Metadata-Version: 2.1
Name: dspca
Version: 1.0.2
Summary: Demixed subspace principal component analysis (dsPCA)
Home-page: http://github.com/ryhattori/dspca
Author: Ryoma Hattori
Author-email: rhattori0204@gmail.com
License: MIT License
Description: Demixed Subspace Principal Component Analysis (dsPCA)
        ===========================================
        
        dsPCA is a dimensionality reduction algorithm for high-dimensional data such as neural population activity. dsPCA describes high-dimensional data by finding best demixed linear coding axes for targeted variables and finding linear dimensions that concicely describe the remaining variance in the data. The demixed coding axes for targeted variables are identified with a supervised approach, and then the axes that capture the remaining variance in the data are identified with an unsupervised approach. Importantly, dsPCA completely removes the linear coding of target signals from data before the unsupervised axis identifications. Therefore, dsPCA decomposes a high-dimensional data into a subspace that fully captures all target signals in the data and a subspace that is free of all target signals.
        
        
        ## Installation
        Run the following command to install the package.
        ~~~~
        pip install dspca
        ~~~~
        
        ## How to use
        dsPCA code is written for Python 3.x    
        Please check an example demo in **dsPCA_demo.ipynb** for the basic implementation.
        
        After installation, import dsPCA
        ~~~~
        from dspca.dspca import dsPCA
        ~~~~
        Provide data ([# of observations] X [# of dimensions to be reduced]) and targets ([# of observations] X [# of targets]) 
        ~~~~
        dsPCA(data=data, targets=targets)
        ~~~~
        The outputs of the function are in the order of
        ~~~~
        projection_target_subspace
        projection_targetfree_subspace
        ax_targets, ax_targetfree
        target_subspace_signal
        targetfree_subspace_signal
        target_subspace_var
        targetfree_subspace_var
        total_var
        dot_target_ax
        ~~~~
        projection_target_subspace
        - Projection of data onto demixed target signal axes.
        
        projection_targetfree_subspace
        - Projection of population activity to the axes of target-free subspace.
        
        ax_targets
        - Axis vectors for the target signal subspace.
        
        ax_targetfree
        - Axis vectors for the target-free signal subspace.
        
        target_subspace_signal
        - Pearson correlation coefficient between the projected data along each target axis and the targeted task-related variables.
        
        targetfree_subspace_signal
        - Pearson correlation coefficient between the projected data along each target-free axis and the targeted task-related variables.
        
        target_subspace_var
        - Data variance along each taraget axis.
        
        targetfree_subspace_var
        - Data variance along each taraget-free axis.
        
        total_var
        - Total data variance of the original input data.
        
        dot_target_ax
        - Matrix with dot products between pairs of target axis vectors.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
