Search Results for "numpy python 2.7" - Page 5
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Showing 382 open source projects for "numpy python 2.7"
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1
Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
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Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
...Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). Surprise, same code in JavaScript for M2 chip in Safari: 12,5 sec single core and 3,3 sec multi core. Python version with numba and numpy on MacOS with M2: 3,78 sec, Intel Ultra 5 225F Linux Fedora 43 GNOME(*Intel): 3,64 sec., W11Intel: 3,73; Faster style in python, MacOS M2: 1,81 sec, *Intel & W11Intel: 2,02 sec.; Ultra faster style in python, MacOS M2: 1,24 s - 1,26 s - 1,34 s, *Intel: 1,48 s - 1,50 s, W11Intel: 1,53 - 1,63. -
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Pybris
B language compiler written in Python targeting RISVM
Pybris is a compiler written in Python using Pyparsing for the B Programming Language. The compiler emits a variant of Bitmario RISVM assembly. The practical goal of the project is to provide a way to develop digital signal processing (DSP) effects for the Competent Audio library that is a friendlier alternative to writing RISVM assembly by hand. Pybris is written for Python 2.7, but has also been tested to run with Python 3.8.10. -
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Abscom
A C11 library of reusable data structures and platform utilities.
Abscom bundles low-level building blocks — dynamic arrays, growable strings, hash functions, an open-addressing hash map, timing helpers, and simple file I/O — under a single umbrella header. On top of that it ships a Python-inspired dynamic runtime that brings var values, lists, dictionaries, sets, JSON, random utilities, and a light object system to plain C, plus a scientific layer with matrices, statistics, general math (scalar utilities, number theory, geometry, complex numbers), CSV, path helpers, and basic threading. A data science layer adds NumPy-style reshaping and generators, Pandas-style numeric CSV, functional utils, and SciKit-Learn-style preprocessing (one-hot encoding, train/test split). ... -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
Contains significant improvements, bug fixes, and additional support. Get it from the releases, or pull the master branch. This package provides a few things. A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data... -
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MakeHuman
This is the main repository for the MakeHuman application as such
This is the main source code for the MakeHuman application as such. See "Getting started" below for instructions on how to get MakeHuman up and running. Mac users should be able to use the same instructions as windows users, although this has not been thoroughly tested. At the point of writing this, the source code is almost ready for a stable release. The testing vision for this code is to build a community release that includes main application and often-used, user-contributed plug-ins. We... -
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Introduction to ML with Python
Notebooks and code for the book "Introduction to Machine Learning
...The included mglearn helper library supplies educational datasets, plotting functions, and figures used throughout the material. Most required datasets are bundled, although the ACL IMDb data must be downloaded separately. Setup instructions cover NumPy, SciPy, scikit-learn, Matplotlib, pandas, Pillow, Graphviz, NLTK, and spaCy. The repository is best treated as a hands-on learning companion whose older dependency assumptions may require adjustment in modern Python environments. -
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Tensor Puzzles
Solve puzzles. Improve your pytorch
Tensor Puzzles is an interactive collection of 21 exercises for learning tensor programming in PyTorch and NumPy. Each puzzle asks the learner to recreate a familiar array operation from first principles. Solutions must fit on one short line and use only a restricted set of indexing, arithmetic, comparison, and broadcasting tools. Standard convenience functions such as sum, view, squeeze, and take are intentionally prohibited. This constraint encourages a deeper understanding of shapes,... -
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bKappa
Cross-platform incremental backup
...If you are looking for that, BorgBackup and restic are good options as of 2024. Warning! Old 1.x versions of bKappa are insecure. Do not use them. Because backup metadata is serialized with the "pickle" Python module (https://docs.python.org/2.7/library/pickle.html), a malicious backup can take over your machine. -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
scikit-learn-videos repository accompanies a video tutorial series designed to teach machine learning using Python’s scikit-learn library. It provides the Jupyter notebooks used in each lesson so learners can reproduce the demonstrations and experiment with the code themselves. The series introduces fundamental machine learning concepts such as classification, regression, model evaluation, feature engineering, and cross-validation using clear examples and real datasets. Each video... -
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Glumpy
Python+Numpy+OpenGL, scalable and beautiful scientific visualization
Glumpy is a Python library that simplifies the development of high-performance, interactive OpenGL visualizations. It abstracts complex OpenGL tasks into Pythonic constructs, making it easier for scientists, artists, and developers to harness the power of the GPU for real-time rendering and data visualization. Glumpy is particularly well-suited for rapid prototyping of graphical applications, and its integration with NumPy and shader programming makes it a powerful tool for both research and creative exploration. -
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Arctic TimeSeries and Tick store
High performance datastore for time series and tick data
Arctic is a timeseries/dataframe database that sits atop MongoDB. Arctic supports serialization of a number of datatypes for storage in the mongo document model. Serializes a number of data types eg. Pandas DataFrames, Numpy arrays, Python objects via pickling etc. so you don't have to handle different datatypes manually. Uses LZ4 compression by default on the client side to get big savings on network / disk. Allows you to version different stages of an object and snapshot the state (In some ways similar to git), and allows you to freely experiment and then just revert back the snapshot. ... -
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GIXY
Nginx configuration static analyzer
Gixy is a tool to analyze Nginx configuration. The main goal of Gixy is to prevent security misconfiguration and automate flaw detection. Currently supported Python versions are 2.7, 3.5, 3.6 and 3.7. Gixy is well tested only on GNU/Linux, other OSs may have some issues. You can find things that Gixy is learning to detect at Issues labeled with "new plugin". By default Gixy will try to analyze Nginx configuration placed in /etc/nginx/nginx.conf. Or something else, you can find all other gixy arguments with the help command: gixy --help. ... -
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Minecraft_python_Edition
Minecraft using pygame opengl pyglet
Minecraft using pygame opengl pyglet from PYTHON REQUIREMENTS: pyopengl==3.1.5 pyglet==1.5.28 numpy pygame keys: w:player move forward a:player move left s:player move back d:player move right e:inventory Esc:pause the game ⏸️ p:enter spectator mode -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
...The project includes more than thirty notebooks that cover a wide range of topics including data analysis, statistical modeling, neural networks, and deep learning. Each notebook introduces theoretical ideas and then demonstrates how to implement them using Python libraries commonly used in data science, such as NumPy, pandas, scikit-learn, and TensorFlow. The repository also includes examples related to natural language processing, computer vision, and data visualization, giving learners exposure to several subfields of machine learning. By organizing the content into modular notebooks, the project allows users to explore topics independently and experiment with the code directly. -
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TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. Licensed under MIT. This training environment was originally designed for tickdata, but also supports OHLC data format. WIP.... -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. ... -
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RC_Filter_Explorer
Multistage RC filter design aid.
For a given total series resistance and total parallel capacitance, a multistage RC filter will give a much sharper rolloff than a single stage filter. The difference is very large when going from one stage to two stages and tapers off as the number of stages increase. The main application of such a filter would be in circuits where the offset and frequency performance of an active filter would be undesirable. The series resistance can be specified and the number of stages increased to... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
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AWS EC2 Instance Connect CLI
All-in-one client for EC2 Instance Connect that handles key brokerage
This is an all-in-one client for EC2 Instance Connect that handles key brokerage and establishing connections to EC2 Instances through an interface near-identical to standard system ssh, sftp, and other utilities. This is a Python client for accessing EC2 instances via AWS EC2 Instance Connect. This module supports Python 2.7.x and 3+. This package is available on PyPI for pip installation, ie, pip install ec2instanceconnectcli. It is strongly encouraged you set up a virtual environment for building and testing. To set up this package you need to have pip installed. ... -
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werpy
Python package for fast Word Error Rate (WER) calculation and analysis
Werpy is a robust yet lightweight Python package, engineered to swiftly compute and analyzes the Word Error Rate (WER) between two text sets. It boasts versatility in managing various types of input data, including strings, lists, and numpy arrays. The library encompasses an extensive array of functionalities, encompassing text normalization to accommodate discrepancies in data collection. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. ... -
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Visdom
A tool for creating, organizing, and sharing data visualizations
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy. Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation. Broadcast visualizations of plots, images, and text for yourself and your collaborators. Organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code. Visdom has... -
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Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
Python ML Jupyter Notebooks is an educational repository that demonstrates how to implement machine learning algorithms and data science workflows using Python. The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. -
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learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
The learn-machine-learning-in-two-months repository is an educational open-source project designed to guide beginners through the process of learning machine learning and deep learning concepts within a structured two-month study plan. The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow. It progressively moves from foundational theory to more advanced subjects including regression, classification, neural networks, and model deployment. The repository emphasizes understanding the underlying principles of machine learning while also providing practical exercises and examples that allow learners to build and experiment with real models. ...
