An open-source library of algorithms to analyse time series in GPU and CPU.

Overview

Khiva

License: MPL 2.0 Gitter chat

Build Documentation Build Linux and Mac OS Build Windows Code Coverage
Documentation Status Build Status Build status Coverage Status

Khiva is an open-source library of efficient algorithms to analyse time series in GPU and CPU. It can be used to extract insights from one or a group of time series. The large number of available methods allow us to understand the nature of each time series. Based on the results of this analysis, users can reduce dimensionality, find out recurrent motifs or discords, understand the seasonality or trend from a given time series, forecasting and detect anomalies.

Khiva provides a mean for time series analytics at scale. These analytics can be exploited in a wide range of use cases across several industries, like energy, finance, e-health, IoT, music industry, etc.

Khiva is inspired by other time series libraries as tsfresh, tslearn and hctsa among others.

Other Matrix Profile implementations

License

This project is licensed under MPL-v2.

Installation

Currently, khiva is supported on Windows, Linux and MacOs, if you need to install the library follow the installation guide.

Contributing

The rules to contribute to this project are described here.

Builds

We have a first approach to generate a build and execute the set of tests on every pull request to the master branch. This process uses travis and appveyor. The status badges of the builds are contained at the beginning of this file.

Referencing Khiva

If you use Khiva in a scientific publication, we would appreciate citations:

@misc{khiva,
 title={Khiva: Accelerated time-series analytics on GPUs and CPU multicores},
 author={Ruiz-Ferrer, Justo and Vilches, Antonio and Torreno, Oscar and Cuesta, David},
 year={2018},
 note={\url{https://github.com/shapelets/khiva}}
}
Comments
  • Segfault and error on Mac with OpenCL and different array types

    Segfault and error on Mac with OpenCL and different array types

    Describe the bug

    Hi, I'm seeing a segfault and error on Mac with OpenCL and different array data types.

    • f32 - success
    • f64 - segfault
    • s32 - error (stack trace below)
      File "/usr/local/lib/python3.9/site-packages/khiva/matrix.py", line 231, in stomp_self_join
        raise Exception(str(error_message.value.decode()))
    Exception: stomp_self_join: ArrayFire Exception (Internal error:998):
    In function cl::Program opencl::buildProgram(const vector<std::__1::string> &, const vector<std::__1::string> &)
    In file src/backend/opencl/compile_module.cpp:128
    OpenCL Device: Intel(R) Iris(TM) 
    

    To Reproduce

    It's easiest to reproduce with the Python library (but think it's probably related to the C++ code).

    from khiva.array import Array, dtype
    from khiva.library import get_backend_info, set_backend, KHIVABackend
    from khiva.matrix import stomp_self_join
    
    # everything works with the CPU backend
    # set_backend(KHIVABackend.KHIVA_BACKEND_CPU)
    
    print(get_backend_info())
    
    # success
    a = Array.from_list([1, 2, 3, 4, 5], dtype.f32)
    stomp_self_join(a, 3)
    
    # segfault
    a = Array.from_list([1, 2, 3, 4, 5], dtype.f64)
    stomp_self_join(a, 3)
    
    # error
    a = Array.from_list([1, 2, 3, 4, 5], dtype.s32)
    stomp_self_join(a, 3)
    

    Expected behavior

    No segfault or error, like with the CPU backend.

    Environment information:

    • OS: Mac OS 11.1
    • Khiva Version: 0.5.0
    • Khiva dependencies versions: ArrayFire 3.7.3, Boost 1.74.0

    Here's the output of get_backend_info():

    ArrayFire v3.7.3 (OpenCL, 64-bit Mac OSX, build default)
    [0] APPLE: Intel(R) Iris(TM) Plus Graphics, 1536 MB
    

    Additional context

    Let me know if there's any I can do to help debug.

    bug 
    opened by ankane 7
  • Tests fail with invalid pointer on free during make

    Tests fail with invalid pointer on free during make

    Not sure if this is on my end or in one of my shared libraries causing the problem.

    Here's the error during the build. https://gist.github.com/zpzim/a7added1e63587e162f871383e33b1dc

    It might be related to Intel TBB. I get this error even when I comment out the entire test function

    opened by zpzim 5
  • Fixing PAA to work with non divisible number of points.

    Fixing PAA to work with non divisible number of points.

