Linear algebra (numpy.linalg) — NumPy v2.0 Manual
Linear algebra (numpy.linalg)# The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient low level implementations of standard linear algebra algorithms. Those libraries may be provided by NumPy itself using C versions of a subset of their reference implementations but, when possible, highly optimized libraries that take advantage of specialized processor functionality are pr
numpy.random.randint — NumPy v1.16 Manual
numpy.random.randint¶ numpy.random.randint(low, high=None, size=None, dtype='l')¶ Return random integers from low (inclusive) to high (exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low). low : int Lowest (signed) integer to be drawn from the distrib
numpy.nan_to_num — NumPy v2.0 Manual
numpy.nan_to_num# numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)[source]# Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords. If x is inexact, NaN is replaced by zero or by the user defined value in nan keyword, infinity is replaced by the largest finite floating po
Overview — NumPy v1.8 Manual
Version: 2.0 Download documentation: Historical versions of documentation Useful links: Installation | Source Repository | Issue Tracker | Q&A Support | Mailing List NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for
numpy.matrix — NumPy v2.0 Manual
numpy.matrix# class numpy.matrix(data, dtype=None, copy=True)[source]# Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power). Note It is no longer recommended to use this class, even for linear algebra. Instead
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