magpie.models.regression.nonlinear

Class SimpleLinearModelExample

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.AbstractNonlinearRegression

magpie.models.regression.nonlinear.SimpleLinearModelExample

All Implemented Interfaces:

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

public class SimpleLinearModelExample
extends AbstractNonlinearRegression

Example for the AbstractNonlinearRegression Class. Fits a regression model to:

Class = a + b * mean_GSvolume_pa

See Also:

Serialized Form

Field Summary

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
SimpleLinearModelExample()

Method Summary

Modifier and Type	Method and Description
protected void	defineCoefficients() Define the names and, if desired, initial guesses of fitting coefficients.
protected void	defineVariables() Define which attributes are used by the function.
protected double	function(double[] variables, double[] coeff) Function defined by the user.
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.

Methods inherited from class magpie.models.regression.AbstractNonlinearRegression

addCoefficient, addCoefficient, addVariable, getCoefficientName, getFittedCoefficient, getMaxIter, getNFittingParameters, getVariableName, makeObjectiveFunction, NCoefficients, NVariables, printUsage, run_protected, runCommand, runNonlinearCommand, setInitialGuess, setMaxIterations, setOptions, train_protected

Methods inherited from class magpie.models.regression.BaseRegression

clone, doRobustRegression, getRobustRegressionQ, robustTraining, 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

SimpleLinearModelExample

public SimpleLinearModelExample()

Method Detail

defineVariables

protected void defineVariables()

Description copied from class: AbstractNonlinearRegression

Define which attributes are used by the function. This function is run a single time during training. As long as variables are defined by the time this function is complete, it does not matter how your code actually does it.

Variables are defined using the AbstractNonlinearRegression.addVariable(java.lang.String) function. *

In AbstractNonlinearRegression.function(double[], double[]) these names are mapped to the variable array in the same order you define them.

Specified by:

defineVariables in class AbstractNonlinearRegression

defineCoefficients

protected void defineCoefficients()

Description copied from class: AbstractNonlinearRegression

Define the names and, if desired, initial guesses of fitting coefficients. As long as coefficients are defined by the time this program exits, it does not matter what this code actually does (i.e. you can define coefficients in other functions).

Coefficients are defined using the AbstractNonlinearRegression.addCoefficient(java.lang.String) function.

In AbstractNonlinearRegression.function(double[], double[]) these names are mapped to the coeff array in the same order you define them.

Specified by:

defineCoefficients in class AbstractNonlinearRegression

function

protected double function(double[] variables,
                          double[] coeff)

Description copied from class: AbstractNonlinearRegression

Function defined by the user. Program supplies attributes in the same order as defined in AbstractNonlinearRegression.defineVariables().

Specified by:

function in class AbstractNonlinearRegression

Parameters:

variables - Values of attributes used as independent variables

coeff - Coefficients of equation

Returns:

Value of equation given those inputs

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).