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by avilchess 3
  • Linking against gtest: conan builds vs regular cmake builds

    Linking against gtest: conan builds vs regular cmake builds

    Describe the bug Linking errors raise when building the project and linking against the libraries installed by conan. Errors sample:

    CMakeFiles/features.dir/featuresTest.cpp.o: In function `testing::AssertionResult testing::internal::CmpHelperEQFailure<long long, int>(char const*, char const*, long long const&, int const&)':
    /home/asem/.conan/data/gtest/1.8.1/bincrafters/stable/package/4a8c5b4cd3b4d45b83fff85d53160ea02ae5fa2d/include/gtest/gtest.h:1435: undefined reference to `testing::internal::EqFailure(char const*, char const*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, bool)'
    

    The linking issue is fixed when linking the khiva tests against the same gtest version 1.8.0 that is built from its main repository.

    Sample from CMakeCache.txt before fixing

    //Path to a file.
    GTEST_INCLUDE_DIRS:PATH=/home/asem/.conan/data/gtest/1.8.1/bincrafters/stable/package/4a8c5b4cd3b4d45b83fff85d53160ea02ae5fa2d/include
    
    //Path to a library.
    GTEST_LIBRARIES:FILEPATH=/home/asem/.conan/data/gtest/1.8.1/bincrafters/stable/package/4a8c5b4cd3b4d45b83fff85d53160ea02ae5fa2d/lib/libgtest.a
    
    //Path to a library.
    GTEST_MAIN_LIBRARIES:FILEPATH=/home/asem/.conan/data/gtest/1.8.1/bincrafters/stable/package/4a8c5b4cd3b4d45b83fff85d53160ea02ae5fa2d/lib/libgtest_main.a
    

    After fixing

    //Path to a file.
    GTEST_INCLUDE_DIRS:PATH=/opt/gtest/include
    
    //Path to a library.
    GTEST_LIBRARIES:FILEPATH=/opt/gtest/lib/libgtest.a
    
    //Path to a library.
    GTEST_MAIN_LIBRARIES:FILEPATH=/opt/gtest/lib/libgtest_main.a
    

    I am just reporting this case since it costed me a lot of time! Couldn't expect that conan might be the reason.

    opened by A-Alaa 3
  • Ruby bindings

    Ruby bindings

    Hi, also wanted to let you know there are now Ruby bindings for Khiva. It's modeled after the Python bindings (with some minor changes to make it more Ruby-like). The C API was really nice to use and easy to follow.

    If you have any feedback, feel free to let me know here or create an issue in the project. Thanks!

    opened by ankane 2
  • Make explicit Arrayfire use config files

    Make explicit Arrayfire use config files

    Make sure you have checked all steps below.

    Description

    • [x] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [x] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [x] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by raulbocanegra 2
  • Improving visvalingam performance by 3x.

    Improving visvalingam performance by 3x.

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by avilchess 2
  • Feature/scamp chains

    Feature/scamp chains

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by jrecuerda 2
  • Change year

    Change year

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by avilchess 2
  • Feature/headers bindings

    Feature/headers bindings

    Description

    • Added headers guards for avoid double inclusions.

    Tests

    • My PR does not need testing because it does not add any functional feature

    Benchmarks

    • [ ] My PR does not need benchmarks because are only changes in include for avoid double inclusions.

    Commits

    • Add headers guards in bindings includes for C and Java.

    License

    opened by ghost 2
  • Fix/operation array

    Fix/operation array

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by avilchess 2
  • Visvalingam runs on both 64 and 32 floats.

    Visvalingam runs on both 64 and 32 floats.

    Description

    Visvalingam now runs transparently for float64 and float32 types.

    Tests

    Added DimensionalityTests.VisvalingamAF to test both scenarios.

    Benchmarks

    N/A

    opened by justoruiz 0
  • Update .appveyor.yml

    Update .appveyor.yml

    Make sure you have checked all steps below.

    Description

    • [ ] Here are some details about my PR, including screenshots of any UI changes:

    Tests

    • [ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

    Benchmarks

    • [ ] My PR adds the following micro benchmarks OR does not need benchmarks for this extremely good reason:

    Commits

    • [ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":
      1. Subject is separated from body by a blank line
      2. Subject is limited to 50 characters
      3. Subject does not end with a period
      4. Subject uses the imperative mood ("add", not "adding")
      5. Body wraps at 72 characters
      6. Body explains "what" and "why", not "how"

    License

    Documentation

    • [ ] In case of new functionality, my PR adds documentation that describes how to use it.
    opened by avilchess 0
  • Fix broken test ClusteringTests.KShapeDouble

