smoothing

问题 I have a 3D sensor which measures v(x,y,z) data. I'm only using the x and y data. Smoothing only x and y would be enough. If I use a log to show the data, it shows me something like this: (time) 0.1 ... (Data log) x = 1.1234566667 (time) 0.2 ... (Data log) x = 1.1245655666 (time) 0.3 ... (data log) x = 1.2344445555 Well the data is more exact actually, but I want to smooth between the 1.1234 value and the 1.2344 value, because for me it's the same, I can use integers to, showing only "x= 1"

问题 I have a function that returns a bounded noise. For example, let's imagine that out input range is [-1, 1]. With my method I can return a bounded/in range noise (depending on the biome we are currently). /// <summary> /// Converts the range. /// </summary> /// <param name="originalStart">The original start.</param> /// <param name="originalEnd">The original end.</param> /// <param name="newStart">The new start.</param> /// <param name="newEnd">The new end.</param> /// <param name="value">The

问题 I have a function that returns a bounded noise. For example, let's imagine that out input range is [-1, 1]. With my method I can return a bounded/in range noise (depending on the biome we are currently). /// <summary> /// Converts the range. /// </summary> /// <param name="originalStart">The original start.</param> /// <param name="originalEnd">The original end.</param> /// <param name="newStart">The new start.</param> /// <param name="newEnd">The new end.</param> /// <param name="value">The

问题 I need to perform image smoothing. I've searched the web but I didn't find anything - every thing I tried doesn't preform like I want. for example: as you see there are bumps or something like stairs, so what should I do so the lines will be straight? thanks.... 回答1: If the resolution of the output image is higher than the resolution of the stairs, then you can do any number of things. To name a few. grayscale (or binary) morphological processing using imclose edge-enhancing smoothing march

问题 In the below picture, the spap2 function is used in Matlab to smooth noisy data. The result is very good. Eigen library supports this functionality Splines. I'm looking for an example in Eigen to obtain similar results. For the Matlab, I've used spap2(4, 4, time, noisyY); Data is provided in this format time noisyData 1.766 6.61202 1.767 11.4159 1.768 8.29416 1.769 8.29416 1.77 8.29416 1.771 6.02606 1.772 4.37819 1.773 4.37819 1.774 4.37819 1.775 3.18094 1.776 2.31109 1.777 1.67911 1.778 1

问题 I made a plot for my data and am now I would like to have the difference in y for every x that was estimated by geom_smooth(). There is a similiar question which unfortunately has no answer. For example, how to get the differences for the following plot (data below): EDIT Two suggestions were made but I still don't know how to calculate the differences. First suggestion was to access the data from the ggplot object. I did so with pb <- ggplot_build(p) pb[["data"]][[1]] That approach kind of

问题 Original question I want to smooth my explanatory variable, something like Speed data of a vehicle, and then use this smoothed values. I searched a lot, and find nothing that directly is my answer. I know how to calculate the kernel density estimation ( density() or KernSmooth::bkde() ) but I don't know then how to calculate the smoothed values of speed. Re-edited question Thanks to @ZheyuanLi, I am able to better explain what I have and what I want to do. So I have re-edited my question as

问题 Original question I want to smooth my explanatory variable, something like Speed data of a vehicle, and then use this smoothed values. I searched a lot, and find nothing that directly is my answer. I know how to calculate the kernel density estimation ( density() or KernSmooth::bkde() ) but I don't know then how to calculate the smoothed values of speed. Re-edited question Thanks to @ZheyuanLi, I am able to better explain what I have and what I want to do. So I have re-edited my question as

问题 I have 2 variables x and y which are Cartesian coordinates at [0,1], and z is the value of a (continuous) variable at these coordinates. The z vector has some important outliers x<-sample(seq(0,1,0.001), replace=F) y<-sample(seq(0,1,0.001), replace=F) z<-runif(1001,min=0,max=1) z[100]<-8;z[400]<-16;z[800]<-4 These outliers I would like to emphasize when presenting these data in a filled.contour I have used until now library(akima) a<-interp(x,y,z) filled.contour(a$x,a$y,a$z) But I am not

问题 I know that the smoothing parameter(lambda) is quite important for fitting a smoothing spline, but I did not see any post here regarding how to select a reasonable lambda (spar=?), I was told that spar normally ranges from 0 to 1. Could anyone share your experience when use smooth.spline()? Thanks. smooth.spline(x, y = NULL, w = NULL, df, spar = NULL, cv = FALSE, all.knots = FALSE, nknots = NULL, keep.data = TRUE, df.offset = 0, penalty = 1, control.spar = list(), tol = 1e-6 * IQR(x)) 回答1: