Search Results for "numpy" - Page 5
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Showing 185 open source projects for "numpy"
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The Neural Process Family
This repository contains notebook implementations
...Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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Weld
High-performance runtime for data analytics applications
...This approach reduces data movement between libraries and enables the system to generate highly optimized machine code for parallel execution. Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. By combining operations from multiple libraries into a single optimized execution plan, Weld can significantly improve performance in analytics and machine learning pipelines. -
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Open Drug Discovery Toolkit (ODDT)
Modular and comprehensive toolkit for use in cheminformatics
...These extensions allow to use of toolkits at all its grace and some features may be backported from others to introduce missing functionalities. Most important and handy property of Molecule in ODDT are Numpy dictionaries containing most properties of supplied molecule. Some of them are straightforward, other require some calculation, ie. atom features. Dictionaries are provided for major entities of molecules. -
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PyHubs
Hubness-aware machine learning in Python
PyHubs is a machine learning library developed in Python containing implementations of hubness-aware machine learning algorithms together with some useful tools for machine learning experiments. According to our recent observation, old versions of PyHubs (such as 1.2.1) does not provide correct results with new versions of numpy (such as 1.16), however, we think that the most recent version of PyHubs (1.3) works correctly with new versions of numpy as well. -
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MCNPydE
MCNP data extraction and display software library
MCNPydE is a Python library for extracting data from MCNP output file. It requires Python, Matplotlib and Numpy. It is a data reduction tool for MCNP output for ease of results analysis and viewing. -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. ... -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. Although the GitHub repository has been archived and is read-only, it is still a valuable snapshot of early, hands-on teaching material for scikit-learn and machine learning in Python. -
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AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning research
...The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. These materials summarize common functions, workflows, and best practices in a concise visual format that makes them easy to consult during development or study sessions. The repository functions as a centralized library where users can quickly access reference materials for both machine learning theory and practical programming tools. ... -
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pyPIDTuneMethods
PID controller design and tuning
pyPIDTuneMethods is a free python-based tool for PID controller design and tuning. pyPIDTuneMethods is based on scipy (numpy, scipy) <http://www.scipy.org/ >, guiqwt <http://code.google.com/p/guiqwt/ >, python control <http://python-control.org/> and pyQt5 <http://www.riverbankcomputing.co.uk >. If you are not familiar with python you can use winpython <http://winpython.sourceforge.net/>. You can also download it from pypi <https://pypi.org/project/pyPIDTuneMethods/>. ... -
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FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same... -
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rest-dev-vnc-docker
Restful / SOAP API Development with common tools in VNC/noVNC Docker
The idea is to use Docker with VNC/noVNC to aggregate all the needed and related Developments tools/IDEs within a single Docker as an agile way to stand up specific collections of tools quick within a Container quick computing needs. REST Development (this GIT) to cover end-to-end needs from JSON/XML, REST connection, Swagger, MongoDB, Test, etc. The use-cases of this kind of VNC/noVNC docker container is just limited by your imaginations and your device or network limitations. Virtually... -
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Ecuacion curva demanda
Determinacion de ecuación de curva de demanda
Usa Pandas, numpy y tkinter de python para desarrollar un algoritmo que al ingresar los datos históricos de demanda de energía devuelve como resultado una ecuación que puede ser usada para modelamiento, simulación y proyecciones -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. ... -
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Python Data Science Tutorials
Common data analysis and machine learning tasks using python
...It combines external tutorials, courses, reference guides, notebooks, articles, and selected code examples. The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. Its machine learning sections cover practical algorithms and libraries, including regression, classification, clustering, support vector machines, and computer vision resources. Additional material addresses text mining, sentiment analysis, serialization with pickle, AutoML, regular expressions, and web scraping. ... -
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pyspread
Python spreadsheet application
...Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Dependencies + Python (>=2.7, <3.0) + numpy (>=1.1.0) + wxPython (>=2.8.10.1, Unicode version required) + matplotlib (>=1.1.1) + pycairo (>=1.8.8) Optional dependencies + python-gnupg (>=0.3.0, for opening own files without approval) + xlrd (>=0.9.2, for loading Excel® files) + xlwt (>=0.9.2, for saving Excel files, pyspread >=v0.3.0 required) + jedi (>=0.8.0, for tab completion and context help in the entry line, pyspread >=v0.3.0 required) + basemap (>=1.0.7, for the weather example pys file) -
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PySptools
Hyperspectral algorithms for Python
...The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. The library is mature enough and is very usable even if the development is at a beta step. -
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CoVaMa
Co-Variation Mapper
...CoVaMa takes NGS alignment data (SAM) and populates large matrices of contingency tables that correspond to every possible pairwise interaction of nucleotides or amino acids in the viral genome. These tables are then analysed for evidence linkage disequilibrium. CoVaMa requires python version 2.7 and Numpy. -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
Data science solutions, insights, dashboards, machine learning, deployment. We start at 100GB. Vaex is a high-performance Python library for lazy Out-of-Core data frames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive... -
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Tangent
Source-to-source debuggable derivatives in pure Python
...Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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Numerical Python
A package for scientific computing with Python
NEWS: NumPy 1.11.2 is the last release that will be made on sourceforge. Wheels for Windows, Mac, and Linux as well as archived source distributions can be found on PyPI. Numerical Python adds a fast and sophisticated array facility to the Python language. NumPy is the most recent and most actively supported package. Numarray and Numeric are no longer supported. -
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...It was designed and tested specifically for CALIFA and other fiber-fed integral-field spectroscopy dataset. It is written in Python and can be executed from the command line. New release is version v0.5. Updated to be compatible with numpy version 1.12. New release is version v0.4. It's now compatible with astropy for fits I/O and computation time is reduced by a factor of 2.
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Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display. As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matplotlib.
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100 numpy exercises
100 numpy exercises (with solutions)
This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation. I've also created some problems myself to reach the 100 limit. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. For extended exercises, make sure to read From Python to NumPy. -
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mds-utils
General purpose utilities for C++ and Python developers
...Amongst them, some type traits for detecting different uBLAS matrix types. 3. some useful classes that allow to treat the old C FILE pointer as a C++ stream. 4. C++ wrappers of the main Python objects, independent of those in Boost Python. Wrappers are provided also for NumPy arrays. 5. C++ classes that help on treating Python file objects as C++ streams. 6. a review and refactor of the indexing support in Python extensions. Now access in write mode is supported too. More details on the Doxygen documentation. Documentation is available through doxygen. Once downloaded and uncompressed, issue the "doxygen" command from the root folder. ...