keel.Algorithms.Genetic_Rule_Learning.M5Rules

Class M5

java.lang.Object

keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

public final class M5
extends java.lang.Object

Field Summary

Fields

Modifier and Type	Field and Description
static int	MODEL_LINEAR_REGRESSION
static int	MODEL_MODEL_TREE
static int	MODEL_REGRESSION_TREE

Constructor Summary

Constructors

Constructor and Description
M5(MyDataset data, double prune_factor, boolean unsmoothed, int verbosity)
M5(parseParameters paramFile) Constructor by parameters file.

Method Summary

Modifier and Type	Method and Description
static double	absDev(int attr, MyDataset inst) Returns the absolute deviation value of the itemsets values of an attribute
void	buildClassifier(MyDataset inst) Construct a model tree by training itemsets
static double	correlation(double[] y1, double[] y2, int n) Returns the correlation coefficient of two double vectors
static java.lang.StringBuffer	deleteTrailingZerosAndDot(java.lang.StringBuffer stringBuffer) Deletes the trailing zeros and decimal point in a stringBuffer
static java.lang.String	doubleToStringF(double value, int width, int afterDecimalPoint) Rounds a double and converts it into a formatted right-justified String.
static java.lang.String	doubleToStringG(double value, int width, int precision) Rounds a double and converts it into a formatted right-justified String.
static boolean	eqDouble(double a, double b) Tests if two double values are equal to each other
static void	errorMsg(java.lang.String err) Prints error message and exits
static long	floorDouble(double value) Returns the largest (closest to positive infinity) long integer value that is not greater than the argument.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
double	getPruningFactor() Get the value of PruningFactor.
M5TreeNode	getTree()
boolean	getUseUnsmoothed() Get the value of UseUnsmoothed.
int	getVerbosity() Get the value of Verbosity.
static boolean	hasEnumAttr(MyDataset inst) Tests if enumerated attribute(s) exists in the itemsets
static boolean	hasMissing(MyDataset inst) Tests if missing value(s) exists in the itemsets
double	measureNumLeaves() return the number of leaves in the tree
double	measureNumLinearModels() return the number of linear models
double	measureNumRules() return the number of rules
static long	roundDouble(double value) Rounds a double
void	setPruningFactor(double v) Set the value of PruningFactor.
void	setUseUnsmoothed(boolean v) Set the value of UseUnsmoothed.
void	setVerbosity(int v) Set the value of Verbosity.
static double	smoothenValue(double p, double q, int n, int k) Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)
static double	sqrSum(int attr, MyDataset inst) Returns the squared sum of the itemsets values of an attribute
static double	stdDev(int attr, MyDataset inst) Returns the standard deviation value of the itemsets values of an attribute
static double	sum(int attr, MyDataset inst) Returns the sum of the itemsets values of an attribute
java.lang.String	toString() Converts the output of the training process into a string
static double	variance(int attr, MyDataset inst) Returns the variance value of the itemsets values of an attribute

Methods inherited from class java.lang.Object

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

Field Detail

MODEL_LINEAR_REGRESSION

public static final int MODEL_LINEAR_REGRESSION

See Also:

Constant Field Values

MODEL_REGRESSION_TREE

public static final int MODEL_REGRESSION_TREE

See Also:

Constant Field Values

MODEL_MODEL_TREE

public static final int MODEL_MODEL_TREE

See Also:

Constant Field Values

Constructor Detail

M5

public M5(parseParameters paramFile)
   throws java.lang.Exception

Constructor by parameters file.

Parameters:

paramFile - the parsed parameters file

Throws:

java.lang.Exception - if the class for the dataset is not numeric.

M5

public M5(MyDataset data,
          double prune_factor,
          boolean unsmoothed,
          int verbosity)
   throws java.lang.Exception

Throws:

java.lang.Exception

Method Detail

buildClassifier

public final void buildClassifier(MyDataset inst)
                           throws java.lang.Exception

Construct a model tree by training itemsets

Parameters:

inst - training itemsets

Throws:

java.lang.Exception - if the classifier can't be built

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Returns:

an array of strings suitable for passing to setOptions

toString

public final java.lang.String toString()

Converts the output of the training process into a string

Overrides:

toString in class java.lang.Object

Returns:

the converted string

measureNumLinearModels

public double measureNumLinearModels()

return the number of linear models

Returns:

the number of linear models

measureNumLeaves

public double measureNumLeaves()

return the number of leaves in the tree

Returns:

the number leaves in the tree (same as # linear models & # rules)

measureNumRules

public double measureNumRules()

return the number of rules

Returns:

the number of rules (same as # linear models & # leaves in the tree)

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)

Returns the value of the named measure

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

java.lang.IllegalArgumentException - if the named measure is not supported

getUseUnsmoothed

public boolean getUseUnsmoothed()

Get the value of UseUnsmoothed.

