root-doc.txt

<h2>Scala for Machine Learning Version 0.99 API Documentation</h2>

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The source code in this library is provided by the author for the sole purpose of illustrating the concepts and algorithms presented in "Scala for Machine Learning"
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See the source code guide <i>SourceCodeGuide.html</i> for Scala programming style, best practice and design patterns.<br>
The implementation of algorithms in each chapter is defined in a dedicated package. The test examples are defined in src/test/scala directory.<br>
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<h4>Chapter 1: Getting started</h4>
<i>org.scalaml.plots</i> Simple line and scatter plot using jFreeChart library<br>
<h4>Chapter 2: Hello World</h4>
<i>org.scalaml.core</i> Core components including monadic representation, time series, type definition and conversion<br>
<i>org.scalaml.stats</i> Basic statistics, normalization and validation of machine learning models<br>
<h4>Chapter 3: Data pre-preprocessing</h4>
<i>org.scalaml.filtering</i> Most common filtering techniques such as moving averages, discrete Fourier transform and Kalman filter<br>

<h4>Chapter 4: Unsupervised learning</h4>
<i>org.scalaml.unsupervised.clustering</i> K-means flat clustering<br>
<i>org.scalaml.unsupervised.em</i> Expectation-maximization clustering algorithm<br>

<h4>Chapter 5: Naive Bayes models</h4>
<i>org.scalaml.supervised.bayes</i> Binomial and multinomial Naive Bayes classifier<br>
<i>org.scalaml.supervised.bayes.text</i> Naive Bayes model for text mining<br>
<h4>Chapter 6: Regression and regularization</h4>
<i>org.scalaml.supervised.regression.linear</i> Covers linear, ordinary least squares and ridge (L2 regularization) regression<br>
<i>org.scalaml.supervised.regression.logistic</i> Implementation of Logistic regression classification<br>
<h4>Chapter 7: Sequential data models</h4>
<i>org.scalaml.supervised.hmm</i> Markovian processes and hidden Markov models<br>
<i>org.scalaml.supervised.crf</i> Conditional Random Fields as applied to text classification<br>
<h4>Chapter 8: Kernel models and support vector machine</h4>
<i>org.scalaml.supervised.svm</i> Introduces support vector machine for binary and multinomial classification, one class outlier detection and support vector regression<br>
<h4>Chapter 9: Artificial neural networks</h4>
<i>org.scalaml.supervised.nnet</i> Multi-layer perceptron training and prediction<br>
<h4>Chapter 10: Genetic algorithms</h4>
<i>org.scalaml.ga</i> Components of a genetic algorithm for optimization and learning<br>
<h4>Chapter 11: Reinforcement learning</h4>
<i>org.scalaml.reinforcement.qlearning</i> Basics of Q-learning and policy generation<br>
<i>org.scalaml.reinforcement.xcs</i> Introduction to the extended learning classifiers systems<br>
<h4>Chapter 12: Scalable frameworks</h4>
<i>org.scalaml.scalability.scala</i> Performance evaluation of Scala parallel collections (Array and maps)<br>
<i>org.scalaml.scalability.akka</i> Evaluation of Akka actors, routing capabilities and futures<br>
<i>org.scalaml.scalability.spark</i> Evaluation of Apache Spark in-memory framework using K-means clustering<br>
<h4>Appendix</h4>
<i>org.scalaml.trading</i> Implementation of technical analysis models for trading securities<br>
<i>org.scalaml.util</i> Utilities classes for configuration and management of maps, files, display and formats<br>
<i>org.scalaml.workflow.data</i> Implementation of I/O functions as data source and sink transformation<br>