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package classifier

The analytics package contains classes, traits and objects for analytics focused on classification.

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  1. class AugNaiveBayes extends BayesClassifier

    The AugNaiveBayes class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The AugNaiveBayes class implements an Integer-Based Tree Augmented 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 supports limited dependency between features/variables. -----------------------------------------------------------------------------

  2. class AugSelNaiveBayes extends BayesClassifier

    The AugSelNaiveBayes class implements an Integer-Based Tree Augmented Selective Naive Bayes Classifier, which is a combinations of two commonly used classifiers for discrete input data.

    The AugSelNaiveBayes class implements an Integer-Based Tree Augmented Selective Naive Bayes Classifier, which is a combinations of two commonly used classifiers 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 supports limited dependency between features/variables. The classifier also uses backward elimination algorithm in an attempt to find the most important subset of features/variables. -----------------------------------------------------------------------------

  3. abstract class BayesClassifier extends ClassifierInt with BayesMetrics

    The BayesClassifier object provides factory methods for building Bayesian classifiers.

    The BayesClassifier object provides factory methods for building Bayesian classifiers. The following types of classifiers are currently supported: NaiveBayes - Naive Bayes classifier SelNaiveBayes - Selective Naive Bayes classifier AugNaiveBayes - Augmented Naive Bayes classifier AugSelNaiveBayes - Augmented Selective Naive Bayes classifier TANBayes - Tree Augmented Naive Bayes classifier SelTAN - Selective Tree Augmented Naive Bayes classifier BayesNetwork2 - Ordering-based Bayesian Network with k = 2 -----------------------------------------------------------------------------

  4. class BayesClfML extends Classifier

    The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data.

    The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification matrix '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.

    See also

    www.aia-i.com/ijai/sample/vol3/no2/173-188.pdf -----------------------------------------------------------------------------

  5. trait BayesMetrics extends AnyRef

    The BayesMetrics trait provides scoring methods.

  6. class BayesNetwork extends BayesClassifier

    The BayesNetwork class implements a Bayesian Network Classifier.

    The BayesNetwork class implements a Bayesian Network Classifier. It classifies a data vector 'z' by determining which of 'k' classes has the highest Joint Probability of 'z' and the outcome (i.e., one of the 'k' classes) of occurring. The Joint Probability calculation is factored into multiple calculations of Conditional Probability. Conditional dependencies are specified using a Directed Acyclic Graph 'DAG'. Nodes are conditionally dependent on their parents only. Conditional probability are recorded in tables. Training is achieved by ... -----------------------------------------------------------------------------

  7. class BayesNetwork2 extends BayesClassifier

    The BayesNetwork2 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data.

    The BayesNetwork2 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data. Each node is limited to have at most 2 parents, and hence the "2" in the class name BayesNetwork2. 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 supports limited dependency between features/variables. ------------------------------------------------------------------------------

  8. trait Classifier extends AnyRef

    The Classifier trait provides a common framework for several classifiers.

    The Classifier trait provides a common framework for several classifiers. A classifier is for bounded responses. When the number of distinct responses cannot be bounded by some integer 'k', a predictor should be used.

  9. abstract class ClassifierInt extends Classifier with Error

    The ClassifierInt abstract class provides a common foundation for several classifiers that operate on integer-valued data.

  10. abstract class ClassifierReal extends Classifier with Error

    The ClassifierReal abstract class provides a common foundation for several classifiers that operate on real-valued data.

  11. class DAG extends AnyRef

    The 'DAG' class provides a data structure for storing directed acyclic graphs.

  12. class DecisionTreeC45 extends ClassifierInt

    The DecisionTreeC45 class implements a Decision Tree classifier using the C4.5 algorithm.

    The DecisionTreeC45 class implements a Decision Tree classifier using the C4.5 algorithm. 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'. Each column in the matrix represents a feature (e.g., Humidity). The 'vc' array gives the number of distinct values per feature (e.g., 2 for Humidity).

  13. class DecisionTreeID3 extends ClassifierInt

    The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm.

    The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm. 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'. Each column in the matrix represents a feature (e.g., Humidity). The 'vc' array gives the number of distinct values per feature (e.g., 2 for Humidity).

