python-weibullfit

MLE 2-parameter-Weibull distribution fit using MLE with numpy or pytorch. Uses Newton-Raphson optimization.

Estimation accuracy

Using PyTorch:

Installation

For now, copy the weibull folder into your project directory to use it.

How-To

Simply import weibull does the job. Subsequently you can use weibull.fit(x) to fit a weibull distribution to your data.

import weibull automatically attempts to load a pytorch implementation to make use of efficient GPU-parallelization to decrease computation time. An alternative numpy implementation is automatically loaded if pytorch fails to load.

Good to know

weibull.fit accepts the following arguments:

• x 1-dimensional ndarray from an (unknown distribution)
• iters Maximum number of iterations
• eps Stopping criterion. Fit is stopped if change within two iterations is smaller than eps.
• use_cuda PyTorch version only. Enable or disable the GPU usage.

Each element x_i in x must satisfy: x_i > 0. Otherwise NaN is returned.