final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

val N0: Double

Attributes: protected
Definition Classes: BayesClassifier

var additive: Boolean

Attributes: protected
Definition Classes: BayesClassifier

def aic(vc: Array[Int], vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = me_default): Double

Compute the 'AIC' for the given Bayesian Network structure and data.

vc: the value count
vcp1: the value count for parent 1
vcp2: the value count for parent 2
popX: the population counts
k: the number of classes
me: the m-estimate value

Definition Classes: BayesMetrics

final def asInstanceOf[T0]: T0

Definition Classes: Any

def calcCMI(idx: IndexedSeq[Int], vca: Array[Int]): MatrixD

Compute the conditional mutual information matrix

idx: indicies of either training or testing region
vca: array of value counts

Definition Classes: BayesClassifier

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: ClassifierInt

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: ClassifierInt

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: TANBayes0 → Classifier

def classify(xx: MatriI): VectoI

Classify all of the row vectors in matrix 'xx'.

xx: the row vectors to classify

Definition Classes: ClassifierInt

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 clone(): AnyRef

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def cmiJoint(p_C: VectoD, p_CX: HMatrix3[Double], p_CXZ: HMatrix5[Double]): MatrixD

Compute conditional mutual information matrix given the marginal probability of C and joint probabilities of CXZ and CX, where C is the class (parent), and X & Z are features.

p_C: the marginal probability of C
p_CX: the joint probability of C and X
p_CXZ: the joint probability of C, X, and Z

Definition Classes: BayesClassifier
See also: en.wikipedia.org/wiki/Conditional_mutual_information

def computeParent(idx: IndexedSeq[Int]): Unit

Compute the parent of each feature based on the correlation matrix.

Compute the parent of each feature based on the correlation matrix. Feature x_i is only a possible candidate for parent of feature x_j if i < j

idx: indicies of either training or testing region

Definition Classes: TANBayes0

def computeVcp(): Unit

Compute the value counts of each parent feature based on the parent vector.

Compute the value counts of each parent feature based on the parent vector. Let 1 be the default value count when there is no parent.

Definition Classes: TANBayes0

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

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

var f_CX: HMatrix3[Int]

Attributes: protected
Definition Classes: BayesClassifier

val f_CXP: HMatrix4[Int]

Attributes: protected
Definition Classes: TANBayes0

var f_CXZ: HMatrix5[Int]

Attributes: protected
Definition Classes: BayesClassifier

var f_X: HMatrix2[Int]

Attributes: protected
Definition Classes: BayesClassifier

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: ClassifierInt

def finalize(): Unit

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

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: ConfusionMat
medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b

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 frequenciesAll(): Unit

Compute frequency counts using the entire data matrix

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: ClassifierInt

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

def getParent: VectoI

Return the parent.

Definition Classes: TANBayes0 → BayesClassifier

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

def logLikelihood(vc: Array[Int], vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = me_default): Double

Compute the Log-Likelihood for the given Bayesian Network structure and data.

vc: the value count
vcp1: the value count for parent 1
vcp2: the value count for parent 2
popX: the population counts
k: the number of classes
me: the m-estimate value

Definition Classes: BayesMetrics

val m: Int

the number of data vectors in training/test-set (# rows)

Attributes: protected
Definition Classes: ClassifierInt

def maxSpanningTree(ch: Array[Set[Int]], elabel: Map[Pair, Double]): MinSpanningTree

Create MaxSpanningTree from conditional mutual information.

ch: the adjacency set
elabel: the edge labels/weights

Definition Classes: TANBayes0

val md: Double

the training-set size as a Double

Attributes: protected
Definition Classes: ClassifierInt

val n: Int

the number of features/variables (# columns)

Attributes: protected
Definition Classes: ClassifierInt

val nd: Double

the feature-set size as a Double

Attributes: protected
Definition Classes: ClassifierInt

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

val nu_y: VectorI

Attributes: protected
Definition Classes: BayesClassifier

var p_C: VectorD

Attributes: protected
Definition Classes: BayesClassifier

val p_X_CP: HMatrix4[Double]

Attributes: protected
Definition Classes: TANBayes0

var parent: VectorI

Attributes: protected
Definition Classes: TANBayes0

def reset(): Unit

Reset or re-initialize the frequency tables from the global frequencies.

Definition Classes: TANBayes → TANBayes0 → Classifier

def shiftToZero(): Unit

Shift the 'x' Matrix so that the minimum value for each column equals zero.

Definition Classes: ClassifierInt

def size: Int

Return the number of data vectors/points in the entire dataset (training + testing),

Definition Classes: ClassifierInt → Classifier

var smooth: Boolean

Attributes: protected
Definition Classes: BayesClassifier

final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes: AnyRef

def test(xx: MatriI, yy: VectoI): Double

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'

Definition Classes: ClassifierInt

def test(itest: IndexedSeq[Int]): Double

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

val tiny: Double

Attributes: protected
Definition Classes: BayesClassifier

def toString(): String

Definition Classes: AnyRef → Any

def toggleSmooth(): Unit

Toggle the value of the 'smooth' property.

Definition Classes: BayesClassifier

def train(itest: IndexedSeq[Int]): TANBayes0

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: TANBayes0 → 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 updateFreq(i: Int): Unit

Decrement frequency counters used in CMI calculations based on the 'i'th row of the data matrix.

i: the index for current data row

Attributes: protected
Definition Classes: TANBayes → TANBayes0 → BayesClassifier

var vc: Array[Int]

Attributes: protected
Definition Classes: TANBayes0

def vc_default: Array[Int]

Return default values for binary input data (value count 'vc' set to 2).

Definition Classes: ClassifierInt

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.

Definition Classes: ClassifierInt

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

Definition Classes: ClassifierInt

val vcp: Array[Int]

Attributes: protected
Definition Classes: TANBayes0

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long, arg1: Int): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @native() @throws( ... )

Packages

TANBayes

Companion object TANBayes

class TANBayes extends TANBayes0

Instance Constructors

Value Members

Inherited from TANBayes0

Inherited from BayesClassifier

Inherited from BayesMetrics

Inherited from ClassifierInt

Inherited from Error

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped