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 data vectors stored as rows of a matrix
- y
the integer-valued classification vector, where y_i = class for row i of 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 fits into, returning the best class, its name and its relative probability.
Given a new discrete data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
- z
the integer vector to classify
- Definition Classes
- Classifier
-
abstract
def
reset(): Unit
Reset the frequency counters.
Reset the frequency counters.
- Definition Classes
- Classifier
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abstract
def
train(itest: IndexedSeq[Int]): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. The indices for the testing dataset are given and the training dataset consists of all the other instances. Must be implemented in any extending class.
- itest
the indices of the instances considered as testing data
- Definition Classes
- Classifier
Concrete Value Members
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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(xx: MatriI): VectoI
Classify all of the row vectors in matrix 'xx'.
Classify all of the row vectors in matrix 'xx'.
- xx
the row vectors to classify
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def
classify(z: VectoD): (Int, String, Double)
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability. Override in classes that require precise real values for classification.
- z
the real vector to classify
- Definition Classes
- ClassifierInt → Classifier
-
def
crossValidate(nx: Int = 10, show: Boolean = false): 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'. FIX - should return a StatVector
- nx
the number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- Definition Classes
- Classifier
-
def
crossValidateRand(nx: Int = 10, show: Boolean = false): 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. FIX - are the comments correct? FIX - should return a StatVector
- nx
number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- Definition Classes
- Classifier
-
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
fit(y: VectoI, yp: VectoI, k: Int = 2): VectoD
Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'.
Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'. Override to add more quality of fit measures.
- y
the actual class labels
- yp
the precicted class labels
- k
the number of class labels
- Definition Classes
- Classifier
- See also
ConfusionMatmedium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b
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def
fitLabel: Seq[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
- Definition Classes
- Classifier
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
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/points in the entire dataset (training + testing),
Return the number of data vectors/points in the entire dataset (training + testing),
- Definition Classes
- ClassifierInt → Classifier
-
def
test(xx: MatriI, yy: VectoI): 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'
-
def
test(itest: IndexedSeq[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.
- itest
indices of the instances considered test data
- Definition Classes
- ClassifierInt → Classifier
-
def
test(testStart: Int, testEnd: Int): Double
Test the quality of the training with a test dataset and return the fraction of correct classifications.
Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.
- testStart
the beginning of test region (inclusive).
- testEnd
the end of test region (exclusive).
- Definition Classes
- Classifier
-
def
train(): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.
- Definition Classes
- Classifier
-
def
train(testStart: Int, testEnd: Int): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.
- testStart
starting index of test region (inclusive) used in cross-validation
- testEnd
ending index of test region (exclusive) used in cross-validation
- Definition Classes
- Classifier
-
def
vc_default: Array[Int]
Return default values for binary input data (value count 'vc' set to 2).
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def
vc_fromData: Array[Int]
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): Array[Int]
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