X-Git-Url: https://git.auder.net/?p=morpheus.git;a=blobdiff_plain;f=pkg%2FR%2Futils.R;h=ff988a063ec034716035ebf58bc43eed01edce77;hp=6d1c36161204b25db17c2c237bcfb77b62ba3556;hb=6dd5c2acccd10635449230faa824b7e8906911bf;hpb=bbdcfe44da4011574dabb19b4f83e2ab199c667a

diff --git a/pkg/R/utils.R b/pkg/R/utils.R
index 6d1c361..ff988a0 100644
--- a/pkg/R/utils.R
+++ b/pkg/R/utils.R
@@ -9,9 +9,9 @@
 #' @export
 normalize = function(X)
 {
-	X = as.matrix(X)
-	norm2 = sqrt( colSums(X^2) )
-	sweep(X, 2, norm2, '/')
+  X = as.matrix(X)
+  norm2 = sqrt( colSums(X^2) )
+  sweep(X, 2, norm2, '/')
 }
 
 # Computes a tensor-vector product
@@ -23,11 +23,11 @@ normalize = function(X)
 #
 .T_I_I_w = function(Te, w)
 {
-	d = length(w)
-	Ma = matrix(0,nrow=d,ncol=d)
-	for (j in 1:d)
-		Ma = Ma + w[j] * Te[,,j]
-	Ma
+  d = length(w)
+  Ma = matrix(0,nrow=d,ncol=d)
+  for (j in 1:d)
+    Ma = Ma + w[j] * Te[,,j]
+  Ma
 }
 
 # Computes the second-order empirical moment between input X and output Y
@@ -39,11 +39,11 @@ normalize = function(X)
 #
 .Moments_M2 = function(X, Y)
 {
-	n = nrow(X)
-	d = ncol(X)
-	M2 = matrix(0,nrow=d,ncol=d)
-	matrix( .C("Moments_M2", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
-		pd=as.integer(d), M2=as.double(M2), PACKAGE="morpheus")$M2, nrow=d, ncol=d)
+  n = nrow(X)
+  d = ncol(X)
+  M2 = matrix(0,nrow=d,ncol=d)
+  matrix( .C("Moments_M2", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
+    pd=as.integer(d), M2=as.double(M2), PACKAGE="morpheus")$M2, nrow=d, ncol=d)
 }
 
 # Computes the third-order empirical moment between input X and output Y
@@ -55,11 +55,11 @@ normalize = function(X)
 #
 .Moments_M3 = function(X, Y)
 {
-	n = nrow(X)
-	d = ncol(X)
-	M3 = array(0,dim=c(d,d,d))
-	array( .C("Moments_M3", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
-		pd=as.integer(d), M3=as.double(M3), PACKAGE="morpheus")$M3, dim=c(d,d,d) )
+  n = nrow(X)
+  d = ncol(X)
+  M3 = array(0,dim=c(d,d,d))
+  array( .C("Moments_M3", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
+    pd=as.integer(d), M3=as.double(M3), PACKAGE="morpheus")$M3, dim=c(d,d,d) )
 }
 
 #' computeMoments
@@ -72,7 +72,7 @@ normalize = function(X)
 #'
 #' @export
 computeMoments = function(X, Y)
-	list( colMeans(Y * X), .Moments_M2(X,Y), .Moments_M3(X,Y) )
+  list( colMeans(Y * X), .Moments_M2(X,Y), .Moments_M3(X,Y) )
 
 # Find the optimal assignment (permutation) between two sets (minimize cost)
 #
@@ -83,9 +83,9 @@ computeMoments = function(X, Y)
 #
 .hungarianAlgorithm = function(distances)
 {
-	n = nrow(distances)
-	.C("hungarianAlgorithm", distances=as.double(distances), pn=as.integer(n),
-		assignment=integer(n), PACKAGE="morpheus")$assignment
+  n = nrow(distances)
+  .C("hungarianAlgorithm", distances=as.double(distances), pn=as.integer(n),
+    assignment=integer(n), PACKAGE="morpheus")$assignment
 }
 
 #' alignMatrices
@@ -105,65 +105,65 @@ computeMoments = function(X, Y)
 #' @export
 alignMatrices = function(Ms, ref, ls_mode)
 {
-	if (!is.matrix(ref) && ref != "mean")
-		stop("ref: matrix or 'mean'")
-	if (!ls_mode %in% c("exact","approx1","approx2"))
-		stop("ls_mode in {'exact','approx1','approx2'}")
+  if (!is.matrix(ref) && ref != "mean")
+    stop("ref: matrix or 'mean'")
+  if (!ls_mode %in% c("exact","approx1","approx2"))
+    stop("ls_mode in {'exact','approx1','approx2'}")
 
-	K <- ncol(Ms[[1]])
-	if (is.character(ref)) #ref=="mean"
-		m_sum = Ms[[1]]
-	L <- length(Ms)
-	for (i in ifelse(is.character(ref),2,1):L)
-	{
-		m_ref = if (is.character(ref)) m_sum / (i-1) else ref
-		m = Ms[[i]] #shorthand
+  K <- ncol(Ms[[1]])
+  if (is.character(ref)) #ref=="mean"
+    m_sum = Ms[[1]]
+  L <- length(Ms)
+  for (i in ifelse(is.character(ref),2,1):L)
+  {
+    m_ref = if (is.character(ref)) m_sum / (i-1) else ref
+    m = Ms[[i]] #shorthand
 
-		if (ls_mode == "exact")
-		{
-			#distances[i,j] = distance between m column i and ref column j
-			distances = apply( m_ref, 2, function(col) ( sqrt(colSums((m-col)^2)) ) )
-			assignment = .hungarianAlgorithm(distances)
-			col <- m[,assignment]
-			if (is.list(Ms)) Ms[[i]] <- col else Ms[,,i] <- col
-		}
-		else
-		{
-			# Greedy matching:
-			#   approx1: li[[i]][,j] is assigned to m[,k] minimizing dist(li[[i]][,j],m[,k'])
-			#   approx2: m[,j] is assigned to li[[i]][,k] minimizing dist(m[,j],li[[i]][,k'])
-			available_indices = 1:K
-			for (j in 1:K)
-			{
-				distances =
-					if (ls_mode == "approx1")
-					{
-						apply(as.matrix(m[,available_indices]), 2,
-							function(col) ( sqrt(sum((col - m_ref[,j])^2)) ) )
-					}
-					else #approx2
-					{
-						apply(as.matrix(m_ref[,available_indices]), 2,
-							function(col) ( sqrt(sum((col - m[,j])^2)) ) )
-					}
-				indMin = which.min(distances)
-				if (ls_mode == "approx1")
-				{
-					col <- m[ , available_indices[indMin] ]
-					if (is.list(Ms)) Ms[[i]][,j] <- col else Ms[,j,i] <- col
-				}
-				else #approx2
-				{
-					col <- available_indices[indMin]
-					if (is.list(Ms)) Ms[[i]][,col] <- m[,j] else Ms[,col,i] <- m[,j]
-				}
-				available_indices = available_indices[-indMin]
-			}
-		}
+    if (ls_mode == "exact")
+    {
+      #distances[i,j] = distance between m column i and ref column j
+      distances = apply( m_ref, 2, function(col) ( sqrt(colSums((m-col)^2)) ) )
+      assignment = .hungarianAlgorithm(distances)
+      col <- m[,assignment]
+      if (is.list(Ms)) Ms[[i]] <- col else Ms[,,i] <- col
+    }
+    else
+    {
+      # Greedy matching:
+      #   approx1: li[[i]][,j] is assigned to m[,k] minimizing dist(li[[i]][,j],m[,k'])
+      #   approx2: m[,j] is assigned to li[[i]][,k] minimizing dist(m[,j],li[[i]][,k'])
+      available_indices = 1:K
+      for (j in 1:K)
+      {
+        distances =
+          if (ls_mode == "approx1")
+          {
+            apply(as.matrix(m[,available_indices]), 2,
+              function(col) ( sqrt(sum((col - m_ref[,j])^2)) ) )
+          }
+          else #approx2
+          {
+            apply(as.matrix(m_ref[,available_indices]), 2,
+              function(col) ( sqrt(sum((col - m[,j])^2)) ) )
+          }
+        indMin = which.min(distances)
+        if (ls_mode == "approx1")
+        {
+          col <- m[ , available_indices[indMin] ]
+          if (is.list(Ms)) Ms[[i]][,j] <- col else Ms[,j,i] <- col
+        }
+        else #approx2
+        {
+          col <- available_indices[indMin]
+          if (is.list(Ms)) Ms[[i]][,col] <- m[,j] else Ms[,col,i] <- m[,j]
+        }
+        available_indices = available_indices[-indMin]
+      }
+    }
 
-		# Update current sum with "label-switched" li[[i]]
-		if (is.character(ref)) #ref=="mean"
-			m_sum = m_sum + Ms[[i]]
-	}
-	Ms
+    # Update current sum with "label-switched" li[[i]]
+    if (is.character(ref)) #ref=="mean"
+      m_sum = m_sum + Ms[[i]]
+  }
+  Ms
 }