Description Usage Arguments Details Value Author(s) References Examples
For the analysis of point process, intensity function plays a central roll. Paiva et al. (2009) proposed to use the intensity function for defining the inner product between point process realizations.
1 |
S1 |
marked point process data. |
S2 |
marked point process data. |
measure |
|
tau |
a parameter for filtering function. |
M |
a precision matrix for filter of marks, i.e., exp( - r' M r) is used for filtering marks. It should be symmetric and positive semi-definite. |
iipmetric
computes intensity inner product metric. Intensity function for the point process realization is estimated by kernel density estimator. This function adopts Gaussian kernels for the sake of computational efficiency.
Similarity or distance between two inputs (marked) point process S1 and S2.
Hideitsu Hino hinohide@cs.tsukuba.ac.jp, Ken Takano, Yuki Yoshikawa, and Noboru Murata
A.R.C. Paiva, I. Park, and J.C. Principe. A reproducing kernel Hilbert space framework for spike train signal processing, Neural Computation, Vol. 21(2), pp. 424-449, 2009.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ##The aftershock data of 26th July 2003 earthquake of M6.2 at the northern Miyagi-Ken Japan.
data(Miyagi20030626)
## time longitude latitude depth magnitude
## split events by 7-hour
sMiyagi <- splitMPP(Miyagi20030626,h=60*60*7,scaleMarks=TRUE)$S
N <- 10
tau <- 0.1
sMat <- matrix(0,N,N)
cat("calculating intensity inner product...")
for(i in 1:(N)){
cat(i," ")
for(j in i:N){
S1 <- sMiyagi[[i]]$time;S2 <- sMiyagi[[j]]$time
sMat[i,j] <- iipmetric(S1,S2,tau=tau,M=diag(1,4))
}
}
sMat <- sMat+t(sMat)
tmpd <- diag(sMat) <- diag(sMat)/2
sMat <- sMat/sqrt(outer(tmpd,tmpd))
image(sMat)
|
calculating intensity inner product...1 2 3 4 5 6 7 8 9 10
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