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

Other available revisons

Version	Changelog	Date
3.5.0	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	March 15, 2012, 09:52:41
3.4.0	New features: Standard DTW added Subsequence DTW added Standard LCS added Fix: LibSvm: fix error when x is a list in learn() method fix code for vc++ fix setup.py (cblas)	January 9, 2012, 12:10:16
3.3.0	New features: Maximum Likelihood Classifier added Classification Tree added KNN: remove labels restrictions Fix: fix elasticnet classifier doc fix PCA (method paramenter): PCA method was always svd setup.py: fix classifiers from this version, mlpy for Windows is compiled with Visual Studio Express 2008 in order to avoid runtime errors	December 19, 2011, 11:35:05
3.2.1	Fix: fix stats import in init PLS: speed improved remove function declaration isn't a prototype warnings from libml clean findpeaks mlpy works with python 3.X add KNN to all	December 9, 2011, 16:12:50
3.2	Version 3.2 New features: PLS added Fix: fix docs in LibSVM and KernelAdatron fix svg logo minor fix in LibSVM and KernelAdatron include stddef.h in fastcluster	December 5, 2011, 16:20:01
3.1	Version 3.1	November 30, 2011, 16:00:02
2.2.1	New features: Elastic Net FSSun speeded up doctests added (mlpy-tests) Documentation improved Several bugs fixed	August 17, 2010, 14:45:50
2.2.0	New features: OLS Ridge Regression Kernel Ridge Regression LASSO LARS Gradient Descent for Regression K-Means Documentation improved Bug fixes: FSSun() SigmaErrorFS fixed	July 13, 2010, 18:25:57
2.1.0	New features: Svm optimal offset option added FSSun for feature weighting/selection added Dlda: adaptive offset for classification implemented Srda: memory usage optimization, speeded up added Tversky kernel for SVM Bug fixes: fixed gaussian weights for SVM	November 24, 2009, 10:27:46
2.0.8	New features: HCluster: sample <-> feature in input data x. Groups are now in 0, ..., N-1 k-medoids added minkowski distance added Documentation improved Bug fixes: canberraq tool fixed Svm(): MatrixKernelGaussian() for Svm.weights() speeded up	September 9, 2009, 15:22:55
2.0.7	New features: New function span_pd(). three_points_pd() deprecated. New Dtw class (dtw() has been removed): Naive and Derivative DTW Symmetric, Asymmetric, Quasi-Symmetric implementation with Slope Constraint Condition P=0 Sakoe-Chiba window condition option Linear space-complexity implementation option (0, 0) boundary condition option canberra() - canberraq(): new option 'dist' returns partial distances canberra - canberraq: partial distances to file(s) added Documentation improved Bug fixes: Derivative DTW algorithm fixed knn_imputing() inf2 bug fixed	August 28, 2009, 15:42:38
2.0.6	New features: DTW and DDTW (Naive Dynamic Time Warping and Derivative Dynamic Time Warping) added documentation improved cwt(): option pad removed, use extmethod and extlen instead (see extend()) extend() function added is_power(n, b) and next_power(n, b) added	July 20, 2009, 17:07:19
2.0.5	Bug fixes: purify() fixed New features: knn_imputing() euclidean squared distance and median method added	June 18, 2009, 14:10:19
2.0.4	_imputing.py: purify() function added imputing.py added; knnimputing() added data_fromfile(): ytype parameter for label type added knn.predict() fixed	June 16, 2009, 13:56:57
2.0.3	canberracore, nncore, svmcore improved misc.c added (away()) Ranking(): onestep fixed new mlpy logo lmatrix_from_numpy() added; canberra*() now work with int64 Svm(): Problem int64 with numpy array fixed	June 3, 2009, 11:15:19
2.0.2	Undecimated Wavelet Trasform (uwt() and iuwt()) added Documentation improved cdf_gaussian_P() added	May 18, 2009, 12:08:40
2.0.1	Three points peaks detection added Miscellaneous documentation improved _wavelet.py removed icwt() sped up	April 27, 2009, 13:30:37
2.0.0	Initial Announcement on mloss.org.	April 17, 2009, 20:36:45
1.2.8	Initial Announcement on mloss.org.	February 15, 2008, 09:32:35

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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