class BayesClfML extends Classifier
The BayesClfML
class implements an Integer-Based Naive Bayes Multi-Label Classifier,
which is a commonly used such classifier for discrete input data. The
classifier is trained using a data matrix 'x' and a classification matrix '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 is naive, because it assumes feature independence and therefore
simply multiplies the conditional probabilities.
- See also
www.aia-i.com/ijai/sample/vol3/no2/173-188.pdf -----------------------------------------------------------------------------
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new
BayesClfML(bayesBuilder: (Int) ⇒ BayesClassifier, nLabels: Int, nFeatures: Int)
- bayesBuilder
the function mapping an integer to a regular Bayes classifier
- nLabels
the number of labels/class variables
- nFeatures
the number of feature variables
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def
buildModel(testStart: Int, testEnd: Int): (Array[Boolean], DAG)
Build a model.
Build a model.
- 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
classify(z: VectoD): (Int, String, Double)
Given a data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability, returning the class, its name and its relative probability.
Given a data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability, returning the class, its name and its relative probability.
- z
the data vector to classify
- Definition Classes
- BayesClfML → Classifier
-
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, returning the class, its name and its relative probability.
Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability, returning the class, its name and its relative probability.
- z
the data vector to classify
- Definition Classes
- BayesClfML → Classifier
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def
clone(): AnyRef
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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).
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final
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eq(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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def
reset(): Unit
Reset or re-initialize all the population and probability vectors and hypermatrices to 0.
Reset or re-initialize all the population and probability vectors and hypermatrices to 0.
- Definition Classes
- BayesClfML → Classifier
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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
- BayesClfML → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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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
- BayesClfML → Classifier
-
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
the indices of the instances considered test data
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def
toString(): String
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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
- BayesClfML → Classifier
-
def
train(testStart: Int, testEnd: 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.
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region (exclusive) used in cross-validation.
- Definition Classes
- BayesClfML → 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
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final
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