set final draft for package

[epclust.git] / epclust / R / computeCoeffs.R

diff --git a/epclust/R/computeCoeffs.R b/epclust/R/computeCoeffs.R
new file mode 100644 (file)
index 0000000..5bc4744
--- /dev/null
+++ b/epclust/R/computeCoeffs.R
@@ -0,0 +1,46 @@
+computeCoeffs = function(data, index, nb_series_per_chunk, wf)
+{
+       coeffs_chunk = NULL
+       if (is.data.frame(data) && index < nrow(data))
+       {
+               #full data matrix
+               coeffs_chunk = curvesToCoeffs(
+                       data[index:(min(index+nb_series_per_chunk-1,nrow(data))),], wf)
+       }
+       else if (is.function(data))
+       {
+               #custom user function to retrieve next n curves, probably to read from DB
+               coeffs_chunk = curvesToCoeffs( data(rank=(index-1)+seq_len(nb_series_per_chunk)), wf )
+       }
+       else if (exists(data_con))
+       {
+               #incremental connection ; TODO: more efficient way to parse than using a temp file
+               ascii_lines = readLines(data_con, nb_series_per_chunk)
+               if (length(ascii_lines > 0))
+               {
+                       series_chunk_file = ".series_chunk"
+                       writeLines(ascii_lines, series_chunk_file)
+                       coeffs_chunk = curvesToCoeffs( read.csv(series_chunk_file), wf )
+                       unlink(series_chunk_file)
+               }
+       }
+       coeffs_chunk
+}
+
+#NOTE: always keep ID in first column
+curvesToCoeffs = function(series, wf)
+{
+       if (!require(wavelets, quietly=TRUE))
+               stop("Couldn't load wavelets library")
+       L = length(series[1,])
+       D = ceiling( log2(L-1) )
+       nb_sample_points = 2^D
+       #TODO: parallel::parApply() ?!
+       res = apply(series, 1, function(x) {
+               interpolated_curve = spline(1:(L-1), x[2:L], n=nb_sample_points)$y
+               W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+               nrj_coeffs = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+               return ( c(x[1], nrj_coeffs) )
+       })
+       return (as.data.frame(res))
+}