The ARMA class provide basic time series analysis capabilities for Auto-
Regressive (AR) and Moving Average (MA) models.
The BayesNetwork class implements a Bayesian Network Classifier.
The CanCorrelation class performs Canonical Correlation Analysis (CCA)
on two random vectors.
The Classifier trait provides a common framework for several classifiers.
The ClassifierInt abstract class provides a common foundation for several
classifiers that operate on integer-valued data.
The ClassifierReal abstract class provides a common foundation for several
classifiers that operate on real-valued data.
The Clusterer trait provides a common framework for several clustering
algorithms.
The 'DAG' class provides a data structure for storing directed acyclic graphs.
The DecisionTreeC45 class implements a Decision Tree classifier using the
C4.
The DecisionTreeID3 class implements a Decision Tree classifier using the
ID3 algorithm.
The HiddenMarkov classes provides Hidden Markov Models (HMM).
Cluster several vectors/points using hierarchical clustering.
The KMeansClustering class cluster several vectors/points using k-means
clustering.
The KNN_Classifier class is used to classify a new vector 'z' into one of
'k' classes.
The LogitRegression class supports logit regression.
The MarkovClustering class implements a Markov Clustering Algorithm (MCL)
and is used to cluster nodes in a graph.
The NMFactorization class factors a matrix 'v' into two non negative matrices
'w' and 'h' such that v = wh approximately.
The NaiveBayes class implements a Gaussian Naive Bayes Classifier, which
is the most commonly used such classifier for continuous input data.
The NaiveBayesInt class implements an Integer-Based Naive Bayes Classifier,
which is a commonly used such classifier for discrete input data.
The NeuralNet class supports basic 3-layer (input, hidden and output) Neural
Networks.
The NonLinRegression class supports non-linear regression.
The Perceptron class supports single-valued 2-layer (input and output)
Neural-Networks.
The PolyRegression class supports polynomial regression.
The Predictor trait provides a common framework for several predictors.
The PrincipalComponents class performs the Principal Component Analysis (PCA)
on data matrix 'x'.
The RandomGraph class generates random undirected graphs with clusters
(as adjacency matrices).
The Reducer trait provides a common framework for several data reduction
algorithms.
The Regression class supports multiple linear regression.
The SimpleRegression class supports simple linear regression.
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.
The ARMATest object is used to test the ARMA class.
The BayesNetworkTest object is used to test the BayesNetwork class.
DecisionTreeC45 is the companion object for the DecisionTreeC45 class.
The DecisionTreeC45Test object is used to test the DecisionTreeC45 class.
DecisionTreeID3 is the companion object for the DecisionTreeID3 class.
The DecisionTreeID3Test object is used to test the DecisionTreeID3 class.
The HiddenMarkovTest object is used to test the HiddenMarkov class.
The HierClusteringTest object is used to test the HierClustering class.
The KMeansClusteringTest object is used to test the KMeansClustering class.
The KNN_ClassifierTest object is used to test the KNN_Classifier class.
The LogitRegressionTest object tests the LogitRegression class.
The MarkovClusteringTest object is used to test the MarkovClustering class.
The NMFactorizationTest object to test NMFactorizationTest class.
NaiveBayesInt is the companion object for the NaiveBayesInt class.
The NaiveBayesTestInt object is used to test the 'NaiveBayesInt' class.
The NaiveBayesIntTest2 object is used to test the 'NaiveBayesInt' class.
The NaiveBayesTest object is used to test the 'NaiveBayes' class.
The NeuralNetTest object is used to test the NeuralNet class.
The NeuralNetTest2 object is used to test the NeuralNet class.
The NonLinRegressionTest object tests the NonLinRegression class:
y = f(x; b) = b0 + exp (b1 * x0).
The Perceptron companion object contains Activation functions.
The PerceptronTest object is used to test the Perceptron class.
The PerceptronTest2 object is used to test the Perceptron class.
The PolyRegressionTest object tests PolyRegression class using the following
regression equation.
The PrincipalComponentsTest object is used to test the PrincipalComponents class.
The Probability object provides methods for operating on univariate and
bivariate probability distributions of discrete random variables 'X' and 'Y'.
The ProbabilityTest object is used to test the Probability object.
The ProbabilityTest2 provides upper bound for 'entropy' and 'entropy_k'.
The RandomGraphTest object is used to test the RandomGraph class.
The RegressionTest object tests Regression class using the following
regression equation.
The RegressionTest2 object tests Regression class using the following
regression equation.
The RegressionTest3 object tests the multi-colinearity method in the
Regression class using the following regression equation.
Object to test SimpleRegression class: y = b dot x = (b0, b1) dot (1.
The SimpleRegressionTest2 object to test SimpleRegression class:
The SupportVectorMachineTest is used to test the SupportVectorMachine class.
The SupportVectorMachineTest2 is used to test the SupportVectorMachine class.
The analytics package contains classes, traits and objects for analytics including classification, clustering and prediction.