gaussian

问题 I have a vector of x and y coordinates drawn from two separate unknown Gaussian distributions. I would like to fit these points to a three dimensional Gauss function and evaluate this function at any x and y . So far the only manner I've found of doing this is using a Gaussian Mixture model with a maximum of 1 component (see code below) and going into the handle of ezcontour to take the X, Y, and Z data out. The problems with this method is firstly that its a very ugly roundabout manner of

问题 This question already has answers here : How to make a Gaussian filter in Matlab (2 answers) Closed 4 years ago . How do I obtain 2D circularly symmetric Gaussian weighting function sampled out to 3 standard deviations (3 x 3) and re scaled to unit volume? 回答1: Try fspecial (Image Processing Toolbox) with the 'gaussian' option. For example, z = fspecial('gaussian', [30 30], 4); generates values on a 30 × 30 grid with sampling step 1 and standard deviation 4 . surf(z) produces the graph The

问题 This question already has answers here : How to make a Gaussian filter in Matlab (2 answers) Closed 4 years ago . How do I obtain 2D circularly symmetric Gaussian weighting function sampled out to 3 standard deviations (3 x 3) and re scaled to unit volume? 回答1: Try fspecial (Image Processing Toolbox) with the 'gaussian' option. For example, z = fspecial('gaussian', [30 30], 4); generates values on a 30 × 30 grid with sampling step 1 and standard deviation 4 . surf(z) produces the graph The

问题 This question already has answers here : How to make a Gaussian filter in Matlab (2 answers) Closed 4 years ago . How do I obtain 2D circularly symmetric Gaussian weighting function sampled out to 3 standard deviations (3 x 3) and re scaled to unit volume? 回答1: Try fspecial (Image Processing Toolbox) with the 'gaussian' option. For example, z = fspecial('gaussian', [30 30], 4); generates values on a 30 × 30 grid with sampling step 1 and standard deviation 4 . surf(z) produces the graph The

问题 How to apply guassian smoothening formula for a graph which is in array? these array are mapped to a color and plotted on the graph. i want the linear gradient of color after applying guassian smoothening.. I want to know the exact guassian smoothening formula too. 回答1: I believe what you're asking for is typically called a "Gaussian blur" in photo-editing applications. It is simply the result of blurring an image using a Gaussian function, resulting in a reduction of visual noise and detail.

问题 How to apply guassian smoothening formula for a graph which is in array? these array are mapped to a color and plotted on the graph. i want the linear gradient of color after applying guassian smoothening.. I want to know the exact guassian smoothening formula too. 回答1: I believe what you're asking for is typically called a "Gaussian blur" in photo-editing applications. It is simply the result of blurring an image using a Gaussian function, resulting in a reduction of visual noise and detail.

问题 For documents like { "_id" : "abc123", "_score" : 3.7613528, "_source" : { "id" : "abc123", "pricePeriods" : [{ "periodTo" : "2016-01-02", "eur" : 1036, "gbp" : 782, "dkk" : 6880, "sek" : 9025, "periodFrom" : "2015-12-26", "nok" : 8065 }, { "periodTo" : "2016-06-18", "eur" : 671, "gbp" : 457, "dkk" : 4625, "sek" : 5725, "periodFrom" : "2016-01-02", "nok" : 5430 } ] } } I would like to have a gauss decay function score on the prices. I have tried like this "query" : { "function_score" : {

问题 I want to construct and 1D plot a uni-variate Gaussian Mixture with say three components in Python where I already have its parameters including mu,sigma,mix coefficients. What I am after has an equivalent in MATLAB i.e. gmdistribution(mu,sigma,p) I think the code should look sth like this: from numpy import * from matplotlib.pylab import * from sklearn import mixture gmm = mixture.GMM(n_components=3) gmm.means_ = np.array([[-1], [0], [3]]) gmm.covars_ = np.array([[1.5], [1], [0.5]]) ** 2 gmm

问题 I'm trying to read my text file and extract 3 main parameters and put them in separate list and apply normalizing on lists of parameters which are (Temperature, Speed, Acceleration) after assigning Gaussian distribution function. For getting good result I split up positive and negative numbers of each parameters' list and apply gaussian distribution function and pick mean value of negative numbers as the real Minimum and pick mean value of positive numbers as the real Maximum instead of

问题 I'm currently using scikit-learn's GaussianNB package. I've noticed that I can choose to return results for the classification several different ways. One way to return a classification is using the predict_log_proba method. Why would I choose to use predict_log_proba versus predict_proba versus predict? 回答1: predict just gives you the class for every example predict_proba gives you the probability for every class, and predict is just taking the class which maximal probability predict_log