Machine-Learning-with-Python - Python codes for common Machine Learning Algorithms

•    Jupyter

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Python codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithm

jlearn - Machine Learning Library, written in J

•    J

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

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

hcluster - Hierarchical Clustering Algorithms

•    Python

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It is a fork of clustering and distance functions from the scipy that removes all the dependencies on scipy. It preserves the API of hcluster 0.2.

clustering hierarchical-clustering

adjclust - Adjacency-constrained hierarchical clustering of a similarity matrix

•    R

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adjclust is a package that provides methods to perform adjacency-constrained hierarchical agglomerative clustering. Adjacency-constrained hierarchical agglomerative clustering is hierarchical agglomerative clustering (HAC) in which each observation is associated to a position, and the clustering is constrained so as only adjacent clusters are merged. It is useful in bioinformatics (e.g. Genome Wide Association Studies or Hi-C data analysis). adjclust provides three user level functions: adjClust, snpClust and hicClust, which are briefly explained below.

hierarchical-clustering gwas hi-c linkage-disequilibrium