class SupportVectorMachine extends ClassifierReal
The SupportVectorMachine class is a translation of Pseudo-Code from a
modified SMO (Modification 2) found at the above URL's into Scala and includes
a few simplifications (e.g., currently only works for linear kernels, dense
data and binary classification).
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Instance Constructors
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new
SupportVectorMachine(x: MatrixD, y: VectoI, fn: Array[String] = Array (), cn: Array[String] = Array ("-", "+"))
- x
the training data points stored as rows in a matrix
- y
the classification of the data points stored in a vector
- fn
the factor names
- cn
the class names
Type Members
- type Pair = (Double, Double)
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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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
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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 continuous data vector z, determine which class it belongs to.
Given a new continuous data vector z, determine which class it belongs to. Classify returns 1 meaning 'z' belongs to the positive class, while -1 means it belongs to the negative class.
- z
the vector to classify
- Definition Classes
- SupportVectorMachine → 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
- Attributes
- protected[java.lang]
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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
- Definition Classes
- AnyRef
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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.
- Attributes
- protected
- Definition Classes
- ClassifierReal
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final
def
getClass(): Class[_]
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- @native()
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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-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 the frequency tables and the probability tables.
Reset or re-initialize the frequency tables and the probability tables.
- Definition Classes
- SupportVectorMachine → 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
Convert svm to a string showing (w, b).
Convert svm to a string showing (w, b).
- Definition Classes
- SupportVectorMachine → AnyRef → Any
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def
train(itest: IndexedSeq[Int]): SupportVectorMachine
Train uses SMO (Sequential Minimum Optimization) algorithm to solves the optimization problem for the weight vector 'w' and the threshold 'b' for the model '(w dot z) - b'.
Train uses SMO (Sequential Minimum Optimization) algorithm to solves the optimization problem for the weight vector 'w' and the threshold 'b' for the model '(w dot z) - b'.
- itest
the indices of the test data
- Definition Classes
- SupportVectorMachine → 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
-
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
update(i1: Int, i2: Int, y1: Int, y2: Int): Unit
Update weights 'w' and error cache 'fCache'.
Update weights 'w' and error cache 'fCache'.
- i1
the index for the first Lagrange multipliers (alpha)
- i2
the index for the second Lagrange multipliers (alpha)
- y1
the first target value
- y2
the second target value
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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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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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