piecewise

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

问题 In my researches, I couldn't find any examples related to a relational value in piecewise functions. Although I received the lower and upper values with the uni_dis method, i'm stuck in the process of transferring these values to the piecewise function. What's the reason? from sympy import Symbol, Piecewise import sympy as sym import sympy.plotting as syp import math a = Symbol('a') b = Symbol('b') x = Symbol('x') function = 1 / abs(a-b) def uni_dis(lower, upper): if lower > upper: lower,

问题 I have a very large datafile, where x= time and y= distance. I would like to figure out what the speed is in different segments. Ideally, I would like Python to calculate the segments and the corresponding linear regression functions. I googled this and think my best option is using the numpy.piecewise to get segmented linear regression. I only keep getting this error # Remove full_output from kwargs, otherwise we're passing it in twice'. The code is use is as follows: y = cleandata["Distance

问题 I would like to get piecewise linear function from set of points. Here is visual example: import matplotlib.pyplot as plt x = [1,2,7,9,11] y = [2,5,9,1,11] plt.plot(x, y) plt.show() So I need a function that would take two lists and would return piecewise linear function back. I do not need regression or any kind of least square fit. I can try to write it myself, but wonder if there is something already written. So far, I only found code returning regression 回答1: try np.interp. It

问题 I define the piecewise function def Li(x): return piecewise(x, [x < 0, x >= 0], [lambda t: sin(t), lambda t: cos(t)]) And when I evaluate Li(1.0) The answer is correct Li(1.0)=array(0.5403023058681398) , But if I write Li(1) the answer is array(0) . I don't understand this behaviour. 回答1: It seems that piecewise() converts the return values to the same type as the input so, when an integer is input an integer conversion is performed on the result, which is then returned. Because sine and

问题 I try to use MILP (Mixed Integer Linear Programming) to calculate the unit commitment problem. (unit commitment: An optimization problem trying to find the best scheduling of generator) There are two optimization variables. Generator power : P (continuous variables). Which line segment on cost curve to use : BN (binary variable). ,Used to linearize the quadratic cost function of the generator. Only one line segment can be opened at a time. So there will be a Constraint. Bn1 + Bn2 + Bn3 <=1

问题 Let's say I have import numpy as np from scipy.interpolate import UnivariateSpline # "true" data; I don't know this function x = np.linspace(0, 100, 1000) d = np.sin(x * 0.5) + 2 + np.cos(x * 0.1) # sample data; that's what I actually measured x_sample = x[::20] d_sample = d[::20] # fit spline s = UnivariateSpline(x_sample, d_sample, k=3, s=0.005) plt.plot(x, d) plt.plot(x_sample, d_sample, 'o') plt.plot(x, s(x)) plt.show() I get What I would now like to have are functions between all the

问题 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, but when one is positive and other negative, it is not able to fit the lines properly. from scipy import optimize from scipy import optimize, interpolate from scipy.optimize import curve_fit import matplotlib.pyplot as plt import

问题 I'm having some issues with the piecewise function I defined in R. I've defined the function as: g1 <- function(x) { if (x <= 0.25) { y <- gs1(x) } else if (x >= 0.75) { y <- gs3(x) } else {y <- gs2(x)} y } where gs1,gs2,gs3 are functions I defined earlier. In order to plot the function g1, I tried: curve(g1) but R displays the following: Warning message: In if (x <= 0.25) { : the condition has length > 1 and only the first element will be used I think the problem might be that R requires the