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MATLAB (an abbreviation of "MATrix LABoratory" [22]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
The earliest reference to a similar formula appears to be Armstrong (1985, p. 348), where it is called "adjusted MAPE" and is defined without the absolute values in the denominator.
The company sold its first order, 10 copies of MATLAB, for $500 to the Massachusetts Institute of Technology (MIT) in February 1985. [8] A few years later, Little and the company moved to Massachusetts. [6] [9] There, Little hired Jeanne O'Keefe, an experienced computer executive, to help formalize the business. [3]
MATLAB also provides a fitglme function to fit generalized linear mixed model models. The Python Statsmodels package supports binomial and poisson implementations. [14] The Julia package MixedModels.jl provides a function called GeneralizedLinearMixedModel that fits a generalized linear mixed model to provided data. [15]
The high-speed test for rejecting non-corner points is operated by examining 4 example pixels, namely pixel 1, 9, 5 and 13. Because there should be at least 12 contiguous pixels that are whether all brighter or darker than the candidate corner, so there should be at least 3 pixels out of these 4 example pixels that are all brighter or darker than the candidate corner.
The Anderson–Darling test assesses whether a sample comes from a specified distribution. It makes use of the fact that, when given a hypothesized underlying distribution and assuming the data does arise from this distribution, the cumulative distribution function (CDF) of the data can be transformed to what should follow a uniform distribution.
The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these.
def ternary_search (f, left, right, absolute_precision)-> float: """Find maximum of unimodal function f() within [left, right]. To find the minimum, reverse the if/else statement or reverse the comparison. """ while abs (right-left) >= absolute_precision: left_third = left + (right-left) / 3 right_third = right-(right-left) / 3 if f (left_third) < f (right_third): left = left_third else: right ...