curve-fitting

I'm trying to understand OpenCV fitLine() algorithm. This is fragment of code from OpenCV: icvFitLine2D function - icvFitLine2D I see that there is some random function that chooses points for approximation, then computes distances from points to fitted line (with choosen points), then choose other points and tries to minimize distance with choosen distType . Can someone clarify what happens from this moment without hard mathematics and assuming no great statistic knowledge?. OpenCV code comments and variable names does not help me in understanding this code. HugoRune (This is an old question,

I currently have a MATLAB program which takes RGB images of traced spiral arms from galaxies and selects the biggest arm component and plots only that. I have tried using matlab's built in curve fitting tool with smoothing spline to fit it and I get the following result: I have tried using interp1 with parametric fitting to only get bad results. Is there a way to fit this type of curve at all? Spektre Your fail is due to that you handle your 2D curve as function which is not the case (you got more y values for the same x ) and that is why the fit fails on the right side (when you hit the non

I'm a developer up in Portland, OR. I'm wondering if anyone can assist: I'm working on Loess fit models using R, once I have the fit accomplished, I'm looking to back-out the equation of the fitted non-linear curve, wondering if there is a way to determine this equation in R? I've been looking but can't find any literature. For me, the graph of the function is great, but without the equation of the graph, I'm kinda dead in the water. Loess doesn't give you an equation [1]. If you just want to get the values returned by the loess function you use predict(loess.object, new.data) [1] From

Here is my problem: polyfit does not take datetime values, so that I converted datetime with mktime producing the polynomial fit works z4 = polyfit(d, y, 3) p4 = poly1d(z4) For the plot however, I would like the datetime description on the axis and didn't # figure out how to do that. Can you help me? fig = plt.figure(1) cx= fig.add_subplot(111) xx = linspace(0, d[3], 100) pylab.plot(d, y, '+', xx, p4(xx),'-g') cx.plot(d, y,'+', color= 'b', label='blub') plt.errorbar(d, y, yerr, marker='.', color='k', ecolor='b', markerfacecolor='b', label="series 1", capsize=0, linestyle='') cx.grid() cx.set

I'd like to be able to perform fits that allows me to fit an arbitrary curve function to data, and allows me to set arbitrary bounds on parameters, for example I want to fit function: f(x) = a1(x-a2)^a3\cdot\exp(-\a4*x^a5) and say: a2 is in following range: (-1, 1) a3 and a5 are positive There is nice scipy curve_fit function, but it doesn't allow to specify parameter bounds. There also is nice http://code.google.com/p/pyminuit/ library that does generic minimalization, and it allows to set bounds on parameters, but in my case it did not coverge. Note: New in version 0.17 of SciPy Let's

Suppose I have a set of x,y coordinates that mark points along contour. Is there a way that I can build a spline representation of the contour that I can evaluate at a particular position along its length and recover interpolated x,y coordinates? It is often not the case that there is a 1:1 correspondence between X and Y values, so univariate splines are no good to me. Bivariate splines would be fine, but as far as I can tell all of the functions for evaluating bivariate splines in scipy.interpolate take x,y values and return z, whereas I need to give z and return x,y (since x,y are points on

I have two NumPy arrays x and y . When I try to fit my data using exponential function and curve_fit (SciPy) with this simple code #!/usr/bin/env python from pylab import * from scipy.optimize import curve_fit x = np.array([399.75, 989.25, 1578.75, 2168.25, 2757.75, 3347.25, 3936.75, 4526.25, 5115.75, 5705.25]) y = np.array([109,62,39,13,10,4,2,0,1,2]) def func(x, a, b, c, d): return a*np.exp(b-c*x)+d popt, pcov = curve_fit(func, x, y) I get wrong coefficients popt [a,b,c,d] = [1., 1., 1., 24.19999988] What is the problem? First comment: since a*exp(b - c*x) = (a*exp(b))*exp(-c*x) = A*exp(-c*x