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Showing 16 open source projects for "windows driver model"
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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MCPower
MCPower — simple Monte Carlo power analysis for complex models
MCPower-GUI is a desktop application that provides a graphical interface for the MCPower Monte Carlo power analysis library. It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario... -
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MAGeCK
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) is a computational tool to identify important genes from the recent genome-scale CRISPR-Cas9 knockout screens technology. For instructions and documentations, please refer to the wiki page. MAGeCK is developed by Wei Li and Han Xu from Dr. Xiaole Shirley Liu's lab at Dana-Farber Cancer Institute/Harvard School of Public Health, and is maintained by Wei Li lab at Children's National Medical Center. We thank the support... -
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Python/FEniCS Examples
phase-field simulation and other examples with Python/FEniCS
The main goal of this project was developing phase-field simulations of lithium dendrite growth with FEniCS programmed in Python. The problem was based in the grand potential-based model of Zijian Hong and Venkatasubramanian Viswanathan (https://doi.org/10.1021/acsenergylett.8b01009) . Some simpler examples were developed before for a first approach with FEniCS: heat equation and combustion model. -
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
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Template Code Set for JModelica
This is a template Python code set to use JModelica easily.
This is a template Python code set which makes it easy to use JModelica to solve optimal control problem. The template includes a sample model definition file (opt_definition.mop) and a .bat file (run_me.bat) to start its calculation. After download the template, immediately you can run JModelica by only double-clicking run_me.bat file, and obtain the optimization result. Please go to the page given below for the information of how to start to use this and its details. -
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That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline-related Inventory Management system. That library uses the Standard Airline IT C++ object model (http://sf.net/projects/stdair).
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PanelCheck is an easy-to-use software tool for visualization of sensory profiling data using different types of plots. The joint information from the implemented plots provide detailed insight into assessor and panel performance.
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MRA
A general recommender system with basic models and MRA
Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide... -
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DeDAY
MLE survival analysis: Gompertz, Weibull, Logistic and mixed morality.
DeDAY (Demography Data Analyses) is a tool of analyzing demography data. It supports Gompertz, Weibull and Logistic distributions. DeDay also supports mixed mortality models based on these distribution such as the Gompertz-Makeham distribution. Distributions such as Gompertz describes only age-dependent mortality, which increases over time. Mixed mortality models, such as in Gompertz-Makeham distribution, consider a more general case where mortality is consist of both age-dependent and... -
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PyParticles
PyParticles is an opensource particles simulation tool box entirely wr
PyParticles is an opensource particles simulation tool box entirely written in python. It support the most popular integrations methods and the most relevant forces model. It also offer a nice looking OpneGL interface or at your preference a Matplotlib based GUI. PyParticles as a forces models implements Gravity, spring, constant force and electrostatic and the user defined vector field force. As a integrations method it includes Euler, Midpoint, Runge Kutta, Störmer Verlet and Leap frog. -
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Model Builder is a graphical tool for designing, simulating and analyzing Mathematical models consisting of a system of ordinary differential equations(ODEs).
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The main goal of this project is to create a system-independent MathML rendering engine in Python. This engine works with an abstract 'plotter' driver class, that can be subclassed for any rendering device needed.
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RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions.
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Tim Peters' FixedPoint.py + Write docs for the Library Reference manual. I expect the existing module docstring will be a good start. + Create a test driver for Python's regression suite. + Have fun modernizing it, if you like (for example,
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ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve,...
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