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Logo JMLR MSVMpack 1.3

by lauerfab - April 23, 2013, 10:44:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5478 views, 2106 downloads, 1 subscription

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Added Matlab interface.

Logo JMLR MultiBoost 1.2.00

by busarobi - April 22, 2013, 15:42:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14147 views, 2472 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:
  • A new fast (sublinear in the number of instances) stump algorithm is implemented. The gain in time is proportional to the sparsity of the features (it is significant when a lot of instances take the most frequent feature value). See Section B.2 in the documentation.
  • A parametrized early stopping option is added in --traintest mode. We stop if the (smoothed) test error does not improve for a certain number of iterations. See Section 4.1.3 in the documentation.

Logo JMLR Waffles 2013-04-06

by mgashler - April 7, 2013, 02:04:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16098 views, 5306 downloads, 1 subscription

About: A broad collection of script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a C++ class library.)

Changes:

See the change log at http://waffles.sourceforge.net/changelog.html


Logo GBAC 0.0.2

by henrydcl - March 26, 2013, 15:48:24 CET [ BibTeX Download ] 425 views, 145 downloads, 2 subscriptions

About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy

Changes:

Readme added. Explanation of the examples in the readme file.


Logo JMLR dlib ml 18.1

by davis685 - March 25, 2013, 23:48:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 53272 views, 9325 downloads, 1 subscription

About: This project is a C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

Changes:

In addition to some bug fixes, this release also brings the following notable improvements to the library:

  • The SURF feature extraction tool has higher matching accuracy than in previous dlib releases.
  • The cutting plane optimizer is now faster
  • A new tool for computing the singular value decomposition of very large matrices
  • A new tool for performing canonical correlation analysis on large datasets
  • A new tool for easily writing parallel for loops

Logo Tapkee 1.0rc1

by blackburn - March 18, 2013, 13:04:41 CET [ Project Homepage BibTeX Download ] 1787 views, 328 downloads, 0 subscriptions

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.

Changes:

Initial Announcement on mloss.org.


Logo JMLR SHOGUN 2.1.0

by sonne - March 17, 2013, 13:59:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41024 views, 8593 downloads, 4 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This release also contains several enhancements, cleanups and bugfixes:

Features

  • Linear Time MMD two-sample test now works on streaming-features, which allows to perform tests on infinite amounts of data. A block size may be specified for fast processing. The below features were also added. By Heiko Strathmann.
  • It is now possible to ask streaming features to produce an instance of streamed features that are stored in memory and returned as a CFeatures* object of corresponding type. See CStreamingFeatures::get_streamed_features().
  • New concept of artificial data generator classes: Based on streaming features. First implemented instances are CMeanShiftDataGenerator and CGaussianBlobsDataGenerator. Use above new concepts to get non-streaming data if desired.
  • Accelerated projected gradient multiclass logistic regression classifier by Sergey Lisitsyn.
  • New CCSOSVM based structured output solver by Viktor Gal
  • A collection of kernel selection methods for MMD-based kernel two- sample tests, including optimal kernel choice for single and combined kernels for the linear time MMD. This finishes the kernel MMD framework and also comes with new, more illustrative examples and tests. By Heiko Strathmann.
  • Alpha version of Perl modular interface developed by Christian Montanari.
  • New framework for unit-tests based on googletest and googlemock by Viktor Gal. A (growing) number of unit-tests from now on ensures basic funcionality of our framework. Since the examples do not have to take this role anymore, they should become more ilustrative in the future.
  • Changed the core of dimension reduction algorithms to the Tapkee library.

Bugfixes

  • Fix for shallow copy of gaussian kernel by Matt Aasted.
  • Fixed a bug when using StringFeatures along with kernel machines in cross-validation which cause an assertion error. Thanks to Eric (yoo)!
  • Fix for 3-class case training of MulticlassLibSVM reported by Arya Iranmehr that was suggested by Oksana Bayda.
  • Fix for wrong Spectrum mismatch RBF construction in static interfaces reported by Nona Kermani.
  • Fix for wrong include in SGMatrix causing build fail on Mac OS X (thanks to @bianjiang).
  • Fixed a bug that caused kernel machines to return non-sense when using custom kernel matrices with subsets attached to them.
  • Fix for parameter dictionary creationg causing dereferencing null pointers with gaussian processes parameter selection.
  • Fixed a bug in exact GP regression that caused wrong results.
  • Fixed a bug in exact GP regression that produced memory errors/crashes.
  • Fix for a bug with static interfaces causing all outputs to be -1/+1 instead of real scores (reported by Kamikawa Masahisa).

