The NumPy mathematical library can be used by any software developer (at any experience level) seeking to integrate complex numerical computing functions into their Python codebase. NumPy is also routinely used in many different data science, machine learning (ML) and scientific Python software packag...
Hash values are computed with hash functions. Python hashableAn object is hashable if it has a hash value which never changes during its lifetime. (It can have different values during multiple invocations of Python programs.) A hashable object needs a __hash__ method. In order to perform ...
The external more_itertools module contains plenty of functions for working on iterables. Many of them accept predicates as arguments. simple.py #!/usr/bin/python from more_itertools import locate def pfn(n): return n > 0 and n % 2 == 0 vals = [-3, 2, 7, 9, -1, 0, 2, 3,...
At the most basic level, interfaces are more flexible because they are objects. ABCs aren't objects, at least in the message-passing smalltalk sense; they are a collection of top-level functions and some rules about how those functions apply to types. If you want to change the answer tois...
we moved from using two function (shapr()andexplain()) to a single function (explain()). In addition, custom models are now explained by passing the prediction function directly toexplain(), quite a few input arguments got new names, and a few functions for edge cases was removed to simp...
Furthermore, the theoretical uncertainties are approximated as Gaussian and obtained by randomly sampling the observables with the input parameters θ distributed according to their probability density functions. With eq. (46), the Δχ2 function, which depends on the Wilson coefficients C, can be ...
inherent concepts and explaining parameters play their roles properly and give validity to the method of choosing representatives, and (iii) experimentally showing that the choice of activation functions is important when training the SENN. This is a preview of subscription content,log in via an ...
scripts: the source codes and utility functions written fully in Python. Setup: git clone https://github.com/maleakhiw/explaining-dataset-shifts.gitcdexplaining-dataset-shifts pip install -r requirements.txt Getting Started Source code to apply shifts, build dimensionality reductors, conduct statistic...
,xt−1j; εti are additive innovation terms; and gi(⋅) are potentially nonlinear functions, specifying how the future values of variable xi depend on the past values of 𝐱. We then say that variable xj does not Granger-cause variable xi, denoted as xj→xi, if and only...
The Scikit-learn package in Python is used to perform and test K-means clustering and obtain the class labels for each sample. Then, this study employs statistical analysis to conduct further self-explaining analysis of facility environments across different typical categories, focusing on analyzing ...