trait Classifier extends AnyRef
The Classifier
trait provides a common framework for several classifiers.
A classifier is for bounded responses. When the number of distinct responses
cannot be bounded by some integer 'k', a predictor should be used.
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abstract
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
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abstract
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
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abstract
def
reset(): Unit
Reset the frequency and probability tables.
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abstract
def
size: Int
Return the size of the feature set.
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abstract
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).
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abstract
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).
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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).
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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).
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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(): 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.
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def
train(itrain: Array[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.
- itrain
the indices of the instances considered train data
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