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Machine Learning Library, written in J
machine-learning convolutional-neural-networks j deep-learning gaussian-mixture-models gaussian-processes self-organizing-map principal-component-analysis k-means hierarchical-clustering lstm ensemble-learning learning rbm restricted-boltzmann-machines multilayer-perceptron-network knn-classifier clustering
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adv
clustering
datasets
densityestimation
dr
energy
ensemble
genetic
gp
impute
kdtree add initial files Jul 25, 2018
knn
linear
mixtures
mlp
optimize
plot
rbf
score
serialize
solverexamples
som
test
text
trees
utils
LICENSE
README.md
init.ijs
iris_som_all.png
iris_som_clusters.png
iris_som_planes.png
iris_som_umatrix.png
jlearn.gif
jlearn.jproj
run.ijs

README.md 

        _____   _____     ________       _       _______     ____  _____  
       |_   _| |_   _|   |_   __  |     / \     |_   __ \   |_   \|_   _| 
         | |     | |       | |_ \_|    / _ \      | |__) |    |   \ | |   
     _   | |     | |   _   |  _| _    / ___ \     |  __ /     | |\ \| |   
    | |__' | _  _| |__/ | _| |__/ | _/ /   \ \_  _| |  \ \_  _| |_\   |_  
    `.____.'(_)|________||________||____| |____||____| |___||_____|\____|

brain

Machine Learning with J.

WIP Machine learning library, written in J. Various algorithm implementations, including MLPClassifiers, MLPRegressors, Mixture Models, K-Means, KNN, RBF-Network, Self-organizing Maps.

som_clusters

Implemented Algorithms

Algorithm Classification Regression Clustering Pattern Matching / Completion Other Notes
Multilayer Perceptron o o n/a n/a Batch SGD with choice of gradient optimizers.
Gaussian Processes x o n/a n/a
RBF Network o o n/a n/a
Decision Tree o x n/a n/a Regressor needs implementing.
AdaBoost Ensemble o x n/a n/a
Gaussian Mixtures n/a n/a o n/a
K-means / K-medians n/a n/a o n/a
Hierarchical Clustering n/a n/a o n/a Agglomerative clustering
K-nearest Neighbours n/a n/a o n/a
Kd-tree o x o n/a
Self-Organizing Maps n/a n/a n/a o
Hopfield Networks n/a x n/a o
Restricted Boltzmann Machines o n/a n/a o Partial implementation.
Convolutional Neural Networks o n/a n/a n/a Experimental. 2D convolutions implemented.
Recurrent Neural Networks o x n/a n/a Experimental. LSTMs implemented.
PCA / KPCA n/a n/a n/a n/a o Dimensionality reduction.
Genetic Algorithm Solver n/a n/a n/a n/a o Optimization.
Symbolic Regression Solver n/a n/a n/a n/a o Function estimation.

Other features

Serialization

Models can be serialized to text files, with a mixture of text and binary packing. The size of the serialized file depends on the size of the model, but will probably range from 10 MB and upwards for NN models (including convnets and rec-nets).

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