Metadata-Version: 2.1
Name: nxcurve
Version: 0.6.2
Summary: draws RNS,QNX and BNX curves and their auc
Home-page: https://github.com/tardigrade33/nxcurve
Author: Nicolas Marin
Author-email: josue.marin1729@gmail.com
License: MIT
Description: # nxcurve
        
        Dimensionality reduction ( DR ) is a data transformation process which provides a low-dimensional ( attribute or variable ) representation of high dimensional data sets. This with the following purposes: noise reduction, storage space reduction, data visualization, efficient data processing and to concentrate the important information in fewer variables than the original set. A performance visual measure in DM is topology preservation. [Quality curves RNX], proposed by Lee and Verleysen, evaluates performance generating a graphical representation of topology preservation.  Nowadays there is a tool for topology conservation evaluation of DM algorithms, developed also by Lee and Verleysen (2009) but this tool is implemented only in Matlab. Here a problem arises because Matlab is limited and cannot be implemented in other technologies. here, we are going to implement, in python, a software evaluation module of curves RNX in order to be used in other technologies.
        
        ## Installation
        
        Use the package manager [pip] to install nxcurve.
        
        ```bash
        pip install nxcurve
        ```
        
        ## Usage
        
        ```python
        from sklearn import manifold, datasets  # datasets
        from nxcurve import quality_curve
        
        n_comp = 2        # number of components to be reduced
        n_nei = 20        # nearest neighbors
        nsamples = 2000   # number of points (samples)
        
        # Creating manifold 
        X, color = datasets.make_swiss_roll(n_samples=nsamples)
        
        # Performing dimensionality reduction
        X_r, err = manifold.locally_linear_embedding(X, n_neighbors=n_nei, n_components=n_comp)
        
        # Drawing RNX curve
        quality_curve(X,X_r,n_nei,'r',True)
        
        """
            input: X original data, X_r reduced data, n_neighbors, option, graph
            output: _NX vector, area under the curve and plot if graph == True
            Any character in the following list generates a new figure: (opt)
            q: Q_NX(K)
            b: N_NX(K)
            r: R_NX(K)
        """
        
        ```
        
        ## Features
          - RNX curve and area under the curve
          - QNX curve and area under the curve
          - BNX curve and area under the curve
        
        ### Development
        - Grahp for the coranking matrix
        - LCMC from a coranking matrix (local continuity meta criterion)
        - Error Handling
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        ## License
        ----
        
        [MIT](https://choosealicense.com/licenses/mit/)
        
        [Quality curves RNX]: <https://www.sciencedirect.com/science/article/abs/pii/S0925231213001471?via%3Dihub>
        [pip]: <https://pypi.org/project/nxcurve/>
        
Keywords: RNX,qualitycurve,QNX,BNX
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta 
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
