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## weka.classifiers.trees.m5
Class PreConstructedLinearModel

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.trees.m5.PreConstructedLinearModel

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler

    public class PreConstructedLinearModel

extends AbstractClassifier
implements java.io.Serializable

This class encapsulates a linear regression function. It is a classifier but does not learn the function itself, instead it is constructed with coefficients and intercept obtained elsewhere. The buildClassifier method must still be called however as this stores a copy of the training data's header for use in printing the model to the console.

Version:
  $Revision: 5928 $
Author:
  Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`PreConstructedLinearModel(double[] coeffs, double intercept)` Constructor

Method Summary
`void` | `buildClassifier(Instances instances)` Builds the classifier.
`double` | `classifyInstance(Instance inst)` Predicts the class of the supplied instance using the linear model.
`double[]` | `coefficients()` Return the array of coefficients
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `intercept()` Return the intercept
`int` | `numParameters()` Return the number of parameters (coefficients) in the linear model
`java.lang.String` | `toString()` Returns a textual description of this linear model

Methods inherited from class weka.classifiers.AbstractClassifier
`debugTipText, distributionForInstance, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

### PreConstructedLinearModel

    public PreConstructedLinearModel(double[] coeffs,
                                     double intercept)

  Constructor

  Parameters:
    `coeffs` - an array of coefficients
    `intercept` - the intercept

Method Detail

### buildClassifier

    public void buildClassifier(Instances instances)
                         throws java.lang.Exception

  Builds the classifier. In this case all that is done is that a copy of the training instances header is saved.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `instances` - an `Instances` value
  Throws:
    `java.lang.Exception` - if an error occurs

### classifyInstance

    public double classifyInstance(Instance inst)
                            throws java.lang.Exception

  Predicts the class of the supplied instance using the linear model.

  Specified by:
    `classifyInstance` in interface `Classifier`
  Overrides:
    `classifyInstance` in class `AbstractClassifier`
  Parameters:
    `inst` - the instance to make a prediction for
  Returns:
    the prediction
  Throws:
    `java.lang.Exception` - if an error occurs

### numParameters

    public int numParameters()

  Return the number of parameters (coefficients) in the linear model

  Returns:
    the number of parameters

### coefficients

    public double[] coefficients()

  Return the array of coefficients

  Returns:
    the coefficients

### intercept

    public double intercept()

  Return the intercept

  Returns:
    the intercept

### toString

    public java.lang.String toString()

  Returns a textual description of this linear model

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    String containing a description of this linear model

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `AbstractClassifier`
  Returns:
    the revision

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