catch-22: CAnonical Time-series CHaracteristics

Related tags

Deep Learningcatch22
Overview

catch22 - CAnonical Time-series CHaracteristics

DOI

About

catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia. The catch22 features are a high-performing subset of the over 7000 features in hctsa.

Features were selected based on their classification performance across a collection of 93 real-world time-series classification problems, as described in our open-access paper:

But what do the features do? You can learn more about what each feature does, with visualizations of how the features behave on real data, here.

The computational pipeline used to generate the catch22 feature set is in the op_importance repository.

For catch22-related information and resources, including a list of publications using catch22, see the catch22 wiki.

Summary of the performance of the catch22 feature set across 93 classification problems, and a comparison to the hctsa feature set (cf. Fig. 4 from our paper):

Installation: Python, R, Matlab, Julia, and compiled C

The fast, C-coded functions in this repository can be used in Python, Matlab, and R following the detailed installation instructions on the wiki.

There is also a native R version, downloadable from CRAN, Rcatch22.

And Julia users can use this Julia package to evaluate the catch22 feature set.

Usage

  • See language-specific usage information in the wiki.
  • Important Note: catch22 features only evaluate dynamical properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).
    • If you think features of the raw distribution may be important for your application, we suggest you add them (in the simplest case, two additional features: the mean and standard deviation) to this feature set.
  • Note that time series are z-scored internally which means e.g., constant time series will lead to NaN outputs.
Comments
  • Why catch22-0.2.0 got deleted from pypi

    Why catch22-0.2.0 got deleted from pypi

    Ideally, the version should be updated or can be made deprecated. But should not be removed from the repository(PyPi). Can you please upload them back with some new versions tags ?

    opened by cahuja1992 5
  • Anaconda Python 3 pip install not working

    Anaconda Python 3 pip install not working

    I tried pip install catch22 and got a long error log. These are the last few lines:

        C/SP_Summaries.c:161:9: error: ‘for’ loop initial declarations are only allowed in C99 mode
                 for(int i = 0; i < nWelch; i ++){
                 ^
        C/SP_Summaries.c:173:9: error: ‘for’ loop initial declarations are only allowed in C99 mode
                 for(int i=0; i<nWelch/5; i++){
                 ^
        error: command 'gcc' failed with exit status 1
        ----------------------------------------
    ERROR: Command errored out with exit status 1: /home/username/anaconda3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-kiwf2hln/catch22/setup.py'"'"'; __file__='"'"'/tmp/pip-install-kiwf2hln/catch22/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-jtj01ddj/install-record.txt --single-version-externally-managed --compile --install-headers /home/username/anaconda3/include/python3.7m/catch22 Check the logs for full command output.
    

    System: Anaconda3, CentOS 7

    Any ideas?

    opened by chanshing 4
  • Generating a shared library for the C implementation (and using that to build pycatch22 etc.)

    Generating a shared library for the C implementation (and using that to build pycatch22 etc.)

    Hello!

    We package and maintain catch22 for Fedora Linux. Now that pycatch22 has been split into its own repo and bundles the C sources, we were wondering if:

    • it would make sense to compile the C sources from here and provide catch22 as a shared library (libcatch22.so.0 types on Linux)
    • use the shared library to link against when building pycatch22 etc.

    The advantage of this is that we won't need to bundle catch22 in pycatch22, and that catch22 is provided as a library for people using C to use.

    @musicinmybrain has created a pull request that generates the shared library (by adding a Makefile and so on) here. The main question here, as you'll see from that pull request, is whether you (upstream) intend to provide it as a C library, and if you do, would you be committing to ABI/shared object versioning etc. to notify users of changes and so on?

    Link to the libtool documentation on versioning: https://www.gnu.org/software/libtool/manual/html_node/Versioning.html#Versioning

    opened by sanjayankur31 3
  • Modified CO_f1ecac feature to use interpolation

    Modified CO_f1ecac feature to use interpolation

    I've modified the CO_f1ecac feature to use interpolation, making it a double (and equivalent to the HCTSA function). See HCTSA's CO_AutoCorr.m and BF_PointOfCrossing.m

    opened by olivercliff 3
  • WIP: catch24 integration

    WIP: catch24 integration

    Adds new .c and .h files for 2 new functions: DN_Mean and DN_Spread_Std which represent the mean and standard deviation, respectively. Adds a boolean argument to core function calls where users can specify if they want to compute catch24 or just catch22. Need to run tests to check everything is working properly, but I have made changes to C code, and R and Python wrappers. @benfulcher changes will need to be made to the MATLAB wrapper too, right?

