Scala Machine Learning

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master
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Light Weight Scala Machine Learning Library

A very light weight Scala machine learning library that provide some basic ML algorithms in Scala. The repo is served as a algorithm gallery. Please enjoy and dive into the algorithm that you will like to learn in its basic level.

Dev-Environment

• Scala 2.13

• Sbt 2.1

This package includes

Classification :

• Naive Bayesian Decision [Code] [Usage]

• K-Nearest Neighborhood (KNN) [Code] [Usage]

• Gaussian Process Classification [Code] [Usage]

• Linear Classification [Code] [Usage]

• Linear Support Vector Machine (linear-SVM) [Code] [Usage]

• Perceptron [Code] [Usage]

• Decision Tree [Code] [Usage]

• Random Forest [Code] [Usage]

• Extreme Learning Machine [Code] [Usage]

Boost :

• Naive Boost [Code] [Usage]

• Weighted Boost [Code] [Usage]

• Gradient Boost [Code] [Usage]

Regression :

• Multiple Linear Regression [Code] [Usage]

• Multivariate Linear Regression - GD [Code] [Usage]

• Stochastic Gradient Decent [Code] [Usage]

• Regression Tree [Code] [Usage]

Clustering :

• Hierarchical [Code] [Usage]

• DBSCAN [Code] [Usage]

• HDBSCAN [Code] [Usage]

• BIRCH [Code] [Usage]

• K-Means [Code] [Usage]

• EM Cluster [Code] [Usage]

• Density Peak Cluster [Code] [Usage]

Neural Net & Deep Learning :

• Neural Network (NN) [Code] [Usage]

• Restricted Boltzmann Machine (RBM) [Code] [Usage]

• Deep Belief Network (DBN) [Code] [Usage]

• Long Short-Term Memory (LSTM) [Code] [Usage]

• Neural Turing Machine - Memory Searching Cognition [Code (in another Repo)]

Optimization :

• Gene Algorithm (GA) [Code] [Usage]

• Minimax [Code] [Usage]

• Monte Carlo Tree Search (MCTS) [Code] [Usage]

• Epsilon Greedy Search [Code] [Usage]

• Upper Confidence Bound [Code] [Usage]

Reinforcement Learning :

• Naive Feedback [Code] [Usage]

• Q-Learning [Code] [Usage]

• Q-Neural Learning [Code] [Usage]

• Deep Q-Network (DQN) [Code] [Usage]

• Dueling DQN (D-DQN) [Code] [Usage]

• Asynchronous Advantage Actor-Critic (A3C) [Code] [Usage]

• Prioritized Experience Replay (PER-DQN) [Code] [Usage]

Feature Analysis :

• Student-T Test [Code] [Usage]

• ANOVA [Code] [Usage]

• Linear Discriminant Analysis [Code] [Usage]

• Quadratic Discriminant Analysis [Code] [Usage]

Feature Transformation :

• One Hot Encoding [Code] [Usage]

Abnormal Detection :

• Isolation Tree [Code] [Usage]

• Isolation Forest [Code] [Usage]

• Random Cut Tree [Code] [Usage]

• Random Cut Forest [Code] [Usage]

TODO

• Rainbow - Deep Reinforcement Learning

• Alpha-go Zero (MCTS-NN) - Deep Reinforcement Learning

• Neural Architect Search (NAS) - Neural Network & Deep Learning

Installation Process

1. Install sbt

2. Clone this project

Test

sbt test

Build Jar

sbt assembly