magpie.models.regression

Class LinearCorrectedRegression

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.LinearCorrectedRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, AbstractRegressionModel, Citable, Commandable, Options, Printable, Savable

public class LinearCorrectedRegression
extends BaseRegression

Apply a linear correction to results from another model. Let f(x) be the submodel. This model finds g(x) = a + b * f(x), such that (y - g(x))^2 is minimized.

Usage: $<submodel>
submodel: Model to be corrected

See Also:

Serialized Form

Field Summary

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
LinearCorrectedRegression()

Method Summary

Modifier and Type	Method and Description
BaseRegression	clone()
int	getNFittingParameters() Number of fitting parameters in a model.
protected java.lang.String	printModel_protected() Internal method that handles printing the model as a string.
java.util.List<java.lang.String>	printModelDescriptionDetails(boolean htmlFormat) Print details of the model.
java.lang.String	printUsage() Print out required format for options.
void	run_protected(Dataset TrainData) Run a model without checking if stuff is trained (use carefully)
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.
void	setSubmodel(BaseModel submodel) Define the model that this class corrects.
protected void	train_protected(Dataset trainData) Train a model without evaluating performance

Methods inherited from class magpie.models.regression.BaseRegression

doRobustRegression, getRobustRegressionQ, robustTraining, runCommand, setRobustRegressionQ

Methods inherited from class magpie.models.BaseModel

about, crossValidate, crossValidate, crossValidate, done, externallyValidate, getAttributeSelector, getCitations, getFilter, getTrainTime, getValidationMethod, handleSetCommand, isTrained, isValidated, loadState, printCommand, printDescription, printModel, resetModel, run, saveCommand, saveState, setAttributeSelector, setComponent, setFilter, train, train

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LinearCorrectedRegression

public LinearCorrectedRegression()

Method Detail

clone

public BaseRegression clone()

Overrides:

clone in class BaseRegression

setOptions

public void setOptions(java.util.List<java.lang.Object> Options)
                throws java.lang.Exception

Description copied from interface: Options

Set any options for this object. If parsing the options fails, return an Exception with the usage as its message.

Parameters:

Options - Array of options as Objects - can be null

Throws:

java.lang.Exception - if problem with inputs

printUsage

public java.lang.String printUsage()

Description copied from interface: Options

Print out required format for options. For consistency, should start with "Usage: ".

Returns:

Usage for this function

setSubmodel

public void setSubmodel(BaseModel submodel)
                 throws java.lang.Exception

Define the model that this class corrects.

Parameters:

submodel - Another regression model

Throws:

java.lang.Exception - If model isn't a regression model

train_protected

protected void train_protected(Dataset trainData)

Description copied from class: BaseModel

Train a model without evaluating performance

Specified by:

train_protected in class BaseModel

Parameters:

trainData - Training data

run_protected

public void run_protected(Dataset TrainData)

Description copied from class: BaseModel

Run a model without checking if stuff is trained (use carefully)

Specified by:

run_protected in class BaseModel

Parameters:

TrainData - Training data

getNFittingParameters

public int getNFittingParameters()

Description copied from interface: AbstractRegressionModel

Number of fitting parameters in a model. Only pertinent to nonlinear regression. Otherwise, this should just return 0.

Returns:

Number of fitting parameters

printModel_protected

protected java.lang.String printModel_protected()

Description copied from class: BaseModel

Internal method that handles printing the model as a string. Should end with a "\n"

Specified by:

printModel_protected in class BaseModel

Returns:

String representation of model

printModelDescriptionDetails

public java.util.List<java.lang.String> printModelDescriptionDetails(boolean htmlFormat)

Description copied from class: BaseModel

Print details of the model. Used by BaseModel.printDescription(boolean).

Implementation note: No not add indentation for details. That is handled by BaseModel.printDescription(boolean). You should also call the super operation to get the Normalizer and Attribute selector settings

Overrides:

printModelDescriptionDetails in class BaseModel

Parameters:

htmlFormat - Whether to use HTML format

Returns:

List describing model details. Each entry is a different line of the description (i.e., in place of newline characters).