curve-fitting

I'm given an array and when I plot it I get a gaussian shape with some noise. I want to fit the gaussian. This is what I already have but when I plot this I do not get a fitted gaussian, instead I just get a straight line. I've tried this many different ways and I just can't figure it out. random_sample=norm.rvs(h) parameters = norm.fit(h) fitted_pdf = norm.pdf(f, loc = parameters[0], scale = parameters[1]) normal_pdf = norm.pdf(f) plt.plot(f,fitted_pdf,"green") plt.plot(f, normal_pdf, "red") plt.plot(f,h) plt.show() You can use fit from scipy.stats.norm as follows: import numpy as np from

Considering the following nice solution for finding cubic Bézier control points for a curve passing through 4 points: How to find control points for a BezierSegment given Start, End, and 2 Intersection Pts in C# - AKA Cubic Bezier 4-point Interpolation I wonder, if there is a straightforward extension to this for making the Bézier curve pass through N points, for N > 2 and maybe N ≤ 20? This is a really old question, but I'm leaving this here for people who have the same question in the future. @divanov has mentioned that there's no Bezier curve passing through N arbitrary points for N >4. I

The curve-fitting problem for 2D data is well known (LOWESS, etc.) but given a set of 3D data points, how do I fit a 3D curve (eg. a smoothing/regression spline) to this data? MORE: I'm trying to find a curve, fitting the data provided by vectors X,Y,Z which have no known relation. Essentially, I have a 3D point cloud, and need to find a 3D trendline. MORE: I apologize for the ambiguity. I tried several approaches (I still haven't tried modifying the linear fit) and a random NN seems to work out best. I.e., I randomly pick a point from the point cloud, find the centroid of it's neighbors

we have to fit about 2000 or odd time series every month, they have very idiosyncratic behavior in particular, some are arma/arima, some are ewma, some are arch/garch with or without seasonality and/or trend (only thing in common is the time series aspect). one can in theory build ensemble model with aic or bic criterion to choose the best fit model but is the community aware of any library which attempts to solve this problem? Google made me aware of the below one by Rob J Hyndman link but are they any other alternatives? Rob Hyndman There are two automatic methods in the forecast package :

I am exploring some data, so the first thing I wanted to do was try to fit a normal (Gaussian) distribution to it. This is my first time trying this in R, so I'm taking it one step at a time. First I pre-binned my data: myhist = data.frame(size = 10:27, counts = c(1L, 3L, 5L, 6L, 9L, 14L, 13L, 23L, 31L, 40L, 42L, 22L, 14L, 7L, 4L, 2L, 2L, 1L) ) qplot(x=size, y=counts, data=myhist) Since I want counts, I need to add a normalization factor (N) to scale up the density: fit = nls(counts ~ N * dnorm(size, m, s), data=myhist, start=c(m=20, s=5, N=sum(myhist$counts)) ) Then I create the fitted data

I am trying to make a gaussian fit over many data points. E.g. I have a 256 x 262144 array of data. Where the 256 points need to be fitted to a gaussian distribution, and I need 262144 of them. Sometimes the peak of the gaussian distribution is outside the data-range, so to get an accurate mean result curve-fitting is the best approach. Even if the peak is inside the range, curve-fitting gives a better sigma because other data is not in the range. I have this working for one data point, using code from http://www.scipy.org/Cookbook/FittingData . I have tried to just repeat this algorithm, but