Returns:

Value of UseUnsmoothed.

setUseUnsmoothed

public void setUseUnsmoothed(boolean v)

Set the value of UseUnsmoothed.

Parameters:

v - Value to assign to UseUnsmoothed.

getPruningFactor

public double getPruningFactor()

Get the value of PruningFactor.

Returns:

Value of PruningFactor.

setPruningFactor

public void setPruningFactor(double v)

Set the value of PruningFactor.

Parameters:

v - Value to assign to PruningFactor.

getTree

public M5TreeNode getTree()

getVerbosity

public int getVerbosity()

Get the value of Verbosity.

Returns:

Value of Verbosity.

setVerbosity

public void setVerbosity(int v)

Set the value of Verbosity.

Parameters:

v - Value to assign to Verbosity.

hasEnumAttr

public static final boolean hasEnumAttr(MyDataset inst)

Tests if enumerated attribute(s) exists in the itemsets

Parameters:

inst - itemsets

Returns:

true if there is at least one; false if none

hasMissing

public static final boolean hasMissing(MyDataset inst)

Tests if missing value(s) exists in the itemsets

Parameters:

inst - itemsets

Returns:

true if there is missing value(s); false if none

sum

public static final double sum(int attr,
                               MyDataset inst)

Returns the sum of the itemsets values of an attribute

Parameters:

attr - an attribute

inst - itemsets

Returns:

the sum value

sqrSum

public static final double sqrSum(int attr,
                                  MyDataset inst)

Returns the squared sum of the itemsets values of an attribute

Parameters:

attr - an attribute

inst - itemsets

Returns:

the squared sum value

stdDev

public static final double stdDev(int attr,
                                  MyDataset inst)

Returns the standard deviation value of the itemsets values of an attribute

Parameters:

attr - an attribute

inst - itemsets

Returns:

the standard deviation value

absDev

public static final double absDev(int attr,
                                  MyDataset inst)

Returns the absolute deviation value of the itemsets values of an attribute

Parameters:

attr - an attribute

inst - itemsets

Returns:

the absolute deviation value

variance

public static final double variance(int attr,
                                    MyDataset inst)

Returns the variance value of the itemsets values of an attribute

Parameters:

attr - an attribute

inst - itemsets

Returns:

the variance value

roundDouble

public static final long roundDouble(double value)

Rounds a double

Parameters:

value - the double value

Returns:

the double rounded

floorDouble

public static final long floorDouble(double value)

Returns the largest (closest to positive infinity) long integer value that is not greater than the argument.

Parameters:

value - the double value

Returns:

the floor integer

doubleToStringF

public static final java.lang.String doubleToStringF(double value,
                                                     int width,
                                                     int afterDecimalPoint)

Rounds a double and converts it into a formatted right-justified String. It is like %f format in C language.

Parameters:

value - the double value

width - the width of the string

afterDecimalPoint - the number of digits after the decimal point

Returns:

the double as a formatted string

doubleToStringG

public static final java.lang.String doubleToStringG(double value,
                                                     int width,
                                                     int precision)

Rounds a double and converts it into a formatted right-justified String. If the double is not equal to zero and not in the range [10e-3,10e7] it is returned in scientific format. It is like %g format in C language.

Parameters:

value - the double value

width - the width of the string

precision - the number of valid digits

Returns:

the double as a formatted string

deleteTrailingZerosAndDot

public static final java.lang.StringBuffer deleteTrailingZerosAndDot(java.lang.StringBuffer stringBuffer)

Deletes the trailing zeros and decimal point in a stringBuffer

Parameters:

stringBuffer - string buffer

Returns:

string buffer with deleted trailing zeros and decimal point

smoothenValue

public static final double smoothenValue(double p,
                                         double q,
                                         int n,
                                         int k)

Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)

Parameters:

p - a double, normally is the prediction of the model at the current node

q - a double, normally is the prediction of the model at the up node

n - the number of itemsets at the up node

k - the smoothing constance, default =15

Returns:

the smoothed value

correlation

public static final double correlation(double[] y1,
                                       double[] y2,
                                       int n)

Returns the correlation coefficient of two double vectors

Parameters:

y1 - double vector 1

y2 - double vector 2

n - the length of two double vectors

Returns:

the correlation coefficient

eqDouble

public static final boolean eqDouble(double a,
                                     double b)

Tests if two double values are equal to each other

Parameters:

a - double 1

b - double 2

Returns:

true if equal; false if not equal

errorMsg

public static final void errorMsg(java.lang.String err)

Prints error message and exits

Parameters:

err - error message