代码搜索：gaussian

找到约 7,040 项符合「gaussian」的源代码

代码结果 7,040
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tex rand-gaussian.tex

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m gaussian_prob.m

function p = gaussian_prob(x, m, C, use_log) % GAUSSIAN_PROB Evaluate a multivariate Gaussian density. % p = gaussian_prob(X, m, C) % p(i) = N(X(:,i), m, C) where C = covariance matrix and each COL
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m sample_gaussian.m

function M = sample_gaussian(mu, Sigma, N) % SAMPLE_GAUSSIAN Draw N random row vectors from a Gaussian distribution % samples = sample_gaussian(mean, cov, N) if nargin==2 N = 1; end % If Y
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m gaussian_sample.m

function x = gsamp(mu, covar, nsamp) %GSAMP Sample from a Gaussian distribution. % % Description % % X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a % D-dimensional Gaussian
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m marginalize_gaussian.m

function [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns) % MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian. % [muX, SXX] = marginalize_gaussian(mu, Sigma, X
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m gmm_to_gaussian.m

function [x,P,wn] = gmm_to_gaussian(g) % Convert gmm to a single Gaussian (ie, compute first two moments of mixture). [g, wn] = gmm_normalise(g); % gmm must be normalised for weighted sums to be
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m kernel_to_gaussian.m

function [x,P,wn] = kernel_to_gaussian(g) % Convert kernels to a single Gaussian (ie, compute first two moments). [g, wn] = kernel_normalise(g); % must be normalised for weighted sums to be meani
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www.eeworm.com/read/168677/9902163

m rxy_gaussian.m

function result = Rxy_gaussian(d_norm,phi_0_deg,sigma_deg,delta_phi_deg) % result = Rxy_gaussian(d_norm,phi_0_deg,sigma_deg,delta_phi_deg) % % Computes the envelope correlation of the signals in th
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www.eeworm.com/read/168677/9902168

m plot_gaussian.m

function plot_gaussian(number_clusters, power, AOA_deg, sigma_deg, ... delta_phi_deg, plot_number, colour) % plot_gaussian(number_clusters, power, AOA_deg, sigma_deg, % delta_
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www.eeworm.com/read/168677/9902181

m normalisation_gaussian.m

function [Q, sigma_deg] = normalisation_gaussian(number_clusters, ... power_lin, AS_deg, delta_phi_deg) % [Q, sigma_deg] = normalisation_gaussian(number_clusters, % power_lin,
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