class DynBayesNetwork extends Classifier
The DynBayesNetwork class provides Dynamic Bayesian Network (DBN) models.
- Alphabetic
- By Inheritance
- DynBayesNetwork
- Classifier
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
- new DynBayesNetwork()
Value Members
-
def
classify(z: VectoI): (Int, String, Double)
Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.
Given a new discrete data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.
- z
the vector to classify
- Definition Classes
- DynBayesNetwork → Classifier
-
def
classify(z: VectoD): (Int, String, Double)
Given a new continuous data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.
Given a new continuous data vector z, determine which class it belongs to, returning the best class, its name and its relative probability.
- z
the vector to classify
- Definition Classes
- DynBayesNetwork → Classifier
-
def
crossValidate(nx: Int = 10): 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 = 10): 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. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency.
- nx
number of crosses and cross-validations (defaults to 10x).
- Definition Classes
- Classifier
-
def
reset(): Unit
Reset the frequency and probability tables.
Reset the frequency and probability tables.
- Definition Classes
- DynBayesNetwork → Classifier
-
def
size: Int
Return the size of the feature set.
Return the size of the feature set.
- Definition Classes
- DynBayesNetwork → Classifier
-
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
the beginning of test region (inclusive).
- testEnd
the end of test region (exclusive).
- Definition Classes
- DynBayesNetwork → 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
- Definition Classes
- Classifier
-
def
train(testStart: Int, testEnd: Int): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- testStart
the beginning of test region (inclusive).
- testEnd
the end of test region (exclusive).
- Definition Classes
- DynBayesNetwork → Classifier
-
def
train(): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- Definition Classes
- Classifier
-
def
train(itest: IndexedSeq[Int]): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- itest
the indices of the instances considered as testing data
- Definition Classes
- Classifier