    I'll remove the drafting tag once I have worked through unit tests, but either of you can feel free to run the additions and check they work if you have time.

    enhancement 
    opened by hendersontrent 3
  • Memory leaks fix

    Memory leaks fix

    Hi,

    First of all, thanks for this great package!

    I noticed that when doing a lot of calls to catch22.catch22_all(data), the memory consumption of grows. This pull request frees C arrays in C/CO_AutoCorr.c, C/helper_functions.c and wrap_Python/catch22_wrap_P3.c. With these changes, the memory consumption is stable. You can test this for yourself by running the following code before and after applying the changes that I am proposing.

    import catch22
    import numpy as np
    from memory_profiler import profile
    
    @profile
    def catch22_all(signal):
        catch22.catch22_all(signal)
        
    for i in range(10000):
        signal = np.random.randn(1000)
        catch22_all(signal)
    
    opened by Olivier-tl 3
  • Correction in CO_Histogram_AMI_even_2_5 implementation

    Correction in CO_Histogram_AMI_even_2_5 implementation

    Referring to this code snippet in CO_AutoCorr.c

    #define tau 2
    #define numBins 5
    
    double CO_HistogramAMI_even_2_5(const double y[], const int size)
    

    Why tau is 2 and numBins is 5? Shouldn't it be vice-versa according to the implementation of hctsa feature

    opened by imraniac 3
  • Error installing R package on Windows

    Error installing R package on Windows

    Hi,

    I have problem to install your package from R...following errors occurred:

    • installing source package 'catch22' ... ** libs c:/Rtools/mingw_64/bin/gcc -I"C:/PROGRA~1/R/R-35~1.1/include" -DNDEBUG -I../inst/include -I"C:/Users/PeterLaurinec/Documents/R/win-library/3.5/Rcpp/include" -O2 -Wall -std=gnu99 -mtune=generic -c CO_AutoCorr.c -o CO_AutoCorr.o In file included from CO_AutoCorr.c:8:0: CO_AutoCorr.c: In function 'CO_AutoCorr': fft.h:16:43: error: '_Imaginary_I' undeclared (first use in this function) #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y))) ^ CO_AutoCorr.c:63:16: note: in expansion of macro 'CMPLX' F[i] = CMPLX(y[i] - m, 0.0); ^ fft.h:16:43: note: each undeclared identifier is reported only once for each function it appears in #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y))) ^ CO_AutoCorr.c:63:16: note: in expansion of macro 'CMPLX' F[i] = CMPLX(y[i] - m, 0.0); ^ CO_AutoCorr.c: In function 'co_autocorrs': fft.h:16:43: error: '_Imaginary_I' undeclared (first use in this function) #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y))) ^ CO_AutoCorr.c:97:16: note: in expansion of macro 'CMPLX' F[i] = CMPLX(y[i] - m, 0.0); ^ CO_AutoCorr.c: In function 'CO_HistogramAMI_even_2_5': CO_AutoCorr.c:347:5: error: variable-sized object may not be initialized double binEdges[numBins+1] = {0}; ^ CO_AutoCorr.c:347:5: warning: excess elements in array initializer CO_AutoCorr.c:347:5: warning: (near initialization for 'binEdges') CO_AutoCorr.c:370:5: error: variable-sized object may not be initialized double binEdges12[(numBins + 1) * (numBins + 1)] = {0}; ^ CO_AutoCorr.c:370:5: warning: excess elements in array initializer CO_AutoCorr.c:370:5: warning: (near initialization for 'binEdges12') CO_AutoCorr.c:414:5: error: variable-sized object may not be initialized double pi[numBins] = {0}; ^ CO_AutoCorr.c:414:5: warning: excess elements in array initializer CO_AutoCorr.c:414:5: warning: (near initialization for 'pi') CO_AutoCorr.c:415:5: error: variable-sized object may not be initialized double pj[numBins] = {0}; ^ CO_AutoCorr.c:415:5: warning: excess elements in array initializer CO_AutoCorr.c:415:5: warning: (near initialization for 'pj') make: *** [C:/PROGRA~1/R/R-35~1.1/etc/x64/Makeconf:208: CO_AutoCorr.o] Error 1 ERROR: compilation failed for package 'catch22'

    Any idea? Thx

    opened by PetoLau 2
  • python3 issue

    python3 issue

    There is a difficulty in compiling using python3. E.g., python3 setup.py build gives the following:

    running build
    running build_ext
    building 'catch22_C' extension
    creating build
    creating build/temp.macosx-10.7-x86_64-3.7
    creating build/C
    gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I../C/ -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include/python3.7m -c catch22_wrap.c -o build/temp.macosx-10.7-x86_64-3.7/catch22_wrap.o
    gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I../C/ -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include/python3.7m -c ../C/MD_hrv.c -o build/temp.macosx-10.7-x86_64-3.7/../C/MD_hrv.o
    gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include -arch x86_64 -I../C/ -I/Users/alex/miniconda3/envs/pipe-516-ram-mating-behaviour/include/python3.7m -c ../C/SP_Summaries.c -o build/temp.macosx-10.7-x86_64-3.7/../C/SP_Summaries.o
    In file included from ../C/CO_AutoCorr.h:9:0,
                     from ../C/SP_Summaries.c:9:
    ../C/SP_Summaries.c: In function 'welch':
    ../C/fft.h:16:43: error: '_Imaginary_I' undeclared (first use in this function)
     #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))
                                               ^
    ../C/SP_Summaries.c:43:20: note: in expansion of macro 'CMPLX'
                 F[i] = CMPLX(xw[i] - m, 0.0);
                        ^~~~~
    ../C/fft.h:16:43: note: each undeclared identifier is reported only once for each function it appears in
     #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))
                                               ^
    ../C/SP_Summaries.c:43:20: note: in expansion of macro 'CMPLX'
                 F[i] = CMPLX(xw[i] - m, 0.0);
                        ^~~~~
    ../C/SP_Summaries.c:71:13: warning: suggest parentheses around comparison in operand of '&' [-Wparentheses]
             if(i>0 & i < Nout-1){
                ~^~
    error: command 'gcc' failed with exit status 1
    

    Can fix? Or just limit to python2?

    opened by benfulcher 2
  • Python package version mismatch

    Python package version mismatch

    In release 0.4.0, the Python package instead has version 0.3.1.

    https://github.com/DynamicsAndNeuralSystems/catch22/blob/2e1a271c6a7437b6a4a754e1adc7e34d7a224c01/wrap_Python/setup.py#L17

    https://github.com/DynamicsAndNeuralSystems/catch22/blob/2e1a271c6a7437b6a4a754e1adc7e34d7a224c01/wrap_Python/setup_P3.py#L17

    opened by musicinmybrain 1
  • Error installing R wrapper (+ fixes for docs)

    Error installing R wrapper (+ fixes for docs)

    Been trying to install the R wrapper without much success (though more success than I've had with the Python and Matlab wrappers!). I noticed a few issues with the documentation along the way, and have noted them here along with the error I get:

    The install docs refer to./C_functions, but this directory appears to be./C in current builds.

    I think the .c and .h files are supposed to be moved to ./wrap_R/catch22/src, rather than ./wrap_R/src (which is specified in the install instructions, but does not actually exist unless I manually create it).

    Running R CMD INSTALL catch22_x.y.tar.gz only “works” in the latter case, and then only if R CMD build catch22 was run after the .c and .h files are copied to ./wrap_R/catch22/src. Attempting to build with these files in ./wrap_R/src causes the following error:

    catch22_wrap.cpp:5:25: fatal error: CO_AutoCorr.h: No such file or directory
    

    The docs should clarify that users should replace catch22_x.y.tar.gz with the tar file generated by the previous step. The name of this fill will depend on their version of catch22.

