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. 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 density functions
based on the Normal (Gaussian) distribution. The classifier is naive, because
it assumes feature independence and therefore simply multiplies the conditional
densities.
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new
NaiveBayesR(x: MatrixD, y: VectorI, fn: Array[String], k: Int, cn: Array[String])
- x
the real-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
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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
calcHistogram(x_j: VectorD, intervals: Int): VectorD
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
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def
calcStats(): Unit
Calculate statistics (sample mean and sample variance) for each class by feature.
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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
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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
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def
clone(): AnyRef
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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).
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finalize(): Unit
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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)
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- ClassifierReal
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val
md: Double
the training-set size as a Double
the training-set size as a Double
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
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notify(): Unit
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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
- NaiveBayesR → 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
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def
test(xx: MatrixD, 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 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(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
- ClassifierReal → Classifier
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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
the indices of the instances considered test data
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def
toString(): String
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def
train(testStart: Int, testEnd: Int): Unit
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.
- testStart
starting index of test region (inclusive) used in cross-validation
- testEnd
ending index of test region (exclusive) used in cross-validation
- Definition Classes
- NaiveBayesR → 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
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def
train(itest: IndexedSeq[Int]): 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.
- itest
the indices of the instances considered as testing data
- Definition Classes
- Classifier
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final
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wait(): Unit
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