class BayesNetwork2 extends ClassifierInt with BayesMetrics
The BayesNetwork2
class implements an Integer-Based Bayesian Network Classifier,
which is a commonly used such classifier for discrete input data. Each node is
limited to have at most 2 parents, and hence the "2" in the class name BayesNetwork2
.
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.
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Instance Constructors
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new
BayesNetwork2(xy: MatriI, fn: Array[String], k: Int, cn: Array[String])
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
- 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
- cn
the names for all classes
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new
BayesNetwork2(x: MatriI, y: VectoI, fn: Array[String], vc: VectoI = null, k: Int, cn: Array[String], thres: Double = 0.3, me: Int = 3)
- 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
- vc
the value count (number of distinct values) for each feature
- 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)
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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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
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
buildModel(testStart: Int = 0, testEnd: Int = 0): Unit
Build the Bayes Networks2 classier model by using the 'AIC' criterion.
Build the Bayes Networks2 classier model by using the 'AIC' criterion. Limited dependencies between variables/features are also supported. Maximum number of parents for each feature is 2.
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region. (exclusive) used in cross-validation.
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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
-
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
-
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 relative probability.
- z
the data vector to classify
- Definition Classes
- BayesNetwork2 → 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
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
computeParent(parent: MatrixI, cor: MatriD, featureOrder: VectorI): MatrixI
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 'x_i' appears before 'x_j' in 'featureOrder'.
- parent
vector holding the parent for each feature/variable
- cor
feature correlation matrix
- featureOrder
keep the order of the features
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def
computeVcp(vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorI, VectorI)
Compute the value counts of each parent feature based on the parent matrix.
Compute the value counts of each parent feature based on the parent matrix.
- vcp1
value count for parent1
- vcp2
value count for parent2
- parent
vector holding the parent for each feature/variable
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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
-
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
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
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- @throws( classOf[java.lang.Throwable] )
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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
- val g_AIC: Array[Double]
- val g_optimalFeatureOrder: Array[VectorI]
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
getFeatureOrder: VectoI
Return the feature order.
-
def
getParent: MatrixI
Return the parent.
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
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
-
val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
-
val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierInt
-
val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
def
reset(): Unit
Reset or re-initialize the frequency tables and the probability tables.
Reset or re-initialize the frequency tables and the probability tables.
- Definition Classes
- BayesNetwork2 → Classifier
-
def
resetHelper(parent: MatrixI, cor: MatriD, featureOrder: VectorI, vcp1: VectorI, vcp2: VectorI, popC: VectorI, probC: VectorD, popX: HMatrix5[Int], probX: HMatrix5[Double]): Unit
Reset or re-initialize the frequency tables and the probability tables.
Reset or re-initialize the frequency tables and the probability tables.
- parent
vector holding the parent for each feature/variable
- cor
feature correlation matrix
- featureOrder
keep the order of the features
- vcp1
value count for parent1
- vcp2
value count for parent2
- popC
frequency counts for classes 0, ..., k-1
- probC
probabilities for classes 0, ..., k-1
- popX
conditional frequency counts for variable/feature j: xj
- probX
conditional probabilities for variable/feature j: xj
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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
-
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
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
- val t_parent: MatrixI
- val t_vcp1: VectorI
- val t_vcp2: VectorI
-
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
-
def
toString(): String
Convert 'this' object to a string.
Convert 'this' object to a string. FIX - implement
- Definition Classes
- BayesNetwork2 → AnyRef → Any
-
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) used in cross-validation.
- testEnd
ending index of test region. (exclusive) used in cross-validation.
- Definition Classes
- BayesNetwork2 → Classifier
-
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
- BayesNetwork2 → 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
train4order(itrain: Array[Int], popC: VectorI, popX: HMatrix5[Int], probC: VectorD, probX: HMatrix5[Double], vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorD, HMatrix5[Double])
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
- popC
frequency counts for classes 0, ..., k-1
- popX
conditional frequency counts for variable/feature j: xj
- probC
probabilities for classes 0, ..., k-1
- probX
conditional probabilities for variable/feature j: xj
- vcp1
value count for parent1
- vcp2
value count for parent2
- parent
vector holding the parent for each feature/variable
-
def
train4order(testStart: Int = 0, testEnd: Int = 0, popC: VectorI, popX: HMatrix5[Int], probC: VectorD, probX: HMatrix5[Double], vcp1: VectorI, vcp2: VectorI, parent: MatrixI): (VectorD, HMatrix5[Double])
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) used in cross-validation.
- testEnd
ending index of test region. (exclusive) used in cross-validation.
- popC
frequency counts for classes 0, ..., k-1
- popX
conditional frequency counts for variable/feature j: xj
- probC
probabilities for classes 0, ..., k-1
- probX
conditional probabilities for variable/feature j: xj
- vcp1
value count for parent1
- vcp2
value count for parent2
- parent
vector holding the parent for each feature/variable
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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
-
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
-
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
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )