diff toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_prob_test.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_prob_test.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,111 @@
+function test_eval_pdf_cond_mixgauss()
+
+%Q = 10; M = 100; d = 20; T = 500;
+Q = 2; M = 3; d = 4; T = 5;
+
+mu = rand(d,Q,M);
+data = randn(d,T);
+%mixmat = mk_stochastic(rand(Q,M));
+mixmat = mk_stochastic(ones(Q,M));
+
+% tied scalar
+Sigma = 0.01;
+
+mu = rand(d,M,Q);
+weights = mixmat';
+N = M*ones(1,Q);
+tic; [B, B2, D] = parzen(data, mu, Sigma, N, weights); toc
+tic; [BC, B2C, DC] = parzenC(data, mu, Sigma, N); toc
+approxeq(B,BC)
+B2C = reshape(B2C,[M Q T]);
+approxeq(B2,B2C)
+DC = reshape(DC,[M Q T]);
+approxeq(D,DC)
+
+
+return
+
+tic; [B, B2] = eval_pdf_cond_mixgauss(data, mu, Sigma, mixmat); toc
+tic; C = eval_pdf_cond_parzen(data, mu, Sigma); toc
+approxeq(B,C)
+
+return;
+
+
+mu = reshape(mu, [d Q*M]);
+
+data = mk_unit_norm(data);
+mu = mk_unit_norm(mu);
+tic; D = 2 -2*(data'*mu); toc % avoid an expensive repmat
+tic; D2 = sqdist(data, mu); toc
+approxeq(D,D2)
+
+
+% D(t,m) = sq dist between data(:,t) and mu(:,m)
+mu = reshape(mu, [d Q*M]);
+D = dist2(data', mu');
+%denom = (2*pi)^(d/2)*sqrt(abs(det(C)));
+denom = (2*pi*Sigma)^(d/2); % sqrt(det(2*pi*Sigma))
+numer = exp(-0.5/Sigma  * D');
+B2 = numer / denom;
+B2 = reshape(B2, [Q M T]);
+
+tic; B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); toc
+
+tic
+A = zeros(Q,T);
+for q=1:Q
+  A(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % sum over m
+end
+toc
+assert(approxeq(A,B))
+
+tic
+A = zeros(Q,T);
+for t=1:T
+  A(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m
+end
+toc
+assert(approxeq(A,B))
+
+    
+
+
+mu = reshape(mu, [d Q M]);
+B3 = zeros(Q,M,T);
+for j=1:Q
+  for k=1:M
+    B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma*eye(d));
+  end
+end
+assert(approxeq(B2, B3))
+
+logB4 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d
+B4 = reshape(exp(logB4), [Q M T]);
+assert(approxeq(B4, B3))
+
+
+
+  
+% tied cov matrix
+
+Sigma = rand_psd(d,d);
+mu = reshape(mu, [d Q*M]);
+D = sqdist(data, mu, inv(Sigma))';
+denom = sqrt(det(2*pi*Sigma));
+numer = exp(-0.5 * D);
+B2 = numer / denom;
+B2 = reshape(B2, [Q M T]);
+
+mu = reshape(mu, [d Q M]);
+B3 = zeros(Q,M,T);
+for j=1:Q
+  for k=1:M
+    B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma);
+  end
+end
+assert(approxeq(B2, B3))
+
+logB4 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D;
+B4 = reshape(exp(logB4), [Q M T]);
+assert(approxeq(B4, B3))