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beta-divergence-metrics-0.0.2


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توضیحات

NumPy and PyTorch implementations of the beta-divergence loss.
ویژگی مقدار
سیستم عامل -
نام فایل beta-divergence-metrics-0.0.2
نام beta-divergence-metrics
نسخه کتابخانه 0.0.2
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Billy Carson
ایمیل نویسنده williamcarsoniv@gmail.com
آدرس صفحه اصلی https://github.com/wecarsoniv/beta-divergence-metrics
آدرس اینترنتی https://pypi.org/project/beta-divergence-metrics/
مجوز -
# Beta-Divergence Loss Implementations This repository contains code for Python implementations of the beta-divergence loss, including implementations compatible [NumPy](https://numpy.org/) and [PyTorch](https://pytorch.org/). ## Dependencies This library is written in Python, and requires Python (with recommended version >= 3.9) to run. In addition to a working PyTorch installation, this library relies on the following libraries and recommended version numbers: * [Python](https://www.python.org/) >= 3.9 * [NumPy](https://numpy.org/) >= 1.22.0 * [SciPy](https://www.scipy.org/) >= 1.7.3 ## Installation To install the latest stable release, use [pip](https://pip.pypa.io/en/stable/). Use the following command to install: $ pip install beta-divergence-metrics ## Usage The [`numpybd.loss`](https://github.com/wecarsoniv/beta-divergence-metrics/blob/main/src/numpybd/loss.py) module contains two beta-divergence function implementations compatible with NumPy and NumPy arrays: one general beta-divergence between two arrays, and a beta-divergence implementation specific to non-negative matrix factorization (NMF). Similarly [`torchbd.loss`](https://github.com/wecarsoniv/beta-divergence-metrics/blob/main/src/torchbd/loss.py) module contains two beta-divergence class implementations compatible with PyTorch and [PyTorch tensors](https://pytorch.org/tutorials/beginner/introyt/tensors_deeper_tutorial.html). Beta-divergence implementations can be imported as follows: ```python # Import beta-divergence loss implementations from numpybd.loss import * from torchbd.loss import * ``` ### Beta-divergence between two NumPy arrays To calculate the beta-divergence between a NumPy array `a` and a target or reference array `b`, use the `beta_div` loss function. The `beta_div` loss function can be used as follows: ```python # Calculate beta-divergence loss between array a and target array b loss_val = beta_div(beta=0, reduction='mean') ``` ### Beta-divergence between two PyTorch tensors To calculate the beta-divergence between tensor `a` and a target or reference tensor `b`, use the `BetaDivLoss` loss function. The `BetaDivLoss` loss function can be instantiated and used as follows: ```python # Instantiate beta-divergence loss object loss_func = BetaDivLoss(beta=0, reduction='mean') # Calculate beta-divergence loss between tensor a and target tensor b loss_val = loss_func(input=a, target=b) ``` ### NMF beta-divergence between NumPy array of data and data reconstruction To calculate the NMF-specific beta-divergence between a NumPy array of data matrix `X` and the product of a scores matrix `H` and a components matrix `W`, use the `nmf_beta_div` loss function. The `nmf_beta_div` loss function can beused as follows: ```python # Calculate beta-divergence loss between data matrix X (target or # reference matrix) and matrix product of H and W loss_val = nmf_beta_div(X=X, H=H, W=W, beta=0, reduction='mean') ``` ### NMF beta-divergence between PyTorch tensor of data and data reconstruction To calculate the NMF-specific beta-divergence between a PyTorch tensor of data matrix `X` and the matrix product of a scores matrix `H` and a components matrix `W`, use the `NMFBetaDivLoss` loss class function. The `NMFBetaDivLoss` loss function can be instantiated and used as follows: ```python # Instantiate NMF beta-divergence loss object loss_func = NMFBetaDivLoss(beta=0, reduction='mean') # Calculate beta-divergence loss between data matrix X (target or # reference matrix) and matrix product of H and W loss_val = loss_func(X=X, H=H, W=W) ``` ### Choosing beta value When instantiating beta-divergence loss objects, the value of beta should be chosen depending on data type and application. For NMF applications, a beta value of 0 (Itakura-Saito divergence) is recommemded. Integer values of beta correspond to the following divergences and loss functions: * beta = 0: [Itakura-Saito divergence](https://en.wikipedia.org/wiki/Itakura-Saito_distance) * beta = 1: [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler_divergence) * beta = 2: [mean-squared error](https://en.wikipedia.org/wiki/Mean_squared_error) ## Issue Tracking and Reports Please use the [GitHub issue tracker](https://github.com/wecarsoniv/beta-divergence-metrics/issues) associated with this repository for issue tracking, filing bug reports, and asking general questions about the package or project.


نحوه نصب


نصب پکیج whl beta-divergence-metrics-0.0.2:

    pip install beta-divergence-metrics-0.0.2.whl


نصب پکیج tar.gz beta-divergence-metrics-0.0.2:

    pip install beta-divergence-metrics-0.0.2.tar.gz