object BayesClassifier
The BayesClassifier
object provides factory methods for building Bayes
classifiers.
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final
def
!=(arg0: Any): Boolean
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final
def
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def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, thres: Double, me: Int): BayesNetwork2
Build a Bayesian Network 2 classification model, passing 'x' and 'y' together in one matrix.
Build a Bayesian Network 2 classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names of the features
- k
the number of classes
- vc
the value count (number of distinct values) for each feature
- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, thres: Double, me: Int): BayesNetwork2
Create a Bayesian Network 2 classification model.
Create a Bayesian Network 2 classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count (number of distinct values) for each feature
- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], fset: Array[Boolean], thres: Double, me: Int, vc: VectoI): SelTAN
Build a Selective Tree Augmented Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
Build a Selective Tree Augmented Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names of the features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the
Boolean
array indicating the selected features- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
- vc
the value count (number of distinct values) for each feature
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], fset: Array[Boolean], thres: Double, me: Int, vc: VectoI): SelTAN
Build a Selective Tree Augmented Naive Bayes Classification model.
Build a Selective Tree Augmented Naive Bayes Classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l the matrix x, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the
Boolean
array indicating the selected features- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
- vc
the value count (number of distinct values) for each feature
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], thres: Double, me: Int, vc: VectoI): TANBayes
Build a Tree Augmented Naive Bayes Classification model, passing 'x' and 'y' together in one matrix.
Build a Tree Augmented Naive Bayes Classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names of the features
- k
the number of classes
- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
- vc
the value count (number of distinct values) for each feature
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], thres: Double, me: Int, vc: VectoI): TANBayes
Build a Tree Augmented Naive Bayes Classification model.
Build a Tree Augmented Naive Bayes Classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- thres
the correlation threshold between 2 features for possible parent-child relationship
- me
use m-estimates (me == 0 => regular MLE estimates)
- vc
the value count (number of distinct values) for each feature
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], fset: ListBuffer[Int], vc: VectoI, me: Int, thres: Double): AugSelNaiveBayes
Build a Augmented Selective Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
Build a Augmented Selective Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the list of selected features
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
- thres
the correlation threshold between 2 features for possible parent-child relationship
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], fset: ListBuffer[Int], vc: VectoI, me: Int, thres: Double): AugSelNaiveBayes
Build a Augmented Selective Naive Bayes classification model.
Build a Augmented Selective Naive Bayes classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l the matrix x, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the list of selected features
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
- thres
the correlation threshold between 2 features for possible parent-child relationship
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Int, thres: Double): AugNaiveBayes
Build a Augmented Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
Build a Augmented Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
- thres
the correlation threshold between 2 features for possible parent-child relationship
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Int, thres: Double): AugNaiveBayes
Build a Augmented Naive Bayes classification model.
Build a Augmented Naive Bayes classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
- thres
the correlation threshold between 2 features for possible parent-child relationship
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], fset: ListBuffer[Int], vc: VectoI, me: Int): SelNaiveBayes
Build a Selective Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
Build a Selective Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the list of selected features
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], fset: ListBuffer[Int], vc: VectoI, me: Int): SelNaiveBayes
Build a Selective Naive Bayes classification model.
Build a Selective Naive Bayes classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix x, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- fset
the list of selected features
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
-
def
apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Int): NaiveBayes
Build a Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
Build a Naive Bayes classification model, passing 'x' and 'y' together in one matrix.
- xy
the data vectors along with their classifications stored as rows of a matrix
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
-
def
apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Int): NaiveBayes
Build a Naive Bayes classification model.
Build a Naive Bayes classification model.
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix x, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
-
final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
list2Array(list: ListBuffer[Int], n: Int): Array[Boolean]
Convert a selected feature set from a list to a
Boolean
array representation.Convert a selected feature set from a list to a
Boolean
array representation.- list
the list of selected features, e.g., (1, 3, 5)
- n
the total number (selected or not) of features
-
val
me_default: Int
The default value for m-estimates (me == 0 => regular MLE estimates) me == 1 => no divide by 0, close to MLE estimates)
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
test(bc: BayesClassifier, name: String): Unit
Test the given Bayes classifier.
Test the given Bayes classifier.
- bc
the Bayes classifier
- name
name of the Bayes classifier
-
def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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