'update'

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

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 (...? is it good ?!)
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))
}