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Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
MATLAB has many toolboxes specifically for the development of AI including the Statistics and Machine Learning Toolbox and Deep Learning Toolbox. These toolboxes provide APIs for the high-level and low-level implementation and use of many types of machine learning models that can integrate with the rest of the MATLAB ecosystem.
Within the Eiffel software development method and language, the terms argument and parameter have distinct uses established by convention. The term argument is used exclusively in reference to a routine's inputs, [7] and the term parameter is used exclusively in type parameterization for generic classes. [8] Consider the following routine ...
GNU Octave is a scientific programming language for scientific computing and numerical computation.Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Mojo def functions use value semantics by default (functions receive a copy of all arguments and any modifications are not visible outside the function), while Python functions use reference semantics (functions receive a reference on their arguments and any modification of a mutable argument inside the function is visible outside). [35]
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...