magpie.models.regression

Class PolynomialRegression

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.PolynomialRegression

All Implemented Interfaces:

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

public class PolynomialRegression
extends BaseRegression

Performs linear regression using all attributes in a Dataset raised to integer exponents. This method creates models that are nonlinear polynomials, like this: f(x,y,z) = a + b * x + c * x2 + d * y + e * y2 + ...

Usage: <Order> [-print_accuracy <figs>]
Order: Maximum Order of terms in polynomial
figs: Optional: Number of significant figures to write when printing model (default: 4)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double[]	coefficients Coefficients of each term in the polynomial
protected int	Order Desired Order of polynomial

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
PolynomialRegression()

Method Summary

Modifier and Type	Method and Description
PolynomialRegression	clone()
static double[][]	expandAttributes(double[][] attributes, int order) Expand attribute array in order to allow it to be used to fit a polynomial model.
static double[]	fitPolynomialModel(double[][] attributes, int order, double[] classVariable) Fit a polynomial model based on a matrix of attribute values.
double[]	getCoefficients() Get coefficients of model.
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)
static double[]	runPolynomialModel(double[][] attributes, int order, double[] coefficients) Run a polynomial model based on a matrix of attribute values.
void	setOptions(java.util.List Options) Set any options for this object.
void	setOrder(int order) Define Order of polynomial.
void	setPrintAccuracy(int digits) Set the number of digits printed when outputting model
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

Field Detail

Order

protected int Order

Desired Order of polynomial

coefficients

protected double[] coefficients

Coefficients of each term in the polynomial

Constructor Detail

PolynomialRegression

public PolynomialRegression()

Method Detail

setOptions

public void setOptions(java.util.List 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

clone

public PolynomialRegression clone()

Overrides:

clone in class BaseRegression

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

setOrder

public void setOrder(int order)

Define Order of polynomial.

Parameters:

order - Desired Order of polynomial.

setPrintAccuracy

public void setPrintAccuracy(int digits)

Set the number of digits printed when outputting model

Parameters:

digits - Number of significant figures

getCoefficients

public double[] getCoefficients()

Get coefficients of model. Arranged: Intercept, x, x², ..., x^N, y, ..., y^N, z, ...

Returns:

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

fitPolynomialModel

public static double[] fitPolynomialModel(double[][] attributes,
                                          int order,
                                          double[] classVariable)

Fit a polynomial model based on a matrix of attribute values. Returns the coefficients of this model in the following Order:

Intercept, Coefficient of attribute1, Coefficient of attribute1², ..., Coefficient of attribute2, ...

Parameters:

attributes - Matrix containing attributes for each entry (entries are rows, attributes columns)

order - Desired Order of polynomial

classVariable - Class variable for each entry

Returns:

Coefficients for model

expandAttributes

public static double[][] expandAttributes(double[][] attributes,
                                          int order)

Expand attribute array in order to allow it to be used to fit a polynomial model. First column is all ones (for the intercept), other columns are for terms listed in the same order as fitPolynomialModel(double[][], int, double[]).

Parameters:

attributes - Matrix containing attributes for each entry (entries are rows, attributes columns)

order - Desired order of polynomial

Returns:

Matrix that can be used to fit this model

runPolynomialModel

public static double[] runPolynomialModel(double[][] attributes,
                                          int order,
                                          double[] coefficients)

Run a polynomial model based on a matrix of attribute values. Coefficients coefficients of this model in the following order:

Intercept, Coefficient of attribute1, Coefficient of attribute1², ..., Coefficient of attribute2, ...

Parameters:

attributes - Matrix containing attributes for each entry (entries are rows, attributes columns)

order - Desired order of polynomial

coefficients - Coefficients of polynomial model

Returns:

Value of model for each entry

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).