curve-fitting

问题 I am trying to fit some data that I have using scipy.optimize.curve_fit. My fit function is: def fitfun(x, a): return np.exp(a*(x - b)) What i want is to define a as the fitting parameter, and b as a parameter that changes depending on the data I want to fit. This means that for one set of data I would want to fit the function: np.exp(a*(x - 10)) while for another set I would like to fit the function np.exp(a*(x - 20)) . In principle, I would like the parameter b to be passed in as any value.

问题 I am new to python and was trying to fit dataset distribution using the following code. The actual data is a list that contains two columns- predicted market price and actual market price. And I was trying to use scipy.curve_fit() but it gave me many lines plotted at the same place. Any help is appreciated. # import the necessary modules and define a func. from scipy.optimize import curve_fit def func(x, a, b, c): return a * x** b + c # my data pred_data = [3.0,1.0,1.0,7.0,6.0,1.0,7.0,4.0,9.0

问题 As start situation, I have an xy-chart with some values on it whose progression resemble an exponential function. I need to write a code that draws a fitting curve on the chart, but I have to use a particular function which is not exponential (because I need to get some coefficients from it). One of the functions i need to use is K(C-x)²/(1+x) whereby k and C are the parameters I need.(They are two and it makes it a lot more complicated) Obviously you can't find this kind of structure on the

问题 Suppose I have a set of univariate data held in the array errors . I would like to fit a PDF to my observed data distribution. My PDF is defined in a function poissvmwalkpdf , whose definition line looks like this: function p = poissvmwalkpdf(theta, mu, kappa, xi) Here, theta is the error (the variable for which values in errors are instances), and mu , kappa , and xi are parameters of the PDF for which I want to find the best fit using maximum-likelihood estimation. This function returns the

问题 I have data sets that I would like to fit to two equations: y1 = a1 + a2 * T / 2 + a3 * T^2 / 3 + a4 * T^3 / 4 + a5 * T^4 / 5 + a6 / T y2 = a1 * lnT + a2 * T + a3 * T^2 / 2 + a4 * T^3 / 3 + a5 * T^4 / 4 + a7 The two polynomials share some parameters (a1 through a5) so I would like to fit these two equations simultaneously. I tried to do it with scipy.optimize.curve_fit: import numpy as np from scipy.optimize import curve_fit def func(T, a1, a2, a3, a4, a5, a6, a7): y1 = a1 + a2 * T / 2 + a3 *

问题 I have a bunch of data, and I want a fitting with a function that I want, for example, 1/(ax^2+bx+c) . My objective is to get a,b,c values. Is there any function of MATLAB that helps with this? I have been checking the fit() function, but I didn't reach a conclusion. Which is the best way? 回答1: The model you give can be solved using simple methods: % model function f = @(a,b,c,x) 1./(a*x.^2+b*x+c); % noise function noise = @(z) 0.005*randn(size(z)); % parameters to find a = +3; b = +4; c = -8

问题 I have a 2400 by 2400 array of data which looks something like this: data = [[-2.302670298082603040e-01 -2.304885241061924717e-01 -2.305029774024092148e-01 -2.304807100897505734e-01 -2.303702531336284665e-01 -2.307144352067780346e-01... [-2.302670298082603040e-01 -2.304885241061924717e-01 -2.305029774024092148e-01 -2.304807100897505734e-01 -2.303702531336284665e-01 -2.307144352067780346e-01... ... and I am trying to fit the following 2D Gaussian function: def Gauss2D(x, mux, muy, sigmax,

问题 I have some data which when plotted looks like the one shown in left on the picture attached. It has some kinks which I wish to delete and smooth-en to get a nice curve [shown on right]. Presently I manually delete the kinks and interpolate the deleted part by polynomial of high order [say 9]. Then I repopulate the deleted fragment and re-draw the curve. This takes a long time and I have quite a number of files to process. Could you folks suggest an efficient way to do this ? [in MATLAB or

问题 I wrote some code with scipy.optimize curve_fit. It works perfectly on my computer: Windows 7 Home Premium with Service Pack 1, 64bit Dell Studio 1558 Intel Core i3 cpu M330@2.13GHz 2.13GHz, 3.86 GB of RAM Python 2.7.3 (default, Apr 10 2012, 23:24:47)[MSC v.1500 64 bit (AMD64)] IPython 0.13.1 Then I moved the script to another machine (COMP2): Microsoft Windows XP Professional Version 2002 Service Pack 3, AMD Athlon(tm) II X4 620 Processor 2.61 GHz, 3.25 GB of RAM, Physical Address Extension

问题 I am trying to perform global fitting with symfit package, following symfit documentation. import numpy as np import symfit as sf import matplotlib.pyplot as plt %matplotlib inline # for ipynb # Generate example data t = np.arange(0.0, 600.1, 30) k = 0.005 C1_0, C2_0 = 1.0, 2.0 C1 = C1_0 * np.exp(-k*t) C2 = C2_0 * np.exp(-k*t) # Construct model x_1, x_2, y_1, y_2 = sf.variables('x_1, x_2, y_1, y_2') kg = sf.Parameter(value=0.01, min=0.0, max=0.1) a_1, a_2 = sf.parameters('a_1, a_2')