How do I compute the derivative of an array, y (say), with respect to another array, x (say) - both arrays from a certain experiment? e.g. y = [1,2,3,4,4,5,6] and x ...
How is the derivative of a f(x) typically calculated programmatically to ensure maximum accuracy? I am implementing the Newton-Raphson method, and it requires taking of the derivative of a function.
To calculate higher order derivatives should be done using truncated taylor series. You could also apply above mentioned class to itself -- the type for the value and derivative values should be a template argument. But this means calculation and storing of derivatives more than once.
ValueError: The number of derivatives at boundaries does not match: expected 2, got 0+0 while trying to use cubic interpolation in pandas on a 2d matrix.
I'm interested in computing partial derivatives in Python. I've seen functions which compute derivatives for single variable functions, but not others. It would be great to find something that did...
Eigen::MatrixXd derivatives(2, 1); derivatives.setZero(); // Derivatives zero for this example. Since these are parametric splines, you have to provide derivatives for both x and y, for each of the two points you have chosen, so the correct size is:
I'm trying to take a second derivative in python with two numpy arrays of data. For example, the arrays in question look like this: import numpy as np x = np.array([ 120. , 121.5, 122. , 12...
I am trying to use the package Deriv, to compute symbolic derivatives of a function depending on one or two variables and a vector of parameters. However, i always obtain the error:
