abstract class ClassifierInt extends Classifier with Error
The ClassifierInt abstract class provides a common foundation for several
classifiers that operate on integer-valued data.
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Instance Constructors
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new
ClassifierInt(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String])
- x
the integer-valued training/test data vectors stored as rows of a matrix
- y
the training/test classification vector, where y_i = class for row i of the matrix x
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
Abstract Value Members
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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
- Definition Classes
- Classifier
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abstract
def
reset(): Unit
Reset the frequency and probability tables.
Reset the frequency and probability tables.
- Definition Classes
- Classifier
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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).
- Definition Classes
- Classifier
Concrete 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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final
def
asInstanceOf[T0]: T0
- Definition Classes
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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.
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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
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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
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
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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
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
featureSelection(TOL: Double = 0.01): Unit
Perform feature selection on the classifier.
Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.
- TOL
tolerance indicating negligible accuracy loss when removing features
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def
finalize(): Unit
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
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val
fset: Array[Boolean]
the set of features to turn on or off.
the set of features to turn on or off. All features are on by default. Used for feature selection.
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- protected
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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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
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val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
- Definition Classes
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final
def
notifyAll(): Unit
- Definition Classes
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def
shiftToZero(): Unit
Shift the 'x' Matrix so that the minimum value for each column equals zero.
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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
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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
indices of the instances considered test data
- Definition Classes
- ClassifierInt → Classifier
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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'
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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
- ClassifierInt → Classifier
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def
toString(): String
- Definition Classes
- AnyRef → Any
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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
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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
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def
vc_default: VectorI
Return default values for binary input data (value count 'vc' set to 2).
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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.
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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
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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