public class CrossFoldLearner extends AbstractVectorClassifier implements OnlineLearner, org.apache.hadoop.io.Writable
MIN_LOG_LIKELIHOOD
Constructor and Description |
---|
CrossFoldLearner() |
CrossFoldLearner(int folds,
int numCategories,
int numFeatures,
PriorFunction prior) |
Modifier and Type | Method and Description |
---|---|
void |
addModel(OnlineLogisticRegression model) |
CrossFoldLearner |
alpha(double alpha) |
double |
auc() |
Vector |
classify(Vector instance)
Compute and return a vector containing
n-1 scores, where
n is equal to numCategories() , given an input
vector instance . |
Vector |
classifyNoLink(Vector instance)
Compute and return a vector of scores before applying the inverse link
function.
|
double |
classifyScalar(Vector instance)
Classifies a vector in the special case of a binary classifier where
AbstractVectorClassifier.classify(Vector) would return a vector with only one element. |
void |
close()
Prepares the classifier for classification and deallocates any temporary data structures.
|
CrossFoldLearner |
copy() |
CrossFoldLearner |
decayExponent(double x) |
OnlineAuc |
getAucEvaluator() |
double |
getLogLikelihood() |
List<OnlineLogisticRegression> |
getModels() |
int |
getNumFeatures() |
double[] |
getParameters() |
PriorFunction |
getPrior() |
int |
getRecord() |
CrossFoldLearner |
lambda(double v) |
CrossFoldLearner |
learningRate(double x) |
double |
logLikelihood() |
int |
numCategories()
Returns the number of categories that a target variable can be assigned to.
|
double |
percentCorrect() |
void |
readFields(DataInput in) |
void |
resetLineCounter() |
void |
setAucEvaluator(OnlineAuc auc) |
void |
setLogLikelihood(double logLikelihood) |
void |
setNumFeatures(int numFeatures) |
void |
setParameters(double[] parameters) |
void |
setPrior(PriorFunction prior) |
void |
setRecord(int record) |
void |
setWindowSize(int windowSize) |
CrossFoldLearner |
stepOffset(int x) |
void |
train(int actual,
Vector instance)
Updates the model using a particular target variable value and a feature vector.
|
void |
train(long trackingKey,
int actual,
Vector instance)
Updates the model using a particular target variable value and a feature vector.
|
void |
train(long trackingKey,
String groupKey,
int actual,
Vector instance)
Updates the model using a particular target variable value and a feature vector.
|
boolean |
validModel() |
void |
write(DataOutput out) |
classify, classifyFull, classifyFull, classifyFull, classifyScalar, logLikelihood
public CrossFoldLearner()
public CrossFoldLearner(int folds, int numCategories, int numFeatures, PriorFunction prior)
public CrossFoldLearner lambda(double v)
public CrossFoldLearner learningRate(double x)
public CrossFoldLearner stepOffset(int x)
public CrossFoldLearner decayExponent(double x)
public CrossFoldLearner alpha(double alpha)
public void train(int actual, Vector instance)
OnlineLearner
train
in interface OnlineLearner
actual
- The value of the target variable. This value should be in the half-open
interval [0..n) where n is the number of target categories.instance
- The feature vector for this example.public void train(long trackingKey, int actual, Vector instance)
OnlineLearner
train
in interface OnlineLearner
trackingKey
- The tracking key for this training example.actual
- The value of the target variable. This value should be in the half-open
interval [0..n) where n is the number of target categories.instance
- The feature vector for this example.public void train(long trackingKey, String groupKey, int actual, Vector instance)
OnlineLearner
train
in interface OnlineLearner
trackingKey
- The tracking key for this training example.groupKey
- An optional value that allows examples to be grouped in the computation of
the update to the model.actual
- The value of the target variable. This value should be in the half-open
interval [0..n) where n is the number of target categories.instance
- The feature vector for this example.public void close()
OnlineLearner
close
in interface Closeable
close
in interface AutoCloseable
close
in interface OnlineLearner
public void resetLineCounter()
public boolean validModel()
public Vector classify(Vector instance)
AbstractVectorClassifier
n-1
scores, where
n
is equal to numCategories()
, given an input
vector instance
. Higher scores indicate that the input vector
is more likely to belong to that category. The categories are denoted by
the integers 0
through n-1
(inclusive), and the
scores in the returned vector correspond to categories 1 through
n-1
(leaving out category 0). It is assumed that the score for
category 0 is one minus the sum of the scores in the returned vector.classify
in class AbstractVectorClassifier
instance
- A feature vector to be classified.n-1
encoding.public Vector classifyNoLink(Vector instance)
AbstractVectorClassifier
The implementation of this method provided by AbstractVectorClassifier
throws an
UnsupportedOperationException
. Your subclass must explicitly override this method to support
this operation.
classifyNoLink
in class AbstractVectorClassifier
instance
- A feature vector to be classified.public double classifyScalar(Vector instance)
AbstractVectorClassifier
AbstractVectorClassifier.classify(Vector)
would return a vector with only one element. As
such, using this method can avoid the allocation of a vector.classifyScalar
in class AbstractVectorClassifier
instance
- The feature vector to be classified.AbstractVectorClassifier.classify(Vector)
public int numCategories()
AbstractVectorClassifier
0
to numCategories()-1
(inclusive).numCategories
in class AbstractVectorClassifier
public double auc()
public double logLikelihood()
public double percentCorrect()
public CrossFoldLearner copy()
public int getRecord()
public void setRecord(int record)
public OnlineAuc getAucEvaluator()
public void setAucEvaluator(OnlineAuc auc)
public double getLogLikelihood()
public void setLogLikelihood(double logLikelihood)
public List<OnlineLogisticRegression> getModels()
public void addModel(OnlineLogisticRegression model)
public double[] getParameters()
public void setParameters(double[] parameters)
public int getNumFeatures()
public void setNumFeatures(int numFeatures)
public void setWindowSize(int windowSize)
public PriorFunction getPrior()
public void setPrior(PriorFunction prior)
public void write(DataOutput out) throws IOException
write
in interface org.apache.hadoop.io.Writable
IOException
public void readFields(DataInput in) throws IOException
readFields
in interface org.apache.hadoop.io.Writable
IOException
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