Search Results for "numpy"

PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling.

... - Implemented a 3d surface plot (GLSurface) based on OpenGL (ScienPlot v1.3.2 and above) - ColorMap surface, trisurface, Pie, Polar plots, and 3D height field, 3dBar, scatter plots (under developing), and more - Column by column plotting/calculation - LaTex commands enclosed by $ symbols can be used for the labels in Graph - Accept txt(Text) and csv(Comma separated values) formatted data - Save, copy, print Graph - Use spread sheets to display data - Textboard to organize the results - Graphs in a publishable quality - Source codes based on: Python Numpy Scipy Matplotlib WxPython Visvis etc. - Special functions - Drag and drop data files - Python console is back (since v1.3.3), capable of reusing column data - Debye and Guinier models for SANS / SAX data - More apps in our Web below

Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model, discrete models, time series analysis and other statistical methods.

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.

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, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. ...