Check the links on mathtools.net. The page for statistics libraries for C++ has links. Another page http://www.thefreecountry.com/sourcecode/mathematics.shtml lists few more. Have you checked the ‘R project’? I think you can call ‘R objects’ from C/C++.
SciPy has added an inverse Box-Cox transformation. https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.inv_boxcox.html scipy.special.inv_boxcox scipy.special.inv_boxcox(y, lmbda) = Compute the inverse of the Box-Cox transformation. Find x such that: y = (x**lmbda – 1) / lmbda if lmbda != 0 log(x) if lmbda == 0 Parameters: y : array_like Data to be transformed. lmbda : array_like Power parameter of the Box-Cox … Read more
Start with identifying the core needs that you think monitoring will solve. Try to answer the two questions “What do I want to know?” and “How do I want to act on that information?”. Examples of “What do I want to know?” Performance over time Largest API users Most commonly used API features Error occurrence … Read more
I know this is a bit late (almost one year!) but I’ve been doing some research on the lognorm function in scipy.stats. A lot of folks seem confused about the input parameters, so I hope to help these people out. The example above is almost correct, but I found it strange to set the mean … Read more
These functions calculate moments of the probability density distribution (that’s why it takes only one parameter) and doesn’t care about the “functional form” of the values. These are meant for “random datasets” (think of them as measures like mean, standard deviation, variance): import numpy as np from scipy.stats import kurtosis, skew x = np.random.normal(0, 2, … Read more
There is no reason r^2 shouldn’t be negative (despite the ^2 in its name). This is also stated in the doc. You can see r^2 as the comparison of your model fit (in the context of linear regression, e.g a model of order 1 (affine)) to a model of order 0 (just fitting a constant), … Read more