function y = fitchangepts(x, icp, statistic) y = nan(size(x)); K = length(icp); nseg = K+1; istart = [1; icp(:)]; istop = [icp(:)-1; length(x)]; if strcmp(statistic,'mean') || strcmp(statistic,'std') for s=1:nseg ix = (istart(s):istop(s))'; y(ix) = mean(x(ix)); end elseif strcmp(statistic,'rms') for s=1:nseg ix = (istart(s):istop(s))'; y(ix) = rms(x(ix)); end else % linear for s=1:nseg ix = (istart(s):istop(s))'; y(ix) = polyval(polyfit(ix,x(ix),1),ix); end end
Test it:
load engineRPM.mat plot(fitchangepts(x,findchangepts(x,'Statistic','linear','MinThreshold',var(x)/2),'linear'))
returns the index at which the mean of ipt
= findchangepts(x
)x
changes most significantly.
If x
is a vector with N elements, then findchangepts
partitions x
into two regions, x(1:ipt-1)
and x(ipt:N)
, that minimize the sum of the residual (squared) error of each region from its local mean.
If x
is an M-by-N matrix, then findchangepts
partitions x
into two regions, x(1:M,1:ipt-1)
and x(1:M,ipt:N)
, returning the column index that minimizes the sum of the residual error of each region from its local M-dimensional mean.
Load a data file containing a recording of a train whistle sampled at 8192 Hz. Find the 10 points at which the root-mean-square level of the signal changes most significantly.
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