mlpy - Machine Learning Python
mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.
If you use mlpy, please cite:
mlpy was used in the following applications.
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 (mlpy.wavelet): Discrete, Undecimated and Continuous Wavelet Transform
Misc: Feature ranking/selection algorithms, Canberra stability indicator, resampling algorithms, error evaluation, peak finding algorithms, Dynamic Time Warping (DTW) distance, Longest Common Subsequence (LCS).
mlpy is completely compatible with PyInstaller
The latest release of mlpy is 3.5.0 (released 2012-03-12). You can download it as a source or as Windows installers (Download). mlpy is also available from the PyPi package repository. You can retrieve the latest code with the command (Mercurial is required):
hg clone http://hg.code.sf.net/p/mlpy/code mlpy-code
If you want to contribute to mlpy send an e-mail to: albanese AT fbk.eu.
If you still have questions, feel free to send an email to the mlpy-general mailing list. To post a message to all the list members, send email to mlpy-general AT googlegroups.com.Old Documentation:
Lead Developer: Davide Albanese (albanese AT fbk.eu)
Contributors: Giuseppe Jurman, Stefano Merler, Roberto Visintainer, Marco Chierici, Lance Hepler.
mlpy is a project of Predictive Models for Biological Predictive Models for Biological and Environmental Data Analysis (MPBA) Research Unit at Fondazione Bruno Kessler and it is co-funded by Associazione Italiana per la Ricerca sul Cancro
Last update: 29 February 2012
by Davide Albanese.