I have 37 linear equations and 36 variables in the form of a matrix equation; A*X=B . The equations don't have an exact answer. I want to use Matlab least square method to find the answers with the least error. I am new to Matlab so any comments will help....

I have a set of 3d data (300 points) that create a surface which looks like two cones or ellipsoids connected to each other. I want a way to find the equation of a best fit ellipsoid or cone to this dataset. The regression method is not important, the easier...

Say I want to fit two arrays x_data_one and y_data_one with an exponential function. In order to do that I might use the following code (in which x_data_one and y_data_one are given dummy definitions): import numpy as np from scipy.optimize import curve_fit def power_law(x, a, b, c): return a *...

I have a set of data, which on plotting x vs y, will give the plot as in this figure.I want to fit a parabola to this data and I've tried using the curve fitting tool in MATLAB. The only problem is that I'm getting an answer as shown here....

Here is my code: clear all close all clc %% abdomen apAbdWat = [5.7 7.4 11.2 14.9 18.6 22.4 26.1 29.8 33.6]; latAbdWat = [7.7 10 15 20 25 30 35 40 45]; apTisAbd = [8.9 11.4 13.9 15.9 18.4 22 24.9 30.7]; latTisAbd = [10.6 14 18 20.6 24...

Is there any way to do anomaly detection in dataset using recursive curve fitting and removing points having the most mean square error with respect to the curve, upto an acceptable threshold? I am using the scipy.optimize.curve_fit function for python 2.7, and I need to work with python preferably. ...

I have data provided in the code which have negative and positive slopes as shown in figure: Using the code applied in this post Fit a curve for data made up of two distinct regimes, I created this code. It works for same slopes either both positive or both negative,...

I think this depends much on the objective function. However, if there are any other ways to limit it - it would be great. At least, my teacher says there is some option. However, I cannot find it from the manual in searching positive. Is there any setting to limit...

I have a zoo data frame, a piece of which looks like: TLT PCY SHY 2015-04-15 122.1545 28.16594 84.63730 2015-04-16 123.9836 28.11196 84.72825 2015-04-17 124.4341 28.07958 84.70804 2015-04-20 125.2258 28.15514 84.74845 2015-04-21 125.9629 28.18753 84.76866 2015-04-22 126.1267 28.28469 84.79897 2015-04-23 126.4923 28.30628 84.85056 2015-04-24 124.5478 28.34947 84.81010 2015-04-27 123.3290 28.36026...

I am wondering how do I fit three points x = ([0.42 0.64 0.96]) and y = ([4.2 5.1 6.0]) with y = k*x^(0.88)? I tried [p,S,mu] = polyfit(x,y,0.88); but MATLAB says only power in integer numbers are accepted. Thanks. EDIT: The idea is I know these three points should...

I have 37 linear equations with 36 variables in the form of matrix: A x = b. (A has 37 rows and 36 columns.) The equations don't have an exact solution so I have used Matlab to find the closest answer using x = A \ b. The problem is...

I have a dataset containing two vectors of points, X and Y that represents measurements of an "exponential-like" phenomenon (i.e. Y = A*exp(b*x)). When fitting it with an exponential equation I'm getting a nice-looking fit, but when I'm using it to compute things it turns out that the fit is...

I'm operating with a 3-D list data in matplotlib. Trying to plot a best-fitted wireframe. My data infrastructure(doesn't represent actual data): x=[1.2, 1.3, 1.6, 2.5, 2,3, 2.8] y=[167.0, 180.3, 177.8,160.4,179.6, 154.3] z=[-0.3, -0.8, -0.75, -1.21, -1.65, -0.68] So far, I've been able to get a fitted wireframe using data =...

I have a list of data that I am trying to fit to a polynomial and I am trying to plot the 95% confidence bands for the parameters as well (in Matlab). If my data are x and y f=fit(x,y,'poly2') plot(f,x,y) ci=confint(f,0.95); a_ci=ci(1,:); b_ci=ci(2,:); I do not know how to...

I want to fit a Lorentzian to my data, so first I want to test my fitting procedure to simulated data: X = linspace(0,100,200); Y = 20./((X-30).^2+20)+0.08*randn(size(X)); starting parameters a3 = ((max(X)-min(X))/10)^2; a2 = (max(X)+min(X))/2; a1 = max(Y)*a3; a0 = [a1,a2,a3]; find minimum for fit afinal = fminsearch(@devsum,a0); afinal is...

I have two arrays: E= [6656400; 13322500; 19980900; 26625600; 33292900; 39942400; 46648900; 53290000] and J=[0.0000000021; 0.0000000047; 0.0000000128; 0.0000000201; 0.0000000659; 0.0000000748; 0.0000001143; 0.0000001397] I want to find the appropriate curve fitting for the above data by applying this equation: J=A0.*(298).^2.*exp(-(W-((((1.6e-19)^3)/(4*pi*2.3*8.854e-12))^0.5).*E.^0.5)./((1.38e-23).*298)) I want to select the starting value of W from 1e-19...

I wrote following script in R: F<-c(1.485, 1.052, .891, .738, .623, .465, .343, .184, .118, .078, 1.80, 2.12, 2.31, 2.83, 3.14, 3.38, 7.70, 15.35, 20.72, 22.93) A<-c(4.2, 4.8, 5.0, 5.2, 5.3, 5.5, 5.6, 5.7, 5.8, 5.9, 3.8, 3.5, 3.4, 2.9, 2.7, 2.5, 1.2, 0.6, 0.5, 0.5) Amplitude <- A/2 Flog...

Unfortunately the power fit with scipy does not return a good fit. I tried to use p0 as an input argument with close values which did not help. I would be very glad if someone could point out to me my problem? # Imports from scipy.optimize import curve_fit import numpy...

I want to fit a polynomial function (max. 3rd order) on each raster cell over all my spectral bands (Landsat 1-7) creating a new raster(stack) representing the coefficients. I got my data (including NA values) in a stack with 6 Layer (Landsat Band 1-7[excluding 6]). I guess somehow I should...

I've been struggling with fitting a distribution to sample data I have in R. I've looked at using the fitdist as well as fitdistr functions, but I seem to be running into problems with both. A quick background; the output of my code should be the most fitting distribution (from...

I am trying to find the tangent lines from a given point outside a closed curve (not on the curve). The curve is defined as 2D x and y coordinates of points，shaped like an irregular ellipse for example. If given a point by the user: (x0,y0) = (-30,80), how can...

I am experimenting with creating high-performance, good-looking pencil tools using SVG paths. I am logging the mouse coordinates to draw a path. To get a high-fidelity path (accurate to the user's movements) I need to log a point for every pixel movement. This has a big disadvantage. It creates a...

Initial Question (Partially Answered) I am using gnuplot's fitting routines to fit a function to some data, and extract a "characteristic decay time constant". (I call this parameter d in my fitting function.) I have used the script code set fit quiet to prevent reams of text being printed to...

I have data like this: y = [0.001 0.0042222222 0.0074444444 0.0106666667 0.0138888889 0.0171111111 0.0203333333 0.0235555556 0.0267777778 0.03] and x = [3.52E-06 9.72E-05 0.0002822918 0.0004929136 0.0006759156 0.0008199029 0.0009092797 0.0009458332 0.0009749509 0.0009892005] and I want y to be a function of x with y = a(0.01 − b*n^−cx). What is the best...

I've done a spline interpolation of a 3D path using 2 2D fits. Using the interpolation condition as well as the requirement to be 2 times differentiable I got the required equations to interpolate my 3D path. However I came to realize, that I disregarded the fact, that the paths...

I would like to fit a bimodal normal distribution to data that looks bimodally distributed, such as the example below (plot(x)): From the MATLAB docs I thought about using the mle function with a function handle to a mixture of two Gaussians: @(x,p,mu1,mu2,sigma1,sigma2)p*normpdf(x,mu1,sigma1)+(1-p)*normpdf(x,mu2,sigma2) However, the mle function fits to the...

due to some problems in Matlab with fixed parameters, I had to switch from the std. fit command to lsqcurvefit. For the normal fit command, one of the output parameters is gof, from which I can calculate the +/- of each parameter and the r^2 value. That should be possible...

I'm trying to make some non-linear fittings with python which involve an integral, and the limits of the integral depends on the independent variable. The code is the following: import numpy as np import scipy as sc import matplotlib.pyplot as plt from scipy.optimize import curve_fit from scipy.integrate import quad T,M=np.genfromtxt("zfc.txt",...

Given that the fitting function is of type: I intend to fit such function to the experimental data (x,y=f(x)) that I have. But then I have some doubts: How do I define my fitting function when there's a summation involved? Once the function defined, i.e. def func(..) return ... is...

I have three (x,y) coordinates that I got from experimental data. The coordinates represents drug solubility at corresponding pH values as appears below: pH(x) Solubility(y) 1.2 12.8 4.5 0.252 6.8 54.9 I want to create a function using these three points to describe the solubility as a function of pH....

This question already has an answer here: How to fit a gaussian to a histogram in R? [duplicate] 1 answer I have created a histogram by reading binary data in R from a file. I am trying to generate a normal Gaussian curve fit to the histogram values but...

I have two curves as show in this figure: I have two equations of these curves with two unknown parameters, but I want to know is it necessary for these two curves to cross each other to be solved? or it is not necessary to be crossed? Because I want...

I am trying to fit on columns this matrix data using this Python code: #!/usr/local/bin/env python import numpy as np import Tkinter #Used for file import import tkFileDialog #Used for file import import os import scipy import scipy.optimize as optimize root = Tkinter.Tk() root.withdraw() #use to hide tkinter window filename...