    Fix broken test ClusteringTests.KShapeDouble

    18/211 Test  #18: ClusteringTests.KShapeDouble .....................................***Failed   18.02 sec
    Running main() from C:\dev\vcpkg\buildtrees\gtest\src\ase-1.10.0-9cde347ce0.clean\googletest\src\gtest_main.cc
    Note: Google Test filter = ClusteringTests.KShapeDouble
    [==========] Running 1 test from 1 test suite.
    [----------] Global test environment set-up.
    [----------] 1 test from ClusteringTests
    [ RUN      ] ClusteringTests.KShapeDouble
    ←[32m[----------]←[0m CPU backend, device: Intel CPU
    [----------] CUDA backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_UHD_Graphics
    unknown file: error: C++ exception with description "ArrayFire Exception (Internal error:998):
    OpenCL Error (-59): Invalid Operation when calling clEnqueueMapBuffer
    In function class af::array __cdecl af::matmul(const class af::array &,const class af::array &,const af_mat_prop,const af_mat_prop)
    In file src\api\cpp\blas.cpp:20" thrown in the test body.
    [  FAILED  ] ClusteringTests.KShapeDouble (17741 ms)
    [----------] 1 test from ClusteringTests (17741 ms total)
    
    [----------] Global test environment tear-down
    [==========] 1 test from 1 test suite ran. (17741 ms total)
    [  PASSED  ] 0 tests.
    [  FAILED  ] 1 test, listed below:
    [  FAILED  ] ClusteringTests.KShapeDouble
    
     1 FAILED TEST
    
    opened by marregui 0
  • Fix broken test ClusteringTests.KShapeFloat

    Fix broken test ClusteringTests.KShapeFloat

    17/211 Test  #17: ClusteringTests.KShapeFloat ......................................***Failed   18.88 sec
    Running main() from C:\dev\vcpkg\buildtrees\gtest\src\ase-1.10.0-9cde347ce0.clean\googletest\src\gtest_main.cc
    Note: Google Test filter = ClusteringTests.KShapeFloat
    [==========] Running 1 test from 1 test suite.
    [----------] Global test environment set-up.
    [----------] 1 test from ClusteringTests
    [ RUN      ] ClusteringTests.KShapeFloat
    ←[32m[----------]←[0m CPU backend, device: Intel CPU
    [----------] CUDA backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_UHD_Graphics
    unknown file: error: C++ exception with description "ArrayFire Exception (Internal error:998):
    OpenCL Error (-59): Invalid Operation when calling clEnqueueMapBuffer
    In function class af::array __cdecl af::matmul(const class af::array &,const class af::array &,const af_mat_prop,const af_mat_prop)
    In file src\api\cpp\blas.cpp:20" thrown in the test body.
    [  FAILED  ] ClusteringTests.KShapeFloat (18594 ms)
    [----------] 1 test from ClusteringTests (18594 ms total)
    
    [----------] Global test environment tear-down
    [==========] 1 test from 1 test suite ran. (18594 ms total)
    [  PASSED  ] 0 tests.
    [  FAILED  ] 1 test, listed below:
    [  FAILED  ] ClusteringTests.KShapeFloat
    
     1 FAILED TEST
    
    opened by marregui 0
  • Fix broken test MatrixTests.FindBestDiscordsMultipleProfiles

    Fix broken test MatrixTests.FindBestDiscordsMultipleProfiles

    166/211 Test #166: MatrixTests.FindBestDiscordsMultipleProfiles .....................***Failed   19.85 sec
    Running main() from C:\dev\vcpkg\buildtrees\gtest\src\ase-1.10.0-9cde347ce0.clean\googletest\src\gtest_main.cc
    Note: Google Test filter = MatrixTests.FindBestDiscordsMultipleProfiles
    [==========] Running 1 test from 1 test suite.
    [----------] Global test environment set-up.
    [----------] 1 test from MatrixTests
    [ RUN      ] MatrixTests.FindBestDiscordsMultipleProfiles
    ←[32m[----------]←[0m CPU backend, device: Intel CPU
    [----------] CUDA backend, device: GeForce_RTX_2060
    C:\Users\MiguelArregui\workspace\khiva\test\matrixTest.cpp(1148): error: Expected equality of these values:
      subsequenceIndicesHost[0]
        Which is: 10
      0
    ←[32m[----------]←[0m OPENCL backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_UHD_Graphics
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_Core(TM)_i7-10875H_CPU @ 2.30GHz
    [  FAILED  ] MatrixTests.FindBestDiscordsMultipleProfiles (19597 ms)
    [----------] 1 test from MatrixTests (19597 ms total)
    
    [----------] Global test environment tear-down
    [==========] 1 test from 1 test suite ran. (19597 ms total)
    [  PASSED  ] 0 tests.
    [  FAILED  ] 1 test, listed below:
    [  FAILED  ] MatrixTests.FindBestDiscordsMultipleProfiles
    
     1 FAILED TEST
    
    opened by marregui 0
  • Fix broken test due to fltpt precission FeaturesTests.FftAggregated

    Fix broken test due to fltpt precission FeaturesTests.FftAggregated

    74/211 Test  #74: FeaturesTests.FftAggregated ......................................***Failed   11.05 sec
    Running main() from C:\dev\vcpkg\buildtrees\gtest\src\ase-1.10.0-9cde347ce0.clean\googletest\src\gtest_main.cc
    Note: Google Test filter = FeaturesTests.FftAggregated
    [==========] Running 1 test from 1 test suite.
    [----------] Global test environment set-up.
    [----------] 1 test from FeaturesTests
    [ RUN      ] FeaturesTests.FftAggregated
    ←[32m[----------]←[0m CPU backend, device: Intel CPU
    [----------] CUDA backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: GeForce_RTX_2060
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_UHD_Graphics
    ←[32m[----------]←[0m OPENCL backend, device: Intel(R)_Core(TM)_i7-10875H_CPU @ 2.30GHz
    C:\Users\MiguelArregui\workspace\khiva\test\featuresTest.cpp(481): error: The difference between fft[3] and f4 is 1.1920928955078125e-06, which exceeds EPSILON, where
    fft[3] evaluates to 3.642664909362793,
    f4 evaluates to 3.6426661014556885, and
    EPSILON evaluates to 9.9999999999999995e-07.
    [  FAILED  ] FeaturesTests.FftAggregated (10790 ms)
    [----------] 1 test from FeaturesTests (10790 ms total)
    
    [----------] Global test environment tear-down
    [==========] 1 test from 1 test suite ran. (10790 ms total)
    [  PASSED  ] 0 tests.
    [  FAILED  ] 1 test, listed below:
    [  FAILED  ] FeaturesTests.FftAggregated
    
    1 FAILED TEST
    
    opened by marregui 0
Releases(v0.5.0)
Owner
Shapelets
Accelerated Time Series Analytics
Shapelets
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
Scikit learn library models to account for data and concept drift.

liquid_scikit_learn Scikit learn library models to account for data and concept drift. This python library focuses on solving data drift and concept d

7 Nov 18, 2021
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
A single Python file with some tools for visualizing machine learning in the terminal.

Machine Learning Visualization Tools A single Python file with some tools for visualizing machine learning in the terminal. This demo is composed of t

Bram Wasti 35 Dec 29, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations.

BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer

KTH Mechanics 8 Mar 31, 2022
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
机器学习检测webshell

ai-webshell-detect 机器学习检测webshell,利用textcnn+简单二分类网络,基于keras,花了七天 检测原理: 从文件熵 文件长度 文件语句提取出特征,然后文件熵与长度送入二分类网络,文件语句送入textcnn 项目原理,介绍,怎么做出来的

Huoji's 56 Dec 14, 2022
MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. Th

Swiggy 66 Dec 06, 2022
Provide an input CSV and a target field to predict, generate a model + code to run it.

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

Max Woolf 1.8k Jan 04, 2023
A Python package for time series classification

pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat

Johann Faouzi 1.4k Jan 01, 2023
ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

Sagnik Roy 13 Jul 02, 2022
Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

Jeong-Yoon Lee 720 Dec 25, 2022
Pandas DataFrames and Series as Interactive Tables in Jupyter

Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook

Marc Wouts 364 Jan 04, 2023
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
Predict the demand for electricity (R) - FRENCH

06.demand-electricity Predict the demand for electricity (R) - FRENCH Prédisez la demande en électricité Prérequis Pour effectuer ce projet, vous devr

1 Feb 13, 2022
Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE)

FFT-accelerated Interpolation-based t-SNE (FIt-SNE) Introduction t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful method for dimensi

Kluger Lab 547 Dec 21, 2022
onelearn: Online learning in Python

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

15 Nov 06, 2022
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Axel 1.4k Jan 06, 2023
CyLP is a Python interface to COIN-OR’s Linear and mixed-integer program solvers (CLP, CBC, and CGL)

CyLP CyLP is a Python interface to COIN-OR’s Linear and mixed-integer program solvers (CLP, CBC, and CGL). CyLP’s unique feature is that you can use i

COIN-OR Foundation 161 Dec 14, 2022