Description:

mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection.

mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3.

mlpy is available both for Python >=2.6 and Python 3.X.

Features

Regression: Least Squares, Ridge Regression, Last Angle Regression, Elastic Net, Kernel Ridge Regression, Support Vector Machines (SVR), Partial Least Squares (PLS)

Classification: Linear Discriminant Analysis (LDA), Basic Perceptron, Elastic Net, Logistic Regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier k-Nearest-Neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier

Clustering: Hierarchical Clustering, Memory-saving Hierarchical Clustering, k-means

Dimensionality Reduction: (Kernel) Fisher Discriminant (FDA), Spectral Regression Discriminant Analysis (SRDA), (kernel) Principal Component Analysis (PCA)

Wavelet Submodule: Discrete, Undecimated and Continuous Wavelet Transform

Feature ranking/selection algorithms, feature lists analysis, resampling, error evaluation, peak finding algorithms

Changes to previous version:

New features:

• LibSvm(): pred_probability() now returns probability estimates; pred_values() added
• LibLinear(): pred_values() and pred_probability() added
• dtw_std: squared Euclidean option added
• LCS for series composed by real values (lcs_real()) added
• Documentation

Fix:

• wavelet submodule: cwt(): it returned only real values in morlet and poul
• IRelief(): remove np. in learn()
• fix rfe_kfda and rfe_w2 when p=1

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows, Unix, Freebsd

Data Formats: None

Tags: Svm, Classification, Clustering, Regression, Rfe, Wavelet, Dtw, Discriminant Analysis

Archive: download here

Comments

jacob Yang (on April 30, 2010, 14:24:11)

when the program is running, there is no output. I don't know when it will be finish.

Michele Filosi (on December 13, 2011, 10:04:04)

Very useful and well implemented!

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