iminuit | Jupyter-friendly Python interface for C++ MINUIT2 | Machine Learning library

 by   scikit-hep Python Version: 2.26.0 License: Non-SPDX

kandi X-RAY | iminuit Summary

kandi X-RAY | iminuit Summary

iminuit is a Python library typically used in Artificial Intelligence, Machine Learning, Jupyter applications. iminuit has no bugs, it has no vulnerabilities, it has build file available and it has low support. However iminuit has a Non-SPDX License. You can install using 'pip install iminuit' or download it from GitHub, PyPI.

Jupyter-friendly Python interface for C++ MINUIT2
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              iminuit has a low active ecosystem.
              It has 236 star(s) with 67 fork(s). There are 15 watchers for this library.
              There were 1 major release(s) in the last 6 months.
              There are 10 open issues and 286 have been closed. On average issues are closed in 34 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of iminuit is 2.26.0

            kandi-Quality Quality

              iminuit has no bugs reported.

            kandi-Security Security

              iminuit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              iminuit has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              iminuit releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed iminuit and discovered the below as its top functions. This is intended to give you an instant insight into iminuit implemented functionality, and help decide if they suit your requirements.
            • Show a plot
            • Return a MnMachinePrecision instance
            • Estimate EDM goal
            • Generate a Minuit
            • Draw the minimization matrix
            • Return the error probability for a given class cl
            • Calculate the mn user profile
            • Calculate the MnContour
            • Convert to table
            • Visualize the model
            • Draw the mn profile
            • Expand a callable
            • Compute the Poisson density of a template
            • Private method to set parameter value
            • Compute the chi - squared chi squared
            • Calculate the poisson distribution
            • Wrapper function
            • Set the loss function
            • Evaluate the function
            • Calculate the chi - squared chi coefficient for a template
            • R Compute the Jacobian of a function
            • Create a Minuit Minuit
            • Draw a contour plot
            • Return a table as a table
            • Create a callable with the given replacement
            • Visualize the components
            Get all kandi verified functions for this library.

            iminuit Key Features

            No Key Features are available at this moment for iminuit.

            iminuit Examples and Code Snippets

            copy iconCopy
            cd .. # or cd anywhere outside your build folder
            picca_deltas.py
            
            Constraints on fitting parameters in iminuit?
            Pythondot img2Lines of Code : 10dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def chi( paras ):
                mpi = paras[ 0 : 32 ]
                s = paras[ 32 ]
                a = np.log( np.sum( np.array( mpi )**2 ) )
                cf = a - np.exp( -s )
                chif = 0
                for i in range( 32 ):
                    chif += ( ( fpi - f( mpi, cf ) ) / error )**2
                retur
            Iminuit fitting algorithm?
            Pythondot img3Lines of Code : 12dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            void MnStrategy::SetMediumStrategy() {
               // set minimum strategy (1) the default
               fStrategy = 1;
               SetGradientNCycles(3);
               SetGradientStepTolerance(0.3);
               SetGradientTolerance(0.05);
               SetHessianNCycles(5);
               SetHessianStepTolera

            Community Discussions

            QUESTION

            Constrain on parameters for Negative Log Likelihood Minimization
            Asked 2020-Jul-11 at 22:44

            I am trying to fit a 5 parameter (a, b, c, d, e) model, where one of the parameters is constrained by another, let's say,

            0< d < 1

            e < |d|

            I am currently using zfit which as far as I know, uses iMinuit

            I have only created the zfit.Parameters and put the limits such that the ranges accessible to them are valid, again, let's say:

            d = zfit.Parameter('d', value=0.5, lower_limit=0.3, upper_limit=1.0, step_size=0.01)

            e = zfit.Parameter('e', value=0.1, lower_limit=0.0, upper_limit=0.3, step_size=0.01)

            It has been working well so far, but I think it is not the right way to do it.

            So my question is, what is the correct way to deal with this kind of constraint?

            Cheers

            ...

            ANSWER

            Answered 2020-Jul-11 at 22:44

            I would use this limits with caution, as they block the variables, ideally, they should be far off the final value.

            There are two ways to achieve what you want:

            • either impose a constraint "mathematically" as a logical consequence, so define one parameter from another using a composed parameter (which is a function of other parameters). If possible, this should be the preferred way.
            • Another option is to impose this restrictions in the likelihood with an additional term. This, however, can have repercussions as you modify the likelihood. The minimizer will find a minimum, but this is maybe not the minimum you have looked for. What you can use are SimpleConstraints and add a penalty term to the likelihood if any of the above is violated (e.g. tf.cast(tf.greater(d, 1), tf.float64) * 100.). Maybe make also sure that minuit is run with use_minuit_grad.

            Source https://stackoverflow.com/questions/62855017

            QUESTION

            Adding constraints to iminuit fitter
            Asked 2017-May-12 at 02:54

            I am trying to add a constraint to a very complicated minimization problem I have but I am not sure how to implement it, even after reading the docs.

            I have a simple example that if answered will help me with my original problem. Here is the code:

            ...

            ANSWER

            Answered 2017-May-12 at 02:54

            Answer to my own question is don't bother using minuit. Use scipy.optimize with method SLSQP. It has equality and inequality constraint methods built in.

            Source https://stackoverflow.com/questions/43462911

            QUESTION

            Iminuit fitting algorithm?
            Asked 2017-Jan-11 at 17:44

            Does anyone knows what algorithm is used for the python iminuit fitting package, when no attribute is specified?

            ...

            ANSWER

            Answered 2017-Jan-11 at 17:44

            The documentation says it uses the Quasi Newton Method and DFP formula.

            You can see in the source code what the default strategy looks like:

            Source https://stackoverflow.com/questions/41597221

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

            Vulnerabilities

            No vulnerabilities reported

            Install iminuit

            You can install using 'pip install iminuit' or download it from GitHub, PyPI.
            You can use iminuit like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • PyPI

            pip install iminuit

          • CLONE
          • HTTPS

            https://github.com/scikit-hep/iminuit.git

          • CLI

            gh repo clone scikit-hep/iminuit

          • sshUrl

            git@github.com:scikit-hep/iminuit.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Consider Popular Machine Learning Libraries

            tensorflow

            by tensorflow

            youtube-dl

            by ytdl-org

            models

            by tensorflow

            pytorch

            by pytorch

            keras

            by keras-team

            Try Top Libraries by scikit-hep

            awkward

            by scikit-hepPython

            awkward-1.0

            by scikit-hepPython

            uproot3

            by scikit-hepPython

            uproot

            by scikit-hepPython

            pyhf

            by scikit-hepPython