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function M = sample_discrete(prob, r, c) % SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib. % M = sample_discrete(prob, r, c) % % Example: sample_discr

d = 2; M = 3; Q = 4; T = 5; Sigma = 10; N = sample_discrete(normalize(ones(1,M)), 1, Q); data = randn(d,T); mu = randn(d,M,Q); [BM, B2M] = parzen(data, mu, Sigma, N); [B, B2] = parzenC(data, mu

% Compare my code with % http://www.media.mit.edu/physics/publications/books/nmm/files/index.html % % cwm.m % (c) Neil Gershenfeld 9/1/97 % 1D Cluster-Weighted Modeling example % clear all fi

function sz = mysize(M) % MYSIZE Like the built-in size, except it returns n if M is a vector of length n, and 1 if M is a scalar. % sz = mysize(M) % % The behavior is best explained by examples

function styles = plotColors() colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... 'r' 'b' 'k' 'g' 'c' 'y' 'm']; symbols = ['o' 'x' '+' '>' '

function styles = plotColors() colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... 'r' 'b' 'k' 'g' 'c' 'y' 'm']; symbols = ['o' 'x' '+' '>' '

function [m1, m2, K11, K12, K21, K22] = partition_matrix_vec(m, K, n1, n2, bs) % PARTITION_MATRIX_VEC Partition a vector and matrix into blocks. % [m1, m2, K11, K12, K21, K22] = partition_matrix_vec

function plot_matrix(G, bw) % PLOT_MATRIX Plot a 2D matrix as a grayscale image, and label the axes % % plot_matrix(M) % % For 0/1 matrices (eg. adjacency matrices), use % plot_matrix(M,1) if

function image_rgb(M) % Show a matrix of integers as a color image. % This is like imagesc, except we know what the mapping is from integer to color. % If entries of M contain integers in {1,2,3},

function M = softeye(K, p) % SOFTEYE Make a stochastic matrix with p on the diagonal, and the remaining mass distributed uniformly % M = softeye(K, p) % % M is a K x K matrix. M = p*eye(K); q