class NullModel extends Classifier
The NullModel
class implements an Integer-Based Null Model Classifier,
which is a simple classifier for discrete input data. The classifier is trained
just using a classification vector 'y'.
Each data instance is classified into one of 'k' classes numbered 0, ..., k-1.
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Instance Constructors
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new
NullModel(y: VectoI, k: Int, cn: Array[String])
- y
the class vector, where y(i) = class for instance i
- k
the number of classes
- cn
the names for all classes
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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def
actualVpredicted(y: VectoI, yp: VectoI): Map[String, Double]
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.
- y
the actual class labels
- yp
the precicted class labels
- Definition Classes
- Classifier
- See also
www.dataschool.io/simple-guide-to-confusion-matrix-terminology
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final
def
asInstanceOf[T0]: T0
- Definition Classes
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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.
Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its relative probability.
- z
the data vector to classify
- Definition Classes
- NullModel → Classifier
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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
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- Classifier
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def
clone(): AnyRef
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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
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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
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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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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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
reset(): Unit
Reset or re-initialize all the frequency counters.
Reset or re-initialize all the frequency counters.
- Definition Classes
- NullModel → Classifier
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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
- NullModel → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
test(itest: IndexedSeq[Int]): Double
Test the quality of the training with a testiing dataset and return the fraction of correct classifications.
Test the quality of the training with a testiing dataset and return the fraction of correct classifications.
- itest
indices of the instances considered test data
- Definition Classes
- NullModel → Classifier
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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
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def
toString(): String
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def
train(itest: IndexedSeq[Int]): NullModel
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.
- itest
indices of the instances considered as testing data
- Definition Classes
- NullModel → Classifier
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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
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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
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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