  14. class DynBayesNetwork extends Classifier

    The DynBayesNetwork class provides Dynamic Bayesian Network (DBN) models.

  15. class HiddenMarkov extends Classifier

    The HiddenMarkov classes provides Hidden Markov Models (HMM).

    The HiddenMarkov classes provides Hidden Markov Models (HMM). An HMM model consists of a probability vector 'pi' and probability matrices 'a' and 'b'. The discrete-time system is characterized by a hidden 'state(t)' and an 'observed(t)' symbol at time 't'.

    pi(j) = P(state(t) = j) a(i, j) = P(state(t+1) = j | state(t) = i) b(i, k) = P(observed(t) = k | state(t) = i)

    model (pi, a, b)

    See also

    www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf

  16. class KNN_Classifier extends ClassifierReal

    The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes.

    The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes. It works by finding its 'knn' nearest neighbors. These neighbors essentially vote according to their classification. The class with most votes is selected as the classification of 'z'. Using a distance metric, the 'knn' vectors nearest to 'z' are found in the training data, which is stored row-wise in the data matrix 'x'. The corresponding classifications are given in the vector 'y', such that the classification for vector 'x(i)' is given by 'y(i)'.

  17. class LDA extends ClassifierReal

    The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.

    The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.

    See also

    en.wikipedia.org/wiki/Linear_discriminant_analysis

  18. class LogisticRegression extends ClassifierReal

    The LogisticRegression class supports (binomial) logistic regression.

    The LogisticRegression class supports (binomial) logistic regression. In this case, 'x' may be multi-dimensional '[1, x_1, ... x_k]'. Fit the parameter vector 'b' in the logistic regression equation

    y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model) and 'y' is now binary.

    See also

    see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

  19. class NaiveBayes extends BayesClassifier

    The NaiveBayes class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The NaiveBayes 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. -----------------------------------------------------------------------------

  20. class NaiveBayesMAP extends BayesClassifier

    The NaiveBayesMAP class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The NaiveBayesMAP class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. Determine the response 'y = f(x)' that maximizes the following probability:

    y = argmax [ P(y) ∏ P(xi | y) ]

    Given several instances, the classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data (row) vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Probabilities for 'y' ('P(y)') are estimated based on frequency counts for each class (y-value) in the training-set. Relative posterior probabilities are computed by multiplying these values by estimated conditional probabilities 'P(xi | y)'. ----------------------------------------------------------------------------- The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities.

    y = argmax [ P(y) ∏ P(xi | y)

    Probabilities are estimated using a Bayesian (Maximum a Posterior) MAP procedure. -----------------------------------------------------------------------------

  21. 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 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. -----------------------------------------------------------------------------

  22. class PGMHD3 extends BayesClassifier

    The PGMHD3 class implements a three level Bayes Classifier for discrete input data.

    The PGMHD3 class implements a three level Bayes 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. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------

  23. class PGMHD3cp extends BayesClassifier

    The PGMHD3cp class implements a three level Bayes Classifier for discrete input data.

    The PGMHD3cp class implements a three level Bayes 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 frequency/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. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------

  24. class PGMHD3fl extends BayesClassifier

    The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels.

    The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels. The classifier is trained using the following data matrices:

    'x' - the mid/features 'X' (level 1) 'z' - the low/feature 'Z' (level 2) 'y' - the top/class 'C' (level 0)

    Each random variable 'C_j, X_k and Z_l' is binary ('vc = 2', 'k = 2'), where '1' indicates occurrence, while '0' indicates no evidence of occurreence. Frequency counts and classification scores are computed from a training-set. -----------------------------------------------------------------------------

  25. class SelNaiveBayes extends BayesClassifier

    The SelNaiveBayes class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The SelNaiveBayes 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. The version is "selective", since features whose impact is small are ignored. ----------------------------------------------------------------------------

  26. class SelTAN extends BayesClassifier

    The SelTAN class implements an Integer-Based Tree Augmented Selective Naive Bayes Classifier, which is a combinations of two commonly used classifiers for discrete input data.

