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{762} 
ref: work0
tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics
date: 06302009 15:07 gmt
revision:0
[head]


http://www.lri.fr/~hansen/cmatutorial.pdf
 
{260}  
Friday March 30 Jen shared an interesting algorithm for spike sorting: dist=pdist(psi); %This finds the Euclidean distances for all of the points (waveforms) in psi; %dist is of the form of a row vector of length m(m1)/2. Could convert into a %distance matrix via squareform function, but is computationally inefficient. %m is the number of waveforms in psit. link=linkage(dist); %This performs a nearest neighbor linkage on the distance matrix and returns %a matrix of size (m1)x3. Cols 1 and 2 contain the indices of the objects %were linked in pairs to form a new cluster. This new cluster is assigned the %index value m+i. There are m1 higher clusters that correspond to the interior %nodes of the hierarchical cluster tree. Col 3 contains the corresponding linkage %distances between the objects paired in the clusters at each row i. [H,T]=dendrogram(link,0); %This creates a dendrogram; 0 instructs the function to plot all nodes in %the tree. H is vector of line handles, and T a vector of the cluster %number assignment for each waveform in psit. It looks real nice in theory, and computes very quickly on 2000 x 32 waveform data (provided you don't want to plot)  however, I'm not sure if it works properly on synthetic data. Here are the commands that i tried: v = [randn(1000, 32); (randn(1000, 32) + rvecrep(ones(1,32),1000))]; [coef, vec] = pca(v); vv = v * vec(:, 1:2); dist = pdist(vv); link = linkage(dist); [H,T]=dendrogram(link,0); figure DensityPlotOpenGL(vv(:,1), vv(:,2))  the fitted dendogram, without PCA
 the fitted dendogram, with PCA
 the asociated PCA plot of the data, clearly showing two clusters. need to figure out how jen made the colorized plots  
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Newmat11  nice, elegant BLAS / FFT and matrix library, with plenty of syntactic sugar. 