Cleanup and API Changes

  • SGStringList is now based on SGReferencedData.
  • "confidences" in context of CLabel and subclasses are now "values".
  • CLinearTimeMMD constructor changes, only streaming features allowed.
  • CDataGenerator will soon be removed and replaced by new streaming- based classes.
  • SGVector, SGMatrix, SGSparseVector, SGSparseVector, SGSparseMatrix refactoring: Now contains load/save routines, relevant functions from CMath, and implementations went to .cpp file.

Logo OpenOpt 0.45

by Dmitrey - March 15, 2013, 14:27:12 CET [ Project Homepage BibTeX Download ] 29086 views, 6195 downloads, 1 subscription

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About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE, MOP etc; general logical constraints, categorical variables, automatic differentiation, interval analysis, many other goodies

Changes:

http://openopt.org/Changelog


About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

Markov decision processes added (Kan & Shelton 2008) [ctmdp.h]

Mean field inference added (Cohn, El-Hay, Friedman, & Kupferman 2009) [meanfieldinf.h]

Factored uniformization for filtering added (Celikkaya & Shelton 2011) [uniformizedfactoredinf.h]

Auxilliary Gibbs sampling added (Rao & Teh 2011) [gibbsauxsampler.h]

Multi-threading for EM added

many speed improvements

unit testing improved [tst/]

new demo "main" programs added [demo/]

file format changed to XML-ish format (with old methods still there for conversion)

matrix switched to Eigen package (with option to return to old matrix)

glpk now included

initial cmake functionality


Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 12943 views, 2918 downloads, 2 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

Changes:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)


Logo WEKA 3.7.9

by mhall - February 24, 2013, 09:13:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29376 views, 4099 downloads, 2 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

http://sourceforge.net/projects/weka/files/weka-3-7/3.7.9/README-3-7-9.txt/view


Logo JMLR scikitlearn 0.13.1

by fabianp - February 23, 2013, 18:00:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6925 views, 2323 downloads, 3 subscriptions

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About: The scikit-learn aims to provide state of the art standard machine learning algorithms in Python.

Changes:

Update for 0.13.1


Logo bob 1.1.2

by anjos - January 15, 2013, 22:50:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1285 views, 226 downloads, 1 subscription

About: Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland.

Changes:

Release 1.1.2


Logo JMLR libDAI 0.3.1

by jorism - September 17, 2012, 14:17:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25606 views, 4754 downloads, 2 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).


Logo JMLR Jstacs 2.0

by keili - July 30, 2012, 13:31:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9513 views, 2091 downloads, 2 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

February 2, 2012: Jstacs 2.0 released

Jstacs 2.0 changes many names and the structure of several packages. It is not code-compatible with Jstacs 1.5 and earlier

RESTRUCTURING and RENAMING:

former ScoringFunction, NormalizableScoringFunction, Model

  • new base-interface SequenceScore
  • new sub-interface StatisticalModel of SequenceScore for all statistical models with sub-iterfaces DifferentiableStatisticalModel and TrainableStatisticalModel
  • new interface DifferentiableSequenceScore replaces ScoringFunction
  • new interface DifferentiableStatisticalModel replaces NormalizableScoringFunction
  • new interface TrainableStatisticalModel replaces Model
  • new abstract class AbstractDifferentiableSequenceScore
  • new abstract class AbstractDifferentiableStatisticalModel replaces AbstractNormalizableScoringFunction
  • new abstract class AbstractTrainableStatisticalModel replaces AbstractModel
  • former Models renamed to TrainSM
  • former ScoringFunction renamed to DiffSS or DiffSM
  • getProbFor removed from TrainableStatisticalModel (former Model) and conceptually replaced by getLogProbFor
  • getLogScore(Sequence,int,int) with changed meaning of arguments: getLogScore(Sequence,start,end) instead of getLogScore(Sequence,start,length)
  • isTrained() replaced by common method isInitialized()

Parameters and Results

  • new super-class of Parameters and Results: AnnotatedEntity
  • common list-type for Parameters and Results: AnnotatedEntityList
  • Renaming: CollectionParameter -> SelectionParameter, MultiSelectionCollectionParameter -> MultiSelectionParameter, new super-class AbstractSelectionParameter
  • major refactoring due to common hierarchy and code-cleanup
  • lazy evaluation of Parameter/ParameterSet hierarchies moved from ParameterSet (loadParameters()) to ParameterSetContainer (constructor on class)
  • SubclassFinder adapted to lazy evaluation

performance measures

  • new abstract super-class AbstractPerformanceMeasure of all performance measures
  • new interface NumericalPerformanceMeasure for all performance measures that return a single number (as opposed, e.g., to curves)
  • new class PerformanceMeasureParameterSet for a collection of general performance measures
  • new class NumericalPerformanceMeasureParameterSet for a collection of NumericalPerformanceMeasures
  • used in evaluate-method of AbstractClassifier and in ClassifierAssessments

further changes

  • Sample renamed to DataSet
  • evaluate and evaluateAll in AbstractClassifier joined
  • new class IndependentProductDiffSS as super-class of IndepedentProductDiffSM (former IndependentProductScoringFunction)
  • new class UniformDiffSS as super-class of UniformDiffSM (former UniformScoringFunction)

NEW FUNCTIONALITY:

  • multi-threaded implementation of Baum-Welch and Viterbi training of hidden Markov models
  • new Interface Singleton that can be used for singleton instances to save memory, current examples: DNAAlphabet, DNAAlphabetContainer, ProteinAlphabet
  • added ProteinAlphabet
  • added possibility to use NaN-values with ContinuousAlphabets
  • added ArbitraryFloatSequence including static methods for DataSet creation for cases where double-precision is not needed
  • new performance measure MaximumFMeasure
  • access to Parameters in ParameterSets and Results in ResultSets by name
  • emitDataSet in BayesianNetworkDiffSM
  • new static method Time.getTimeInstance that returns UserTime or RealTime depending on availability of shared lib
  • SubclassFinder allows for adding own base packages
  • new method overlaps() in LocatedSequenceAnnotationWithLength
  • AbstractTerminationCondition used in ScoreClassifier and sub-classes
  • public method propagateESS in HMMFactory
  • new method generateLog in DirichletMRG for drawing log-values
  • added DifferentiableStatisticalModelFactory

BUGFIXES/IMPROVEMENTS:

  • bugfix in propagation of equivalent sample size in HMMFactory
  • bugfix in random initialization of BasicHigherOrderTransition
  • improved Alignment implementation
  • SafeOutputStream with new static factory method getSafeOutputStream, write methods now work on Objects

DOCUMENTATION:

  • improved Javadocs in many classes and packages
  • new Cookbook with extensive documentation and explanation

MISC:

  • output of NonParsableException more verbose
  • Exceptions in multi-threaded code now lead to exit of program instead of only stopping the thread
  • update of RServe/RClient

Logo MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 37982 views, 7201 downloads, 2 subscriptions

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

Changes:

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

Logo Theano 0.5

by jaberg - February 23, 2012, 23:14:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8479 views, 1500 downloads, 1 subscription

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.5 (23 February 2012)

Highlight:

  • Moved to github: http://github.com/Theano/Theano/
  • Old trac ticket moved to assembla ticket: http://www.assembla.com/spaces/theano/tickets
  • Theano vision: http://deeplearning.net/software/theano/introduction.html#theano-vision (Many people)
  • Theano with GPU works in some cases on Windows now. Still experimental. (Sebastian Urban)
  • Faster dot() call: New/Better direct call to cpu and gpu ger, gemv, gemm and dot(vector, vector). (James, Frederic, Pascal)
  • C implementation of Alloc. (James, Pascal)
  • theano.grad() now also work with sparse variable. (Arnaud)
  • Macro to implement the Jacobian/Hessian with theano.tensor.{jacobian,hessian} (Razvan)
  • See the Interface changes.

Interface Behavior Changes:

  • The current default value of the parameter axis of theano.{max,min,argmax,argmin,max_and_argmax} is now the same as numpy: None. i.e. operate on all dimensions of the tensor. (Frederic Bastien, Olivier Delalleau) (was deprecated and generated a warning since Theano 0.3 released Nov. 23rd, 2010)
  • The current output dtype of sum with input dtype [u]int* is now always [u]int64. You can specify the output dtype with a new dtype parameter to sum. The output dtype is the one using for the summation. There is no warning in previous Theano version about this. The consequence is that the sum is done in a dtype with more precision than before. So the sum could be slower, but will be more resistent to overflow. This new behavior is the same as numpy. (Olivier, Pascal)
  • When using a GPU, detect faulty nvidia drivers. This was detected when running Theano tests. Now this is always tested. Faulty drivers results in in wrong results for reduce operations. (Frederic B.)

Interface Features Removed (most were deprecated):

  • The string modes FAST_RUN_NOGC and STABILIZE are not accepted. They were accepted only by theano.function(). Use Mode(linker='c|py_nogc') or Mode(optimizer='stabilize') instead.
  • tensor.grad(cost, wrt) now always returns an object of the "same type" as wrt (list/tuple/TensorVariable). (Ian Goodfellow, Olivier)
  • A few tag.shape and Join.vec_length left have been removed. (Frederic)
  • The .value attribute of shared variables is removed, use shared.set_value() or shared.get_value() instead. (Frederic)
  • Theano config option "home" is not used anymore as it was redundant with "base_compiledir". If you use it, Theano will now raise an error. (Olivier D.)
  • scan interface changes: (Razvan Pascanu)
    • The use of return_steps for specifying how many entries of the output to return has been removed. Instead, apply a subtensor to the output returned by scan to select a certain slice.
    • The inner function (that scan receives) should return its outputs and updates following this order: [outputs], [updates], [condition]. One can skip any of the three if not used, but the order has to stay unchanged.

Interface bug fix:

  • Rop in some case should have returned a list of one Theano variable, but returned the variable itself. (Razvan)

New deprecation (will be removed in Theano 0.6, warning generated if you use them):

  • tensor.shared() renamed to tensor._shared(). You probably want to call theano.shared() instead! (Olivier D.)

Bug fixes (incorrect results):

  • On CPU, if the convolution had received explicit shape information, they where not checked at runtime. This caused wrong result if the input shape was not the one expected. (Frederic, reported by Sander Dieleman)
  • Theoretical bug: in some case we could have GPUSum return bad value. We were not able to reproduce this problem
    • patterns affected ({0,1}*nb dim, 0 no reduction on this dim, 1 reduction on this dim): 01, 011, 0111, 010, 10, 001, 0011, 0101 (Frederic)
  • div by zero in verify_grad. This hid a bug in the grad of Images2Neibs. (James)
  • theano.sandbox.neighbors.Images2Neibs grad was returning a wrong value. The grad is now disabled and returns an error. (Frederic)
  • An expression of the form "1 / (exp(x) +- constant)" was systematically matched to "1 / (exp(x) + 1)" and turned into a sigmoid regardless of the value of the constant. A warning will be issued if your code was affected by this bug. (Olivier, reported by Sander Dieleman)
  • When indexing into a subtensor of negative stride (for instance, x[a:b:-1][c]), an optimization replacing it with a direct indexing (x[d]) used an incorrect formula, leading to incorrect results. (Pascal, reported by Razvan)
  • The tile() function is now stricter in what it accepts to allow for better error-checking/avoiding nonsensical situations. The gradient has been disabled for the time being as it only implemented (incorrectly) one special case. The reps argument must be a constant (not a tensor variable), and must have the same length as the number of dimensions in the x argument; this is now checked. (David)

Scan fixes:

