distance and hirerachical clustering with template => compute with float precision available

R/acprob.R

@@ -58,32 +58,6 @@ W <- function(x,h,D=NULL,kernel="gaussien")
 }
 
 
-WsansC <- function(x,h,D=NULL,kernel="gaussien")
-{
-    x   <- as.matrix(x)
-    n   <- dim(x)[1]
-    p   <- dim(x)[2]
-    if (is.null(D)) {
-        D <- diag(1,p)
-    }
-
-    som    <- 0
-    result <- 0
-    for(i in 1:(n-1) )
-        {
-        for(j in (i+1):n)
-            {
-            Delta <- x[i,]-x[j,]
-            norm <- sqrt(t(Delta) %*%D %*%Delta)
-            k <- K ( norm /h,kernel)   # K ( |Delta|/h )
-            k <- as.numeric(k)
-            som <- som + k
-            result <- result + k * Delta %*% t(Delta)
-            }
-        }
-    result /   som
-}
-
 varrob <- function(x,h,D=NULL,kernel="gaussien")
 {
     x   <- as.matrix(x)
@@ -120,32 +94,6 @@ varrob <- function(x,h,D=NULL,kernel="gaussien")
 }
 
 
-varrobsansC <- function(x,h,D=NULL,kernel="gaussien")
-{
-    n   <- dim(x)[1]
-    p   <- dim(x)[2]
-    if (is.null(D)) {
-        D <- diag(1,p)
-    }
-    x   <- as.matrix(x)
-    x   <- scale(x ,center = TRUE, scale = FALSE)
-    som <- 0
-    res <- 0
-    for (i in 1:n )
-    {
-        k <- K (  sqrt(t(x[i,]) %*%D %*% x[i,]) /h,kernel)
-        k <- as.numeric(k)
-        res <- res + k * x[i,] %*% t( x[i,])
-        som <- som + k
-    }
-    S <- res / som
-    Sinv <- solve(S)
-    solve ( Sinv -  D / h )
-
-}
-
-
-
 acpgen <- function(x,h1,h2,center=TRUE,reduce=TRUE,kernel="gaussien")
 {
     # CENTRONS, ET REDUISONS

R/dist.R

-Dist <- function(x, method="euclidean", diag=FALSE, upper=FALSE)
+Dist <- function(x, method="euclidean", nbproc = 1, diag=FALSE, upper=FALSE)
 {
 
   if(class(x) == "exprSet")
@@ -25,6 +25,7 @@ Dist <- function(x, method="euclidean", diag=FALSE, upper=FALSE)
 	    d = double(N*(N - 1)/2),
 	    diag  = as.integer(FALSE),
 	    method= as.integer(method),
+            nbproc = as.integer(nbproc),
             ierr=as.integer(0),
 	    DUP = FALSE,
             NAOK=TRUE,

R/hcluster.R

@@ -10,7 +10,7 @@
 
 
 
-hcluster <- function (x, method = "euclidean", diag = FALSE, upper = FALSE, link = "complete", members = NULL)
+hcluster <- function (x, method = "euclidean", diag = FALSE, upper = FALSE, link = "complete", members = NULL, nbproc = 2, doubleprecision = TRUE)
 {
 
   if(class(x) == "exprSet")
@@ -46,6 +46,10 @@ hcluster <- function (x, method = "euclidean", diag = FALSE, upper = FALSE, link
   if (length(members) != N) 
     stop("Invalid length of members")
   n <- N
+
+  precision <- 1
+  if(doubleprecision)
+    precision <- 2
   
   hcl <- .C("hcluster",
             x = as.double(x),
@@ -59,6 +63,8 @@ hcluster <- function (x, method = "euclidean", diag = FALSE, upper = FALSE, link
             order = integer(n),
             crit = double(n),
             members = as.double(members),
+            nbprocess  = as.integer(nbproc),
+            precision  = as.integer(precision),
             res  = as.integer (1),
             DUP = FALSE,
             NAOK=TRUE,