    The SelTAN class implements an Integer-Based Tree Augmented Selective Naive Bayes Classifier, which is a combinations of two commonly used classifiers 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 supports limited dependency between features/variables. The classifier also uses backward elimination algorithm in an attempt to find the most important subset of features/variables. -----------------------------------------------------------------------------

  27. class SupportVectorMachine extends ClassifierReal

    The SupportVectorMachine class is a translation of Pseudo-Code from a modified SMO (Modification 2) found at the above URL's into Scala and includes a few simplifications (e.g., currently only works for linear kernels, dense data and binary classification).

  28. class TANBayes extends BayesClassifier

    The TANBayes class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The TANBayes class implements an Integer-Based Tree Augmented 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 supports limited dependency between features/variables. -----------------------------------------------------------------------------

  29. class TabuFeatures extends AnyRef

    The TabuFeatures keeps track of pairs of features, so they are not repeatedly tried.

Value Members

  1. val BASE_DIR: String

    The relative path for base directory

  2. object AugNaiveBayes

    AugNaiveBayes is the companion object for the AugNaiveBayes class.

  3. object AugNaiveBayesTest extends App

    The AugNaiveBayesTest object is used to test the AugNaiveBayes class.

    The AugNaiveBayesTest object is used to test the AugNaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-AugNaiveBayes-example.pdf > run-main scalation.analytics.classifier.AugNaiveBayesTest

  4. object AugNaiveBayesTest2 extends App

    The AugNaiveBayesTest2 object is used to test the AugNaiveBayes class.

    The AugNaiveBayesTest2 object is used to test the AugNaiveBayes class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.AugNaiveBayesTest2

  5. object AugNaiveBayesTest3 extends App

    The AugNaiveBayesTest3 object is used to test the AugNaiveBayes class.

    The AugNaiveBayesTest3 object is used to test the AugNaiveBayes class. > run-main scalation.analytics.classifier.AugNaiveBayesTest3

  6. object AugSelNaiveBayes

    AugSelNaiveBayes is the companion object for the AugSelNaiveBayes class.

  7. object AugSelNaiveBayesTest extends App

    The AugSelNaiveBayesTest object is used to test the AugSelNaiveBayes class.

    The AugSelNaiveBayesTest object is used to test the AugSelNaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-AugSelNaiveBayes-example.pdf > run-main scalation.analytics.classifier.AugSelNaiveBayesTest

  8. object AugSelNaiveBayesTest2 extends App

    The AugSelNaiveBayesTest2 object is used to test the AugSelNaiveBayes class.

    The AugSelNaiveBayesTest2 object is used to test the AugSelNaiveBayes class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.AugSelNaiveBayesTest2

  9. object AugSelNaiveBayesTest3 extends App

    The AugSelNaiveBayesTest3 object is used to test the AugSelNaiveBayes class.

    The AugSelNaiveBayesTest3 object is used to test the AugSelNaiveBayes class. > run-main scalation.analytics.classifier.AugSelNaiveBayesTest3

  10. object BayesClassifier

    The BayesClassifier object provides factory methods for building Bayes classifiers.

  11. object BayesClassifierTest extends App

    The BayesClassifierTest object is used to test the BayesClassifier class.

    The BayesClassifierTest object is used to test the BayesClassifier class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.BayesClassifierTest

  12. object BayesClassifierTest10 extends App

    The BayesClassifierTest10 object is used to test the BayesClassifier class.

    The BayesClassifierTest10 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest10

  13. object BayesClassifierTest11 extends App

    The BayesClassifierTest11 object is used to test the BayesClassifier class.

    The BayesClassifierTest11 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest11

  14. object BayesClassifierTest2 extends App

    The BayesClassifierTest2 object is used to test the BayesClassifier class.

    The BayesClassifierTest2 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest2

  15. object BayesClassifierTest3 extends App

    The BayesClassifierTest3 object is used to test the BayesClassifier class.

    The BayesClassifierTest3 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest3

  16. object BayesClassifierTest4 extends App

    The BayesClassifierTest4 object is used to test the BayesClassifier class.

    The BayesClassifierTest4 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest4

  17. object BayesClassifierTest5 extends App

    The BayesClassifierTest5 object is used to test the BayesClassifier class.

