Project details for MLPY Machine Learning Py

Screenshot MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ]

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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
URL: Project Homepage
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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