best-fit-curve

问题 I'm trying to calculate a best-fit curve for data using a 3-6 order polynomial. I found this tutorial: Cubic Regression (best fit line) in JavaScript First, I can't seem to get my outputs to remotely match the curve. jsbin here: http://jsbin.com/qukuqigobu/1/edit?html,js,console,output var data_x = [500,1000,1500,2000,2500,3000,3500,4000,4500,5000,5500,6000,6500,7000]; var data_y = [50,80,100,160,210,265,340,390,440,470,500,500,495,460]; var cubic = function(params,x) { return params[0] * x*x

问题 I'm trying to calculate a best-fit curve for data using a 3-6 order polynomial. I found this tutorial: Cubic Regression (best fit line) in JavaScript First, I can't seem to get my outputs to remotely match the curve. jsbin here: http://jsbin.com/qukuqigobu/1/edit?html,js,console,output var data_x = [500,1000,1500,2000,2500,3000,3500,4000,4500,5000,5500,6000,6500,7000]; var data_y = [50,80,100,160,210,265,340,390,440,470,500,500,495,460]; var cubic = function(params,x) { return params[0] * x*x

问题 I am trying to apply a best fit line to time series showing NDVI over time but I keep running into errors. my x, in this case, are different dates as strings that are not evenly spaced and y is the NDVI value for use each date. When I use the poly1d function in numpy I get the following error: TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32') I have attached a sample of the data set I am working with # plot Data and and

问题 I am trying to find a trend in several datasets. The trends involve finding the best fit line, but if i imagine the procedure would not be too different for any other model (just possibly more time consuming). There are 3 conceivable scenarios: All good data where all the data fits a single trend with a low variability All bad data where all or most of the data exhibits tremendous variability and the entire dataset must be discarded. Partial good data where some of the data may be good while

问题 I am trying to fit a power-law function, and in order to find the best fit parameter. However, I find that if the initial guess of parameter is different, the "best fit" output is different. Unless I find the right initial guess, I can get the best optimizing, instead of local optimizing. Is there any way to find the **appropriate initial guess ** ????. My code is listed below. Please feel free make any input. Thanks! import numpy as np import pandas as pd from scipy.optimize import curve_fit

问题 I have a requirement to create bezier curve in my project. For that purpose I am drawing a view with paint, but the problem is that I am not getting the exact shape for my need as mentioned in the picture below. So kindly help me with your solutions and changes or modifications in my code. Thanks in advance. Code I am using to create Bezier Curve: public class DrawView extends View { public DrawView (Context context) { super (context); } protected void onDraw (Canvas canvas) { super.onDraw

问题 I have a requirement to create bezier curve in my project. For that purpose I am drawing a view with paint, but the problem is that I am not getting the exact shape for my need as mentioned in the picture below. So kindly help me with your solutions and changes or modifications in my code. Thanks in advance. Code I am using to create Bezier Curve: public class DrawView extends View { public DrawView (Context context) { super (context); } protected void onDraw (Canvas canvas) { super.onDraw

问题 data <- dput(data): structure(list(x = 1:16, y = c(-79.62962963, -84.72222222, -88.42592593, -74.07407407, -29.62962963, 51.38888889, 79.62962963, 96.2962963, 87.96296296, 88.42592593, 73.14814815, 12.96296296, -63.42592593, -87.03703704, -87.5, -87.96296296)), .Names = c("x", "y"), row.names = c(NA, 16L), class = "data.frame") I calculated in R with ggplot2 a smooth line for my dataset: p1 <- ggplot(data, aes(x=x(°), y=(%))) library(splines) library(MASS) (p2 <- p1 + stat_smooth(method = "lm

问题 A similar question has been answered in the past, but my part of their question was not answered (Matlab curve fitting tool, cftool, generate code function does not give the same fit). I have a set of data points that are meant to show the "ideal" curve for the mechanism I am studying. When I ask the Curve Fitting Toolbox in Matlab to find a 2-term exponential, I get a great fit (R-square: 0.9998, Adjusted R-square: 0.9997). However, when I generate code for this fit, it changes the

问题 How do I fit a curve on a barplot? I have an equation, the diffusion equation, which has some unknown parameters, these parameters make the curve larger, taller, etc. On the other hand I have a barplot coming from a simulation. I would like to fit the curve on the barplot, and find the best parameters for the curve, how can I do that? This is what I obtained by 'manual fitting', so basically I changed manually all the parameters for hours. However is there a way to do this with python? To