    The BayesClassifierTest5 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest5

  18. object BayesClassifierTest6 extends App

    The BayesClassifierTest6 object is used to test the BayesClassifier class.

    The BayesClassifierTest6 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest6

  19. object BayesClassifierTest7 extends App

    The BayesClassifierTest7 object is used to test the BayesClassifier class.

    The BayesClassifierTest7 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest7

  20. object BayesClassifierTest8 extends App

    The BayesClassifierTest8 object is used to test the BayesClassifier class.

    The BayesClassifierTest8 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest8

  21. object BayesClassifierTest9 extends App

    The BayesClassifierTest9 object is used to test the BayesClassifier class.

    The BayesClassifierTest9 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest9

  22. object BayesClfMLTest extends App

    The BayesClfMLTest object is used to test the BayesClfML class.

    The BayesClfMLTest object is used to test the BayesClfML class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.BayesClfMLTest

  23. object BayesNetwork2

    The BayesNetwork2 object is the companion object for the BayesNetwork2 class.

  24. object BayesNetwork2Test extends App

    The BayesNetwork2Test object is used to test the BayesNetwork2 class.

    The BayesNetwork2Test object is used to test the BayesNetwork2 class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-BayesNetwork2-example.pdf > run-main scalation.analytics.classifier.BayesNetwork2Test

  25. object BayesNetwork2Test2 extends App

    The BayesNetwork2Test2 object is used to test the BayesNetwork2 class.

    The BayesNetwork2Test2 object is used to test the BayesNetwork2 class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.BayesNetwork2Test2

  26. object BayesNetwork2Test3 extends App

    The BayesNetwork2Test3 object is used to test the BayesNetwork2 class.

    The BayesNetwork2Test3 object is used to test the BayesNetwork2 class. > run-main scalation.analytics.classifier.BayesNetwork2Test3

  27. object BayesNetworkTest extends App

    The BayesNetworkTest object is used to test the BayesNetwork class.

    The BayesNetworkTest object is used to test the BayesNetwork class. Ex: Classify whether a person has a Back Ache.

    See also

    www.eng.tau.ac.il/~bengal/BN.pdf > run-main sclation.analytics.classifier.BayesNetworkTest

  28. object ClassifierInt

    The ClassifierInt companion object provides methods to read in data matrices in a combined 'xy' format that can be later decomposed into 'x' the feature data matrix and 'y' the classification vector.

  29. object DecisionTreeC45

    DecisionTreeC45 is the companion object for the DecisionTreeC45 class.

  30. object DecisionTreeC45Test extends App

    The DecisionTreeC45Test object is used to test the DecisionTreeC45 class.

    The DecisionTreeC45Test object is used to test the DecisionTreeC45 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

    See also

    http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm

  31. object DecisionTreeID3

    DecisionTreeID3 is the companion object for the DecisionTreeID3 class.

  32. object DecisionTreeID3Test extends App

    The DecisionTreeID3Test object is used to test the DecisionTreeID3 class.

    The DecisionTreeID3Test object is used to test the DecisionTreeID3 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

    See also

    http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm

  33. object DynBayesNetworkTest extends App

    The DynBayesNetworkTest object is used to test the DynBayesNetwork class.

    The DynBayesNetworkTest object is used to test the DynBayesNetwork class. > run-main scalation.analytics.classifier.DynBayesNetworkTest

  34. object HiddenMarkovTest extends App

    The HiddenMarkovTest object is used to test the HiddenMarkov class.

    The HiddenMarkovTest object is used to test the HiddenMarkov class. Given model '(pi, a, b)', determine the probability of the observations 'ob'.

    See also

    www.cs.sjsu.edu/~stamp/RUA/HMM.pdf (exercise 1). > run-main scalation.analytics.classifieu.HiddenMarkovTest

  35. object HiddenMarkovTest2 extends App

    The HiddenMarkovTest2 object is used to test the HiddenMarkov class.

