You first create the filter with fspecial and then convolve the image with the filter using imfilter (which works on multidimensional images as in the example). You specify sigma and hsize in fspecial. Code: %%# Read an image I = imread(‘peppers.png’); %# Create the gaussian filter with hsize = [5 5] and sigma = 2 … Read more
This is an interesting question. In general, the best way to know the difference between two python implementations is to inspect the code yourself: import inspect, random str_gauss = inspect.getsource(random.gauss) str_nv=inspect.getsource(random.normalvariate) and then you print each of the strings to see how the sources differ. A quick look at the codes show that not only … Read more
In general terms if you really care about getting the the exact same result as MATLAB, the easiest way to achieve this is often by looking directly at the source of the MATLAB function. In this case, edit fspecial: … case ‘gaussian’ % Gaussian filter siz = (p2-1)/2; std = p3; [x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1)); arg … Read more
With the excellent matplotlib and numpy packages from matplotlib import pyplot as mp import numpy as np def gaussian(x, mu, sig): return ( 1.0 / (np.sqrt(2.0 * np.pi) * sig) * np.exp(-np.power((x – mu) / sig, 2.0) / 2) ) x_values = np.linspace(-3, 3, 120) for mu, sig in [(-1, 1), (0, 2), (2, 3)]: … Read more
Here is corrected code: import pylab as plb import matplotlib.pyplot as plt from scipy.optimize import curve_fit from scipy import asarray as ar,exp x = ar(range(10)) y = ar([0,1,2,3,4,5,4,3,2,1]) n = len(x) #the number of data mean = sum(x*y)/n #note this correction sigma = sum(y*(x-mean)**2)/n #note this correction def gaus(x,a,x0,sigma): return a*exp(-(x-x0)**2/(2*sigma**2)) popt,pcov = curve_fit(gaus,x,y,p0=[1,mean,sigma]) plt.plot(x,y,’b+:’,label=”data”) … Read more
Since this question has garnered a lot of views, I thought I’d post code for the final answer that I found, in part, by posting to the Eigen forums. The code uses Boost for the univariate normal and Eigen for matrix handling. It feels rather unorthodox, since it involves using the “internal” namespace, but it … Read more
You can create a Gaussian kernel from scratch as noted in MATLAB documentation of fspecial. Please read the Gaussian kernel creation formula in the algorithms part in that page and follow the code below. The code is to create an m-by-n matrix with sigma = 1. m = 5; n = 5; sigma = 1; … Read more
If you can use numpy, there is numpy.random.multivariate_normal(mean, cov[, size]). For example, to get 10,000 2D samples: np.random.multivariate_normal(mean, cov, 10000) where mean.shape==(2,) and cov.shape==(2,2).
You should use the fact that a Gaussian kernel is separable, i. e. you can express a 2D convolution as a combination of two 1D convolutions. If the filter is large, it may also make sense to use the fact that convolution in the spatial domain is equivalent to multiplication in the frequency (Fourier) domain. … Read more
It sounds like you want a truncated normal distribution. Using scipy, you could use scipy.stats.truncnorm to generate random variates from such a distribution: import matplotlib.pyplot as plt import scipy.stats as stats lower, upper = 3.5, 6 mu, sigma = 5, 0.7 X = stats.truncnorm( (lower – mu) / sigma, (upper – mu) / sigma, loc=mu, … Read more