeCopies(fullModel, 5);
Evaluation eval = new Evaluation(trainingSet);
// make some splits
for (int j = 0; j < 5; j++) {
Instances test = trainingSet.testCV(5, j);
// unlearn these test instances
for (int k = 0; k < test.numInstances(); k++) {
test.instance(k).setWeight(-test.instance(k).weight());
((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));
// reset the weight back to its original value
test.instance(k).setWeight(-test.instance(k).weight());
}
eval.evaluateModel(copies[j], test);
}
return eval.incorrect();
}
开发者ID:williamClanton,项目名称:jbossBA,代码行数:31,代码来源:NBTreeNoSplit.java
示例4: makeOnlineClassifier
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
public static Classifier makeOnlineClassifier(String wekaClassifier, String[] options) throws Exception {
switch (WekaOnlineClassificationAlgorithms.valueOf(wekaClassifier)) {
case naiveBayes:
NaiveBayesUpdateable naiveBayesUpdateable = new NaiveBayesUpdateable();
setOptionsForWekaPredictor(options, naiveBayesUpdateable);
return naiveBayesUpdateable;
case locallyWeightedLearner:
LWL lwl = new LWL();
setOptionsForWekaPredictor(options, lwl);
return lwl;
case nearestNeighbors:
IBk ibk = new IBk();
setOptionsForWekaPredictor(options, ibk);
return ibk;
case onlineDecisionTree:
HoeffdingTree tree = new HoeffdingTree();
setOptionsForWekaPredictor(options, tree);
return tree;
case stochasticGradientDescent:
SGD sgd = new SGD();
setOptionsForWekaPredictor(options, sgd);
return sgd;
default:
return new NaiveBayesUpdateable();
}
}
开发者ID:LakkiB,项目名称:mlstorm,代码行数:27,代码来源:WekaUtils.java
示例5: buildClassifier
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Build the no-split node
*
* @param instances an <code>Instances</code> value
* @exception Exception if an error occurs
*/
public final void buildClassifier(Instances instances) throws Exception {
m_nb = new NaiveBayesUpdateable();
m_disc = new Discretize();
m_disc.setInputFormat(instances);
Instances temp = Filter.useFilter(instances, m_disc);
m_nb.buildClassifier(temp);
if (temp.numInstances() >= 5) {
m_errors = crossValidate(m_nb, temp, new Random(1));
}
m_numSubsets = 1;
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:18,代码来源:NBTreeNoSplit.java
示例6: AggregateableFilteredClassifierUpdateable
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
public AggregateableFilteredClassifierUpdateable() {
m_Classifier = new NaiveBayesUpdateable();
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:4,代码来源:AggregateableFilteredClassifierUpdateable.java
示例7: handleNumericAttribute
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Creates split on numeric attribute.
*
* @exception Exception if something goes wrong
*/
private void handleNumericAttribute(Instances trainInstances)
throws Exception {
m_c45S = new C45Split(m_attIndex, 2, m_sumOfWeights, true);
m_c45S.buildClassifier(trainInstances);
if (m_c45S.numSubsets() == 0) {
return;
}
m_errors = 0;
Instances[] trainingSets = new Instances[m_complexityIndex];
trainingSets[0] = new Instances(trainInstances, 0);
trainingSets[1] = new Instances(trainInstances, 0);
int subset = -1;
// populate the subsets
for (int i = 0; i < trainInstances.numInstances(); i++) {
Instance instance = trainInstances.instance(i);
subset = m_c45S.whichSubset(instance);
if (subset != -1) {
trainingSets[subset].add((Instance) instance.copy());
} else {
double[] weights = m_c45S.weights(instance);
for (int j = 0; j < m_complexityIndex; j++) {
Instance temp = (Instance) instance.copy();
if (weights.length == m_complexityIndex) {
temp.setWeight(temp.weight() * weights[j]);
} else {
temp.setWeight(temp.weight() / m_complexityIndex);
}
trainingSets[j].add(temp);
}
}
}
/*
* // compute weights (weights of instances per subset m_weights = new
* double [m_complexityIndex]; for (int i = 0; i < m_complexityIndex; i++) {
* m_weights[i] = trainingSets[i].sumOfWeights(); }
* Utils.normalize(m_weights);
*/
Random r = new Random(1);
int minNumCount = 0;
for (int i = 0; i < m_complexityIndex; i++) {
if (trainingSets[i].numInstances() > 5) {
minNumCount++;
// Discretize the sets
Discretize disc = new Discretize();
disc.setInputFormat(trainingSets[i]);
trainingSets[i] = Filter.useFilter(trainingSets[i], disc);
trainingSets[i].randomize(r);
trainingSets[i].stratify(5);
NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);
// add the errors for this branch of the split
m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
} else {
for (int j = 0; j < trainingSets[i].numInstances(); j++) {
m_errors += trainingSets[i].instance(j).weight();
}
}
}
// Check if minimum number of Instances in at least two
// subsets.
if (minNumCount > 1) {
m_numSubsets = m_complexityIndex;
}
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:78,代码来源:NBTreeSplit.java
示例8: handleNumericAttribute
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Creates split on numeric attribute.