    The HiddenMarkovTest2 object is used to test the HiddenMarkov class. Train the model (pi, a, b) based on the observed data. > run-main scalation.analytics.classifier.HiddenMarkovTest2

  36. object KNN_ClassifierTest extends App

    The KNN_ClassifierTest object is used to test the KNN_Classifier class.

  37. object LDATest extends App

    The LDATest is used to test the LDA class.

    The LDATest is used to test the LDA class.

    See also

    people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > run-main scalation.analytics.classifier.LDATest

  38. object LogisticRegressionTest extends App

    The LogisticRegressionTest object tests the LogisticRegression class.

    The LogisticRegressionTest object tests the LogisticRegression class.

    See also

    www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aci = 29.533, pseudo_rSq = 0.4178 > run-main scalation.analytics.classifier.LogisticRegressionTest

  39. object LogisticRegressionTest2 extends App

    The LogisticRegressionTest object tests the LogisticRegression class.

    The LogisticRegressionTest object tests the LogisticRegression class.

    See also

    www.stat.wisc.edu/~mchung/teaching/.../GLM.logistic.Rpackage.pdf > run-main scalation.analytics.classifier.classifier.LogisticRegressionTest2

    statmaster.sdu.dk/courses/st111/module03/index.html

  40. object NaiveBayes

    NaiveBayes is the companion object for the NaiveBayes class.

  41. object NaiveBayesMAP

    NaiveBayesMAP is the companion object for the NaiveBayesMAP class.

  42. object NaiveBayesMAPTest extends App

    The NaiveBayesTestMAP object is used to test the NaiveBayesMAP class.

    The NaiveBayesTestMAP object is used to test the NaiveBayesMAP class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.NaiveBayesMAPTest

  43. object NaiveBayesMAPTest2 extends App

    The NaiveBayesMAPTest2 object is used to test the 'NaiveBayesMAP' class.

    The NaiveBayesMAPTest2 object is used to test the 'NaiveBayesMAP' class. Given whether a person is Fast and/or Strong, classify them as making Y = 1 or not making Y = 0 the football team. > run-main scalation.analytics.classifier.NaiveBayesMAPTest2

  44. object NaiveBayesMAPTest3 extends App

    The NaiveBayesMAPTest3 object is used to test the 'NaiveBayesMAP' class.

    The NaiveBayesMAPTest3 object is used to test the 'NaiveBayesMAP' class. > run-main scalation.analytics.classifier.NaiveBayesMAPTest3

  45. object NaiveBayesMLTest2 extends App

    The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class.

    The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.NaiveBayesMLTest2

  46. object NaiveBayesMLTest3 extends App

    The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class.

    The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class. > run-main scalation.analytics.classifier.NaiveBayesMLTest3

  47. object NaiveBayesRTest extends App

    The NaiveBayesRTest object is used to test the NaiveBayesR class.

    The NaiveBayesRTest object is used to test the NaiveBayesR class. * Ex: Classify whether a person is male (M) or female (F) based on the measured features.

    See also

    en.wikipedia.org/wiki/Naive_Bayes_classifier > run-main scalation.analytics.classifier.NaiveBayesRTest

  48. object NaiveBayesTest extends App

    The NaiveBayesTest object is used to test the NaiveBayes class.

    The NaiveBayesTest object is used to test the NaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.NaiveBayesTest

  49. object NaiveBayesTest2 extends App

    The NaiveBayesTest2 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest2 object is used to test the 'NaiveBayes' class. Given whether a person is Fast and/or Strong, classify them as making Y = 1 or not making Y = 0 the football team. > run-main scalation.analytics.classifier.NaiveBayesTest2

  50. object NaiveBayesTest3 extends App

    The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.

    See also

    docs.roguewave.com/imsl/java/7.3/manual/api/com/imsl/datamining/NaiveBayesClassifierEx2.html > run-main scalation.analytics.classifier.NaiveBayesTest3

    archive.ics.uci.edu/ml/datasets/Lenses

  51. object NaiveBayesTest4 extends App

    The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest4 object is used to test the 'NaiveBayes' class. > run-main scalation.analytics.classifier.NaiveBayesTest4

  52. object PGMHD3

    PGMHD3 is the companion object for the PGMHD3 class.

