curve-fitting

Anyone here that could help me with the following problem? The following code calculates the best polynomial fit to a given data-set, that is; a polynomial of a specified degree. Unfortunately, whatever the data-set may be, usually at degree 6 or higher, MATLAB gets a totally wrong fit. Usually the fit curves totally away from the data in a sort of exponantial-looking-manner downwards. (see the example: degree = 8). x=[1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5] % experimental x-values y=[4.3 6.2 10.1 13.5 19.8 22.6 24.7 29.2] % experimental y-values degree=8; % specify the degree A = zeros(length(x)

I need to fit 10 data points ( x,y ) into this equation: ay² + bxy + cx + dy + e = x² It's told that this is a ellipse-like equation. I can't do it with usual curve fitting tools because it is not really a function (one x corresponds to 2 y s). I can't either use ellipse curve fitting because there is no c*x and d*y in an ellipse equation. Any ideas? Thanks in advance. EDIT: Both Oil and AK4749 gave right answer! Thank you guys! It is a linear system with variables [a b c d e]. You can use \ to solve it: x=rand(10,1); y=rand(10,1); [y.^2,x.*y,x,y,ones(numel(x),1)]\x.^2 ans = -0.4437 %% a 1

I use scipy.odr in order to make a fit with uncertainties on both x and y following this question Correct fitting with scipy curve_fit including errors in x? After the fit I would like to compute the uncertainties on the parameters. Thus I look at the square root of the diagonal elements of the covariance matrix. I get : >>> print(np.sqrt(np.diag(output.cov_beta))) [ 0.17516591 0.33020487 0.27856021] But in the Output there is also output.sd_beta which is, according to the doc on odr Standard errors of the estimated parameters, of shape (p,). But, it does not give me the same results : >>>

I'm trying to fit some data and stuff, I know there is a simple command to do this with python/numpy/matplotlib, but I can't find it. I think it is something like popt,popc = numpy.curvefit(f,x) where popt is the paramters of f , popc is the fit quality and f is a predefined function of f. Does any of you know it? Take a look at scipy.optimize.curve_fit : scipy.optimize.curve_fit(f, xdata, ydata, p0=None, sigma=None, **kw) Use non-linear least squares to fit a function, f, to data. Found it . Curve_fit from optimize in scipy 来源： https://stackoverflow.com/questions/8280871/curve-fitting-with

I'm trying to compute the best fit of two forms of an exponential to some x, y data (the data file can be downloaded from here ) Here's the code: from scipy.optimize import curve_fit import numpy as np # Get x,y data data = np.loadtxt('data.txt', unpack=True) xdata, ydata = data[0], data[1] # Define first exponential function def func(x, a, b, c): return a * np.exp(b * x) + c # Get parameters estimate popt, pcov = curve_fit(func, xdata, ydata) print popt # Define second exponential function (one more parameter) def func2(x, a, b, c, d): return a * np.exp(b * x + c) + d # Get parameters

I have a range of particle size distribution data arranged by percentage volume fraction, like so:; size % 6.68 0.05 9.92 1.15 etc. I need to fit this data to a lognormal distribution, which I planned to do using python's stats.lognorm.fit function, but this seems to expect the input as an array of variates rather than binned data, judging by what I've read . I was planning to use a for loop to iterate through the data and .extend each size entry to a placeholder array the required number of times to create an array with a list of variates that corresponds to the binned data. This seems really

I have the following dose response data and wish to plot dose response model and global fit curve. [xdata = drug concentration; ydata(0-5) = response values at different concentrations of the drug]. I plotted the Std Curve without an issue. Std Curve Data fit: df <- data.frame(xdata = c(1000.00,300.00,100.00,30.00,10.00,3.00,1.00,0.30, 0.10,0.03,0.01,0.00), ydata = c(91.8,95.3,100,123,203,620,1210,1520,1510,1520,1590, 1620)) nls.fit <- nls(ydata ~ (ymax*xdata / (ec50 + xdata)) + Ns*xdata + ymin, data=df, start=list(ymax=1624.75, ymin = 91.85, ec50 = 3, Ns = 0.2045514)) Dose Response Curve Data