Search Results for "algorithms framework" - Page 5
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Showing 128 open source projects for "algorithms framework"
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plot.py
direct data plotting and evaluation
The Plot.py project tries to supply a measurement data visualization and treatment framework being easy to use while keeping the freedom for advanced users to execute additional data treatment algorithms. Plotting is done via gnuplot and the script used to produce the graphs can be exported for later use/changes. Many raw experimental data types (mostly of x-ray and neutron scattering experiments) are supported with more to be added on user request. -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
Lihang is an open-source repository that provides educational notes, mathematical derivations, and code implementations based on the book Statistical Learning Methods by Li Hang. The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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Phaistos is a framework for all-atom Monte Carlo simulations of proteins. It incorporates several advanced probabilistic models of protein structure for conformational sampling, efficient move-algorithms and the OPLS and PROFASI forcefields.
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The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded.
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Python Open Source Echosounder Toolkit
real-time visualization of network data broadcasts from echosounders
...The Python Open Source Echosounder Toolkit (pyOSET) was designed to facilitate this process by providing near real-time visualization of network data broadcasts from multiple echosounder systems, and to serve as a framework for implementation of algorithms to detect, locate, and identify fish or the seabed. An Alpha release of the software is available under the GNU General Public License, version 2. Many practical aspects of the user interface are still in the planning and development phase. Those who wish to participate in the development of pyOSET are encouraged to subscribe to the Developer's Mailing List. ... -
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scipion-xmipp
Image processing framework to integrate EM software packages.
...Xmipp is a well-known package in the EM image processing. It is integrated into Scipion and contains the algorithms in the core libraries and command line programs. -
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fitGCP
Fitting genome coverage distributions with mixture models
...fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. Please find the accompanying paper here: http://dx.doi.org/10.1093/bioinformatics/btt147 -
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ant_farm
Python-based reverse-engineering tool
ant_farm provides a GUI framework for integrating all of those python tools you have written over the years to parse files, execute algorithms, display data etc. -
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UnsupervisedPy
unsupervised learning algorithms for python
This is a library for python containing popular machine learning algorithms under the unsupervised learning framework. Algorithms implemented up to now: K-Means Intelligent K-Means Weighted K-Means Minkowski Weighted K-Means Intelligent Minkowski Weighted K-Means Partition Around Medoids (PAM) Build (initialization for PAM) Minkowski Weighted PAM (with and without Build) Ward Method -
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JBoost is a simple, robust system for classification. JBoost contains implementations of several boosting algorithms in an alternating decision tree framework. In addition, JBoost provides extensible software for adding more learning algorithms.
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This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.
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Emulica provides manufacturing control engineers and researchers with generic modeling components to build industry-scale virtual (emulated) shop floor systems, and thus test control approaches.
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Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
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MIVF - Medical Imaging and Visualization Factory, is a framework for medical applications. It supplies a platform, in which image processing and 3D visualization algorithms can be employed as reusable components (functional modules or plugins).
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PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.
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HDRFlow is a framework to process high-dynamic range (HDR) and RAW images. It's written in C++, and is both cross-platform and hardware accelerated on modern GPUs.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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TAROT is a easy-to-use framework for Monte Carlo simulations in python. Calculations between different kinds of randomly distributed numbers are made as easy as basic arithmetics. Tarot provides an interactive graphical interface for interpretation.
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pyMVC is a Model-View-Controller implementation library and framework written in Python for fast and high-grade software development.
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pySPACE
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. ... -
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ACF is a framework for writing model-checkers. ACF is built on the fundamental observation that the structure of most model-checking algorithms is independent of the formalism used to describe the system.
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The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).