    Fixing the error related to the location of the C files got me as far as the following error. Any advice would be appreciated:

    (r-environment) # [email protected] in ~/Software/catch22/wrap_R [17:55:06]
    $ R CMD INSTALL catch22_0.1.tar.gz
    * installing to library \u2018/home/despoB/dlurie/anaconda3/envs/r-environment/lib/R/library\u2019
    * installing *source* package \u2018catch22\u2019 ...
    ** libs
    gcc -std=gnu99 -I/home/despoB/dlurie/anaconda3/envs/r-environment/lib/R/include -DNDEBUG -I../inst/include -I"/home/despoB/dlurie/anaconda3/envs/r-environment/lib/R/library/Rcpp/include" -I/home/despoB/dlurie/anaconda3/envs/r-environment/include   -fpic  -I/home/despoB/dlurie/anaconda3/envs/r-environment/include  -c CO_AutoCorr.c -o CO_AutoCorr.o
    In file included from CO_AutoCorr.c:8:0:
    CO_AutoCorr.c: In function \u2018CO_AutoCorr\u2019:
    fft.h:16:43: error: \u2018_Imaginary_I\u2019 undeclared (first use in this function)
     #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))
                                               ^
    CO_AutoCorr.c:63:16: note: in expansion of macro \u2018CMPLX\u2019
             F[i] = CMPLX(y[i] - m, 0.0);
                    ^
    fft.h:16:43: note: each undeclared identifier is reported only once for each function it appears in
     #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))
                                               ^
    CO_AutoCorr.c:63:16: note: in expansion of macro \u2018CMPLX\u2019
             F[i] = CMPLX(y[i] - m, 0.0);
                    ^
    CO_AutoCorr.c: In function \u2018co_autocorrs\u2019:
    fft.h:16:43: error: \u2018_Imaginary_I\u2019 undeclared (first use in this function)
     #define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))
                                               ^
    CO_AutoCorr.c:97:16: note: in expansion of macro \u2018CMPLX\u2019
             F[i] = CMPLX(y[i] - m, 0.0);
                    ^
    make: *** [CO_AutoCorr.o] Error 1
    ERROR: compilation failed for package \u2018catch22\u2019
    * removing \u2018/home/despoB/dlurie/anaconda3/envs/r-environment/lib/R/library/catch22\u2019
    
    opened by danlurie 1
  • 14 * possible use of wrong binary operator ?

    14 * possible use of wrong binary operator ?

    fedora/pass300/20220906/catch22.spec.out:IN_AutoMutualInfoStats.c:44:19: warning: suggest parentheses around comp arison in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:PD_PeriodicityWang.c:66:20: warning: suggest parentheses around comparis on in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:PD_PeriodicityWang.c:72:25: warning: suggest parentheses around comparis on in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:SB_BinaryStats.c:40:20: warning: suggest parentheses around comparison i n operand of '|' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:SB_BinaryStats.c:78:20: warning: suggest parentheses around comparison i n operand of '|' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:SP_Summaries.c:86:13: warning: suggest parentheses around comparison in operand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:splinefit.c:606:14: warning: suggest parentheses around comparison in op erand of '&' [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/IN_AutoMutualInfoStats.c:44:19: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/PD_PeriodicityWang.c:66:20: warning: suggest parentheses around com parison in operand of ‘&’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/PD_PeriodicityWang.c:72:25: warning: suggest parentheses around com parison in operand of ‘&’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/SB_BinaryStats.c:40:20: warning: suggest parentheses around compari son in operand of ‘|’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/SB_BinaryStats.c:78:20: warning: suggest parentheses around compari son in operand of ‘|’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/SP_Summaries.c:86:13: warning: suggest parentheses around compariso n in operand of ‘&’ [-Wparentheses] fedora/pass300/20220906/catch22.spec.out:../C/splinefit.c:606:14: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses]

    I checked the first two, presumably the rest are the same.

    opened by dcb314 1
  • Installation failure: `for’ loop initial declarations are only allowed in C99 mode`

    Installation failure: `for’ loop initial declarations are only allowed in C99 mode`

    When running pip install catch22, the C compilation seems to complain. I tried it with python 3.7-3.9.

