public class SGD extends RandomizableClassifier implements UpdateableClassifier, OptionHandler, Aggregateable<SGD>
-F Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression), 2 = squared loss (regression), 3 = epsilon insensitive loss (regression), 4 = Huber loss (regression). (default = 0)
-L The learning rate. If normalization is turned off (as it is automatically for streaming data), then the default learning rate will need to be reduced (try 0.0001). (default = 0.01).
-R <double> The lambda regularization constant (default = 0.0001)
-E <integer> The number of epochs to perform (batch learning only, default = 500)
-C <double> The epsilon threshold (epsilon-insenstive and Huber loss only, default = 1e-3)
-N Don't normalize the data
-M Don't replace missing values
-S <num> Random number seed. (default 1)
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
Modifier and Type | Field and Description |
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static int |
EPSILON_INSENSITIVE
The epsilon insensitive loss function
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static int |
HINGE
the hinge loss function.
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static int |
HUBER
The Huber loss function
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static int |
LOGLOSS
the log loss function.
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static int |
SQUAREDLOSS
the squared loss function.
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static Tag[] |
TAGS_SELECTION
Loss functions to choose from
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BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
Constructor and Description |
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SGD() |
Modifier and Type | Method and Description |
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SGD |
aggregate(SGD toAggregate)
Aggregate an object with this one
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void |
buildClassifier(Instances data)
Method for building the classifier.
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double[] |
distributionForInstance(Instance inst)
Computes the distribution for a given instance
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java.lang.String |
dontNormalizeTipText()
Returns the tip text for this property
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java.lang.String |
dontReplaceMissingTipText()
Returns the tip text for this property
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java.lang.String |
epochsTipText()
Returns the tip text for this property
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java.lang.String |
epsilonTipText()
Returns the tip text for this property
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void |
finalizeAggregation()
Call to complete the aggregation process.
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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boolean |
getDontNormalize()
Get whether normalization has been turned off.
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boolean |
getDontReplaceMissing()
Get whether global replacement of missing values has been disabled.
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int |
getEpochs()
Get current number of epochs
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double |
getEpsilon()
Get the epsilon threshold on the error for epsilon insensitive and Huber
loss functions
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double |
getLambda()
Get the current value of lambda
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double |
getLearningRate()
Get the learning rate.
|
SelectedTag |
getLossFunction()
Get the current loss function.
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java.lang.String[] |
getOptions()
Gets the current settings of the classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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double[] |
getWeights() |
java.lang.String |
globalInfo()
Returns a string describing classifier
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java.lang.String |
lambdaTipText()
Returns the tip text for this property
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java.lang.String |
learningRateTipText()
Returns the tip text for this property
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
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java.lang.String |
lossFunctionTipText()
Returns the tip text for this property
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static void |
main(java.lang.String[] args)
Main method for testing this class.
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void |
reset()
Reset the classifier.
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void |
setDontNormalize(boolean m)
Turn normalization off/on.
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void |
setDontReplaceMissing(boolean m)
Turn global replacement of missing values off/on.
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void |
setEpochs(int e)
Set the number of epochs to use
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void |
setEpsilon(double e)
Set the epsilon threshold on the error for epsilon insensitive and Huber
loss functions
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void |
setLambda(double lambda)
Set the value of lambda to use
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void |
setLearningRate(double lr)
Set the learning rate.
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void |
setLossFunction(SelectedTag function)
Set the loss function to use.
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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java.lang.String |
toString()
Prints out the classifier.
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void |
updateClassifier(Instance instance)
Updates the classifier with the given instance.
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getSeed, seedTipText, setSeed
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
makeCopy
public static final int HINGE
public static final int LOGLOSS
public static final int SQUAREDLOSS
public static final int EPSILON_INSENSITIVE
public static final int HUBER
public static final Tag[] TAGS_SELECTION
public Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClassifier
Capabilities
public java.lang.String epsilonTipText()
public void setEpsilon(double e)
e
- the value of epsilon to usepublic double getEpsilon()
public java.lang.String lambdaTipText()
public void setLambda(double lambda)
lambda
- the value of lambda to usepublic double getLambda()
public void setLearningRate(double lr)
lr
- the learning rate to use.public double getLearningRate()
public java.lang.String learningRateTipText()
public java.lang.String epochsTipText()
public void setEpochs(int e)
e
- the number of epochs to usepublic int getEpochs()
public void setDontNormalize(boolean m)
m
- true if normalization is to be disabled.public boolean getDontNormalize()
public java.lang.String dontNormalizeTipText()
public void setDontReplaceMissing(boolean m)
m
- true if global replacement of missing values is to be turned off.public boolean getDontReplaceMissing()
public java.lang.String dontReplaceMissingTipText()
public void setLossFunction(SelectedTag function)
function
- the loss function to use.public SelectedTag getLossFunction()
public java.lang.String lossFunctionTipText()
public java.util.Enumeration<Option> listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableClassifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-F Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression), 2 = squared loss (regression), 3 = epsilon insensitive loss (regression), 4 = Huber loss (regression). (default = 0)
-L The learning rate. If normalization is turned off (as it is automatically for streaming data), then the default learning rate will need to be reduced (try 0.0001). (default = 0.01).
-R <double> The lambda regularization constant (default = 0.0001)
-E <integer> The number of epochs to perform (batch learning only, default = 500)
-C <double> The epsilon threshold (epsilon-insenstive and Huber loss only, default = 1e-3)
-N Don't normalize the data
-M Don't replace missing values
-S <num> Random number seed. (default 1)
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
setOptions
in interface OptionHandler
setOptions
in class RandomizableClassifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableClassifier
public java.lang.String globalInfo()
public void reset()
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in interface Classifier
data
- the set of training instances.java.lang.Exception
- if the classifier can't be built successfully.public void updateClassifier(Instance instance) throws java.lang.Exception
updateClassifier
in interface UpdateableClassifier
instance
- the new training instance to include in the modeljava.lang.Exception
- if the instance could not be incorporated in the
model.public double[] distributionForInstance(Instance inst) throws java.lang.Exception
distributionForInstance
in interface Classifier
distributionForInstance
in class AbstractClassifier
inst
- the instance for which distribution is computedjava.lang.Exception
- if the distribution can't be computed successfullypublic double[] getWeights()
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
public SGD aggregate(SGD toAggregate) throws java.lang.Exception
aggregate
in interface Aggregateable<SGD>
toAggregate
- the object to aggregatejava.lang.Exception
- if the supplied object can't be aggregated for some
reasonpublic void finalizeAggregation() throws java.lang.Exception
finalizeAggregation
in interface Aggregateable<SGD>
java.lang.Exception
- if the aggregation can't be finalized for some reasonpublic static void main(java.lang.String[] args)