Rule

java.lang.Object
- keel.Algorithms.Rule_Learning.Ripper.Rule

```
public class Rule
extends java.lang.Object
```
Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2

Since:

JDK1.2

Version:

1.1

Author:

Written by Alberto Fernández (University of Granada) 01/07/2008, Modified by Xavi Solé (La Salle, Ramón Llull University - Barcelona) 03/12/2008

Field Summary

Fields
Modifier and Type Field and Description

static int EQUAL
Flag for equal operator

static int GREATER
Flag for greater operator

static int LOWER
Flag for lower operator

Fields
Modifier and Type	Field and Description
`static int`	`EQUAL` Flag for equal operator
`static int`	`GREATER` Flag for greater operator
`static int`	`LOWER` Flag for lower operator

Constructor Summary

Constructors
Constructor and Description

Rule()
Constructs an empty rule.

Constructors
Constructor and Description
`Rule()` Constructs an empty rule.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`int`	`apply(MyDataset data)` It returns the number of the instances covered by the rule in a given dataset
`int`	`apply(MyDataset data, Mask select)` Returns the number of the instances covered by the rule in a given dataset
`int`	`apply(MyDataset data, Mask select, int ignore)` Returns the number of the instances covered by the rule in a given dataset.
`keel.Algorithms.Rule_Learning.Ripper.Stats`	`apply(MyDataset data, Mask positives, Mask negatives)` It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
`Rule`	`getCopy()` It returns a copy of this rule
`static double`	`getExceptionCost(MyDataset data, int tp, int tn, int fp, int fn)` Static version.
`double`	`getExceptionCost(MyDataset data, Mask positives, Mask negatives)` Returns the Minimum Data Length of a dataset given a theory (this rule).
`SimpleRule`	`getSimpleRule(int i)` Returns the i-ieth simple rule of this rule.
`java.lang.String`	`getType()` It returns the right side (class) of the rule.
`void`	`grow(int attribute, double value, int operator)` Adds a simple rule to this rule.
`void`	`grow(SimpleRule sr)` Adds a simple rule to this rule.
`boolean`	`isEqual(Rule r)` Return wether this rule is equal to another given rule
`void`	`prune(int pos)` Deletes a simple rule from this chain
`void`	`setType(java.lang.String new_class)` It sets the right side of the rule.
`int`	`size()` Returns the size (number of simple rules) of the rule
`static double`	`subsetDL(double t, double k, double p)` Subset description length: S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
`double`	`theoryDL(MyDataset data)` The description length of the theory for a given rule.
`java.lang.String`	`toString()` Returns a string representation of this Rule, containing the String representation of each SimpleRule.

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

- Field Detail
  - GREATER
```
public static int GREATER
```
    Flag for greater operator
  - LOWER
```
public static int LOWER
```
    Flag for lower operator
  - EQUAL
```
public static int EQUAL
```
    Flag for equal operator
- Constructor Detail
  - Rule
```
public Rule()
```
    Constructs an empty rule.
- Method Detail
  - apply
```
public int apply(MyDataset data,
                 Mask select,
                 int ignore)
```
    Returns the number of the instances covered by the rule in a given dataset. This method allows to ignore a simple rule from that rule.
    
    Parameters:
    
    data - MyDataset the dataset
    
    select - Mask the mask with the active entries of the dataset
    
    ignore - int id of the single rule that it will be ignore in the applying of the rule
    
    Returns:
    
    number of instances (from the active entries) covered by the rule
  - apply
```
public int apply(MyDataset data,
                 Mask select)
```
    Returns the number of the instances covered by the rule in a given dataset
    
    Parameters:
    
    data - MyDataset the dataset
    
    select - Mask the mask with the active entries of the dataset
    
    Returns:
    
    number of instances (from the active entries) covered by the rule
  - apply
```
public int apply(MyDataset data)
```
    It returns the number of the instances covered by the rule in a given dataset
    
    Parameters:
    
    data - MyDataset the dataset
    
    Returns:
    
    number of instances (from the active entries) covered by the rule
  - apply
```
public keel.Algorithms.Rule_Learning.Ripper.Stats apply(MyDataset data,
                                                        Mask positives,
                                                        Mask negatives)
```
    It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
    