  • computing grad of a function of grad of scan (reported by Justin Bayer, fix by Razvan) before : most of the time crash, but could be wrong value with bad number of dimensions (so a visible bug) now : do the right thing.
  • gradient with respect to outputs using multiple taps (reported by Timothy, fix by Razvan) before : it used to return wrong values now : do the right thing. Note: The reported case of this bug was happening in conjunction with the save optimization of scan that give run time errors. So if you didn't manually disable the same memory optimization (number in the list4), you are fine if you didn't manually request multiple taps.
  • Rop of gradient of scan (reported by Timothy and Justin Bayer, fix by Razvan) before : compilation error when computing R-op now : do the right thing.
  • save memory optimization of scan (reported by Timothy and Nicolas BL, fix by Razvan) before : for certain corner cases used to result in a runtime shape error now : do the right thing.
  • Scan grad when the input of scan has sequences of different lengths. (Razvan, reported by Michael Forbes)
  • Scan.infer_shape now works correctly when working with a condition for the number of loops. In the past, it returned n_steps as the length, which is not always true. (Razvan)
  • Scan.infer_shape crash fix. (Razvan)

New features:

  • AdvancedIncSubtensor grad defined and tested (Justin Bayer)
  • Adding 1D advanced indexing support to inc_subtensor and set_subtensor (James Bergstra)
  • tensor.{zeros,ones}_like now support the dtype param as numpy (Frederic)
  • Added configuration flag "exception_verbosity" to control the verbosity of exceptions (Ian)
  • theano-cache list: list the content of the theano cache (Frederic)
  • theano-cache unlock: remove the Theano lock (Olivier)
  • tensor.ceil_int_div to compute ceil(a / float(b)) (Frederic)
  • MaxAndArgMax.grad now works with any axis (The op supports only 1 axis) (Frederic)
    • used by tensor.{max,min,max_and_argmax}
  • tensor.{all,any} (Razvan)
  • tensor.roll as numpy: (Matthew Rocklin, David Warde-Farley)
  • Theano with GPU works in some cases on Windows now. Still experimental. (Sebastian Urban)
  • IfElse now allows to have a list/tuple as the result of the if/else branches.
    • They must have the same length and corresponding type (Razvan)
  • Argmax output dtype is now int64 instead of int32. (Olivier)
  • Added the element-wise operation arccos. (Ian)
  • Added sparse dot with dense grad output. (Yann Dauphin)
    • Optimized to Usmm and UsmmCscDense in some case (Yann)
    • Note: theano.dot and theano.sparse.structured_dot() always had a gradient with the same sparsity pattern as the inputs. The new theano.sparse.dot() has a dense gradient for all inputs.
  • GpuAdvancedSubtensor1 supports broadcasted dimensions. (Frederic)
  • TensorVariable.zeros_like() and SparseVariable.zeros_like()
  • theano.sandbox.cuda.cuda_ndarray.cuda_ndarray.device_properties() (Frederic)
  • theano.sandbox.cuda.cuda_ndarray.cuda_ndarray.mem_info() return free and total gpu memory (Frederic)
  • Theano flags compiledir_format. Keep the same default as before: compiledir_%(platform)s-%(processor)s-%(python_version)s. (Josh Bleecher Snyder)
    • We also support the "theano_version" substitution.
  • IntDiv c code (faster and allow this elemwise to be fused with other elemwise) (Pascal)
  • Internal filter_variable mechanism in Type. (Pascal, Ian)
    • Ifelse works on sparse.
    • It makes use of gpu shared variable more transparent with theano.function updates and givens parameter.
  • Added a_tensor.transpose(axes) axes is optional (James)
    • theano.tensor.transpose(a_tensor, kwargs) We where ignoring kwargs, now it is used as the axes.
  • a_CudaNdarray_object[*] = int, now works (Frederic)
  • tensor_variable.size (as numpy) computes the product of the shape elements. (Olivier)
  • sparse_variable.size (as scipy) computes the number of stored values. (Olivier)
  • sparse_variable[N, N] now works (Li Yao, Frederic)
  • sparse_variable[M:N, O:P] now works (Li Yao, Frederic, Pascal) M, N, O, and P can be Python int or scalar tensor variables, None, or omitted (sparse_variable[:, :M] or sparse_variable[:M, N:] work).
  • tensor.tensordot can now be moved to GPU (Sander Dieleman, Pascal, based on code from Tijmen Tieleman's gnumpy, http://www.cs.toronto.edu/~tijmen/gnumpy.html)
  • Many infer_shape implemented on sparse matrices op. (David W.F.)
  • Added theano.sparse.verify_grad_sparse to easily allow testing grad of sparse op. It support testing the full and structured gradient.
  • The keys in our cache now store the hash of constants and not the constant values themselves. This is significantly more efficient for big constant arrays. (Frederic B.)
  • 'theano-cache list' lists key files bigger than 1M (Frederic B.)
  • 'theano-cache list' prints an histogram of the number of keys per compiled module (Frederic B.)
  • 'theano-cache list' prints the number of compiled modules per op class (Frederic B.)
  • The Theano flag "nvcc.fastmath" is now also used for the cuda_ndarray.cu file.
  • Add the header_dirs to the hard part of the compilation key. This is currently used only by cuda, but if we use library that are only headers, this can be useful. (Frederic B.)
  • The Theano flag "nvcc.flags" is now included in the hard part of the key. This mean that now we recompile all modules for each value of "nvcc.flags". A change in "nvcc.flags" used to be ignored for module that were already compiled. (Frederic B.)
  • Alloc, GpuAlloc are not always pre-computed (constant_folding optimization) at compile time if all their inputs are constant. (Frederic B., Pascal L., reported by Sander Dieleman)
  • New Op tensor.sort(), wrapping numpy.sort (Hani Almousli)

