CCMI | Classifier based information

 by   sudiptodip15 Python Version: Current License: No License

kandi X-RAY | CCMI Summary

kandi X-RAY | CCMI Summary

CCMI is a Python library. CCMI has no bugs, it has no vulnerabilities and it has low support. However CCMI build file is not available. You can download it from GitHub.

Code for reproducing key results in the paper CCMI : Classifier based Conditional Mutual Information Estimation by Sudipto Mukherjee, Himanshu Asnani and Sreeram Kannan. If you use the code, please cite our paper. The code can be used for mutual information and conditional mutual information estimation; conditional independence testing.
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            kandi-support Support

              CCMI has a low active ecosystem.
              It has 16 star(s) with 4 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              CCMI has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of CCMI is current.

            kandi-Quality Quality

              CCMI has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              CCMI does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              CCMI releases are not available. You will need to build from source code and install.
              CCMI has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CCMI and discovered the below as its top functions. This is intended to give you an instant insight into CCMI implemented functionality, and help decide if they suit your requirements.
            • Estimate the CMI estimate
            • Split x y z z z coordinates
            • Generate bootstrap data
            • Simulate a CGAN model
            • Train the model
            • Wrapper for CCIT
            • Creates training data
            • Wrapper for conditional kNN
            • Bootstrap the model
            • Compute the KL divergence between points x and x
            • Compute the k - means distance between points x and y
            • Calculate the entropy of a set of points
            • Write samples to a file
            • Split x y z coordinates
            • Compute the entropy of a histogram
            • Generate conditional index
            • Calculate the CDM for a set of points
            • Generate conditional conditional index
            • Get the MINE estimator
            • Generate CMI data
            • Generate CMI data for a Gauss channel
            • Creates a post - non - linear
            • Generate the CMI data mean and noise
            • Generate CMI data variable C
            • Generate CMI data for a sparse dependency
            • Generate the cmi dependency network
            • Generate CMI data variable for a Gauss channel
            Get all kandi verified functions for this library.

            CCMI Key Features

            No Key Features are available at this moment for CCMI.

            CCMI Examples and Code Snippets

            Finds optimal binary search tree .
            pythondot img1Lines of Code : 67dot img1License : Permissive (MIT License)
            copy iconCopy
            def find_optimal_binary_search_tree(nodes):
                """
                This function calculates and prints the optimal binary search tree.
                The dynamic programming algorithm below runs in O(n^2) time.
                Implemented from CLRS (Introduction to Algorithms) book.
              
            Runs the algorithms algorithm .
            javadot img2Lines of Code : 8dot img2License : Permissive (MIT License)
            copy iconCopy
            private void doGraphFrameAlgorithms(GraphFrame graph) {
            
                    graph.pageRank().maxIter(20).resetProbability(0.15).run().vertices().show();
            
                    graph.connectedComponents().run().show();
            
                    graph.triangleCount().run().show();
                }  
            Returns a stream of all algorithms known to this algorithm .
            javadot img3Lines of Code : 3dot img3License : Permissive (MIT License)
            copy iconCopy
            public Stream getAllAlgorithms() {
                    return RandomGeneratorFactory.all().map(RandomGeneratorFactory::name);
                }  

            Community Discussions

            QUESTION

            Existing SSIS Packages Suddenly fail to Validate custom components XML
            Asked 2018-May-04 at 15:48

            We have been using and running SSIS packages for SQL Server 2014 with custom components that have been used since before I arrived at my work place. We upgraded to Visual Studio 2017, and have been fine doing so for months. Recently--maybe in line with a recent update--Visual Studio ssdt fails to load these packages because of errors regarding validating the xml behind these components. The packages continue to run on the server, and creating new packages with these custom components still works as expected. The packages also still open in Visual Studio 2013. We've gone back to earlier versions of the same package in code control. Reinstalled earlier versions of Visual Studio, installed the ssdt tools as a stand-alone, but nothing seems to work.

            There is a slew or errors regarding being unable to load values that are part of the definition of these components, and bad references to these in the precedence constraints because of the failure to load these components in the first place, like:

            Error loading CCMI Call Import.dtsx: Error loading value "@" from node "DTS:ForEachEnumerator". C:\Users\..Call Import.dtsx 1

            When I remove all references to the custom components in the precedence constraints, it will attempt to load the package without the "corrupted" pieces, but because these custom components are containers, in some cases this leaves out most of the work that's been done. I've removed some of these "bad" values it can't load on a lark, but it keeps erroring on the next attribute still associated with the container after I remove the one previously referenced in the error.

            I can't open the package, so I cant copy and paste the components into a new package.

            Anyone have any ideas?

            ...

            ANSWER

            Answered 2018-May-04 at 15:48

            I was able to fix some of the issues after some study of the XML of these custom components, and wanted to share in case it helped someone, although I doubt the exact same issues I came across would affect many other people.

            I wasn't getting any useful error messages for a custom foreach loop container, but when I tried to copy it from a working VS 2013 screen into VS 2017, I did finally get an error stating something like (lost the exact quote) "missing attribute for the server target version". I then found that the working examples of the custom component that worked had this node that was missing from the XML that was "broken":

            Source https://stackoverflow.com/questions/50107122

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

            Vulnerabilities

            No vulnerabilities reported

            Install CCMI

            You can download it from GitHub.
            You can use CCMI 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 .
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            gh repo clone sudiptodip15/CCMI

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            git@github.com:sudiptodip15/CCMI.git

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