class SelTAN extends BayesClassifier
The SelTAN
class implements an Integer-Based Tree Augmented Selective
Naive Bayes Classifier, which is a combinations of two commonly used classifiers
for discrete input data. The classifier is trained using a data matrix 'x' and a
classification vector 'y'. Each data vector in the matrix is classified into one
of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on
the population of each class in the training-set. Relative posterior probabilities
are computed by multiplying these by values computed using conditional probabilities.
The classifier supports limited dependency between features/variables. The classifier
also uses backward elimination algorithm in an attempt to find the most important
subset of features/variables.
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Instance Constructors
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new
SelTAN(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], fset: Array[Boolean] = null, thres: Double = 0.3, me: Int = me_default, vc: VectoI = null)
- 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
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
aic(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Int = me_default): Double
Compute the 'AIC' for the given Bayesian Network structure and data.
Compute the 'AIC' for the given Bayesian Network structure and data.
- vc
the value count
- vcp1
the value count for parent 1
- vcp2
the value count for parent 2
- popX
the population counts
- k
the number of classes
- me
the m-estimate value
- Definition Classes
- BayesMetrics
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final
def
asInstanceOf[T0]: T0
- Definition Classes
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def
buildModel(testStart: Int = 0, testEnd: Int = 0): (Array[Boolean], DAG)
Build the Tree Augmented Selective Naive Bayes classier model by using backward-elimination Selective algorithm.
Build the Tree Augmented Selective Naive Bayes classier model by using backward-elimination Selective algorithm. Limited dependencies between variables/features are also supported.
- testStart
starting index of test region (inclusive)
- testEnd
ending index of test region (exclusive)
- Definition Classes
- SelTAN → BayesClassifier
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def
calcCorrelation: MatriD
Calculate the correlation matrix for the feature vectors 'fea'.
Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.
- Definition Classes
- ClassifierInt
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def
calcCorrelation2(zrg: Range, xrg: Range): MatriD
Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).
Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.
- zrg
the range of Z-columns
- xrg
the range of X-columns
- Definition Classes
- ClassifierInt
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def
classify(z: VectoI): (Int, String, Double)
Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.
Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its realtive probability.
- z
the data vector to classify
- Definition Classes
- SelTAN → Classifier
-
def
classify(z: VectoD): (Int, String, Double)
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector.
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector. Return the best class, its name and its relative probability
- z
the vector to classify
- Definition Classes
- ClassifierInt → Classifier
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def
clone(): AnyRef
- Attributes
- protected[java.lang]
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- @throws( ... )
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def
computeParent(): Unit
Compute the parent of each feature based on the correlation matrix.
Compute the parent of each feature based on the correlation matrix. Feature x_i is only a possible candidate for parent of feature x_j if i < j.
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def
computeVcp(): Unit
Compute the value count of each parent feature based on the parent vector.
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def
condMutualInformation(pz: VectorD, ptz: HMatrix3[Double], pxyz: HMatrix5[Double]): MatrixD
Compute conditional mutual information for XY given Z from frequency counts
Compute conditional mutual information for XY given Z from frequency counts
- pz
the probability of Z
- ptz
the probability of X given Z, or Y given Z
- pxyz
the probability of Y and Y given Z
- Definition Classes
- BayesClassifier
- See also
http://www.cs.technion.ac.il/~dang/journal_papers/friedman1997Bayesian.pdf, p.12
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def
crossValidate(nx: Int = 5): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.
- nx
the number of crosses and cross-validations (defaults to 5x).
- Definition Classes
- Classifier
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def
crossValidateRand(nx: Int = 5): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.
- nx
the number of crosses and cross-validations (defaults to 5x).
- Definition Classes
- Classifier
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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
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
- method
the method where the error occurred
- message
the error message
- Definition Classes
- Error
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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def
logLikelihood(vc: VectoI, vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Int = me_default): Double
Compute the Log-Likelihood for the given Bayesian Network structure and data.
Compute the Log-Likelihood for the given Bayesian Network structure and data.
- vc
the value count
- vcp1
the value count for parent 1
- vcp2
the value count for parent 2
- popX
the population counts
- k
the number of classes
- me
the m-estimate value
- Definition Classes
- BayesMetrics
-
val
m: Int
the number of data vectors in training/test-set (# rows)
the number of data vectors in training/test-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
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def
reset(): Unit
Reset or re-initialize the frequency tables and the probability tables with the updated parent vector.
Reset or re-initialize the frequency tables and the probability tables with the updated parent vector.
- Definition Classes
- SelTAN → Classifier
-
def
shiftToZero(): Unit
Shift the 'x' Matrix so that the minimum value for each column equals zero.
Shift the 'x' Matrix so that the minimum value for each column equals zero.
- Definition Classes
- ClassifierInt
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def
size: Int
Return the number of data vectors in training/test-set (# rows).
Return the number of data vectors in training/test-set (# rows).
- Definition Classes
- ClassifierInt → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(itest: VectorI): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- itest
indices of the instances considered test data
- Definition Classes
- ClassifierInt → Classifier
-
def
test(xx: MatrixI, yy: VectorI): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- xx
the integer-valued test vectors stored as rows of a matrix
- yy
the test classification vector, where 'yy_i = class' for row 'i' of 'xx'
- Definition Classes
- ClassifierInt
-
def
test(testStart: Int, testEnd: Int): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
- testStart
beginning of test region (inclusive)
- testEnd
end of test region (exclusive)
- Definition Classes
- ClassifierInt → Classifier
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def
toString(): String
- Definition Classes
- AnyRef → Any
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def
train(itrain: Array[Int]): Unit
Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.
Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.
- itrain
indices of the instances considered train data
- Definition Classes
- SelTAN → Classifier
-
def
train(testStart: Int = 0, testEnd: Int = 0): Unit
Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.
Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.
- testStart
starting index of test region (inclusive)
- testEnd
ending index of test region (exclusive)
- Definition Classes
- SelTAN → Classifier
-
def
train(): Unit
Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions.
Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions. Assumes that conditional densities follow the Normal (Gaussian) distribution.
- Definition Classes
- Classifier
-
def
vc_default: VectorI
Return default values for binary input data (value count 'vc' set to 2).
Return default values for binary input data (value count 'vc' set to 2).
- Definition Classes
- ClassifierInt
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def
vc_fromData: VectorI
Return value counts calculated from the input data.
Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.
- Definition Classes
- ClassifierInt
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def
vc_fromData2(rg: Range): VectorI
Return value counts calculated from the input data.
Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.
- rg
the range of columns to be considered
- Definition Classes
- ClassifierInt
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final
def
wait(): Unit
- Definition Classes
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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final
def
wait(arg0: Long): Unit
- Definition Classes
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