model-fitting

问题 I have x-y scatter data, which exhibit bell-shaped (i.e. normal distribution shaped) behaviour over the course of a year. These are primary production data from high latitudes (more in detail here, the article is paywalled, but I hope that the figures are visible). Question How do I fit a normal distribution shaped curve on scatter data in ggplot2? Example data x <- structure(list(yday = c(238, 238, 238, 242, 242, 250, 250, 253, 254, 169, 199, 208, 230, 21, 37, 88, 94, 102, 125, 125, 95, 98,

问题 I'm trying to fit a function to some data in Python using the LMFIT library for nonlinear functions. It's easy enough, but I want to know if there's a way to restrict some properties of the fitted values. For example, in the following code I fit my data to optimize values A, B and C. But I also want the ratio of A to B to be pi/4 times some integer. Is there a way to impose this restriction? from lmfit import Model import numpy from numpy import cos, sin, pi, linspace Upload data: data =

问题 i am trying to fit distributions. The fitting is finished, but i need a measurement, to choose the best model. Many papers are using the Kolomogorov-Smirnov (KS) test. I tried to implement that, and i am getting very low p-value results. The implementation: #Histigram plot binwidth = np.arange(0,int(out_threshold1),1) n1, bins1, patches = plt.hist(h1, bins=binwidth, normed=1, facecolor='#023d6b', alpha=0.5, histtype='bar') #Fitting gevfit4 = gev.fit(h1) pdf_gev4 = gev.pdf(lnspc, *gevfit4) plt

问题 Lets say i have 3 response variables A,C and M and i want to fit a model for all possible models ie fit Y ~ A, Y ~ C, Y ~ M, Y ~ A * C, Y ~ A * M, Y ~ C * M, etc. Is there a quick way to do this without manually specifiying the interactions each time? i do not want to write M1 = glm(Y ~ A , data = subs, family = "poisson") M2 = glm(Y ~ C , data = subs, family = "poisson") M3 = glm(Y ~ M , data = subs, family = "poisson") M4 = glm(Y ~ A*C , data = subs, family = "poisson") ... In reality i

问题 After applying different filtering and segmentation techniques, I end up with an image like this: I have access to some contours detection functions that return a list of points on the edge of that object, or returns a fitted polygon (with many edges though, much more than 4). I want a way to fit a quadrilateral to that shape as I know it is a front face of a shoebox that is supposed to be a quadrilateral. Due to the perspective view, the parallelity is not conserved so I have no constraints

问题 After applying different filtering and segmentation techniques, I end up with an image like this: I have access to some contours detection functions that return a list of points on the edge of that object, or returns a fitted polygon (with many edges though, much more than 4). I want a way to fit a quadrilateral to that shape as I know it is a front face of a shoebox that is supposed to be a quadrilateral. Due to the perspective view, the parallelity is not conserved so I have no constraints

问题 After applying different filtering and segmentation techniques, I end up with an image like this: I have access to some contours detection functions that return a list of points on the edge of that object, or returns a fitted polygon (with many edges though, much more than 4). I want a way to fit a quadrilateral to that shape as I know it is a front face of a shoebox that is supposed to be a quadrilateral. Due to the perspective view, the parallelity is not conserved so I have no constraints

来源： https://stackoverflow.com/questions/42713276/valueerror-unknown-label-type-while-implementing-mlpclassifier

问题 I am dealing with the relationship: y = h * R + x * v * h where: x = (N - M) * exp(-Q * u) + M which gives the principal equation: y = h * R + v * h * (N - M) * exp(-Q * u) + v * h * M All uppercase letters are constants, and all lowercase letters are variables. I have real data for all the variables, but I either do not know the values of the constants (R and Q), or want to check the ability of the data to fit the values of the constants (N and M). I want to use nls() to fit the equation

问题 I am looking for a piece of software (python preferred, but really anything for which a jupyter kernel exists) to fit a data sample to a mixture of t-distributions. I searched quite a while already and it seems to be that this is a somehwat obscure endeavor as most search results turn up for mixture of gaussians (what I am not interested here). TThe most promising candidates so far are the "AdMit" and "MitSEM" R packages. However I do not know R and find the description of these packages