Showing 100 open source projects for "python for windows"

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  • 1
    Kalman and Bayesian Filters in Python

    Kalman and Bayesian Filters in Python

    Kalman Filter book using Jupyter Notebook

    Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to...
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  • 2
    Zipline

    Zipline

    Zipline, a Pythonic algorithmic trading library

    Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python...
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  • 3
    Fun4Me

    Fun4Me

    A package for functional annotation for metagenomes

    This package includes a few programs for rapid functional annotation for metagenomic sequences, including, 1) Gene prediction by FragGeneScan; 2) Similarity search by RAPSearch2; 3) Functional annotation in GO (Gene Ontology) and EC (Enzyme Commission) based on similarity search results; 4) From EC to metabolic pathway reconstruction by MinPath. Inputs: Just sequencing reads (or assemblies) Outputs: Protein-coding genes (or gene fragments); similarity search; functional annotations...
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  • 4
    StructPie

    StructPie

    A set of C libraries to implement data structures and algorithms

    Struct-Pie (Structures Pie) is a set of C shared libraries to implement data structures and algorithms so that they can be used/integrated easily into C projects. LIFO & FIFO Stack, Binary Search Tree, Priority Queue and a Hash Table are implemented and included in this package. Future releases will have many other data structures. The hash table in this package uses separate chaining to avoid collision. In the "hash_table" directory, the hash table implementation uses linked lists....
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  • 5
    interactive-coding-challenges

    interactive-coding-challenges

    120+ interactive Python coding interview challenges

    Interactive Coding Challenges is a collection of practice problems designed to strengthen data structures, algorithms, and problem-solving skills. The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference...
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  • 6
    Smart Algorithm

    Smart Algorithm

    Repository implementing a variety of intelligent algorithms

    Smart-Algorithm is a repository implementing a variety of intelligent / metaheuristic optimization algorithms (e.g. Genetic Algorithm, Ant Colony, Particle Swarm, Immune Algorithm). The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune...
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  • 7
    MADDPG

    MADDPG

    Code for the MADDPG algorithm from a paper

    MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in...
    Downloads: 4 This Week
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  • 8
    FuzzyWuzzy

    FuzzyWuzzy

    Fuzzy string matching in Python

    We’ve made it our mission to pull in event tickets from every corner of the internet, showing you them all on the same screen so you can compare them and get to your game/concert/show as quickly as possible. Of course, a big problem with most corners of the internet is labeling. One of our most consistently frustrating issues is trying to figure out whether two ticket listings are for the same real-life event (that is, without enlisting the help of our army of interns). To pick an example...
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  • 9
    Baselines

    Baselines

    High-quality implementations of reinforcement learning algorithms

    Unlike the other two, openai/baselines is not currently a maintained or prominent repo in the OpenAI organization (and I found no strong reference in OpenAI’s main GitHub). Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new...
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  • 10
    Coach

    Coach

    Enables easy experimentation with state of the art algorithms

    Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments...
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  • 11
    Active Learning

    Active Learning

    Framework and examples for active learning with machine learning model

    Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main...
    Downloads: 4 This Week
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  • 12
    Code Catalog in Python

    Code Catalog in Python

    Algorithms and data structures for review for coding interview

    code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying...
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  • 13
    Omniglot

    Omniglot

    Omniglot data set for one-shot learning

    This repository hosts the Omniglot dataset for one-shot learning, containing handwritten characters across multiple alphabets along with stroke data. It includes both MATLAB and Python starter scripts (e.g. demo.m, demo.py) to illustrate how to load the images and stroke sequences and run baseline experiments (such as classification by modified Hausdorff distance). The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each...
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  • 14
    Data Algorithm/leetcode/lintcode

    Data Algorithm/leetcode/lintcode

    Data Structure and Algorithm notes

    This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress.
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  • 15
    zTrader
    Various tools for automating some trading operations on the most popular cryptocurrency exchanges.
    Downloads: 0 This Week
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  • 16
    zCharter
    Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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  • 17
    TextTeaser

    TextTeaser

    TextTeaser is an automatic summarization algorithm

    textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative....
    Downloads: 14 This Week
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  • 18
    Pygorithm

    Pygorithm

    A Python module for learning all major algorithms

    A Python module to learn all the major algorithms on the go! Purely for educational purposes. If you are using Python 2.7 use pip instead. Depending on your permissions, you might need to use pip install, user pygorithm to install. To see all the available functions in a module, you can just type help() with the module name as an argument.
    Downloads: 0 This Week
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  • 19

    Simple Python Fixed-Point Module

    Pure-python binary fixed-point arithmetic library

    This Python module provides basic facilities for mathematics on fixed-point numbers. The number of fractional binary digits is tunable, allowing near-arbitrary precision arithmetic. Arithmetic & functions such as sqrt, exp, log, sin & cos are available. For the latest releases please visit https://github.com/rwpenney/spfpm
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  • 20
    ipytracer

    ipytracer

    Algorithm Visualizer for IPython/Jupyter Notebook

    Algorithm Visualizer for IPython/Jupyter Notebook. If you use the display(TracerObject) code from where you want to see, you can use it without any special modification.
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  • 21
    Illustrated Algorithms

    Illustrated Algorithms

    Interactive algorithm visualizations

    Inspired by Grokking Algorithms and python-execution-trace, this project aims to reveal the mechanics behind algorithms via interactive visualizations of their execution. Visual representations of variables and operations augment the control flow, alongside actual source code. You can fast forward and rewind the execution to closely observe how an algorithm works. The same code that is displayed next to the illustration is also decorated using babel-plugin-trace-execution and executed to...
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  • 22
    Algorithms in Python

    Algorithms in Python

    Data Structures and Algorithms in Python

    Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational...
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  • 23
    Modular toolkit for Data Processing MDP
    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. The implementation of new algorithms is easy and intuitive. The...
    Downloads: 3 This Week
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  • 24

    ViennaCL

    Linear algebra and solver library using CUDA, OpenCL, and OpenMP

    ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects.
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    Downloads: 19 This Week
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  • 25
    SteppedStateMachine

    SteppedStateMachine

    Creates and operates a stepped state machine

    Implements a stepped state machine, i.e. a state machine which executes a single state transition at a time. Because of this, no data, e.g. state data, can be stored between executions. Instead, any such data must be stored in persistent storage between executions. This permits operation of the state machine as a CGI program in a web server. A WSGI or fastCGI or other such web server is not required. Received symbols may be received from sources outside the state machine, or may be...
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