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When it gets to the amplitude which minimises the cost function it simply stops the algorithm as it appears to have converged and makes little or no changes in the sigma parameter.This program is a software to minimalizatiom logic function by karnaugh map and use bool algebra. The algorithm simply takes steps in amplitude as this is the steepest gradient. One needs to make a large number of 'unit' sized steps in y (amplitude) to get to the minimum from the point x,y = (0,0), where as you only need less than one 'unit' sized step to get to the minimum in x (sigma). Make sure to make note of the different scales for the x and y axis. Results and Discussion: According to the results. Consider the following plot in parameter space (Colour is the sum of the squared residuals of the fit for given parameters and the white cross shows the optimal solution): 59 software (1995-2008) was used for nonlinear fitting of Freundlich to sorption data. You can also solve the problem by scaling the amplitude: the amplitude is so large the parameter space is distorted and the gradient descent simply follows the direction of greatest change in the amplitude and effectively ignores the sigma. I've found this works well for the fitting normal distributions but you could consider other methods. Here I just found the maximum y value and used this to determine the initial parameters. You should write a function which can provide reasonable estimates of the starting parameters. Which as you can see provides a reasonable fit: Popt2, pcov2 = curve_fit(gaussian, xdata2, ydata, p0=initial_guess) You should try providing reasonable starting parameters (by using the p0 argument of curve_fit) to avoid this: #.
The problem is your second attempt at fitting a gaussian is getting stuck in a local minimum while searching parameter space: curve_fit is a wrapper for least_squares which uses gradient descent to minimize the cost function and this is liable to get stuck in local minima. There are 11 companies in the Pcms Datafit Inc. Popt1, pcov1 = curve_fit(gaussian, xdata1, ydata) has 68 total employees across all of its locations and generates 22.30 million in sales (USD). Xdata1 = np.linspace(-9,4,20, endpoint=True) # works fine I am using _fit in Spyder with Python 3.7.1 on Windows 7. Changing some of the values of the data, it is easy to make it work for both cases, but one can also easily find cases in which it does not work well for xdata1 and also in which covariance of the parameters is not estimated. I am having a hard time trying to understand why my Gaussian fit to a set of data ( ydata) does not work well if I shift the interval of x-values corresponding to that data ( xdata1 to xdata2).