class NaiveBayesR extends ClassifierReal
The NaiveBayesR class implements a Gaussian Naive Bayes Classifier, which
is the most commonly used such classifier for continuous 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. Class probabilities are calculated based on the frequency of
each class in the training-set. Relative probabilities are computed by
multiplying these by values computed using conditional density functions
based on the Normal (Gaussian) distribution. The classifier is naive, because
it assumes feature independence and therefore simply multiplies the conditional
densities.
-----------------------------------------------------------------------------
- Alphabetic
- By Inheritance
- NaiveBayesR
- ClassifierReal
- Error
- Classifier
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
NaiveBayesR(x: MatriD, y: VectoI, fn_: Strings = null, k: Int = 2, cn_: Strings = null)
- x
the real-valued data vectors stored as rows of a matrix
- y
the class vector, where y_i = class for row i of the matrix x, x(i)
- fn_
the names for all features/variables
- k
the number of classes
- cn_
the names for all classes
Value Members
-
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
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
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
-
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
-
def
calcHistogram(x_j: VectoD, intervals: Int): VectoD
Compute the counts for each interval in the histogram.
Compute the counts for each interval in the histogram.
- x_j
the vector for feature j given class c.
- intervals
the number intervals
-
def
calcStats(): Unit
Calculate statistics (sample mean and sample variance) for each class by feature.
-
def
classify(z: VectoD): (Int, String, Double)
Given a continuous data vector z, classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.
Given a continuous 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
- NaiveBayesR → Classifier
-
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
-
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
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
var
cn: Strings
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
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
-
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
-
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 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
-
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
- ClassifierReal
-
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
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
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
-
val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
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()
-
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
- NaiveBayesR → Classifier
-
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
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
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
-
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
-
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
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
train(itest: IndexedSeq[Int]): NaiveBayesR
Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions.
Train the classifier, i.e., calculate statistics and create conditional density 'cd' functions. Assumes that conditional densities follow the Normal (Gaussian) distribution.
- itest
the indices of the instances considered as testing data@param testStart starting index of test region (inclusive) used in cross-validation
- Definition Classes
- NaiveBayesR → 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
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
-
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( ... )