gaussian

I am trying to use SciPy's gaussian_kde function to estimate the density of multivariate data. In my code below I sample a 3D multivariate normal and fit the kernel density but I'm not sure how to evaluate my fit. import numpy as np from scipy import stats mu = np.array([1, 10, 20]) sigma = np.matrix([[4, 10, 0], [10, 25, 0], [0, 0, 100]]) data = np.random.multivariate_normal(mu, sigma, 1000) values = data.T kernel = stats.gaussian_kde(values) I saw this but not sure how to extend it to 3D. Also not sure how do I even begin to evaluate the fitted density? How do I visualize this? There are

I want generate a number in Gaussian and Uniform distributions in matlab. I know this function randi and rand() but all of them are in normal (Gaussian) distribution. How can a generate a random number in uniform distribution? Use rand(dimensions) for a Uniform Distribution between 0 and 1. Use randn(dimensions) * sqrt(sigma) + mu for a Gaussian Distribution with a mean of mu and variance of sigma . randn is the function to generate Gaussian distributed variables ( randi and rand produce uniformly distributed ones). raj You can generate any distribution from rand(). For example , lets say you

I have some data points and their mean point. I need to find whether those data points (with that mean) follows a Gaussian distribution. Is there a function in MATLAB which can do that kind of a test? Or do I need to write a test of my own? I tried looking at different statistical functions provided by MATLAB. I am very new to MATLAB so I might have overlooked the right function. cheers Check this documentation page on all available hypothesis tests . From those, for your purpose you can use: Chi-square goodness-of-fit test Lilliefors test z-test t-test Kolmogorov-Smirnov test ... among others

I'd like to implement my own Gaussian kernel in Python, just for exercise. I'm using: sklearn.svm.SVC(kernel=my_kernel) but I really don't understand what is going on. I expect the function my_kernel to be called with the columns of the X matrix as parameters, instead I got it called with X , X as arguments. Looking at the examples things are not clearer. What am I missing? This is my code: ''' Created on 15 Nov 2014 @author: Luigi ''' import scipy.io import numpy as np from sklearn import svm import matplotlib.pyplot as plt def svm_class(fileName): data = scipy.io.loadmat(fileName) X = data[

My knowledge of maths is limited which is why I am probably stuck. I have a spectra to which I am trying to fit two Gaussian peaks. I can fit to the largest peak, but I cannot fit to the smallest peak. I understand that I need to sum the Gaussian function for the two peaks but I do not know where I have gone wrong. An image of my current output is shown: The blue line is my data and the green line is my current fit. There is a shoulder to the left of the main peak in my data which I am currently trying to fit, using the following code: import matplotlib.pyplot as pt import numpy as np from

I have two gaussian plots: x = np.linspace(-5,9,10000) plot1=plt.plot(x,mlab.normpdf(x,2.5,1)) plot2=plt.plot(x,mlab.normpdf(x,5,1)) and I want to find the point at where the two curves intersect. Is there a way of doing this? In particular I want to find the value of the x-coordinate where they meet. You want to find the x's such that both gaussian functions have the same height.(i.e intersect) You can do so by equating two gaussian functions and solve for x. In the end you will get a quadratic equation with coefficients relating to the gaussian means and variances. Here is the final result: