MixingRuleRegression (magpie API)

java.lang.Object
- magpie.models.BaseModel
- - magpie.models.regression.BaseRegression
  - - magpie.models.regression.MixingRuleRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, AbstractRegressionModel, Citable, Commandable, Options, Printable, Savable
```
public class MixingRuleRegression
extends BaseRegression
```
Predicts the class of a CompositionEntry based on the composition-weighted mean. Only works on datasets that fulfill CompositionDataset.
Special features:
- Can determine class based on the average of a property, or the average of its inverse (1/p = x_1/p_1 + ...).
- Can "correct" weighted property using a linear model: class = a + b * meanProperty
- Can fit elemental properties for a certain element
Usage: <property> [-invert] [-correct] [-fit <ElementNames>]
property: Name of property to use for mixing rule model
-invert: Whether to compute the harmonic mean.
-correct: Fit linear correction factors to mixing rule equation
ElementNames: List elements whose elemental property should be fitted
Implemented Print Commands:
fitted - Print fitted elemental properties (if any)
See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected double[]`	`CorrectionFactors` Coefficients used for correction factor.
`protected java.util.List<java.lang.String>`	`FittedElements` Elements to fit using least squares
`protected double[]`	`LookupTable` Lookup table for property of interest
`protected java.lang.String`	`PropertyName` Property used for weighting
`protected boolean`	`UseCorrection` Whether to apply correction factors
`protected boolean`	`UseInverse` Whether to compute harmonic mean.

Fields inherited from class magpie.models.BaseModel
AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors
Constructor and Description

MixingRuleRegression()

Constructors
Constructor and Description
`MixingRuleRegression()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`BaseRegression`	`clone()`
`protected void`	`fitCorrectionFactors(CompositionDataset Data)` Fit correction factors to the mixing rule model.
`protected void`	`fitElementProperties(CompositionDataset Data)` Fits properties for elements specified by user.
`int`	`getNFittingParameters()` Number of fitting parameters in a model.
`java.lang.String`	`getPropertyName()` Retrieve the property used for alloy mean
`boolean`	`getUseInverse()`
`java.lang.String`	`printCommand(java.util.List<java.lang.String> Command)` Handles more complicated printing commands.
`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`	`setFittedElements(java.util.List<java.lang.String> elements)` Set which elements to fit.
`void`	`setOptions(java.util.List OptionsObj)` Set any options for this object.
`void`	`setPropertyName(java.lang.String PropertyName)` Define which property to use for alloy mean
`void`	`setUseCorrection(boolean UseCorrection)` Whether to fit a linear model to correct differences between the alloy mean and class variable
`void`	`setUseInverse(boolean UseInverse)` Define whether to take the average of the property, or the average of the inverse of the property.
`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, 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
  - PropertyName
```
protected java.lang.String PropertyName
```
    Property used for weighting
  - LookupTable
```
protected double[] LookupTable
```
    Lookup table for property of interest
  - UseInverse
```
protected boolean UseInverse
```
    Whether to compute harmonic mean.
  - CorrectionFactors
```
protected double[] CorrectionFactors
```
    Coefficients used for correction factor. 0 = Intercept. 1 = Slope.
  - UseCorrection
```
protected boolean UseCorrection
```
    Whether to apply correction factors
  - FittedElements
```
protected java.util.List<java.lang.String> FittedElements
```
    Elements to fit using least squares
- Constructor Detail
  - MixingRuleRegression
```
public MixingRuleRegression()
```
- Method Detail
  - clone
```
public BaseRegression clone()
```
    Overrides:
    
    clone in class BaseRegression
  - setPropertyName
```
public void setPropertyName(java.lang.String PropertyName)
```
    Define which property to use for alloy mean
    
    Parameters:
    
    PropertyName - Name of desired property
  - getPropertyName
```
public java.lang.String getPropertyName()
```
    Retrieve the property used for alloy mean
    
    Returns:
    
    Name of desired property
  - setUseInverse
```
public void setUseInverse(boolean UseInverse)
```
    Define whether to take the average of the property, or the average of the inverse of the property.
    
    Parameters:
    
    UseInverse - Whether to use the inverse
    
    See Also:
    
    MixingRuleRegression
  - getUseInverse
```
public boolean getUseInverse()
```
    Returns:
    
    Whether the inverse of the target property is averaged
  - setUseCorrection
```
public void setUseCorrection(boolean UseCorrection)
```
    Whether to fit a linear model to correct differences between the alloy mean and class variable
    
    Parameters:
    
    UseCorrection - Whether to use corrections
  - setOptions
```
public void setOptions(java.util.List OptionsObj)
                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:
    
    OptionsObj - 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
  - setFittedElements
```
public void setFittedElements(java.util.List<java.lang.String> elements)
```
    Set which elements to fit.
    
    Parameters:
    
    elements - Elements to be fit to minimize model error. List of element abbreviations.
  - 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
  - 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
  - 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
  - fitElementProperties
```
protected void fitElementProperties(CompositionDataset Data)
```
    Fits properties for elements specified by user. For now, uses OLS regression.
    
    Parameters:
    
    Data - Dataset to use for fitting
  - 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).
  - fitCorrectionFactors
```
protected void fitCorrectionFactors(CompositionDataset Data)
```
    Fit correction factors to the mixing rule model.
    
    Parameters:
    
    Data - Dataset to use for training
  - printCommand
```
public java.lang.String printCommand(java.util.List<java.lang.String> Command)
                              throws java.lang.Exception
```
    Description copied from interface: Printable
    
    Handles more complicated printing commands.
    
    Specified by:
    
    printCommand in interface Printable
    
    Overrides:
    
    printCommand in class BaseModel
    
    Parameters:
    
    Command - Command specifying what to print
    
    Returns:
    
    String formatted as requested
    
    Throws:
    
    java.lang.Exception - If command not understood

Class MixingRuleRegression

Field Summary

Fields inherited from class magpie.models.BaseModel

Constructor Summary

Method Summary

Methods inherited from class magpie.models.regression.BaseRegression

Methods inherited from class magpie.models.BaseModel

Methods inherited from class java.lang.Object

Field Detail

PropertyName

LookupTable

UseInverse

CorrectionFactors

UseCorrection

FittedElements

Constructor Detail

MixingRuleRegression

Method Detail

clone

setPropertyName

getPropertyName

setUseInverse

getUseInverse

setUseCorrection

setOptions

printUsage

setFittedElements

train_protected

getNFittingParameters

run_protected

fitElementProperties

printModel_protected

printModelDescriptionDetails

fitCorrectionFactors

printCommand