class KNN_Classifier extends ClassifierReal
The KNN_Classifier class is used to classify a new vector 'z' into one of
'k' classes. It works by finding its 'kappa' nearest neighbors. These neighbors
essentially vote according to their classification. The class with most
votes is selected as the classification of 'z'. Using a distance metric,
the 'kappa' vectors nearest to 'z' are found in the training data, which is
stored row-wise in the data matrix 'x'. The corresponding classifications
are given in the vector 'y', such that the classification for vector 'x(i)'
is given by 'y(i)'.
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new
KNN_Classifier(x: MatriD, y: VectoI, fn: Array[String] = null, k: Int = 2, cn: Array[String] = Array ("no", "yes"), kappa: Int = 3)
- x
the vectors/points of classified data stored as rows of a matrix
- y
the classification of each vector in x
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- kappa
the number of nearest neighbors to consider
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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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.
- Definition Classes
- ClassifierReal
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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
- Definition Classes
- ClassifierReal
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def
classify(z: VectoD): (Int, String, Double)
Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'kappa' nearest neighbors.
Given a new point/vector 'z', determine which class it belongs to (i.e., the class getting the most votes from its 'kappa' nearest neighbors. Return the best class, its name and its votes
- z
the vector to classify
- Definition Classes
- KNN_Classifier → Classifier
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def
classify(xx: MatriD): 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
- Definition Classes
- ClassifierReal
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def
classify(z: VectoI): (Int, String, Double)
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles.
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability
- z
the vector to classify
- Definition Classes
- ClassifierReal → 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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def
distance(x: VectoD, z: VectoD): Double
Compute a distance metric between vectors/points 'x' and 'z'.
Compute a distance metric between vectors/points 'x' and 'z'. The squared Euclidean norm used for efficiency, but may use other norms.
- x
the first vector/point
- z
the second vector/point
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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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
- Definition Classes
- ClassifierReal
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def
finalize(): Unit
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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
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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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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- ClassifierReal
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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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def
kNearest(z: VectoD): Unit
Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array.
Find the 'kappa' nearest neighbors (top-'kappa') to vector 'z' and store in the 'topK' array. Break ties by flipping a fair coin.
- z
the vector to be classified
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val
m: Int
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierReal
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val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierReal
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
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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 'topK' and counters.
Reset or re-initialize 'topK' and counters.
- Definition Classes
- KNN_Classifier → Classifier
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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
- ClassifierReal → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
test(xx: MatriD, 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 real-valued test vectors stored as rows of a matrix
- yy
the test classification vector, where 'yy_i = class for row i of xx'
- Definition Classes
- ClassifierReal
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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
- ClassifierReal → 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]): KNN_Classifier
Training involves resetting the data structures before each classification.
Training involves resetting the data structures before each classification. It uses lazy training, so most of it is done during classification.
- itest
the indices of the test data
- Definition Classes
- KNN_Classifier → 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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def
vc_default: Array[Int]
Return default values for binary input data (value count 'vc' set to 2).
Return default values for binary input data (value count 'vc' set to 2). Also may be used for binning into two categories.
- Definition Classes
- ClassifierReal
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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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