I am experimenting with gradient descent and want to plot a contour of the gradient given independent variables x and y.
The optimization objective is to estimate a point given only a list of points and the distances to each of those points. I have a list of vectors of form [(x_1, y_1, d_1), ..., (x_n, y_n, d_n)] where d_i is the measured distance from the point to be estimated to the point (x_i, y_i), and I have a function g(x, y) that returns the gradient at the point (x, y). (The function g(x, y) uses the training vectors to calculate the gradient.)
The gradient descent algorithm works fine and arrives at a close estimate to the actual point coordinates. I want now to visualize the gradient as a contour map. I have the following for x and y values:
xlist = np.linspace(min([v[0] for v in vectors])-1, max([v[0] for v in vectors])+1, 100)
ylist = np.linspace(min([v[1] for v in vectors])-1, max([v[1] for v in vectors])+1, 100)
X, Y = np.meshgrid(xlist, ylist)
But now I need a Z value that maps each pair of coordinates in the grid mesh to g(x, y), and it needs to be the correct shape for the matplotlib contour plot. The examples I have seen have been useless because they all simply multiplied the x and y arrays to generate z values (which obviously will not work in this case), and all the tips, tricks, and SO answers I have encountered ultimately did not help.
How do I use my custom function g(x, y) to create the 2D Z array necessary for constructing a valid contour plot?