New optimizations:

  • AdvancedSubtensor1 reuses preallocated memory if available (scan, c|py_nogc linker) (Frederic)
  • dot22, dot22scalar work with complex. (Frederic)
  • Generate Gemv/Gemm more often. (James)
  • Remove scan when all computations can be moved outside the loop. (Razvan)
  • scan optimization done earlier. This allows other optimizations to be applied. (Frederic, Guillaume, Razvan)
  • exp(x) * sigmoid(-x) is now correctly optimized to the more stable form sigmoid(x). (Olivier)
  • Added Subtensor(Rebroadcast(x)) => Rebroadcast(Subtensor(x)) optimization. (Guillaume)
  • Made the optimization process faster. (James)
  • Allow fusion of elemwise when the scalar op needs support code. (James)
  • Better opt that lifts transpose around dot. (James)

Crashes fixed:

  • T.mean crash at graph building time. (Ian)
  • "Interactive debugger" crash fix. (Ian, Frederic)
  • Do not call gemm with strides 0, some blas refuse it. (Pascal Lamblin)
  • Optimization crash with gemm and complex. (Frederic)
  • GPU crash with elemwise. (Frederic, some reported by Chris Currivan)
  • Compilation crash with amdlibm and the GPU. (Frederic)
  • IfElse crash. (Frederic)
  • Execution crash fix in AdvancedSubtensor1 on 32 bit computers. (Pascal)
  • GPU compilation crash on MacOS X. (Olivier)
  • Support for OSX Enthought Python Distribution 7.x. (Graham Taylor, Olivier)
  • When the subtensor inputs had 0 dimensions and the outputs 0 dimensions. (Frederic)
  • Crash when the step to subtensor was not 1 in conjunction with some optimization. (Frederic, reported by Olivier Chapelle)
  • Runtime crash related to an optimization with subtensor of alloc (reported by Razvan, fixed by Frederic)
  • Fix dot22scalar cast of integer scalars (Justin Bayer, Frederic, Olivier)
  • Fix runtime crash in gemm, dot22. FB
  • Fix on 32bits computer: make sure all shape are int64.(Olivier)
  • Fix to deque on python 2.4 (Olivier)
  • Fix crash when not using c code (or using DebugMode) (not used by default) with numpy 1.6*. Numpy has a bug in the reduction code that made it crash. (Pascal)
  • Crashes of blas functions (Gemv on CPU; Ger, Gemv and Gemm on GPU) when matrices had non-unit stride in both dimensions (CPU and GPU), or when matrices had negative strides (GPU only). In those cases, we are now making copies. (Pascal)
  • More cases supported in AdvancedIncSubtensor1. (Olivier D.)
  • Fix crash when a broadcasted constant was used as input of an elemwise Op and needed to be upcasted to match the op's output. (Reported by John Salvatier, fixed by Pascal L.)
  • Fixed a memory leak with shared variable (we kept a pointer to the original value) (Ian G.)

Known bugs:

  • CAReduce with nan in inputs don't return the good output (Ticket <https://www.assembla.com/spaces/theano/tickets/763>_).
    • This is used in tensor.{max,mean,prod,sum} and in the grad of PermuteRowElements.

Sandbox:

  • cvm interface more consistent with current linker. (James)
  • Now all tests pass with the linker=cvm flags.
  • vm linker has a callback parameter. (James)
  • review/finish/doc: diag/extract_diag. (Arnaud Bergeron, Frederic, Olivier)
  • review/finish/doc: AllocDiag/diag. (Arnaud, Frederic, Guillaume)
  • review/finish/doc: MatrixInverse, matrix_inverse. (Razvan)
  • review/finish/doc: matrix_dot. (Razvan)
  • review/finish/doc: det (determinent) op. (Philippe Hamel)
  • review/finish/doc: Cholesky determinent op. (David)
  • review/finish/doc: ensure_sorted_indices. (Li Yao)
  • review/finish/doc: spectral_radius_boud. (Xavier Glorot)
  • review/finish/doc: sparse sum. (Valentin Bisson)
  • review/finish/doc: Remove0 (Valentin)
  • review/finish/doc: SquareDiagonal (Eric)

Sandbox New features (not enabled by default):

  • CURAND_RandomStreams for uniform and normal (not picklable, GPU only) (James)
  • New sandbox.linalg.ops.pinv(pseudo-inverse) op (Razvan)

Documentation:

  • Many updates. (Many people)
  • Updates to install doc on MacOS. (Olivier)
  • Updates to install doc on Windows. (David, Olivier)
  • Doc on the Rop function (Ian)
  • Added how to use scan to loop with a condition as the number of iteration. (Razvan)
  • Added how to wrap in Theano an existing python function (in numpy, scipy, ...). (Frederic)
  • Refactored GPU installation of Theano. (Olivier)

Others:

  • Better error messages in many places. (Many people)
  • PEP8 fixes. (Many people)
  • Add a warning about numpy bug when using advanced indexing on a tensor with more than 232 elements (the resulting array is not correctly filled and ends with zeros). (Pascal, reported by David WF)
  • Added Scalar.ndim=0 and ScalarSharedVariable.ndim=0 (simplify code) (Razvan)
  • New min_informative_str() function to print graph. (Ian)
  • Fix catching of exception. (Sometimes we used to catch interrupts) (Frederic, David, Ian, Olivier)
  • Better support for utf string. (David)
  • Fix pydotprint with a function compiled with a ProfileMode (Frederic)
    • Was broken with change to the profiler.
  • Warning when people have old cache entries. (Olivier)
  • More tests for join on the GPU and CPU. (Frederic)
  • Do not request to load the GPU module by default in scan module. (Razvan)
  • Fixed some import problems. (Frederic and others)
  • Filtering update. (James)
  • On Windows, the default compiledir changed to be local to the computer/user and not transferred with roaming profile. (Sebastian Urban)
  • New theano flag "on_shape_error". Defaults to "warn" (same as previous behavior): it prints a warning when an error occurs when inferring the shape of some apply node. The other accepted value is "raise" to raise an error when this happens. (Frederic)
  • The buidbot now raises optimization/shape errors instead of just printing a warning. (Frederic)
  • better pycuda tests (Frederic)
  • check_blas.py now accept the shape and the number of iteration as parameter (Frederic)
  • Fix opt warning when the opt ShapeOpt is disabled (enabled by default) (Frederic)
  • More internal verification on what each op.infer_shape return. (Frederic, James)
  • Argmax dtype to int64 (Olivier)
  • Improved docstring and basic tests for the Tile Op (David).

Logo MIToolbox 1.03

by apocock - February 13, 2012, 19:45:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5756 views, 1122 downloads, 1 subscription

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, and Renyi entropy/mutual information. Works with discrete distributions, and expects column vectors of features.

Changes:

Updated documentation & link to JMLR publication.


Logo FEAST 1.00

by apocock - February 13, 2012, 19:00:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5728 views, 1438 downloads, 1 subscription

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF.

Changes:

Initial Announcement on mloss.org.


Logo JMLR LWPR 1.2.4

by sklanke - February 6, 2012, 19:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22046 views, 2664 downloads, 1 subscription

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]

Changes:

Version 1.2.4

  • Corrected typo in lwpr.c (wrong function name for multi-threaded helper function on Unix systems) Thanks to Jose Luis Rivero

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