class NaiveBayes0 extends BayesClassifier
The NaiveBayes0 class implements an Integer-Based Naive Bayes Classifier,
which is a commonly used such classifier for discrete input data. The
classifier is trained using a data matrix 'x' and a classification vector 'y'.
Each data vector in the matrix is classified into one of 'k' classes numbered
0, ..., k-1. Prior probabilities are calculated based on the population of
each class in the training-set. Relative posterior probabilities are computed
by multiplying these by values computed using conditional probabilities. The
classifier is naive, because it assumes feature independence and therefore
simply multiplies the conditional probabilities.
This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------
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Instance Constructors
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new
NaiveBayes0(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI = null, me: Double = me_default)
- 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
- vc
the value count (number of distinct values) for each feature
- me
use m-estimates (me == 0 => regular MLE estimates)
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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val
N0: Double
- Attributes
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- BayesClassifier
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var
additive: Boolean
- Attributes
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- BayesClassifier
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def
aic(vc: VectoI, 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
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def
calcCMI(idx: IndexedSeq[Int], vca: Array[Int]): MatrixD
Compute the conditional mutual information matrix
Compute the conditional mutual information matrix
- idx
indicies of either training or testing region
- vca
array of value counts
- Definition Classes
- BayesClassifier
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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
- 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(z: VectoD): (Int, String, Double)
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector.
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector. Return the best class, its name and its relative probability
- z
the vector to classify
- Definition Classes
- ClassifierInt → Classifier
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def
clone(): AnyRef
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def
cmiJoint(p_C: VectorD, 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.
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
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def
crossValidate(nx: Int = 10): 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'.
- nx
the number of crosses and cross-validations (defaults to 5x).
- Definition Classes
- Classifier
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def
crossValidateRand(nx: Int = 10): 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.
- nx
number of crosses and cross-validations (defaults to 10x).
- Definition Classes
- Classifier
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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val
f_C: VectorI
- Attributes
- protected
- Definition Classes
- BayesClassifier
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var
f_CX: HMatrix3[Int]
- Attributes
- protected
- Definition Classes
- BayesClassifier
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var
f_CXZ: HMatrix5[Int]
- Attributes
- protected
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- BayesClassifier
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var
f_X: HMatrix2[Int]
- Attributes
- protected
- Definition Classes
- BayesClassifier
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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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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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def
frequencies(indices: IndexedSeq[Int]): Unit
Count the frequencies for 'y' having class 'i' and 'x' for cases 0, 1, ...
Count the frequencies for 'y' having class 'i' and 'x' for cases 0, 1, ... This is the quick/decremental version.
- indices
indices of the instances considered training data
- Attributes
- protected
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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[_]
- Definition Classes
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def
getParent: Any
Return the parent (override as needed).
Return the parent (override as needed).
- Definition Classes
- BayesClassifier
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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def
lclassify(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 log-probability. This version adds log-probabilities.
- z
the data vector to classify
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def
logLikelihood(vc: VectoI, 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
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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
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- 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
p_C: VectorD
- Attributes
- protected
- Definition Classes
- BayesClassifier
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val
p_X_C: HMatrix3[Double]
- Attributes
- protected
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def
reset(): Unit
Reset or re-initialize all the population and probability vectors and hypermatrices to 0.
Reset or re-initialize all the population and probability vectors and hypermatrices to 0.
- Definition Classes
- 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 in training/test-set (# rows).
Return the number of data vectors in training/test-set (# rows).
- Definition Classes
- ClassifierInt → Classifier
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var
smooth: Boolean
- Attributes
- protected
- Definition Classes
- BayesClassifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
test(itest: VectorI): 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
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def
test(xx: MatrixI, yy: VectorI): 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
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def
test(testStart: Int, testEnd: 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.
- testStart
beginning of test region (inclusive)
- testEnd
end of test region (exclusive)
- Definition Classes
- ClassifierInt → Classifier
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val
tiny: Double
- Attributes
- protected
- Definition Classes
- BayesClassifier
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def
toString(): String
- Definition Classes
- AnyRef → Any
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def
toggleSmooth(): Unit
Toggle the value of the 'smooth' property.
Toggle the value of the 'smooth' property.
- Definition Classes
- BayesClassifier
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def
train(itest: IndexedSeq[Int]): Unit
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
- NaiveBayes0 → Classifier
-
def
train(testStart: Int, testEnd: Int): Unit
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.
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region (exclusive) used in cross-validation.
- Definition Classes
- NaiveBayes0 → Classifier
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def
train(): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- Definition Classes
- Classifier
-
def
updateFreq(i: Int): Unit
Increment frequency counters based on the 'i'th row of the data matrix.
Increment frequency counters based on the 'i'th row of the data matrix.
- i
the index for current data row
- Attributes
- protected
- Definition Classes
- NaiveBayes0 → BayesClassifier
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def
vc_default: VectorI
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
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def
vc_fromData: VectorI
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
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def
vc_fromData2(rg: Range): VectorI
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
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val
vca: Array[Int]
- Attributes
- protected
-
def
vlog(p: VectorD): VectorD
Take the log of the given probability vector.
Take the log of the given probability vector.
- p
the given probability vector
- Attributes
- protected
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final
def
wait(): Unit
- Definition Classes
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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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