@@ -1,5 +1,5 @@

 Package: preprocessCore

-Version: 1.13.3

+Version: 1.13.4

 Title: A collection of pre-processing functions

 Author: Benjamin Milo Bolstad <bmb@bmbolstad.com>

 Maintainer: Benjamin Milo Bolstad <bmb@bmbolstad.com>

@@ -65,6 +65,7 @@

  ** Nov 19, 2008 - add *_via_subset code

  ** Jan 15, 2009 - fix VECTOR_ELT/STRING_ELT issues

  ** Dec 1, 2010 - change how  PTHREAD_STACK_MIN is used

+ ** Jan 5, 2011 - use_target issue when target distribution length != nrow(x) fixed

  **

  ***********************************************************/

@@ -2612,7 +2613,7 @@ static double linear_interpolate_helper(double v, double *x, double *y, int n)

-void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset, double *target, int *targetrows, int start_col, int end_col){

+static void using_target_via_subset_part1(double *data, int *rows, int *cols, int *in_subset, double *target, int *targetrows, int start_col, int end_col, int subset_count){

   int i,j,ind,target_ind;

@@ -2627,91 +2628,94 @@ void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset,

   int targetnon_na = *targetrows;

   int non_na = 0;

-  int subset_count = 0;

-

   double *sample_percentiles;

   double *datvec;

-

-  /* Two parts to the algorithm */

-  /* Part 1: Adjust the elements not in the "subset" */

-  for (i = 0; i <  *rows; i++){

-    if (in_subset[i] == 1){

-      subset_count++;

-    }

-  }

-  if (*rows > subset_count){

-    /* We have non subset elements to deal with */

-    sample_percentiles = (double *)Calloc(subset_count, double);

-    datvec = (double *)Calloc(*rows,double);

-    dimat = (dataitem **)Calloc(1,dataitem *);

-    dimat[0] = (dataitem *)Calloc(*rows,dataitem);

+  sample_percentiles = (double *)Calloc(subset_count, double);

+  datvec = (double *)Calloc(*rows,double);

+  dimat = (dataitem **)Calloc(1,dataitem *);

+  dimat[0] = (dataitem *)Calloc(*rows,dataitem);

-    for (j = start_col; j <= end_col; j++){

-      non_na = 0;

-      for (i =0; i < *rows; i++){

-	if (ISNA(data[j*(*rows) + i]) || (in_subset[i] == 0)){

-	  

-	} else {

-	  dimat[0][non_na].data = data[j*(*rows) + i];

-	  dimat[0][non_na].rank = i;

-	  non_na++;

-	}

-      }	   

-      qsort(dimat[0],non_na,sizeof(dataitem),sort_fn);

-      get_ranks(ranks,dimat[0],non_na);

-      

-      for (i=0; i < non_na; i++){

-	sample_percentiles[i] = (double)(ranks[i] - 1)/(double)(non_na-1);

-	datvec[i] = dimat[0][i].data;

+  for (j = start_col; j <= end_col; j++){

+    

+    /* First figure out percentiles of the "subset" data */

+    non_na = 0;

+    for (i =0; i < *rows; i++){

+      if (!ISNA(data[j*(*rows) + i]) && (in_subset[i] == 1)){

+	dimat[0][non_na].data = data[j*(*rows) + i];

+	dimat[0][non_na].rank = i;

+	non_na++;

       }

-      

-      for  (i =0; i < *rows; i++){

+    }	   

+    qsort(dimat[0],non_na,sizeof(dataitem),sort_fn);

+    get_ranks(ranks,dimat[0],non_na);

+    

+    for (i=0; i < non_na; i++){

+      sample_percentiles[i] = (double)(ranks[i] - 1)/(double)(non_na-1);

+      datvec[i] = dimat[0][i].data;

+    }

+    

+    /* Now try to estimate what percentile of the "subset" data each datapoint in the "non-subset" data falls */

+    for  (i =0; i < *rows; i++){

       /*Linear interpolate to get sample percentile */

-	if (in_subset[i] == 0 && !ISNA(data[j*(*rows) + i])){

-	  samplepercentile = linear_interpolate_helper(data[j*(*rows) + i], datvec, sample_percentiles, non_na);

-	  target_ind_double = 1.0 + ((double)(targetnon_na) - 1.0) * samplepercentile;

-	  target_ind_double_floor = floor(target_ind_double + 4*DOUBLE_EPS);

-	  

-	  target_ind_double = target_ind_double - target_ind_double_floor;

-	  

-	  if (fabs(target_ind_double) <=  4*DOUBLE_EPS){

-	    target_ind_double = 0.0;

-	  }

-	  

-	  

-	  if (target_ind_double  == 0.0){

-	    target_ind = (int)floor(target_ind_double_floor + 0.5); /* nearbyint(target_ind_double_floor); */	

-	    ind = dimat[0][i].rank;

-	    data[j*(*rows) +i] = row_mean[target_ind-1];

-	  } else if (target_ind_double == 1.0){

-	    target_ind = (int)floor(target_ind_double_floor + 1.5); /* (int)nearbyint(target_ind_double_floor + 1.0); */ 

-	    ind = dimat[0][i].rank;

-	    data[j*(*rows) +i] = row_mean[target_ind-1];

+      if (in_subset[i] == 0 && !ISNA(data[j*(*rows) + i])){

+	samplepercentile = linear_interpolate_helper(data[j*(*rows) + i], datvec, sample_percentiles, non_na);

+	target_ind_double = 1.0 + ((double)(targetnon_na) - 1.0) * samplepercentile;

+	target_ind_double_floor = floor(target_ind_double + 4*DOUBLE_EPS);

+	

+	target_ind_double = target_ind_double - target_ind_double_floor;

+	

+	if (fabs(target_ind_double) <=  4*DOUBLE_EPS){

+	  target_ind_double = 0.0;

+	}

+	if (target_ind_double  == 0.0){

+	  target_ind = (int)floor(target_ind_double_floor + 0.5); /* nearbyint(target_ind_double_floor); */	

+	  ind = dimat[0][i].rank;

+	  data[j*(*rows) +i] = row_mean[target_ind-1];

+	} else if (target_ind_double == 1.0){

+	  target_ind = (int)floor(target_ind_double_floor + 1.5); /* (int)nearbyint(target_ind_double_floor + 1.0); */ 

+	  ind = dimat[0][i].rank;

+	  data[j*(*rows) +i] = row_mean[target_ind-1];

+	} else {

+	  target_ind = (int)floor(target_ind_double_floor + 0.5); /* nearbyint(target_ind_double_floor); */	

+	  ind = dimat[0][i].rank;

+	  if ((target_ind < *targetrows) && (target_ind > 0)){

+	    data[j*(*rows) +i] = (1.0- target_ind_double)*row_mean[target_ind-1] + target_ind_double*row_mean[target_ind];

+	  } else if (target_ind >= *targetrows){

+	    data[j*(*rows) +i] = row_mean[*targetrows-1];

 	  } else {

-	    target_ind = (int)floor(target_ind_double_floor + 0.5); /* nearbyint(target_ind_double_floor); */	

-	    ind = dimat[0][i].rank;

-	    if ((target_ind < *targetrows) && (target_ind > 0)){

-	      data[j*(*rows) +i] = (1.0- target_ind_double)*row_mean[target_ind-1] + target_ind_double*row_mean[target_ind];

-	    } else if (target_ind >= *targetrows){

-	      data[j*(*rows) +i] = row_mean[*targetrows-1];

-	    } else {

-	      data[j*(*rows) +i] = row_mean[0];

-	    }

+	    data[j*(*rows) +i] = row_mean[0];

 	  }

 	}

       }

-    } 

-    Free(dimat[0]);

-    Free(dimat);

-    Free(datvec);

-    Free(sample_percentiles);

+    }

+  } 

+  Free(dimat[0]);

+  Free(dimat);

+  Free(datvec);

+  Free(sample_percentiles);

+}

-  }

+static void using_target_via_subset_part2(double *data, int *rows, int *cols, int *in_subset, double *target, int *targetrows, int start_col, int end_col, int subset_count){

+

+  int i,j,ind,target_ind;

+  

+  dataitem **dimat;

+

+  double *row_mean = target;

+

+  double *ranks = (double *)Calloc((*rows),double);

+  double samplepercentile;

+  double target_ind_double,target_ind_double_floor;

+

+  int targetnon_na = *targetrows;

+  int non_na = 0;

+  

+  double *sample_percentiles;

+  double *datvec;

-  /* Part 2: Adjust the elements in the subset*/

   if (*rows == targetnon_na){

     /* now assign back distribution */

     /* this is basically the standard story */

@@ -2722,9 +2726,7 @@ void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset,

     for (j = start_col; j <= end_col; j++){

       non_na = 0;

       for (i =0; i < *rows; i++){

-	if (ISNA(data[j*(*rows) + i]) || (in_subset[i] == 0)){

-	  

-	} else {

+	if (!ISNA(data[j*(*rows) + i]) && (in_subset[i] == 1)){

 	  dimat[0][non_na].data = data[j*(*rows) + i];

 	  dimat[0][non_na].rank = i;

 	  non_na++;

@@ -2790,9 +2792,7 @@ void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset,

     for (j = start_col; j <= end_col; j++){

       non_na = 0;

       for (i =0; i < *rows; i++){	

-	if (ISNA(data[j*(*rows) + i])){

-

-	} else {

+	if (!ISNA(data[j*(*rows) + i]) && (in_subset[i] == 1)){

 	  dimat[0][non_na].data = data[j*(*rows) + i];

 	  dimat[0][non_na].rank = i;

 	  non_na++;

@@ -2841,6 +2841,47 @@ void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset,

   Free(dimat[0]);

   Free(dimat);

   Free(ranks);

+

+

+}

+

+void using_target_via_subset(double *data, int *rows, int *cols, int *in_subset, double *target, int *targetrows, int start_col, int end_col){

+

+  int i,j,ind,target_ind;

+  

+  dataitem **dimat;

+

+  double *row_mean = target;

+

+  double *ranks = (double *)Calloc((*rows),double);

+  double samplepercentile;

+  double target_ind_double,target_ind_double_floor;

+

+  int targetnon_na = *targetrows;

+  int non_na = 0;

+  

+  int subset_count = 0;

+

+  double *sample_percentiles;

+  double *datvec;

+  

+

+  /* Two parts to the algorithm */

+ 

+  /* First find out if the enitirety of the data is in the subset */

+  for (i = 0; i <  *rows; i++){

+    if (in_subset[i] == 1){

+      subset_count++;

+    }

+  }

+   /* Part 1: Adjust the elements not in the "subset" */

+  if (*rows > subset_count){

+     /* We have non subset elements to deal with */	

+     using_target_via_subset_part1(data, rows, cols, in_subset, target, targetrows, start_col, end_col,subset_count);

+  }

+

+  /* Part 2: Adjust the elements in the "subset"*/

+  using_target_via_subset_part2(data, rows, cols, in_subset, target, targetrows, start_col, end_col,subset_count);

 }