Ruleset

java.lang.Object
- keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset

```
public class Ruleset
extends java.lang.Object
```
Representation of a disjuction of rules with a common consecuent. It may be represented as: if (rule1 || rule2) then output=consecuent

Since:

JDK1.2

Version:

1.1

Author:

Written by Antonio Alejandro Tortosa (University of Granada) 01/07/2008, Modified by Xavi Solé (La Salle, Ramón Llull University - Barcelona) 16/12/2008

Constructor Summary

Constructors
Constructor and Description

Ruleset()
Constructs an empty ruleset.

Constructors
Constructor and Description
`Ruleset()` Constructs an empty ruleset.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addRule(Rule r)` Adds a new rule to the ruleset.
`keel.Algorithms.Rule_Learning.C45RulesSA.Stats`	`apply(MyDataset data)` It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
`keel.Algorithms.Rule_Learning.C45RulesSA.Stats`	`apply(MyDataset data, Mask positives, Mask negatives)` It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
`double`	`biased(double N, double E, double ExpE)` Biased function.
`double`	`getExceptionCost(MyDataset data)` Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
`double`	`getExceptionCost(MyDataset data, Mask positives, Mask negatives)` Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
`double`	`getExceptionCost(MyDataset data, Mask positives, Mask negatives, keel.Algorithms.Rule_Learning.C45RulesSA.IncrementalMask rulesetMask)` Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
`double`	`getMDL(MyDataset data)` Returns the Minimum Data Length of a dataset given a theory (this ruleset).
`Rule`	`getRule(int pos)` Returns the rule in the i-th position of the ruleset.
`double`	`getTheoryCost(MyDataset data)` The description length of the theory for the ruleset.
`java.lang.String`	`getType()` Returns the common output (consecuent) of the rules in the ruleset.
`void`	`insertRule(Rule r, int pos)` Inserts a new rule in a given position of the ruleset.
`void`	`pulish(MyDataset data, Mask positives, Mask negatives)` Remove the rules that increase the DL value of the set.
`void`	`removeRule(int pos)` Deletes a given rule of the ruleset.
`void`	`setType(java.lang.String type)` Sets the common output (consecuent) of the rules in the ruleset.
`int`	`size()` Returns the size (number of rules) of the ruleset.
`java.lang.String`	`toString()` Returns a string representation of this Ruleset, containing the String representation of each Rule.

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

- Constructor Detail
  - Ruleset
```
public Ruleset()
```
    Constructs an empty ruleset.
- Method Detail
  - addRule
```
public void addRule(Rule r)
```
    Adds a new rule to the ruleset.
    
    Parameters:
    
    r - Rule the new rule
  - apply
```
public keel.Algorithms.Rule_Learning.C45RulesSA.Stats apply(MyDataset data)
```
    It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset. This methods takes into account the right part (consecuent) of the rules
    
    Parameters:
    
    data - MyDataset the dataset
    
    Returns:
    
    number of true positives, false positives, true negatives and false negatives of the whole ruleset in the following order: {tp,tn,fp,fn}
  - apply
```
public keel.Algorithms.Rule_Learning.C45RulesSA.Stats apply(MyDataset data,
                                                            Mask positives,
                                                            Mask negatives)
```
    It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset. (This methods doesn't take into account the right part (consecuent) of the rules).
    
    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 whole ruleset in the following order: {tp,tn,fp,fn}
  - getExceptionCost
```
public double getExceptionCost(MyDataset data,
                               Mask positives,
                               Mask negatives)
```
    Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset). 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 ruleset.
  - biased
```
public double biased(double N,
                     double E,
                     double ExpE)
```
    Biased function.
    
    Parameters:
    
    N - N parameter given.
    
    E - E parameter given.
    
    ExpE - exponent given.
    
    Returns:
    
    biased value.
  - getExceptionCost
```
public double getExceptionCost(MyDataset data)
```
    Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset). See [Quinlan95]
    
    Parameters:
    
    data - MyDataset the datasets
    
    Returns:
    
    the MDL of data given this ruleset.
  - getMDL
```
public double getMDL(MyDataset data)
```
    Returns the Minimum Data Length of a dataset given a theory (this ruleset). See [Quinlan95]
    
    Parameters:
    
    data - MyDataset the datasets
    
    Returns:
    
    the MDL of data given this ruleset.
  - getExceptionCost
```
public double getExceptionCost(MyDataset data,
                               Mask positives,
                               Mask negatives,
                               keel.Algorithms.Rule_Learning.C45RulesSA.IncrementalMask rulesetMask)
```
    Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset). See [Quinlan95]
    
    Parameters:
    
    data - MyDataset the datasets
    
    positives - Mask active positive entries of data
    
    negatives - Mask active negative entries of data
    
    rulesetMask - the combine mask of all rules in the ruleset.
    
    Returns:
    
    the MDL of data given this ruleset.
  - getTheoryCost
```
public double getTheoryCost(MyDataset data)
```
    The description length of the theory for the ruleset. Computed as the addition of the theory cost for each rule:
    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.
    
    Parameters:
    
    data - MyDataset the dataset
    
    Returns:
    
    the description length of the theory for the ruleset
  - pulish
```
public void pulish(MyDataset data,
                   Mask positives,
                   Mask negatives)
```
    Remove the rules that increase the DL value of the set.
    
    Parameters:
    
    data - the dataset
    
    positives - the positives exemples
    
    negatives - the negatives exemples
  - getRule
```
public Rule getRule(int pos)
```
    Returns the rule in the i-th position of the ruleset.
    
    Parameters:
    
    pos - int position of the rule in the ruleset
    
    Returns:
    
    the rule in the pos-th position of the ruleset.
  - getType
```
public java.lang.String getType()
```
    Returns the common output (consecuent) of the rules in the ruleset.
    
    Returns:
    
    the common output (consecuent) of the rules in the ruleset.
  - insertRule
```
public void insertRule(Rule r,
                       int pos)
```
    Inserts a new rule in a given position of the ruleset.
    
    Parameters:
    
    r - Rule the new rule
    
    pos - int the position where r must be inserted
  - removeRule
```
public void removeRule(int pos)
```
    Deletes a given rule of the ruleset.
    
    Parameters:
    
    pos - int position of the rule in the ruleset.
  - setType
```
public void setType(java.lang.String type)
```
    Sets the common output (consecuent) of the rules in the ruleset.
    
    Parameters:
    
    type - String the common output (consecuent) of the rules in the ruleset.
  - size
```
public int size()
```
    Returns the size (number of rules) of the ruleset.
    
    Returns:
    
    the size (number of rules) of the ruleset.
  - toString
```
public java.lang.String toString()
```
    Returns a string representation of this Ruleset, containing the String representation of each Rule.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of this Ruleset, containing the String representation of each Rule.

Class Ruleset

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Ruleset

Method Detail

addRule

apply

apply

getExceptionCost

biased

getExceptionCost

getMDL

getExceptionCost

getTheoryCost

pulish

getRule

getType

insertRule

removeRule

setType

size

toString