    Parameters:
    
    data - MyDataset the dataset
    
    positives - active positive instances of data
    
    negatives - active negative instances of data
    
    Returns:
    
    number of true positives, false positives, true negatives and false negatives of the rule in the following order: {tp,tn,fp,fn}
  - getExceptionCost
```
public double getExceptionCost(MyDataset data,
                               Mask positives,
                               Mask negatives)
```
    Returns the Minimum Data Length of a dataset given a theory (this rule). See [Quinlan95]
    
    Parameters:
    
    data - MyDataset the datasets
    
    positives - Mask active positive entries of data
    
    negatives - Mask active negative entries of data
    
    Returns:
    
    the MDL of data given this rule
  - subsetDL
```
public static double subsetDL(double t,
                              double k,
                              double p)
```
    Subset description length:
    S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
    
    Parameters:
    
    t - the number of elements in a known set
    
    k - the number of elements in a subset
    
    p - the expected proportion of subset known by recipient
    
    Returns:
    
    the subset description length
  - theoryDL
```
public double theoryDL(MyDataset data)
```
    The description length of the theory for a given rule. Computed as:
    0.5* [||k||+ S(t, k, k/t)]
    where k is the number of antecedents of the rule; t is the total possible antecedents that could appear in a rule; ||K|| is the universal prior for k , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) is the subset encoding length.
    Details see Quilan: "MDL and categorical theories (Continued)",ML95
    
    Parameters:
    
    data - MyDataset the dataset
    
    Returns:
    
    the theory DL, weighted if weight != 1.0
  - getExceptionCost
```
public static double getExceptionCost(MyDataset data,
                                      int tp,
                                      int tn,
                                      int fp,
                                      int fn)
```
    Static version. Returns the Minimum Data Length of a dataset given a theory (this rule). See [Quinlan95]
    
    Parameters:
    
    data - MyDataset the datasets
    
    tp - int true positives
    
    tn - int true negatives
    
    fp - int false positives
    
    fn - int false negatives
    
    Returns:
    
    the MDL of data given this rule
  - getSimpleRule
```
public SimpleRule getSimpleRule(int i)
```
    Returns the i-ieth simple rule of this rule.
    
    Parameters:
    
    i - position of the simple rule
    
    Returns:
    
    the i-ieth simple rule of this rule.
  - grow
```
public void grow(int attribute,
                 double value,
                 int operator)
```
    Adds a simple rule to this rule.
    
    Parameters:
    
    attribute - int attribute id (position of the attribute)
    
    value - double attribute's value
    
    operator - int rule operator
  - grow
```
public void grow(SimpleRule sr)
```
    Adds a simple rule to this rule.
    
    Parameters:
    
    sr - SimpleRule the simple rule
  - setType
```
public void setType(java.lang.String new_class)
```
    It sets the right side of the rule.
    
    Parameters:
    
    new_class - double new class of the rule
  - getType
```
public java.lang.String getType()
```
    It returns the right side (class) of the rule.
    
    Returns:
    
    the right side (class) of the rule.
  - getCopy
```
public Rule getCopy()
```
    It returns a copy of this rule
    
    Returns:
    
    a copy of this rule
  - prune
```
public void prune(int pos)
```
    Deletes a simple rule from this chain
    
    Parameters:
    
    pos - int position of the simple rule of the rule
  - size
```
public int size()
```
    Returns the size (number of simple rules) of the rule
    
    Returns:
    
    the size (number of simple rules) of the rule
  - isEqual
```
public boolean isEqual(Rule r)
```
    Return wether this rule is equal to another given rule
    
    Parameters:
    
    r - Rule the given rule
    
    Returns:
    
    true if this rule is equal to the given rule
  - toString
```
public java.lang.String toString()
```
    Returns a string representation of this Rule, containing the String representation of each SimpleRule.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of this Rule, containing the String representation of each SimpleRule.

Class Rule

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

GREATER

LOWER

EQUAL

Constructor Detail

Rule

Method Detail

apply

apply

apply

apply

getExceptionCost

subsetDL

theoryDL

getExceptionCost

getSimpleRule

grow

grow

setType

getType

getCopy

prune

size

isEqual

toString