gaussian

问题 I have a set of data. I want to build a one class distribution from that data. Based on the learned distribution I want to get a probability value for each of the data instance. Based on this probability values (thresholding) I want to build a classifier to classify a particular data instance is comming from that distribution or not. In this case, lets say I have a data of 50x100000 where 50 is the dimension of each data instance, the number of instances are 100000. I am leaning a Gaussian

问题 I have a data set (come from reality) like a<-c(12.4314579038074, 30.1197692762127, 31.8986680511062, 21.5000657793742, 24.2201952026304, 21.4083286311931, 30.543892400514, 11.3243039196637, 18.6629847817322, 68.5846251690826, 8.15853135278713, 20.0269675183568, 23.761746451965, 15.7934365856289, 20.2308964167179, 16.1739058219803, 16.80324393405, 17.8825865979249, 11.4033338599819, 24.6766819397457, 21.6401012626284, 34.9473576269376, 21.2387163543256, 38.8393540982342, 11.1324409747932, 27

问题 I have a sparse 3D array of values. I am trying to turn each "point" into a fuzzy "sphere", by applying a Gaussian filter to the array. I would like the original value at the point (x,y,z) to remain the same. I just want to create falloff values around this point... But applying the Gaussian filter changes the original (x,y,z) value as well. I am currently doing this: dataCube = scipy.ndimage.filters.gaussian_filter(dataCube, 3, truncate=8) Is there a way for me to normalize this, or do

问题 I am attempting to use a MATLAB script that requires the use of the Image Processing Toolbox function fspecial() . I do not have the Image Processing Toolbox, but do have the Signal Processing Toolbox which contains suite of tools for the creation of filters. Sadly, I am largely ignorant on filter creation and am looking to see if I can get some help determining if I can replicate the following line of code using the filter creation tools in the Signal Processing Toolbox: fspecial('gaussian',

问题 Would a 100-by-3 dimensional feature matrix be mapped into a 100 dimensional or into a infinite dimensional feature space, if the mapping would not be bypassed by the Gaussian RBF Kernel? Following this reasoning (The RBF kernel of Support Vector Machine) I would tend to say the feature matrix would be mapped to a inifinite dimensional feature space. Here a summary of the content: Given a m-by-n feature matrix X . Each n-dimensional instance x of X is used to define a n-dimensional normal

问题 I've been working on a function to generate gaussian distributed random randoms between zero and 1. This website here was a great help as I basically copied the algorithm for Polar Form to get an understanding of the procedure, but I am having trouble keeping the value between 0 and 1, including 0 but excluding 1. I believe the mathematical notation for this is [0, 1) if I'm correct. Any insight you could provide would be great. On Unix, this compiles with; gcc fileName .c -lm #include

问题 I am preparing to implement a Gaussian Filter in C#, and in preparation I am reading literature on this. However I have disagreeing sources. One book (Japanese text: Practical Image Processing Introduction by Uchimura) specifies that the equation to calculate the template is w(u,v)= (1/2*pi*sigma^2) exp(-(x^2+v^2)/(2*sigma^2)). I think that is correct, although the author links size and sigma as SIZE = 3*sigma . Finally an excellent book (Feature Extraction & Image Processing for Computer

问题 I need to perform Gaussian Elimination using SSE and I am not sure how to access each element(32 bits) from the 128 bit registers(each storing 4 elements). This is the original code(without using SSE): unsigned int i, j, k; for (i = 0; i < num_elements; i ++) /* Copy the contents of the A matrix into the U matrix. */ for(j = 0; j < num_elements; j++) U[num_elements * i + j] = A[num_elements*i + j]; for (k = 0; k < num_elements; k++){ /* Perform Gaussian elimination in place on the U matrix. *

问题 I have a 2400 by 2400 array of data which looks something like this: data = [[-2.302670298082603040e-01 -2.304885241061924717e-01 -2.305029774024092148e-01 -2.304807100897505734e-01 -2.303702531336284665e-01 -2.307144352067780346e-01... [-2.302670298082603040e-01 -2.304885241061924717e-01 -2.305029774024092148e-01 -2.304807100897505734e-01 -2.303702531336284665e-01 -2.307144352067780346e-01... ... and I am trying to fit the following 2D Gaussian function: def Gauss2D(x, mux, muy, sigmax,

问题 I have a question as follows: Suppose I have an image(size=360x640(row by col)), and I have a center coordinate that's say is (20, 100). What I want is to generate a probability distribution that has the highest value in that center (20,100), and lower probability value in the neighbor and much more lower value farer than the center. All I figure out is to put a multivariate gaussian (since the dimension is 2D) and set mean to the center(20,100). But is that correct and how do I design the