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Blue collar PR: Remove superfluous semicolons and convert tabs to spaces #120

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Blue collar PR: Remove superfluous semicolons and convert tabs to spaces #120

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c933974
Remove superfluous semicolons, convert tabs to spaces, and remove DOS…
Jun 11, 2016
f3e4c43
Thought better of it - rolled back the dos2unix stuff. Just do tabs t…
javadba Jun 11, 2016
4d8d9b6
Apparently bidmach_init.scala did not previously have the dos newline…
javadba Jun 11, 2016
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154 changes: 77 additions & 77 deletions src/main/scala/BIDMach/Clustering.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ class PAMmodel(val opts:PAMmodel.Options = new PAMmodel.Options) {
}

def dists(a:FMat):FMat = {
val dd = if (Mat.hasCUDA > 0) a xTG a else a xT a;
val dd = if (Mat.hasCUDA > 0) a xTG a else a xT a
val d1 = getdiag(dd)
dd ~ dd * 2.0f
dd ~ d1 - dd
Expand All @@ -44,9 +44,9 @@ class PAMmodel(val opts:PAMmodel.Options = new PAMmodel.Options) {
var j = 0
while (j < ncache && continue) {
if (centmap(iss(j, ii)) > 0) {
imin(ii) = centmap(iss(j, ii)) - 1
vmin(ii) = ds(j, ii)
continue = false
imin(ii) = centmap(iss(j, ii)) - 1
vmin(ii) = ds(j, ii)
continue = false
}
j += 1
}
Expand All @@ -71,9 +71,9 @@ class PAMmodel(val opts:PAMmodel.Options = new PAMmodel.Options) {
var idepth = 0
while (isamps.length > 0) {
val isx = centmap(iss(isamps, idepth), 0)
val ifound = find(isx > 0)
imin(isamps(ifound)) = isx(ifound) - 1
vmin(isamps(ifound)) = ds(isamps(ifound), idepth)
val ifound = find(isx > 0)
imin(isamps(ifound)) = isx(ifound) - 1
vmin(isamps(ifound)) = ds(isamps(ifound), idepth)
Mat.nflops += 4*isamps.length
isamps = isamps(find(isx == 0))
idepth += 1
Expand All @@ -89,37 +89,37 @@ class PAMmodel(val opts:PAMmodel.Options = new PAMmodel.Options) {
}

def pointdiffs(ds:FMat, iss:IMat, vd:DMat):DMat = {
val deltas = dzeros(nsamps,1) // Array to hold improvements in distance over vd
var i = 0
while (i < nsamps) { // Calculate improvements over vd for new candidate centers
var j = 0
while (j < nsamps && ds(j,i) < vd(i)) { // using sorted order of ds
deltas(iss(j,i)) += ds(j,i) - vd(i)
j += 1
}
maxdepth = math.max(maxdepth, j)
Mat.nflops += 16*j
i += 1
}
deltas
val deltas = dzeros(nsamps,1) // Array to hold improvements in distance over vd
var i = 0
while (i < nsamps) { // Calculate improvements over vd for new candidate centers
var j = 0
while (j < nsamps && ds(j,i) < vd(i)) { // using sorted order of ds
deltas(iss(j,i)) += ds(j,i) - vd(i)
j += 1
}
maxdepth = math.max(maxdepth, j)
Mat.nflops += 16*j
i += 1
}
deltas
}

def pointdiffs2(ds:FMat, iss:IMat, vd:FMat):FMat = {
val deltas = zeros(nsamps,1) // Array to hold improvements in distance over vd
var ii = icol(0->nsamps)
var idepth = 0
while (ii.length > 0) { // Calculate improvements over vd for new candidate centers
ii = ii(find(ds(ii,idepth) < vd(ii,0)))
var j = 0
while (j < ii.length) {
deltas(iss(ii(j),idepth)) += (ds(ii(j),idepth) - vd(ii(j)))
j += 1
}
Mat.nflops += 16*j
idepth += 1
maxdepth = math.max(maxdepth, idepth)
}
deltas
val deltas = zeros(nsamps,1) // Array to hold improvements in distance over vd
var ii = icol(0->nsamps)
var idepth = 0
while (ii.length > 0) { // Calculate improvements over vd for new candidate centers
ii = ii(find(ds(ii,idepth) < vd(ii,0)))
var j = 0
while (j < ii.length) {
deltas(iss(ii(j),idepth)) += (ds(ii(j),idepth) - vd(ii(j)))
j += 1
}
Mat.nflops += 16*j
idepth += 1
maxdepth = math.max(maxdepth, idepth)
}
deltas
}

def sortgen(dd:FMat):(FMat,IMat) = {
Expand Down Expand Up @@ -151,52 +151,52 @@ class PAMmodel(val opts:PAMmodel.Options = new PAMmodel.Options) {
var itry = 0
while (itry < ntrys) {
println("Try %d" format itry)
val rr = rand(nsamps,1) // Get a random permutation for the centers
val (rs,irs) = sort2(rr,1)
val icenters = irs(0->ncenters,0) // Pick centers from the permutation
val ics = icol(0->nsamps)
val (vdists, imin) = mindists(ds, iss, ics, icenters) // Get min distances from points to centers, and best center ids
println(" pass=0, mean dist=%f" format mean(vdists,1).v)
val vtmp = vdists.copy
val itmp = imin.copy
var nchanged = 1
var ipass = 0
var totchanged = 0
while (nchanged > 0 && ipass < options.maxpasses) { // Keep making passes until no improvements
ipass += 1
nchanged = 0
var ipc = 0
while (ipc < ncenters) { // Try to improve this center (ipc)
vtmp <-- vdists // Copy distances
val ifix = find(imin == ipc) // Find points in cluster with this center
val tcents = icenters((0->ipc) \ ((ipc+1)->ncenters),0) // List of centers minus the current one
mindists(ds, iss, ifix, tcents, vtmp, itmp) // vtmp holds distances to centers minus the current center
val deltas = pointdiffs(ds, iss, vtmp) // deltas holds improvements for each potential center over vtmp
val (vs,is) = mini2(deltas) // Find best new center
if (vs.v + sum(vtmp).v < sum(vdists).v && is.v != icenters(ipc,0)) { // Is the new center better than the old (and not equal to it)?
icenters(ipc) = is.v // If yes, update the center list
mindists(ds, iss, ics, icenters, vdists, imin) // Compute new distances and centers
nchanged += 1
if (options.verb) println(" pass=%d, ipc=%d, mean dist=%f, nchanged=%d" format (ipass, ipc, mean(vdists,1).v, nchanged))
}
ipc += 1
}
println(" pass=%d, mean dist=%f, nchanged=%d, nspills=%d" format (ipass, mean(vdists,1).v, nchanged, nspills))
totchanged += nchanged
}
val mv = mean(vdists).v
if (mv < bestvd) {
bestc = icenters
bestv = vdists
besti = imin
bestvd = mv
}
itry += 1
val rr = rand(nsamps,1) // Get a random permutation for the centers
val (rs,irs) = sort2(rr,1)
val icenters = irs(0->ncenters,0) // Pick centers from the permutation
val ics = icol(0->nsamps)
val (vdists, imin) = mindists(ds, iss, ics, icenters) // Get min distances from points to centers, and best center ids
println(" pass=0, mean dist=%f" format mean(vdists,1).v)
val vtmp = vdists.copy
val itmp = imin.copy
var nchanged = 1
var ipass = 0
var totchanged = 0
while (nchanged > 0 && ipass < options.maxpasses) { // Keep making passes until no improvements
ipass += 1
nchanged = 0
var ipc = 0
while (ipc < ncenters) { // Try to improve this center (ipc)
vtmp <-- vdists // Copy distances
val ifix = find(imin == ipc) // Find points in cluster with this center
val tcents = icenters((0->ipc) \ ((ipc+1)->ncenters),0) // List of centers minus the current one
mindists(ds, iss, ifix, tcents, vtmp, itmp) // vtmp holds distances to centers minus the current center
val deltas = pointdiffs(ds, iss, vtmp) // deltas holds improvements for each potential center over vtmp
val (vs,is) = mini2(deltas) // Find best new center
if (vs.v + sum(vtmp).v < sum(vdists).v && is.v != icenters(ipc,0)) { // Is the new center better than the old (and not equal to it)?
icenters(ipc) = is.v // If yes, update the center list
mindists(ds, iss, ics, icenters, vdists, imin) // Compute new distances and centers
nchanged += 1
if (options.verb) println(" pass=%d, ipc=%d, mean dist=%f, nchanged=%d" format (ipass, ipc, mean(vdists,1).v, nchanged))
}
ipc += 1
}
println(" pass=%d, mean dist=%f, nchanged=%d, nspills=%d" format (ipass, mean(vdists,1).v, nchanged, nspills))
totchanged += nchanged
}
val mv = mean(vdists).v
if (mv < bestvd) {
bestc = icenters
bestv = vdists
besti = imin
bestvd = mv
}
itry += 1
}
val t3=gflop
val vdists2 = mini(dd(?,bestc),2)
println("Optimum in %f secs, %f gflops, mean dist=%f, verify=%f, maxdepth=%d, nspills=%d\nTotal time %f seconds" format
(t3._2, t3._1, bestvd, mean(DMat(vdists2),1).v, maxdepth, nspills, t3._2+ft2._2+ft1._2))
(t3._2, t3._1, bestvd, mean(DMat(vdists2),1).v, maxdepth, nspills, t3._2+ft2._2+ft1._2))
}

}
Expand Down
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