  53. object PGMHD3Test extends App

    The PGMHD3Test object is used to test the PGMHD3 class.

    The PGMHD3Test object is used to test the PGMHD3 class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3Test

  54. object PGMHD3Test2 extends App

    The PGMHD3Test2 object is used to test the 'PGMHD3' class.

    The PGMHD3Test2 object is used to test the 'PGMHD3' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.PGMHD3Test2

  55. object PGMHD3Test3 extends App

    The PGMHD3Test3 object is used to test the 'PGMHD3' class.

    The PGMHD3Test3 object is used to test the 'PGMHD3' class. > run-main scalation.analytics.classifier.PGMHD3Test3

  56. object PGMHD3cp

    PGMHD3cp is the companion object for the PGMHD3cp class.

  57. object PGMHD3cpTest extends App

    The PGMHD3cpTest object is used to test the PGMHD3cp class.

    The PGMHD3cpTest object is used to test the PGMHD3cp class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3cpTest

  58. object PGMHD3cpTest2 extends App

    The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class.

    The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.PGMHD3cpTest2

  59. object PGMHD3cpTest3 extends App

    The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class.

    The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class. > run-main scalation.analytics.classifier.PGMHD3cpTest3

  60. object PGMHD3fl

    PGMHD3fl is the companion object for the PGMHD3fl class.

  61. object PGMHD3flTest extends App

    The PGMHD3flTest object is used to test the PGMHD3fl class.

    The PGMHD3flTest object is used to test the PGMHD3fl class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3flTest

  62. object SelNaiveBayes

    SelNaiveBayes is the companion object for the SelNaiveBayes class.

  63. object SelNaiveBayesTest extends App

    The SelNaiveBayesTest object is used to test the SelNaiveBayes class.

    The SelNaiveBayesTest object is used to test the SelNaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.SelNaiveBayesTest

  64. object SelNaiveBayesTest2 extends App

    The SelNaiveBayesTest2 object is used to test the SelNaiveBayes class.

    The SelNaiveBayesTest2 object is used to test the SelNaiveBayes class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.SelNaiveBayesTest2

  65. object SelNaiveBayesTest3 extends App

    The SelNaiveBayesTest3 object is used to test the SelNaiveBayes class.

    The SelNaiveBayesTest3 object is used to test the SelNaiveBayes class. > run-main scalation.analytics.classifier.SelNaiveBayesTest3

  66. object SelTAN

    SelTAN is the companion object for the SelTAN class.

  67. object SelTANTest extends App

    The SelTANTest object is used to test the SelTAN class.

    The SelTANTest object is used to test the SelTAN class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-SelTAN-example.pdf > run-main scalation.analytics.classifier.SelTANTest

  68. object SelTANTest2 extends App

    The SelTANTest2 object is used to test the SelTAN class.

    The SelTANTest2 object is used to test the SelTAN class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.SelTANTest2

  69. object SelTANTest3 extends App

    The SelTanTest3 object is used to test the SelTAN class > run-main scalation.analytic.classifier.SelTANTest3

  70. object SupportVectorMachineTest extends App

    The SupportVectorMachineTest is used to test the SupportVectorMachine class.

    The SupportVectorMachineTest is used to test the SupportVectorMachine class. > run-main scalation.analytics.classifier.SupportVectorMachineTest

  71. object SupportVectorMachineTest2 extends App

    The SupportVectorMachineTest2 is used to test the SupportVectorMachine class.

  72. object TANBayes

    The TANBayes object is the companion object for the TANBayes class.

  73. object TANBayesTest extends App

    The TANBayesTest object is used to test the TANBayes class.

    The TANBayesTest object is used to test the TANBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-AugNaiveBayes-example.pdf > run-main scalation.analytics.classifier.TANBayesTest

  74. object TANBayesTest2 extends App

    The TANBayesTest2 object is used to test the TANBayes class.

    The TANBayesTest2 object is used to test the TANBayes class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.TANBayesTest2

  75. object TANBayesTest3 extends App

    The TANBayesTest3 object is used to test the TANBayes class.

    The TANBayesTest3 object is used to test the TANBayes class. > run-main scalation.analytics.classifier.TANBayesTest3

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