     C/CO_AutoCorr.c:531:9: error: ‘for’ loop initial declarations are only allowed in C99 mode
                 for(int j = 0; j < numBins; j++){
                 ^
        error: command '/usr/bin/gcc' failed with exit code 1
        ----------------------------------------
    ERROR: Command errored out with exit status 1: /home/cxx579/anaconda3/envs/test_env/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-l6lnk30h/catch22_0230d4e651e84fa1a6f4489c53513e34/setup.py'"'"'; __file__='"'"'/tmp/pip-install-l6lnk30h/catch22_0230d4e651e84fa1a6f4489c53513e34/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-g8rdn06o/install-record.txt --single-version-externally-managed --compile --install-headers /home/cxx579/anaconda3/envs/test_env/include/python3.9/catch22 Check the logs for full command output.
    

    I am not an expert in C and how it tights up with the Python wrapper but it might. have to do with this? Any ideas how to fix this. Thx :D

    This issue can be fixed by

    export CFLAGS="-std=c99"
    

    However, it might be good to change the for loop instantiation that is more compatible by declaring the the iteration variable outside the loop like:

    int i;
    for (i=0;i<10;i++) { ..
    
    opened by angerhang 0
  • Segmentation Faults on Small Datasets

    Segmentation Faults on Small Datasets

    What Happened

    Get a segmentation fault when running catch22.catch22_all on short lists/numpy.arrays.

    What I Expected to Happen

    Returns a dictionary of features (perhaps with a lot of NaN-types due to the short timeseries)

    Minimum Complete Verifable Example

    >>> import catch22
    >>> catch22.catch22_all([1,2])
    Segmentation fault
    

    Further Details

    Can check that this doesn't happen with longer arrays like so:

    import catch22
    timeseries = list(range(10))
    while timeseries:
        print(len(timeseries))
        catch22.catch22_all(timeseries)
        timeseries = timeseries[:-1]
    
    opened by evanharwin 4
  • Failed to install catch22

    Failed to install catch22

    I am using Sagemaker notebook and failed to install catch22 package.

    Tried to add 'extra_compile_args = ['-std=c99']' before running 'pip install catch22'. Getting the following error:

    C/SC_FluctAnal.c:12:5: note: use option -std=c99 or -std=gnu99 to compile your code

    opened by Sidshroff 0
  • wrap_Python setup.py should add

    wrap_Python setup.py should add "extra_compile_args = ['-std=c99']" (GCC 7.5.0)

    or else the compilation will fail in Linux. setup_P3.py :

    the c++ extension module
    extension_mod = Extension("catch22_C",
            sources=["catch22_wrap_P3.c"] + sourceFileList,
            include_dirs=[sourceDir],
            extra_compile_args = ['-std=c99']
    )
    

    Python:

        active environment : base
        active env location : /opt/anaconda
                shell level : 1
           user config file : /root/.condarc
     populated config files : /root/.condarc
              conda version : 4.9.2
        conda-build version : 3.17.8
             python version : 3.7.3.final.0
           virtual packages : __glibc=2.27=0
                              __unix=0=0
                              __archspec=1=x86_64
    

    gcc

    Using built-in specs.
    COLLECT_GCC=gcc
    COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/7/lto-wrapper
    OFFLOAD_TARGET_NAMES=nvptx-none
    OFFLOAD_TARGET_DEFAULT=1
    Target: x86_64-linux-gnu
    Configured with: ../src/configure -v --with-pkgversion='Ubuntu 7.5.0-3ubuntu1~18.04' --with-bugurl=file:///usr/share/doc/gcc-7/README.Bugs --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++ --prefix=/usr --with-gcc-major-version-only --program-suffix=-7 --program-prefix=x86_64-linux-gnu- --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --enable-bootstrap --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-libmpx --enable-plugin --enable-default-pie --with-system-zlib --with-target-system-zlib --enable-objc-gc=auto --enable-multiarch --disable-werror --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-offload-targets=nvptx-none --without-cuda-driver --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu
    Thread model: posix
    gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)
    
    opened by xiaoluffy 0
  • Failed to install

    Failed to install

    Hi, I failed to install it in Python, would you mind checking out what is the problem? And how to use catch22? Is there a GUI or maybe a handbook for detailed method? image

    opened by IrenXu 7
Releases(v0.4.0)
  • v0.4.0(Jun 21, 2022)