man/acpgen.Rd

@@ -2,7 +2,7 @@
 \alias{acpgen}
 \alias{K}
 \alias{W}
-\alias{WsansC}
+
 
 
 \title{Generalised principal component analysis}
@@ -12,7 +12,6 @@
 acpgen(x,h1,h2,center=TRUE,reduce=TRUE,kernel="gaussien")
 K(u,kernel="gaussien")
 W(x,h,D=NULL,kernel="gaussien")
-WsansC(x,h,D=NULL,kernel="gaussien")
 }
 
 \arguments{

man/dist.Rd

 \name{Dist}
 \title{Distance Matrix Computation}
 \usage{
-Dist(x, method = "euclidean", diag = FALSE, upper = FALSE)
+Dist(x, method = "euclidean", nbproc = 1, diag = FALSE, upper = FALSE)
 }
 \alias{Dist}
 
@@ -15,6 +15,7 @@ Dist(x, method = "euclidean", diag = FALSE, upper = FALSE)
     \code{"canberra"}, \code{"binary"}, \code{"pearson"},
     \code{"correlation"} or \code{"spearman"}.
     Any unambiguous substring can be given.}
+  \item{nbproc}{integer, Number of subprocess for parallelization}
   \item{diag}{logical value indicating whether the diagonal of the
     distance matrix should be printed by \code{print.dist}.}
   \item{upper}{logical value indicating whether the upper triangle of the
@@ -111,7 +112,7 @@ Dist(x, method = "euclidean", diag = FALSE, upper = FALSE)
   \code{\link[cluster]{daisy}} in the \file{cluster} package with more
   possibilities in the case of \emph{mixed} (contiuous / categorical)
   variables.
- \code{\link[stats]{dist}}  \code{\link{hclust}}.
+ \code{\link[stats]{dist}}  \code{\link{hcluster}}.
 }
 \examples{
 x <- matrix(rnorm(100), nrow=5)
@@ -119,6 +120,10 @@ Dist(x)
 Dist(x, diag = TRUE)
 Dist(x, upper = TRUE)
 
+
+## compute dist with 8 threads
+Dist(x,nbproc=8)
+
 }
 \keyword{multivariate}
 \keyword{cluster}

man/hcluster.Rd

@@ -6,7 +6,8 @@
 }
 \usage{
 hcluster(x, method = "euclidean", diag = FALSE, upper = FALSE,
-         link = "complete", members = NULL)
+         link = "complete", members = NULL, nbproc = 2,
+         doubleprecision = TRUE)
 }
 \arguments{
   \item{x}{
@@ -29,6 +30,10 @@ hcluster(x, method = "euclidean", diag = FALSE, upper = FALSE,
     \code{"average"}, \code{"mcquitty"}, \code{"median"} or
     \code{"centroid"}.}
   \item{members}{\code{NULL} or a vector with length size of \code{d}.}
+  \item{nbproc}{integer, number of subprocess for parallelization}
+  \item{doubleprecision}{True: use of double precision for distance
+    matrix computation; False: use simple precision}
+  
 }
 \value{
   An object of class \bold{hclust} which describes the
@@ -87,7 +92,7 @@ hcluster(x, method = "euclidean", diag = FALSE, upper = FALSE,
   by Antoine Lucas \url{http://mulcyber.toulouse.inra.fr/projects/amap/}.
 }
 \seealso{
-  \code{\link{Dist}}, \code{\link{hclusterpar}},
+  \code{\link{Dist}},
   \code{\link[stats]{hclust}}, \code{\link[stats]{kmeans}}.
 }
 
@@ -118,6 +123,29 @@ opar <- par(mfrow = c(1, 2))
 plot(hc,  labels = FALSE, hang = -1, main = "Original Tree")
 plot(hc1, labels = FALSE, hang = -1, main = "Re-start from 10 clusters")
 par(opar)
+
+
+## other combinaison are possible
+
+hc <- hcluster(USArrests,method = "euc",link = "ward", nbproc= 1,
+doubleprecision = TRUE)
+hc <- hcluster(USArrests,method = "max",link = "single", nbproc= 2,
+doubleprecision = TRUE)
+hc <- hcluster(USArrests,method = "man",link = "complete", nbproc= 1,
+doubleprecision = TRUE)
+hc <- hcluster(USArrests,method = "can",link = "average", nbproc= 2,
+doubleprecision = TRUE)
+hc <- hcluster(USArrests,method = "bin",link = "mcquitty", nbproc= 1,
+doubleprecision = FALSE)
+hc <- hcluster(USArrests,method = "pea",link = "median", nbproc= 2,
+doubleprecision = FALSE)
+hc <- hcluster(USArrests,method = "cor",link = "centroid", nbproc= 1,
+doubleprecision = FALSE)
+hc <- hcluster(USArrests,method = "spe",link = "complete", nbproc= 2,
+doubleprecision = FALSE)
+
+
+
 }
 \keyword{multivariate}
 \keyword{cluster}

man/varrob.Rd

 \name{VarRob}
 \alias{varrob}
-\alias{varrobsansC}
+
 
 \title{Robust variance}
 \description{Compute a robust variance}
 
 \usage{
 varrob(x,h,D=NULL,kernel="gaussien")
-varrobsansC(x,h,D=NULL,kernel="gaussien")
 }
 
 \arguments{

src/distance.cpp

+
+#include "distance.h"
+#include "distance_template.h"
+
+
+
+/**
+ * R_distance: compute parallelized distance. Function called direclty by R
+ * \brief compute distance and call function thread_dist
+ * that call one of function R_euclidean or R_...
+ * \param x input matrix
+ * \param nr,nc number of row and columns
+ *        nr individuals with nc values.
+ * \param d distance half matrix.
+ * \param diag if we compute diagonal of dist matrix (usualy: no).
+ * \param method 1, 2,... method used
+ * \param nbprocess: number of threads to create
+ * \param ierr error return; 1 good; 0 missing values
+ */
+void R_distance(double *x, int *nr, int *nc, double *d, int *diag, int *method,int *nbprocess, int * ierr)
+{
+#ifndef __MINGW_H
+  if(*nbprocess != 1)
+    amaptemplate::distancepar<double>(x,nr,nc, d,diag,method,
+				      nbprocess, ierr);
+  else
+#endif
+    amaptemplate::T_distance<double>(x,nr, nc, d, diag, 
+				   method, ierr);
+  
+
+}
+
+
+
+
+/**
+ * Sort, and return order and rank
+ * \brief This function is used by R_spearman.
+ * order and rank must be initialised with 0:(n-1)
+ * make sort, return x sorted
+ * order = order(x) - 1 
+ * rank = rank(x) -1
+ * 
+ */
+
+void rsort_rank_order(double *x, int *order, int *rank, int * n)
+{
+    double v;
+    int i, j, h, iv;
+
+
+    for (h = 1; h <= *n / 9; h = 3 * h + 1);
+    for (; h > 0; h /= 3)
+	for (i = h; i < *n; i++) {
+	    v = x[i]; iv = order[i];
+	    j = i;
+	    while (j >= h && (x[j - h] > v))
+	      {
+		x[j] = x[j - h]; order[j] = order[j-h];
+		rank[order[j]] = j; 
+		j -= h; 
+	      }
+	    x[j] = v; order[j] = iv; 
+	    rank[order[j]] = j; 
+	}
+
+}

src/distance.h

+
+extern "C"
+{
+
+  /**
+   * R_distance: compute parallelized distance. Function called direclty by R
+   * \brief compute distance and call function thread_dist
+   * that call one of function R_euclidean or R_...
+   * \param x input matrix
+   * \param nr,nc number of row and columns
+   *        nr individuals with nc values.
+   * \param d distance half matrix.
+   * \param diag if we compute diagonal of dist matrix (usualy: no).
+   * \param method 1, 2,... method used
+   * \param nbprocess: number of threads to create
+   * \param ierr error return; 1 good; 0 missing values
+   */
+  void R_distance(double *x, int *nr, int *nc, double *d, int *diag, 
+		  int *method,int * nbprocess, int * ierr);
+
+
+
+  void rsort_rank_order(double *x, int *order, int *rank, int * n);
+
+
+
+}

src/distance_template.cpp

+/*! \file distance.c
+ * \brief all functions requiered for R dist function and C hcluster function.
+ *
+ *  \date Created: probably in 1995
+ *  \date Last modified: Time-stamp: <2005-10-09 13:12:06 antoine>
+ *
+ *  \author R core members, and lately: Antoine Lucas 
+ *
+ *  
+ *  R : A Computer Language for Statistical Data Analysis
+ *  Copyright (C) 1995, 1996  Robert Gentleman and Ross Ihaka
+ *  Copyright (C) 1998, 2001  Robert Gentleman, Ross Ihaka and the
+ *                            R Development Core Team
+ *
+ *  This program is free software; you can redistribute it and/or modify
+ *  it under the terms of the GNU General Public License as published by
+ *  the Free Software Foundation; either version 2 of the License, or
+ *  (at your option) any later version.
+ *
+ *  This program is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *  GNU General Public License for more details.
+ *
+ *  You should have received a copy of the GNU General Public License
+ *  along with this program; if not, write to the Free Software
+ *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+ *
+ */
+
+
+#define _AMAP_DISTANCE_TEMPLATE_CPP 1
+
+#ifdef HAVE_CONFIG_H
+#include <config.h>
+#endif
+
+#include "distance_template.h"
+#include "distance.h"
+
+#include <float.h>
+#include <R_ext/Arith.h>
+#include <R_ext/Error.h>
+#include "mva.h"
+#include <limits>
+#include <pthread.h>
+
+#define MAX( A , B )  ( ( A ) > ( B ) ? ( A ) : ( B ) )
+#define MIN( A , B )  ( ( A ) < ( B ) ? ( A ) : ( B ) )
+
+
+namespace amaptemplate
+{
+
+  /** \brief Distance euclidean (i.e. sqrt(sum of square) )
+   */
+  template <class T> T  R_euclidean(double *x, int nr, int nc, int i1, int i2,int * flag, void ** opt)
+  {
+    T dev, dist;
+    int count, j;
+
+    count= 0;
+    dist = 0;
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	dev = (x[i1] - x[i2]);
+	dist += dev * dev;
+	count++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0)
+      { 
+	*flag = 0;
+	return NA_REAL;
+      }
+
+    if(count != nc) dist /= ((T)count/nc);
+    return sqrt(dist);
+  }
+
+  /** \brief Distance maximum (supremum norm)
+   */
+  template <class T> T R_maximum(double *x, int nr, int nc, int i1, int i2, int * flag, void ** opt)
+  {
+    T dev, dist;
+    int count, j;
+
+    count = 0;
+    // TODO: use c++ limits
+    dist = std::numeric_limits<T>::min();
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	dev = fabs(x[i1] - x[i2]);
+	if(dev > dist)
+	  dist = dev;
+	count++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0)
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    return dist;
+  }
+
+  /** \brief Distance manhattan
+   */
+  template< class T> T R_manhattan(double *x, int nr, int nc, int i1, int i2, int * flag, void ** opt)
+  {
+    T dist;
+    int count, j;
+
+    count = 0;
+    dist = 0;
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	dist += fabs(x[i1] - x[i2]);
+	count++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0)
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    if(count != nc) dist /= ((T)count/nc);
+    return dist;
+  }
+
+  /** \brief Distance canberra
+   */
+  template <class T> T R_canberra(double *x, int nr, int nc, int i1, int i2, int * flag, void ** opt)
+  {
+    T dist, sum, diff;
+    int count, j;
+
+    count = 0;
+    dist = 0;
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	sum = fabs(x[i1] + x[i2]);
+	diff = fabs(x[i1] - x[i2]);
+	if (sum > DBL_MIN || diff > DBL_MIN) {
+	  dist += diff/sum;
+	  count++;
+	}
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0)
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    if(count != nc) dist /= ((T)count/nc);
+    return dist;
+  }
+
+  /** \brief Distance binary
+   */
+  template <class T> T R_dist_binary(double *x, int nr, int nc, int i1, int i2,int * flag, void ** opt)
+  {
+    int total, count, dist;
+    int j;
+
+    total = 0;
+    count = 0;
+    dist = 0;
+
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	if(x[i1] || x[i2]){
+	  count++;
+	  if( ! (x[i1] && x[i2]) ) dist++;
+	}
+	total++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+
+    if(total == 0)
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    if(count == 0) return 0;
+    return (T) dist / count;
+  }
+
+  /** \brief Pearson / Pearson centered (correlation)
+   *  \note Added by A. Lucas
+   */
+  template<class T> T R_pearson(double *x, int nr, int nc, int i1, int i2,int * flag, void ** opt)
+  {
+    T num,sum1,sum2, dist;
+    int count,j;
+
+    count= 0;
+    num = 0;
+    sum1 = 0;
+    sum2 = 0;
+
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	num += (x[i1] * x[i2]);
+	sum1 += (x[i1] * x[i1]);
+	sum2 += (x[i2] * x[i2]);
+	count++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0) 
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    dist = 1 - ( num / sqrt(sum1 * sum2) );
+    return dist;
+  }
+
+
+  /** \brief Distance correlation (Uncentered Pearson)
+   *  \note Added by A. Lucas
+   */
+  template<class T> T R_correlation(double *x, int nr, int nc, int i1, int i2,int * flag, void ** opt)
+  {
+    T num,denum,sumx,sumy,sumxx,sumyy,sumxy;
+    int count,j;
+
+    count= 0;
+    sumx=0;
+    sumy=0;
+    sumxx=0;
+    sumyy=0;
+    sumxy=0;
+
+
+    for(j = 0 ; j < nc ; j++) {
+      if(R_FINITE(x[i1]) && R_FINITE(x[i2])) {
+	sumxy += (x[i1] * x[i2]);
+	sumx += x[i1];
+	sumy += x[i2];
+	sumxx += x[i1] * x[i1];
+	sumyy += x[i2] * x[i2];
+	count++;
+      }
+      i1 += nr;
+      i2 += nr;
+    }
+    if(count == 0)
+      {
+	*flag = 0;
+	return NA_REAL;
+      }
+    num = sumxy - ( sumx*sumy /count );
+    denum = sqrt( (sumxx - (sumx*sumx /count ) )* (sumyy - (sumy*sumy /count ) ) );
+    return 1 - (num / denum);
+  }
+
+  /** \brief Spearman distance (rank base metric)
+   *  \note Added by A. Lucas
+   */
+  template <class T> T R_spearman(double *x, int nr, int nc, int i1, int i2,int * flag, void ** opt)
+  {
+    int j;
+    double * data_tri;
+    int * order_tri;
+    int * rank_tri;
+    int n;
+    T diffrang=0;
+
+    /* initialisation of variables */
+    data_tri = (double * ) opt[0];
+    order_tri = (int * )   opt[1];
+    rank_tri  = (int * )   opt[2];
+    for(j = 0 ; j < nc ; j++) {
+      if(!(R_FINITE(x[i1]) && R_FINITE(x[i2])))
+	{
+	  *flag = 0;
+	  return NA_REAL;	
+	}
+      order_tri[j] = rank_tri[j] = j;