*
* @exception Exception if something goes wrong
*/
private void handleNumericAttribute(Instances trainInstances)
throws Exception {
m_c45S = new C45Split(m_attIndex, 2, m_sumOfWeights, true);
m_c45S.buildClassifier(trainInstances);
if (m_c45S.numSubsets() == 0) {
return;
}
m_errors = 0;
Instances [] trainingSets = new Instances [m_complexityIndex];
trainingSets[0] = new Instances(trainInstances, 0);
trainingSets[1] = new Instances(trainInstances, 0);
int subset = -1;
// populate the subsets
for (int i = 0; i < trainInstances.numInstances(); i++) {
Instance instance = trainInstances.instance(i);
subset = m_c45S.whichSubset(instance);
if (subset != -1) {
trainingSets[subset].add((Instance)instance.copy());
} else {
double [] weights = m_c45S.weights(instance);
for (int j = 0; j < m_complexityIndex; j++) {
Instance temp = (Instance)instance.copy();
if (weights.length == m_complexityIndex) {
temp.setWeight(temp.weight() * weights[j]);
} else {
temp.setWeight(temp.weight() / m_complexityIndex);
}
trainingSets[j].add(temp);
}
}
}
/* // compute weights (weights of instances per subset
m_weights = new double [m_complexityIndex];
for (int i = 0; i < m_complexityIndex; i++) {
m_weights[i] = trainingSets[i].sumOfWeights();
}
Utils.normalize(m_weights); */
Random r = new Random(1);
int minNumCount = 0;
for (int i = 0; i < m_complexityIndex; i++) {
if (trainingSets[i].numInstances() > 5) {
minNumCount++;
// Discretize the sets
Discretize disc = new Discretize();
disc.setInputFormat(trainingSets[i]);
trainingSets[i] = Filter.useFilter(trainingSets[i], disc);
trainingSets[i].randomize(r);
trainingSets[i].stratify(5);
NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);
// add the errors for this branch of the split
m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
} else {
for (int j = 0; j < trainingSets[i].numInstances(); j++) {
m_errors += trainingSets[i].instance(j).weight();
}
}
}
// Check if minimum number of Instances in at least two
// subsets.
if (minNumCount > 1) {
m_numSubsets = m_complexityIndex;
}
}
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:78,代码来源:NBTreeSplit.java
示例9: handleNumericAttribute
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Creates split on numeric attribute.
*
* @exception Exception if something goes wrong
*/
private void handleNumericAttribute(Instances trainInstances)
throws Exception {
m_c45S = new C45Split(m_attIndex, 2, m_sumOfWeights);
m_c45S.buildClassifier(trainInstances);
if (m_c45S.numSubsets() == 0) {
return;
}
m_errors = 0;
Instances [] trainingSets = new Instances [m_complexityIndex];
trainingSets[0] = new Instances(trainInstances, 0);
trainingSets[1] = new Instances(trainInstances, 0);
int subset = -1;
// populate the subsets
for (int i = 0; i < trainInstances.numInstances(); i++) {
Instance instance = trainInstances.instance(i);
subset = m_c45S.whichSubset(instance);
if (subset != -1) {
trainingSets[subset].add((Instance)instance.copy());
} else {
double [] weights = m_c45S.weights(instance);
for (int j = 0; j < m_complexityIndex; j++) {
Instance temp = (Instance)instance.copy();
if (weights.length == m_complexityIndex) {
temp.setWeight(temp.weight() * weights[j]);
} else {
temp.setWeight(temp.weight() / m_complexityIndex);
}
trainingSets[j].add(temp);
}
}
}
/* // compute weights (weights of instances per subset
m_weights = new double [m_complexityIndex];
for (int i = 0; i < m_complexityIndex; i++) {
m_weights[i] = trainingSets[i].sumOfWeights();
}
Utils.normalize(m_weights); */
Random r = new Random(1);
int minNumCount = 0;
for (int i = 0; i < m_complexityIndex; i++) {
if (trainingSets[i].numInstances() > 5) {
minNumCount++;
// Discretize the sets
Discretize disc = new Discretize();
disc.setInputFormat(trainingSets[i]);
trainingSets[i] = Filter.useFilter(trainingSets[i], disc);
trainingSets[i].randomize(r);
trainingSets[i].stratify(5);
NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
fullModel.buildClassifier(trainingSets[i]);
// add the errors for this branch of the split
m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
} else {
for (int j = 0; j < trainingSets[i].numInstances(); j++) {
m_errors += trainingSets[i].instance(j).weight();
}
}
}
// Check if minimum number of Instances in at least two
// subsets.
if (minNumCount > 1) {
m_numSubsets = m_complexityIndex;
}
}
开发者ID:williamClanton,项目名称:jbossBA,代码行数:78,代码来源:NBTreeSplit.java
示例10: getNaiveBayesModel
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Get the naive bayes model at this node
*
* @return a <code>NaiveBayesUpdateable</code> value
*/
public NaiveBayesUpdateable getNaiveBayesModel() {
return m_nb;
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:9,代码来源:NBTreeNoSplit.java
示例11: NBNode
import weka.classifiers.bayes.NaiveBayesUpdateable; //导入依赖的package包/类
/**
* Construct a new NBNode
*
* @param header the instances structure of the data we're learning from
* @param nbWeightThreshold the weight mass to see before allowing naive Bayes
* to predict
* @throws Exception if a problem occurs
*/
public NBNode(Instances header, double nbWeightThreshold) throws Exception {
m_nbWeightThreshold = nbWeightThreshold;
m_bayes = new NaiveBayesUpdateable();
m_bayes.buildClassifier(header);
}
开发者ID:mydzigear,项目名称:repo.kmeanspp.silhouette_score,代码行数:14,代码来源:NBNode.java
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