class NaiveBayes extends NaiveBayes0
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
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Instance Constructors
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new
NaiveBayes(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc_: Array[Int] = null, me: Double = me_default, PARALLELISM: Int = ...)
- x
the integer-valued data vectors stored as rows of a matrix
- y
the class vector, where y(l) = class for row l of the matrix x, x(l)
- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- me
use m-estimates (me == 0 => regular MLE estimates)
- PARALLELISM
the level of parallelism, the number of threads to use
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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var
additive: Boolean
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- Definition Classes
- BayesClassifier
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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.
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
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
calcCMI(idx: IndexedSeq[Int], vca: Array[Int]): MatrixD
Compute the conditional mutual information matrix
Compute the conditional mutual information matrix
- 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
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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
- ClassifierInt
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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
- NaiveBayes0 → Classifier
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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
- Definition Classes
- ClassifierInt
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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
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def
clone(): AnyRef
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def
cmiJoint(p_y: VectorD, p_Xy: HMatrix3[Double], p_XyZ: 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.
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_y
the marginal probability of y
- p_Xy
the joint probability of X and y
- p_XyZ
the joint probability of C, X, and Z
- Definition Classes
- BayesClassifier
- See also
en.wikipedia.org/wiki/Conditional_mutual_information
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var
cn: Strings
- Attributes
- protected
- Definition Classes
- ClassifierInt
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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
- ClassifierInt
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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 predicted 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
-
def
fitMap(y: VectoI, yp: VectoI, k: Int = 2): Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- y
the actual class labels
- yp
the predicted class labels
- k
the number of class labels
- Definition Classes
- Classifier
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
var
fn: Strings
- Attributes
- protected
- Definition Classes
- ClassifierInt
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def
frequenciesAll(): Unit
Compute frequency counts using the entire data matrix
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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
- ClassifierInt
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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
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.
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)
the number of data vectors in training/test-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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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
- ClassifierInt
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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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
- ClassifierInt
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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var
nu_X: HMatrix2[Int]
- Attributes
- protected
- Definition Classes
- BayesClassifier
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var
nu_Xy: HMatrix3[Int]
- Attributes
- protected
- Definition Classes
- BayesClassifier
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var
nu_XyZ: HMatrix5[Int]
- Attributes
- protected
- Definition Classes
- BayesClassifier
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val
nu_y: VectorI
- Attributes
- protected
- Definition Classes
- BayesClassifier
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val
p_Xy: HMatrix3[Double]
- Attributes
- protected
- Definition Classes
- NaiveBayes0
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var
p_y: VectorD
- Attributes
- protected
- Definition Classes
- BayesClassifier
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def
reset(): Unit
Reset or re-initialize all the frequency tables from global frequencies.
Reset or re-initialize all the frequency tables from global frequencies.
- Definition Classes
- NaiveBayes → NaiveBayes0 → Classifier
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def
shiftToZero(): Unit
Shift the 'x' Matrix so that the minimum value for each column equals zero.
Shift the 'x' Matrix so that the minimum value for each column equals zero.
- Definition Classes
- ClassifierInt
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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
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var
smooth: Boolean
- Attributes
- protected
- Definition Classes
- BayesClassifier
-
def
split(indices: IndexedSeq[Int], k: Int): Array[IndexedSeq[Int]]
Split 'indices' into 'k' arrays of equal sizes (perhaps except for the last one)
Split 'indices' into 'k' arrays of equal sizes (perhaps except for the last one)
- indices
the ParSeq to be splitted
- k
the number of pieces the vector is to be splitted
- Definition Classes
- BayesClassifier
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(itest: Array[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
- BayesClassifier
-
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'
- 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.
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.
Toggle the value of the 'smooth' property.
- Definition Classes
- BayesClassifier
-
def
train(itest: IndexedSeq[Int]): NaiveBayes0
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. This is the quick version that uses the "subtraction" method to achieve efficiency.
- itest
indices of the instances considered testing data
- Definition Classes
- NaiveBayes0 → 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, nu_y: VectoI, nu_Xy: HMatrix3[Int]): Unit
Decrement frequency counters based on the 'i'th row of the data matrix.
Decrement frequency counters based on the 'i'th row of the data matrix.
- i
the index for current data row
- nu_y
frequency table of class y = c
- nu_Xy
joint frequency table of X and
- Attributes
- protected
- Definition Classes
- NaiveBayes → NaiveBayes0
-
def
updateFreq(i: Int, nu_y: VectoI, nu_X: HMatrix2[Int], nu_Xy: HMatrix3[Int], nu_XyZ: HMatrix5[Int]): Unit
Increment/Decrement frequency counters based on the 'i'th row of the data matrix.
Increment/Decrement frequency counters based on the 'i'th row of the data matrix. Only to be used for CMI frequency calculations.
- i
the index for current data row
- Attributes
- protected
- Definition Classes
- BayesClassifier
-
var
vc: Array[Int]
- Attributes
- protected
- Definition Classes
- NaiveBayes0
-
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).
- 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
-
final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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def
wait(arg0: Long): Unit
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