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fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M ) input: X - input samples, Nx2 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian (each mean is a 2x1 vector) covar - fitted covariance for each gaussian. this is a 2x2xM matrix. t - probability of each gaussian in the complete distribution iter - number of iterations done by the function-fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M ) input: X - input samples, Nx2 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian (each mean is a 2x1 vector) covar - fitted covariance for each gaussian. this is a 2x2xM matrix. t - probability of each gaussian in the complete distribution iter - number of iterations done by the function
Date : 2025-12-30 Size : 2kb User : resident e

fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function- fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm format: [u,sig,t,iter] = fit_mix_gaussian( X,M ) input: X - input samples, Nx1 vector M - number of gaussians which are assumed to compose the distribution output: u - fitted mean for each gaussian sig - fitted standard deviation for each gaussian t - probability of each gaussian in the complete distribution iter- number of iterations done by the function
Date : 2025-12-30 Size : 1kb User : resident e
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