  • v0.3.1(Jun 9, 2022)

    Adds a catch24 option to compute Mean (DN_Mean) and Standard Deviation (DN_Spread_Std) as features in addition to the standard catch22. Deletes R wrapper from repository as native R implementation Rcatch22 is on CRAN and requires no manual compilation. Interpolates the CO_f1ecac feature to now return a double instead of an integer for more nuanced performance in feature-based time-series analysis applications, such as time-series classification.

    Source code(tar.gz)
    Source code(zip)
  • v0.2.1(Jan 11, 2021)

  • v0.2.0(Jan 10, 2021)

  • v0.1.0(Jun 21, 2020)

Owner
Carl H Lubba
Carl H Lubba
Code and data for ImageCoDe, a contextual vison-and-language benchmark

ImageCoDe This repository contains code and data for ImageCoDe: Image Retrieval from Contextual Descriptions. Data All collected descriptions for the

McGill NLP 27 Dec 02, 2022
Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

MASTER-PyTorch PyTorch reimplementation of "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021). This projec

Wenwen Yu 255 Dec 29, 2022
Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

Conditional Generation Using Polynomial Expansions Official implementation of the conditional image generation experiments as described on the NeurIPS

Grigoris 4 Aug 07, 2022
Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)

Face-Detection-with-MTCNN Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to sol

Chetan Hirapara 3 Oct 07, 2022
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

1 Dec 14, 2021
CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

CLIP (Contrastive Language–Image Pre-training) Experiments (Evaluation) Model Dataset Acc (%) ViT-B/32 (Paper) CIFAR100 65.1 ViT-B/32 (Our) CIFAR100 6

Myeongjun Kim 52 Jan 07, 2023
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Shayne O'Brien 471 Dec 16, 2022
Machine Learning Framework for Operating Systems - Brings ML to Linux kernel

KML: A Machine Learning Framework for Operating Systems & Storage Systems Storage systems and their OS components are designed to accommodate a wide v

File systems and Storage Lab (FSL) 186 Nov 24, 2022
Rust bindings for the C++ api of PyTorch.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

Laurent Mazare 2.3k Dec 30, 2022
Ensembling Off-the-shelf Models for GAN Training

Data-Efficient GANs with DiffAugment project | paper | datasets | video | slides Generated using only 100 images of Obama, grumpy cats, pandas, the Br

MIT HAN Lab 1.2k Dec 26, 2022
Dataset Condensation with Contrastive Signals

Dataset Condensation with Contrastive Signals This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC). T

3 May 19, 2022
Generalized Data Weighting via Class-level Gradient Manipulation

Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas

18 Nov 12, 2022
NeurIPS 2021 Datasets and Benchmarks Track

AP-10K: A Benchmark for Animal Pose Estimation in the Wild Introduction | Updates | Overview | Download | Training Code | Key Questions | License Intr

AP-10K 82 Dec 11, 2022
PyTorch implementation of SimSiam: Exploring Simple Siamese Representation Learning

SimSiam: Exploring Simple Siamese Representation Learning This is a PyTorch implementation of the SimSiam paper: @Article{chen2020simsiam, author =

Facebook Research 834 Dec 30, 2022
deep-prae

Deep Probabilistic Accelerated Evaluation (Deep-PrAE) Our work presents an efficient rare event simulation methodology for black box autonomy using Im

Safe AI Lab 4 Apr 17, 2021
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi

Arthur Juliani 76 Jan 07, 2023
[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach This is the repo to host the dataset TextSeg and code for TexRNe

SHI Lab 174 Dec 19, 2022
Alphabetical Letter Recognition

BayeesNetworks-Image-Classification Alphabetical Letter Recognition In these demo we are using "Bayees Networks" Our database is composed by Learning

Mohammed Firass 4 Nov 30, 2021
Code for Paper "Evidential Softmax for Sparse MultimodalDistributions in Deep Generative Models"

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset

Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v

Meta Research 118 Jan 07, 2023