wildboar | wildboar is a Python module for temporal machine learning | Machine Learning library

 by   isaksamsten Python Version: v1.0.12 License: LGPL-3.0

kandi X-RAY | wildboar Summary

kandi X-RAY | wildboar Summary

wildboar is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Numpy applications. wildboar has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.

wildboar is a Python module for temporal machine learning and fast distance computations built on top of scikit-learn and numpy distributed under the GNU Lesser General Public License Version 3. It is currently maintained by Isak Samsten.
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            kandi-support Support

              wildboar has a low active ecosystem.
              It has 15 star(s) with 3 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 17 have been closed. On average issues are closed in 123 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of wildboar is v1.0.12

            kandi-Quality Quality

              wildboar has 0 bugs and 0 code smells.

            kandi-Security Security

              wildboar has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              wildboar code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              wildboar is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
              Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

            kandi-Reuse Reuse

              wildboar releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 4197 lines of code, 247 functions and 58 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed wildboar and discovered the below as its top functions. This is intended to give you an instant insight into wildboar implemented functionality, and help decide if they suit your requirements.
            • Emulate emmotiers
            • Fit the embedding
            • Validate input data
            • Check the number of timesteps in X
            • Calculate motifs
            • Check whether x is an instance of target_type
            • Calculate a matrix of data
            • Compute the distance between x and y
            • Setup the wildboar
            • Splits the data into two outliers
            • Get the feature engine
            • Fit the model
            • Calculate the distance between x and y
            • Plot the model
            • Compute density of outliers
            • Gets a feature engine
            • Splits a set of points
            • Calculate k - means outlier outliers
            • Refresh the repo
            • R Weighted KL divergence
            • Compute the clustering
            • Create a feature engine
            • Calculate the distance between two sequences
            • Generate arc curves
            • Plot a frequency domain
            • Return a generator that yields all datasets in the specified repository
            • Plot the importances
            Get all kandi verified functions for this library.

            wildboar Key Features

            No Key Features are available at this moment for wildboar.

            wildboar Examples and Code Snippets

            wildboar,Usage
            Pythondot img1Lines of Code : 6dot img1License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
            from wildboar.ensemble import ShapeletForestClassifier
            from wildboar.datasets import load_two_lead_ecg
            x_train, x_test, y_train, y_test = load_two_lead_ecg(merge_train_test=False)
            c = ShapeletForestClassifier()
            c.fit(x_train, y_train)
            c.score(x_test,  
            Compilation
            Pythondot img2Lines of Code : 2dot img2License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
            python setup.py install
            
            pip install -r requirements.txt
              
            Binaries
            Pythondot img3Lines of Code : 1dot img3License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
            pip install wildboar
              

            Community Discussions

            QUESTION

            Add an additional staple to geom_col with value zero R
            Asked 2020-Oct-13 at 12:32

            I have a dataset of different species and different damage. As you can see in my data, I only have two different "damages" in my code, but in a matter of fact, I got three different "damages". I want to plot my data and for each Species (Wolf, Bear, and Wildboar). I want staples for damage1 (grazing), damage2(stomp), and damage3, etc. But I want the staple for the damage3 to be "zero". I want to show in some way that there were zero species in damage3.

            ...

            ANSWER

            Answered 2020-Oct-13 at 08:15

            Would adding text on top of bars with count work ?

            Source https://stackoverflow.com/questions/64331064

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install wildboar

            wildboar is available through pip and can be installed with:. Universal binaries are compiled for GNU/Linux and Python 3.7, 3.8 and 3.9.

            Support

            HTML documentation: https://isaksamsten.github.io/wildboar
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          • HTTPS

            https://github.com/isaksamsten/wildboar.git

          • CLI

            gh repo clone isaksamsten/wildboar

          • sshUrl

            git@github.com:isaksamsten/wildboar.git

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