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Plot normalized uniform mixture

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问题 I need to reproduce the normalized density p(x) below, but the code given does not generate a normalized PDF. clc, clear % Create three distribution objects with different parameters pd1 = makedist('Uniform','lower',2,'upper',6); pd2 = makedist('Uniform','lower',2,'upper',4); pd3 = makedist('Uniform','lower',5,'upper',6); % Compute the pdfs x = -1:.01:9; pdf1 = pdf(pd1,x); pdf2 = pdf(pd2,x); pdf3 = pdf(pd3,x); % Sum of uniforms pdf = (pdf1 + pdf2 + pdf3); % Plot the pdfs figure; stairs(x,pdf,

Plot normalized uniform mixture

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GaussianMixture initialization using component parameters - sklearn

阅读更多关于 GaussianMixture initialization using component parameters - sklearn

问题 I want to use sklearn.mixture.GaussianMixture to store a gaussian mixture model so that I can later use it to generate samples or a value at a sample point using score_samples method. Here is an example where the components have the following weight, mean and covariances import numpy as np weights = np.array([0.6322941277066596, 0.3677058722933399]) mu = np.array([[0.9148052872961359, 1.9792961751316835], [-1.0917396392992502, -0.9304220945910037]]) sigma = np.array([[[2.267889129267119, 0

GaussianMixture initialization using component parameters - sklearn

阅读更多关于 GaussianMixture initialization using component parameters - sklearn

I want to use sklearn.mixture.GaussianMixture to store a gaussian mixture model so that I can later use it to generate samples or a value at a sample point using score_samples method. Here is an example where the components have the following weight, mean and covariances import numpy as np weights = np.array([0.6322941277066596, 0.3677058722933399]) mu = np.array([[0.9148052872961359, 1.9792961751316835], [-1.0917396392992502, -0.9304220945910037]]) sigma = np.array([[[2.267889129267119, 0.6553245618368836], [0.6553245618368835, 0.6571014653342457]], [[0.9516607767206848, -0.7445831474157608],