[18/55] [partial] incubator-systemml git commit: [SYSTEMML-482] [SYSTEMML-480] Adding a Git attributes file to enfore Unix-styled line endings, and normalizing all of the line endings.

[du...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_bivariate2.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_bivariate2.dml b/src/test/scripts/applications/parfor/parfor_bivariate2.dml
index f6dd4d5..d0734a9 100644
--- a/src/test/scripts/applications/parfor/parfor_bivariate2.dml
+++ b/src/test/scripts/applications/parfor/parfor_bivariate2.dml
@@ -1,256 +1,256 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-
-/*
- *
- * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
- *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
- *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
- *
- * Seven inputs:  
- *    $1) D  - input data
- *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
- *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
- *    $4) K1 - kind for attributes in S1 
- *    $5) K2 - kind for attributes in S2
- *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
- *    $6) numPairs - total number of pairs (m*n)
- *    $7) maxC - maximum number of categories in any categorical attribute
- * 
- * One output:    
- *    $6) output directory in which following four statistics files are created
- *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
- *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
- *        + categorical.counts - 
- *        + categorical.means - 
- *        + categorical.variances - 
- *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
- *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
- */
-
-D = read($1, rows=$7, cols=$8);  # input data set
-S1 = read($2, rows=1, cols=$9); # attribute set 1
-S2 = read($3, rows=1, cols=$9); # attribute set 2
-K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
-K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
-numPairs = $10; # number of attribute pairs (|S1|*|S2|)
-maxC = $11;     # max number of categories in any categorical attribute
-
-s1size = ncol(S1);
-s2size = ncol(S2);
-
-# R, chisq, cramers, spearman, eta, anovaf
-numstats = 8;
-basestats = matrix(0, rows=numstats, cols=numPairs);
-cat_counts = matrix(0, rows=maxC, cols=numPairs);
-cat_means = matrix(0, rows=maxC, cols=numPairs);
-cat_vars = matrix(0, rows=maxC, cols=numPairs);
-
-
-parfor( i in 1:s1size, par=4, mode=LOCAL, check=0, opt=NONE) {
-    a1 = castAsScalar(S1[,i]);
-    k1 = castAsScalar(K1[1,i]);
-    A1 = D[,a1];
-
-    parfor( j in 1:s2size, par=4, mode=REMOTE_MR, check=0, opt=NONE) {
-        pairID = (i-1)*s2size+j; 
-        a2 = castAsScalar(S2[,j]);
-        k2 = castAsScalar(K2[1,j]);
-        A2 = D[,a2];
-    
-        if (k1 == k2) {
-            if (k1 == 1) {
-                # scale-scale
-                print("[" + i + "," + j + "] scale-scale");
-                r = bivar_ss(A1,A2);   
-                basestats[1,pairID] = r;
-            } else {
-                # nominal-nominal or ordinal-ordinal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-
-                if ( k1 == 3 ) {
-                    # ordinal-ordinal
-                    print("[" + i + "," + j + "] ordinal-ordinal");
-                    sp = bivar_oo(A1, A2);
-                    basestats[6,pairID] = sp;
-                }
-            }
-        } 
-        else {
-            if (k1 == 1 | k2 == 1) {
-                # Scale-nominal/ordinal      TODO MB correctness errors
-                print("[" + i + "," + j + "] scale-categorical");
-                
-               if ( k1 == 1 ) {
-                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
-                }
-                else {
-                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
-                }
-                basestats[7,pairID] = eta;
-                basestats[8,pairID] = f;
-                cat_counts[,pairID] = counts;
-                cat_means[,pairID] = means;
-                cat_vars[,pairID] = vars; 
-            }
-            else {
-                # nominal-ordinal or ordinal-nominal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-            }
-        }
-    }
-}
-
-write(basestats, $6 + "/bivar.stats");
-write(cat_counts, $6 + "/category.counts");
-write(cat_means, $6 + "/category.means");
-write(cat_vars, $6 + "/category.variances");
-
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
-
-    # Contingency Table
-    F = table(A,B);
-
-    # Chi-Squared
-    W = sum(F);
-    r = rowSums(F);
-    c = colSums(F);
-    E = (r %*% c)/W;
-    T = (F-E)^2/E;
-    chi_squared = sum(T);
-
-    # compute p-value
-    degFreedom = (nrow(F)-1)*(ncol(F)-1);
-    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
-
-    # Cramer's V
-    R = nrow(F);
-    C = ncol(F);
-    q = min(R,C);
-    cramers_v = sqrt(chi_squared/(W*(q-1)));
-
-    # Assign return values
-    chisq = chi_squared;
-    df = as.double(degFreedom);
-    pval = pValue;
-    cramersv = cramers_v;
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
-
-    # Unweighted co-variance
-    covXY = cov(X,Y);
-
-    # compute standard deviations for both X and Y by computing 2^nd central moment
-    W = nrow(X);
-    m2X = moment(X,2);
-    m2Y = moment(Y,2);
-    sigmaX = sqrt(m2X * (W/(W-1.0)) );
-    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
-
-    # Pearson's R
-    R = covXY / (sigmaX*sigmaY);
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-# Y points to SCALE variable
-# A points to CATEGORICAL variable
-bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
-
-    # mean and variance in target variable
-    W = nrow(A);
-    my = mean(Y);
-    varY = moment(Y,2) * W/(W-1.0)
-
-    # category-wise (frequencies, means, variances)
-    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
-    CMeans = aggregate(target=Y, groups=A, fn="mean");
-    CVars =  aggregate(target=Y, groups=A, fn="variance");
-
-    # number of categories
-    R = nrow(CFreqs);
-
-    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
-
-    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
-    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
-    AnovaF = anova_num/anova_den;
-}
-
-# -----------------------------------------------------------------------------------------------------------
-# Function to compute ranks
-# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
-computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
-    Ranks = cumsum(X) - X/2 + 1/2;
-}
-
-#-------------------------------------------------------------------------
-
-bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
-
-    # compute contingency table
-    F = table(A,B);
-
-    catA = nrow(F);  # number of categories in A
-    catB = ncol(F);  # number of categories in B
-
-    # compute category-wise counts for both the attributes
-    R = rowSums(F);
-    S = colSums(F);
-
-    # compute scores, both are column vectors
-    [C] = computeRanks(R);
-    meanX = mean(C,R); 
-
-    columnS = t(S);
-    [D] = computeRanks(columnS);
-
-    # scores (C,D) are individual values, and counts (R,S) act as weights
-    meanY = mean(D,columnS);
-
-    W = sum(F); # total weight, or total #cases
-    varX = moment(C,R,2)*(W/(W-1.0));
-    varY = moment(D,columnS,2)*(W/(W-1.0));
-    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
-
-    sp = covXY/(sqrt(varX)*sqrt(varY));
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+
+/*
+ *
+ * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
+ *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
+ *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
+ *
+ * Seven inputs:  
+ *    $1) D  - input data
+ *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
+ *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
+ *    $4) K1 - kind for attributes in S1 
+ *    $5) K2 - kind for attributes in S2
+ *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
+ *    $6) numPairs - total number of pairs (m*n)
+ *    $7) maxC - maximum number of categories in any categorical attribute
+ * 
+ * One output:    
+ *    $6) output directory in which following four statistics files are created
+ *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
+ *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
+ *        + categorical.counts - 
+ *        + categorical.means - 
+ *        + categorical.variances - 
+ *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
+ *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
+ */
+
+D = read($1, rows=$7, cols=$8);  # input data set
+S1 = read($2, rows=1, cols=$9); # attribute set 1
+S2 = read($3, rows=1, cols=$9); # attribute set 2
+K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
+K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
+numPairs = $10; # number of attribute pairs (|S1|*|S2|)
+maxC = $11;     # max number of categories in any categorical attribute
+
+s1size = ncol(S1);
+s2size = ncol(S2);
+
+# R, chisq, cramers, spearman, eta, anovaf
+numstats = 8;
+basestats = matrix(0, rows=numstats, cols=numPairs);
+cat_counts = matrix(0, rows=maxC, cols=numPairs);
+cat_means = matrix(0, rows=maxC, cols=numPairs);
+cat_vars = matrix(0, rows=maxC, cols=numPairs);
+
+
+parfor( i in 1:s1size, par=4, mode=LOCAL, check=0, opt=NONE) {
+    a1 = castAsScalar(S1[,i]);
+    k1 = castAsScalar(K1[1,i]);
+    A1 = D[,a1];
+
+    parfor( j in 1:s2size, par=4, mode=REMOTE_MR, check=0, opt=NONE) {
+        pairID = (i-1)*s2size+j; 
+        a2 = castAsScalar(S2[,j]);
+        k2 = castAsScalar(K2[1,j]);
+        A2 = D[,a2];
+    
+        if (k1 == k2) {
+            if (k1 == 1) {
+                # scale-scale
+                print("[" + i + "," + j + "] scale-scale");
+                r = bivar_ss(A1,A2);   
+                basestats[1,pairID] = r;
+            } else {
+                # nominal-nominal or ordinal-ordinal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+
+                if ( k1 == 3 ) {
+                    # ordinal-ordinal
+                    print("[" + i + "," + j + "] ordinal-ordinal");
+                    sp = bivar_oo(A1, A2);
+                    basestats[6,pairID] = sp;
+                }
+            }
+        } 
+        else {
+            if (k1 == 1 | k2 == 1) {
+                # Scale-nominal/ordinal      TODO MB correctness errors
+                print("[" + i + "," + j + "] scale-categorical");
+                
+               if ( k1 == 1 ) {
+                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
+                }
+                else {
+                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
+                }
+                basestats[7,pairID] = eta;
+                basestats[8,pairID] = f;
+                cat_counts[,pairID] = counts;
+                cat_means[,pairID] = means;
+                cat_vars[,pairID] = vars; 
+            }
+            else {
+                # nominal-ordinal or ordinal-nominal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+            }
+        }
+    }
+}
+
+write(basestats, $6 + "/bivar.stats");
+write(cat_counts, $6 + "/category.counts");
+write(cat_means, $6 + "/category.means");
+write(cat_vars, $6 + "/category.variances");
+
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
+
+    # Contingency Table
+    F = table(A,B);
+
+    # Chi-Squared
+    W = sum(F);
+    r = rowSums(F);
+    c = colSums(F);
+    E = (r %*% c)/W;
+    T = (F-E)^2/E;
+    chi_squared = sum(T);
+
+    # compute p-value
+    degFreedom = (nrow(F)-1)*(ncol(F)-1);
+    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
+
+    # Cramer's V
+    R = nrow(F);
+    C = ncol(F);
+    q = min(R,C);
+    cramers_v = sqrt(chi_squared/(W*(q-1)));
+
+    # Assign return values
+    chisq = chi_squared;
+    df = as.double(degFreedom);
+    pval = pValue;
+    cramersv = cramers_v;
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
+
+    # Unweighted co-variance
+    covXY = cov(X,Y);
+
+    # compute standard deviations for both X and Y by computing 2^nd central moment
+    W = nrow(X);
+    m2X = moment(X,2);
+    m2Y = moment(Y,2);
+    sigmaX = sqrt(m2X * (W/(W-1.0)) );
+    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
+
+    # Pearson's R
+    R = covXY / (sigmaX*sigmaY);
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+# Y points to SCALE variable
+# A points to CATEGORICAL variable
+bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
+
+    # mean and variance in target variable
+    W = nrow(A);
+    my = mean(Y);
+    varY = moment(Y,2) * W/(W-1.0)
+
+    # category-wise (frequencies, means, variances)
+    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
+    CMeans = aggregate(target=Y, groups=A, fn="mean");
+    CVars =  aggregate(target=Y, groups=A, fn="variance");
+
+    # number of categories
+    R = nrow(CFreqs);
+
+    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
+
+    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
+    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
+    AnovaF = anova_num/anova_den;
+}
+
+# -----------------------------------------------------------------------------------------------------------
+# Function to compute ranks
+# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
+computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
+    Ranks = cumsum(X) - X/2 + 1/2;
+}
+
+#-------------------------------------------------------------------------
+
+bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
+
+    # compute contingency table
+    F = table(A,B);
+
+    catA = nrow(F);  # number of categories in A
+    catB = ncol(F);  # number of categories in B
+
+    # compute category-wise counts for both the attributes
+    R = rowSums(F);
+    S = colSums(F);
+
+    # compute scores, both are column vectors
+    [C] = computeRanks(R);
+    meanX = mean(C,R); 
+
+    columnS = t(S);
+    [D] = computeRanks(columnS);
+
+    # scores (C,D) are individual values, and counts (R,S) act as weights
+    meanY = mean(D,columnS);
+
+    W = sum(F); # total weight, or total #cases
+    varX = moment(C,R,2)*(W/(W-1.0));
+    varY = moment(D,columnS,2)*(W/(W-1.0));
+    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
+
+    sp = covXY/(sqrt(varX)*sqrt(varY));
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_bivariate3.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_bivariate3.dml b/src/test/scripts/applications/parfor/parfor_bivariate3.dml
index 1443c6e..990a6fe 100644
--- a/src/test/scripts/applications/parfor/parfor_bivariate3.dml
+++ b/src/test/scripts/applications/parfor/parfor_bivariate3.dml
@@ -1,256 +1,256 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-
-/*
- *
- * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
- *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
- *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
- *
- * Seven inputs:  
- *    $1) D  - input data
- *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
- *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
- *    $4) K1 - kind for attributes in S1 
- *    $5) K2 - kind for attributes in S2
- *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
- *    $6) numPairs - total number of pairs (m*n)
- *    $7) maxC - maximum number of categories in any categorical attribute
- * 
- * One output:    
- *    $6) output directory in which following four statistics files are created
- *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
- *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
- *        + categorical.counts - 
- *        + categorical.means - 
- *        + categorical.variances - 
- *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
- *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
- */
-
-D = read($1, rows=$7, cols=$8);  # input data set
-S1 = read($2, rows=1, cols=$9); # attribute set 1
-S2 = read($3, rows=1, cols=$9); # attribute set 2
-K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
-K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
-numPairs = $10; # number of attribute pairs (|S1|*|S2|)
-maxC = $11;     # max number of categories in any categorical attribute
-
-s1size = ncol(S1);
-s2size = ncol(S2);
-
-# R, chisq, cramers, spearman, eta, anovaf
-numstats = 8;
-basestats = matrix(0, rows=numstats, cols=numPairs);
-cat_counts = matrix(0, rows=maxC, cols=numPairs);
-cat_means = matrix(0, rows=maxC, cols=numPairs);
-cat_vars = matrix(0, rows=maxC, cols=numPairs);
-
-
-parfor( i in 1:s1size, par=4, mode=REMOTE_MR, check=0, opt=NONE) {
-    a1 = castAsScalar(S1[,i]);
-    k1 = castAsScalar(K1[1,i]);
-    A1 = D[,a1];
-
-    parfor( j in 1:s2size, par=4, mode=LOCAL, check=0, opt=NONE) {
-        pairID = (i-1)*s2size+j; 
-        a2 = castAsScalar(S2[,j]);
-        k2 = castAsScalar(K2[1,j]);
-        A2 = D[,a2];
-    
-        if (k1 == k2) {
-            if (k1 == 1) {
-                # scale-scale
-                print("[" + i + "," + j + "] scale-scale");
-                r = bivar_ss(A1,A2);   
-                basestats[1,pairID] = r;
-            } else {
-                # nominal-nominal or ordinal-ordinal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-
-                if ( k1 == 3 ) {
-                    # ordinal-ordinal
-                    print("[" + i + "," + j + "] ordinal-ordinal");
-                    sp = bivar_oo(A1, A2);
-                    basestats[6,pairID] = sp;
-                }
-            }
-        } 
-        else {
-            if (k1 == 1 | k2 == 1) {
-                # Scale-nominal/ordinal      TODO MB correctness errors
-                print("[" + i + "," + j + "] scale-categorical");
-                
-               if ( k1 == 1 ) {
-                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
-                }
-                else {
-                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
-                }
-                basestats[7,pairID] = eta;
-                basestats[8,pairID] = f;
-                cat_counts[,pairID] = counts;
-                cat_means[,pairID] = means;
-                cat_vars[,pairID] = vars; 
-            }
-            else {
-                # nominal-ordinal or ordinal-nominal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-            }
-        }
-    }
-}
-
-write(basestats, $6 + "/bivar.stats");
-write(cat_counts, $6 + "/category.counts");
-write(cat_means, $6 + "/category.means");
-write(cat_vars, $6 + "/category.variances");
-
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
-
-    # Contingency Table
-    F = table(A,B);
-
-    # Chi-Squared
-    W = sum(F);
-    r = rowSums(F);
-    c = colSums(F);
-    E = (r %*% c)/W;
-    T = (F-E)^2/E;
-    chi_squared = sum(T);
-
-    # compute p-value
-    degFreedom = (nrow(F)-1)*(ncol(F)-1);
-    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
-
-    # Cramer's V
-    R = nrow(F);
-    C = ncol(F);
-    q = min(R,C);
-    cramers_v = sqrt(chi_squared/(W*(q-1)));
-
-    # Assign return values
-    chisq = chi_squared;
-    df = as.double(degFreedom);
-    pval = pValue;
-    cramersv = cramers_v;
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
-
-    # Unweighted co-variance
-    covXY = cov(X,Y);
-
-    # compute standard deviations for both X and Y by computing 2^nd central moment
-    W = nrow(X);
-    m2X = moment(X,2);
-    m2Y = moment(Y,2);
-    sigmaX = sqrt(m2X * (W/(W-1.0)) );
-    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
-
-    # Pearson's R
-    R = covXY / (sigmaX*sigmaY);
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-# Y points to SCALE variable
-# A points to CATEGORICAL variable
-bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
-
-    # mean and variance in target variable
-    W = nrow(A);
-    my = mean(Y);
-    varY = moment(Y,2) * W/(W-1.0)
-
-    # category-wise (frequencies, means, variances)
-    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
-    CMeans = aggregate(target=Y, groups=A, fn="mean");
-    CVars =  aggregate(target=Y, groups=A, fn="variance");
-
-    # number of categories
-    R = nrow(CFreqs);
-
-    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
-
-    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
-    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
-    AnovaF = anova_num/anova_den;
-}
-
-# -----------------------------------------------------------------------------------------------------------
-# Function to compute ranks
-# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
-computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
-    Ranks = cumsum(X) - X/2 + 1/2;
-}
-
-#-------------------------------------------------------------------------
-
-bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
-
-    # compute contingency table
-    F = table(A,B);
-
-    catA = nrow(F);  # number of categories in A
-    catB = ncol(F);  # number of categories in B
-
-    # compute category-wise counts for both the attributes
-    R = rowSums(F);
-    S = colSums(F);
-
-    # compute scores, both are column vectors
-    [C] = computeRanks(R);
-    meanX = mean(C,R); 
-
-    columnS = t(S);
-    [D] = computeRanks(columnS);
-
-    # scores (C,D) are individual values, and counts (R,S) act as weights
-    meanY = mean(D,columnS);
-
-    W = sum(F); # total weight, or total #cases
-    varX = moment(C,R,2)*(W/(W-1.0));
-    varY = moment(D,columnS,2)*(W/(W-1.0));
-    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
-
-    sp = covXY/(sqrt(varX)*sqrt(varY));
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+
+/*
+ *
+ * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
+ *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
+ *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
+ *
+ * Seven inputs:  
+ *    $1) D  - input data
+ *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
+ *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
+ *    $4) K1 - kind for attributes in S1 
+ *    $5) K2 - kind for attributes in S2
+ *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
+ *    $6) numPairs - total number of pairs (m*n)
+ *    $7) maxC - maximum number of categories in any categorical attribute
+ * 
+ * One output:    
+ *    $6) output directory in which following four statistics files are created
+ *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
+ *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
+ *        + categorical.counts - 
+ *        + categorical.means - 
+ *        + categorical.variances - 
+ *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
+ *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
+ */
+
+D = read($1, rows=$7, cols=$8);  # input data set
+S1 = read($2, rows=1, cols=$9); # attribute set 1
+S2 = read($3, rows=1, cols=$9); # attribute set 2
+K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
+K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
+numPairs = $10; # number of attribute pairs (|S1|*|S2|)
+maxC = $11;     # max number of categories in any categorical attribute
+
+s1size = ncol(S1);
+s2size = ncol(S2);
+
+# R, chisq, cramers, spearman, eta, anovaf
+numstats = 8;
+basestats = matrix(0, rows=numstats, cols=numPairs);
+cat_counts = matrix(0, rows=maxC, cols=numPairs);
+cat_means = matrix(0, rows=maxC, cols=numPairs);
+cat_vars = matrix(0, rows=maxC, cols=numPairs);
+
+
+parfor( i in 1:s1size, par=4, mode=REMOTE_MR, check=0, opt=NONE) {
+    a1 = castAsScalar(S1[,i]);
+    k1 = castAsScalar(K1[1,i]);
+    A1 = D[,a1];
+
+    parfor( j in 1:s2size, par=4, mode=LOCAL, check=0, opt=NONE) {
+        pairID = (i-1)*s2size+j; 
+        a2 = castAsScalar(S2[,j]);
+        k2 = castAsScalar(K2[1,j]);
+        A2 = D[,a2];
+    
+        if (k1 == k2) {
+            if (k1 == 1) {
+                # scale-scale
+                print("[" + i + "," + j + "] scale-scale");
+                r = bivar_ss(A1,A2);   
+                basestats[1,pairID] = r;
+            } else {
+                # nominal-nominal or ordinal-ordinal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+
+                if ( k1 == 3 ) {
+                    # ordinal-ordinal
+                    print("[" + i + "," + j + "] ordinal-ordinal");
+                    sp = bivar_oo(A1, A2);
+                    basestats[6,pairID] = sp;
+                }
+            }
+        } 
+        else {
+            if (k1 == 1 | k2 == 1) {
+                # Scale-nominal/ordinal      TODO MB correctness errors
+                print("[" + i + "," + j + "] scale-categorical");
+                
+               if ( k1 == 1 ) {
+                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
+                }
+                else {
+                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
+                }
+                basestats[7,pairID] = eta;
+                basestats[8,pairID] = f;
+                cat_counts[,pairID] = counts;
+                cat_means[,pairID] = means;
+                cat_vars[,pairID] = vars; 
+            }
+            else {
+                # nominal-ordinal or ordinal-nominal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+            }
+        }
+    }
+}
+
+write(basestats, $6 + "/bivar.stats");
+write(cat_counts, $6 + "/category.counts");
+write(cat_means, $6 + "/category.means");
+write(cat_vars, $6 + "/category.variances");
+
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
+
+    # Contingency Table
+    F = table(A,B);
+
+    # Chi-Squared
+    W = sum(F);
+    r = rowSums(F);
+    c = colSums(F);
+    E = (r %*% c)/W;
+    T = (F-E)^2/E;
+    chi_squared = sum(T);
+
+    # compute p-value
+    degFreedom = (nrow(F)-1)*(ncol(F)-1);
+    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
+
+    # Cramer's V
+    R = nrow(F);
+    C = ncol(F);
+    q = min(R,C);
+    cramers_v = sqrt(chi_squared/(W*(q-1)));
+
+    # Assign return values
+    chisq = chi_squared;
+    df = as.double(degFreedom);
+    pval = pValue;
+    cramersv = cramers_v;
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
+
+    # Unweighted co-variance
+    covXY = cov(X,Y);
+
+    # compute standard deviations for both X and Y by computing 2^nd central moment
+    W = nrow(X);
+    m2X = moment(X,2);
+    m2Y = moment(Y,2);
+    sigmaX = sqrt(m2X * (W/(W-1.0)) );
+    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
+
+    # Pearson's R
+    R = covXY / (sigmaX*sigmaY);
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+# Y points to SCALE variable
+# A points to CATEGORICAL variable
+bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
+
+    # mean and variance in target variable
+    W = nrow(A);
+    my = mean(Y);
+    varY = moment(Y,2) * W/(W-1.0)
+
+    # category-wise (frequencies, means, variances)
+    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
+    CMeans = aggregate(target=Y, groups=A, fn="mean");
+    CVars =  aggregate(target=Y, groups=A, fn="variance");
+
+    # number of categories
+    R = nrow(CFreqs);
+
+    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
+
+    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
+    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
+    AnovaF = anova_num/anova_den;
+}
+
+# -----------------------------------------------------------------------------------------------------------
+# Function to compute ranks
+# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
+computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
+    Ranks = cumsum(X) - X/2 + 1/2;
+}
+
+#-------------------------------------------------------------------------
+
+bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
+
+    # compute contingency table
+    F = table(A,B);
+
+    catA = nrow(F);  # number of categories in A
+    catB = ncol(F);  # number of categories in B
+
+    # compute category-wise counts for both the attributes
+    R = rowSums(F);
+    S = colSums(F);
+
+    # compute scores, both are column vectors
+    [C] = computeRanks(R);
+    meanX = mean(C,R); 
+
+    columnS = t(S);
+    [D] = computeRanks(columnS);
+
+    # scores (C,D) are individual values, and counts (R,S) act as weights
+    meanY = mean(D,columnS);
+
+    W = sum(F); # total weight, or total #cases
+    varX = moment(C,R,2)*(W/(W-1.0));
+    varY = moment(D,columnS,2)*(W/(W-1.0));
+    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
+
+    sp = covXY/(sqrt(varX)*sqrt(varY));
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_bivariate4.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_bivariate4.dml b/src/test/scripts/applications/parfor/parfor_bivariate4.dml
index 9b85bad..a19957f 100644
--- a/src/test/scripts/applications/parfor/parfor_bivariate4.dml
+++ b/src/test/scripts/applications/parfor/parfor_bivariate4.dml
@@ -1,258 +1,258 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-
-/*
- *
- * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
- *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
- *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
- *
- * Seven inputs:  
- *    $1) D  - input data
- *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
- *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
- *    $4) K1 - kind for attributes in S1 
- *    $5) K2 - kind for attributes in S2
- *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
- *    $6) numPairs - total number of pairs (m*n)
- *    $7) maxC - maximum number of categories in any categorical attribute
- * 
- * One output:    
- *    $6) output directory in which following four statistics files are created
- *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
- *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
- *        + categorical.counts - 
- *        + categorical.means - 
- *        + categorical.variances - 
- *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
- *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
- */
-
-D = read($1, rows=$7, cols=$8);  # input data set
-S1 = read($2, rows=1, cols=$9); # attribute set 1
-S2 = read($3, rows=1, cols=$9); # attribute set 2
-K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
-K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
-numPairs = $10; # number of attribute pairs (|S1|*|S2|)
-maxC = $11;     # max number of categories in any categorical attribute
-
-s1size = ncol(S1);
-s2size = ncol(S2);
-
-#numpairs = s1size * s2size;
-#print(s1size + ", " + s2size + ", " + numpairs);
-
-# R, chisq, cramers, spearman, eta, anovaf
-numstats = 8;
-basestats = matrix(0, rows=numstats, cols=numPairs);
-cat_counts = matrix(0, rows=maxC, cols=numPairs);
-cat_means = matrix(0, rows=maxC, cols=numPairs);
-cat_vars = matrix(0, rows=maxC, cols=numPairs);
-
-
-parfor( i in 1:s1size, check=0) {
-    a1 = castAsScalar(S1[,i]);
-    k1 = castAsScalar(K1[1,i]);
-    A1 = D[,a1];
-
-    parfor( j in 1:s2size, check=0) {
-        pairID = (i-1)*s2size+j; 
-        a2 = castAsScalar(S2[,j]);
-        k2 = castAsScalar(K2[1,j]);
-        A2 = D[,a2];
-    
-        if (k1 == k2) {
-            if (k1 == 1) {
-                # scale-scale
-                print("[" + i + "," + j + "] scale-scale");
-                r = bivar_ss(A1,A2);   
-                basestats[1,pairID] = r;
-            } else {
-                # nominal-nominal or ordinal-ordinal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-
-                if ( k1 == 3 ) {
-                    # ordinal-ordinal
-                    print("[" + i + "," + j + "] ordinal-ordinal");
-                    sp = bivar_oo(A1, A2);
-                    basestats[6,pairID] = sp;
-                }
-            }
-        } 
-        else {
-            if (k1 == 1 | k2 == 1) {
-                # Scale-nominal/ordinal     
-                print("[" + i + "," + j + "] scale-categorical");
-                
-               if ( k1 == 1 ) {
-                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
-                }
-                else {
-                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
-                }
-                basestats[7,pairID] = eta;
-                basestats[8,pairID] = f;
-                cat_counts[,pairID] = counts;
-                cat_means[,pairID] = means;
-                cat_vars[,pairID] = vars; 
-            }
-            else {
-                # nominal-ordinal or ordinal-nominal
-                print("[" + i + "," + j + "] categorical-categorical");
-                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
-                basestats[2,pairID] = chisq;
-                basestats[3,pairID] = df;
-                basestats[4,pairID] = pval;
-                basestats[5,pairID] = cramersv;
-            }
-        }
-    }
-}
-
-write(basestats, $6 + "/bivar.stats");
-write(cat_counts, $6 + "/category.counts");
-write(cat_means, $6 + "/category.means");
-write(cat_vars, $6 + "/category.variances");
-
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
-
-    # Contingency Table
-    F = table(A,B);
-
-    # Chi-Squared
-    W = sum(F);
-    r = rowSums(F);
-    c = colSums(F);
-    E = (r %*% c)/W;
-    T = (F-E)^2/E;
-    chi_squared = sum(T);
-
-    # compute p-value
-    degFreedom = (nrow(F)-1)*(ncol(F)-1);
-    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
-
-    # Cramer's V
-    R = nrow(F);
-    C = ncol(F);
-    q = min(R,C);
-    cramers_v = sqrt(chi_squared/(W*(q-1)));
-
-    # Assign return values
-    chisq = chi_squared;
-    df = as.double(degFreedom);
-    pval = pValue;
-    cramersv = cramers_v;
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
-
-    # Unweighted co-variance
-    covXY = cov(X,Y);
-
-    # compute standard deviations for both X and Y by computing 2^nd central moment
-    W = nrow(X);
-    m2X = moment(X,2);
-    m2Y = moment(Y,2);
-    sigmaX = sqrt(m2X * (W/(W-1.0)) );
-    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
-
-    # Pearson's R
-    R = covXY / (sigmaX*sigmaY);
-}
-
-# -----------------------------------------------------------------------------------------------------------
-
-# Y points to SCALE variable
-# A points to CATEGORICAL variable
-bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
-
-    # mean and variance in target variable
-    W = nrow(A);
-    my = mean(Y);
-    varY = moment(Y,2) * W/(W-1.0)
-
-    # category-wise (frequencies, means, variances)
-    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
-    CMeans = aggregate(target=Y, groups=A, fn="mean");
-    CVars =  aggregate(target=Y, groups=A, fn="variance");
-    
-    # number of categories
-    R = nrow(CFreqs);
-
-    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
-
-    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
-    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
-    AnovaF = anova_num/anova_den;
-}
-
-
-# -----------------------------------------------------------------------------------------------------------
-# Function to compute ranks
-# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
-computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
-    Ranks = cumsum(X) - X/2 + 1/2;
-}
-
-#-------------------------------------------------------------------------
-
-bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
-
-    # compute contingency table
-    F = table(A,B);
-    
-    catA = nrow(F);  # number of categories in A
-    catB = ncol(F);  # number of categories in B
-
-    # compute category-wise counts for both the attributes
-    R = rowSums(F);
-    S = colSums(F);
-
-    # compute scores, both are column vectors
-    [C] = computeRanks(R);
-    meanX = mean(C,R); 
-
-    columnS = t(S);
-    [D] = computeRanks(columnS);
-
-    # scores (C,D) are individual values, and counts (R,S) act as weights
-    meanY = mean(D,columnS);
-
-    W = sum(F); # total weight, or total #cases
-    varX = moment(C,R,2)*(W/(W-1.0));
-    varY = moment(D,columnS,2)*(W/(W-1.0));
-    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
-
-    sp = covXY/(sqrt(varX)*sqrt(varY));
-}
-
-# -----------------------------------------------------------------------------------------------------------
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+
+/*
+ *
+ * For a given pair of attribute sets, compute bivariate statistics between all attribute pairs 
+ *   Given, S_1 = {A_11, A_12, ... A_1m} and S_2 = {A_21, A_22, ... A_2n} 
+ *          compute bivariate stats for m*n pairs (A_1i, A_2j), (1<= i <=m) and (1<= j <=n)
+ *
+ * Seven inputs:  
+ *    $1) D  - input data
+ *    $2) S1 - First attribute set {A_11, A_12, ... A_1m}
+ *    $3) S2 - Second attribute set {A_21, A_22, ... A_2n}
+ *    $4) K1 - kind for attributes in S1 
+ *    $5) K2 - kind for attributes in S2
+ *             kind=1 for scale, kind=2 for nominal, kind=3 for ordinal
+ *    $6) numPairs - total number of pairs (m*n)
+ *    $7) maxC - maximum number of categories in any categorical attribute
+ * 
+ * One output:    
+ *    $6) output directory in which following four statistics files are created
+ *        + bivar.stats - matrix with all 8 bivariate statistics computed for different attribute pairs
+ *                        (R, (chi-sq, df, pval, cramersv), spearman, Eta, F)
+ *        + categorical.counts - 
+ *        + categorical.means - 
+ *        + categorical.variances - 
+ *          -> Values in these three matrices are applicable only for scale-categorical attribute pairs. 
+ *          k^th column in these matrices denote the attribute pair (A_1i,A_2j) where i*j = k.
+ */
+
+D = read($1, rows=$7, cols=$8);  # input data set
+S1 = read($2, rows=1, cols=$9); # attribute set 1
+S2 = read($3, rows=1, cols=$9); # attribute set 2
+K1 = read($4, rows=1, cols=$9); # kind for attributes in S1
+K2 = read($5, rows=1, cols=$9); # kind for attributes in S2
+numPairs = $10; # number of attribute pairs (|S1|*|S2|)
+maxC = $11;     # max number of categories in any categorical attribute
+
+s1size = ncol(S1);
+s2size = ncol(S2);
+
+#numpairs = s1size * s2size;
+#print(s1size + ", " + s2size + ", " + numpairs);
+
+# R, chisq, cramers, spearman, eta, anovaf
+numstats = 8;
+basestats = matrix(0, rows=numstats, cols=numPairs);
+cat_counts = matrix(0, rows=maxC, cols=numPairs);
+cat_means = matrix(0, rows=maxC, cols=numPairs);
+cat_vars = matrix(0, rows=maxC, cols=numPairs);
+
+
+parfor( i in 1:s1size, check=0) {
+    a1 = castAsScalar(S1[,i]);
+    k1 = castAsScalar(K1[1,i]);
+    A1 = D[,a1];
+
+    parfor( j in 1:s2size, check=0) {
+        pairID = (i-1)*s2size+j; 
+        a2 = castAsScalar(S2[,j]);
+        k2 = castAsScalar(K2[1,j]);
+        A2 = D[,a2];
+    
+        if (k1 == k2) {
+            if (k1 == 1) {
+                # scale-scale
+                print("[" + i + "," + j + "] scale-scale");
+                r = bivar_ss(A1,A2);   
+                basestats[1,pairID] = r;
+            } else {
+                # nominal-nominal or ordinal-ordinal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+
+                if ( k1 == 3 ) {
+                    # ordinal-ordinal
+                    print("[" + i + "," + j + "] ordinal-ordinal");
+                    sp = bivar_oo(A1, A2);
+                    basestats[6,pairID] = sp;
+                }
+            }
+        } 
+        else {
+            if (k1 == 1 | k2 == 1) {
+                # Scale-nominal/ordinal     
+                print("[" + i + "," + j + "] scale-categorical");
+                
+               if ( k1 == 1 ) {
+                    [eta,f, counts, means, vars] = bivar_sc(A1,A2);
+                }
+                else {
+                    [eta,f, counts, means, vars] = bivar_sc(A2,A1);
+                }
+                basestats[7,pairID] = eta;
+                basestats[8,pairID] = f;
+                cat_counts[,pairID] = counts;
+                cat_means[,pairID] = means;
+                cat_vars[,pairID] = vars; 
+            }
+            else {
+                # nominal-ordinal or ordinal-nominal
+                print("[" + i + "," + j + "] categorical-categorical");
+                [chisq, df, pval, cramersv]  = bivar_cc(A1,A2);
+                basestats[2,pairID] = chisq;
+                basestats[3,pairID] = df;
+                basestats[4,pairID] = pval;
+                basestats[5,pairID] = cramersv;
+            }
+        }
+    }
+}
+
+write(basestats, $6 + "/bivar.stats");
+write(cat_counts, $6 + "/category.counts");
+write(cat_means, $6 + "/category.means");
+write(cat_vars, $6 + "/category.variances");
+
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_cc = function(Matrix[Double] A, Matrix[Double] B) return (Double chisq, Double df, Double pval, Double cramersv) {
+
+    # Contingency Table
+    F = table(A,B);
+
+    # Chi-Squared
+    W = sum(F);
+    r = rowSums(F);
+    c = colSums(F);
+    E = (r %*% c)/W;
+    T = (F-E)^2/E;
+    chi_squared = sum(T);
+
+    # compute p-value
+    degFreedom = (nrow(F)-1)*(ncol(F)-1);
+    pValue = pchisq(target=chi_squared, df=degFreedom, lower.tail=FALSE);
+
+    # Cramer's V
+    R = nrow(F);
+    C = ncol(F);
+    q = min(R,C);
+    cramers_v = sqrt(chi_squared/(W*(q-1)));
+
+    # Assign return values
+    chisq = chi_squared;
+    df = as.double(degFreedom);
+    pval = pValue;
+    cramersv = cramers_v;
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+bivar_ss = function(Matrix[Double] X, Matrix[Double] Y) return (Double R) {
+
+    # Unweighted co-variance
+    covXY = cov(X,Y);
+
+    # compute standard deviations for both X and Y by computing 2^nd central moment
+    W = nrow(X);
+    m2X = moment(X,2);
+    m2Y = moment(Y,2);
+    sigmaX = sqrt(m2X * (W/(W-1.0)) );
+    sigmaY = sqrt(m2Y * (W/(W-1.0)) );
+
+    # Pearson's R
+    R = covXY / (sigmaX*sigmaY);
+}
+
+# -----------------------------------------------------------------------------------------------------------
+
+# Y points to SCALE variable
+# A points to CATEGORICAL variable
+bivar_sc = function(Matrix[Double] Y, Matrix[Double] A) return (Double Eta, Double AnovaF, Matrix[Double] CFreqs, Matrix[Double] CMeans, Matrix[Double] CVars ) {
+
+    # mean and variance in target variable
+    W = nrow(A);
+    my = mean(Y);
+    varY = moment(Y,2) * W/(W-1.0)
+
+    # category-wise (frequencies, means, variances)
+    CFreqs = aggregate(target=Y, groups=A, fn="count"); 
+    CMeans = aggregate(target=Y, groups=A, fn="mean");
+    CVars =  aggregate(target=Y, groups=A, fn="variance");
+    
+    # number of categories
+    R = nrow(CFreqs);
+
+    Eta = sqrt(1 - ( sum((CFreqs-1)*CVars) / ((W-1)*varY) ));
+
+    anova_num = sum( (CFreqs*(CMeans-my)^2) )/(R-1);
+    anova_den = sum( (CFreqs-1)*CVars )/(W-R);
+    AnovaF = anova_num/anova_den;
+}
+
+
+# -----------------------------------------------------------------------------------------------------------
+# Function to compute ranks
+# takes a column vector as input, and produces a vector of same size in which each cell denotes to the computed score for that category
+computeRanks = function(Matrix[Double] X) return (Matrix[Double] Ranks) {
+    Ranks = cumsum(X) - X/2 + 1/2;
+}
+
+#-------------------------------------------------------------------------
+
+bivar_oo = function(Matrix[Double] A, Matrix[Double] B) return (Double sp) {
+
+    # compute contingency table
+    F = table(A,B);
+    
+    catA = nrow(F);  # number of categories in A
+    catB = ncol(F);  # number of categories in B
+
+    # compute category-wise counts for both the attributes
+    R = rowSums(F);
+    S = colSums(F);
+
+    # compute scores, both are column vectors
+    [C] = computeRanks(R);
+    meanX = mean(C,R); 
+
+    columnS = t(S);
+    [D] = computeRanks(columnS);
+
+    # scores (C,D) are individual values, and counts (R,S) act as weights
+    meanY = mean(D,columnS);
+
+    W = sum(F); # total weight, or total #cases
+    varX = moment(C,R,2)*(W/(W-1.0));
+    varY = moment(D,columnS,2)*(W/(W-1.0));
+    covXY = sum( t(F/(W-1) * (C-meanX)) * (D-meanY) );
+
+    sp = covXY/(sqrt(varX)*sqrt(varY));
+}
+
+# -----------------------------------------------------------------------------------------------------------

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr.R
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr.R b/src/test/scripts/applications/parfor/parfor_corr.R
index 5f8c315..854e593 100644
--- a/src/test/scripts/applications/parfor/parfor_corr.R
+++ b/src/test/scripts/applications/parfor/parfor_corr.R
@@ -1,48 +1,48 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-args <- commandArgs(TRUE)
-options(digits=22)
-
-library("Matrix")
-
-V1 <- readMM(paste(args[1], "V.mtx", sep=""))
-V <- as.matrix(V1);
-
-m <- nrow(V);
-n <- ncol(V); 
-W <- m;
-
-R <- array(0,dim=c(n,n))
-
-for( i in 1:(n-1) )
-{
-   X <- V[ ,i];                 
-      
-   for( j in (i+1):n )  
-   {
-      Y <- V[ ,j];  
-      R[i,j] <- cor(X, Y)  
-      #print(R[i,j]);
-   }
-}   
-
-writeMM(as(R, "CsparseMatrix"), paste(args[2], "Rout", sep=""));
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+args <- commandArgs(TRUE)
+options(digits=22)
+
+library("Matrix")
+
+V1 <- readMM(paste(args[1], "V.mtx", sep=""))
+V <- as.matrix(V1);
+
+m <- nrow(V);
+n <- ncol(V); 
+W <- m;
+
+R <- array(0,dim=c(n,n))
+
+for( i in 1:(n-1) )
+{
+   X <- V[ ,i];                 
+      
+   for( j in (i+1):n )  
+   {
+      Y <- V[ ,j];  
+      R[i,j] <- cor(X, Y)  
+      #print(R[i,j]);
+   }
+}   
+
+writeMM(as(R, "CsparseMatrix"), paste(args[2], "Rout", sep=""));

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr0.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr0.dml b/src/test/scripts/applications/parfor/parfor_corr0.dml
index 7a340bd..a711d0a 100644
--- a/src/test/scripts/applications/parfor/parfor_corr0.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr0.dml
@@ -1,51 +1,51 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0,rows=n,cols=n); 
-
-for( i in 1:(n-1) )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   for( j in (i+1):n )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      #print("R[("+i+","+j+")]="+rXY);
-      R[i,j] = rXY; 
-      
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0,rows=n,cols=n); 
+
+for( i in 1:(n-1) )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   for( j in (i+1):n )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      #print("R[("+i+","+j+")]="+rXY);
+      R[i,j] = rXY; 
+      
+   }
+}   
+
 write(R, $4);       
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr1.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr1.dml b/src/test/scripts/applications/parfor/parfor_corr1.dml
index 8bb0368..b2d5a14 100644
--- a/src/test/scripts/applications/parfor/parfor_corr1.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr1.dml
@@ -1,50 +1,50 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1), par=4, mode=LOCAL, opt=NONE )  
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n, par=4, mode=LOCAL, opt=NONE )
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      #print("R[("+i+","+j+")]="+rXY); 
-      R[i,j] = rXY;       
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1), par=4, mode=LOCAL, opt=NONE )  
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n, par=4, mode=LOCAL, opt=NONE )
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      #print("R[("+i+","+j+")]="+rXY); 
+      R[i,j] = rXY;       
+   }
+}   
+
 write(R, $4);       
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr2.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr2.dml b/src/test/scripts/applications/parfor/parfor_corr2.dml
index c5f5c31..9e10534 100644
--- a/src/test/scripts/applications/parfor/parfor_corr2.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr2.dml
@@ -1,50 +1,50 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1), par=4, mode=LOCAL, opt=NONE )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n, par=4, mode=REMOTE_MR, opt=NONE )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      print("R[{"+i+","+j+"}]="+rXY); #test robustness of ProgramConverter
-      R[i,j] = rXY;       
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1), par=4, mode=LOCAL, opt=NONE )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n, par=4, mode=REMOTE_MR, opt=NONE )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      print("R[{"+i+","+j+"}]="+rXY); #test robustness of ProgramConverter
+      R[i,j] = rXY;       
+   }
+}   
+
 write(R, $4);     
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr3.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr3.dml b/src/test/scripts/applications/parfor/parfor_corr3.dml
index 46295a3..95fc38f 100644
--- a/src/test/scripts/applications/parfor/parfor_corr3.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr3.dml
@@ -1,51 +1,51 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1), par=4, mode=REMOTE_MR, opt=NONE )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n, par=4, mode=LOCAL, opt=NONE )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      print("R[{"+i+","+j+"}]="+rXY); #test robustness of ProgramConverter
-      R[i,j] = rXY; 
-      
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1), par=4, mode=REMOTE_MR, opt=NONE )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n, par=4, mode=LOCAL, opt=NONE )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      print("R[{"+i+","+j+"}]="+rXY); #test robustness of ProgramConverter
+      R[i,j] = rXY; 
+      
+   }
+}   
+
 write(R, $4); 
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr4.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr4.dml b/src/test/scripts/applications/parfor/parfor_corr4.dml
index 62e643d..16b4767 100644
--- a/src/test/scripts/applications/parfor/parfor_corr4.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr4.dml
@@ -1,51 +1,51 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1) )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      print("R[{"+i+","+j+"}]="+rXY); 
-      R[i,j] = rXY; 
-      
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1) )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      print("R[{"+i+","+j+"}]="+rXY); 
+      R[i,j] = rXY; 
+      
+   }
+}   
+
 write(R, $4); 
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr5.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr5.dml b/src/test/scripts/applications/parfor/parfor_corr5.dml
index 0da9539..1663b8f 100644
--- a/src/test/scripts/applications/parfor/parfor_corr5.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr5.dml
@@ -1,51 +1,51 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1), profile=1 )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n, profile=1 )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      print("R[{"+i+","+j+"}]="+rXY); 
-      R[i,j] = rXY; 
-      
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1), profile=1 )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n, profile=1 )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      print("R[{"+i+","+j+"}]="+rXY); 
+      R[i,j] = rXY; 
+      
+   }
+}   
+
 write(R, $4); 
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/816e2db8/src/test/scripts/applications/parfor/parfor_corr6.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/parfor/parfor_corr6.dml b/src/test/scripts/applications/parfor/parfor_corr6.dml
index 166b3d7..2ee26cc 100644
--- a/src/test/scripts/applications/parfor/parfor_corr6.dml
+++ b/src/test/scripts/applications/parfor/parfor_corr6.dml
@@ -1,51 +1,51 @@
-#-------------------------------------------------------------
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-# 
-#   http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-#-------------------------------------------------------------
-
-V = read($1,rows=$2,cols=$3);
-m = $2;
-n = $3;
-W = m;
-
-R = matrix(0, rows=n,cols=n); 
-
-parfor( i in 1:(n-1), log=debug )
-{
-   X = V[,i];                 
-   m2X = moment(X,2);
-   sigmaX = sqrt(m2X * (W/(W-1.0)) );
-      
-   parfor( j in (i+1):n )  
-   {  
-      Y = V[,j];
-
-      #corr computation    
-      m2Y = moment(Y,2);
-      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
-      covXY = cov(X,Y);      
-      rXY = covXY / (sigmaX*sigmaY); 
-      
-      print("R[{"+i+","+j+"}]="+rXY); 
-      R[i,j] = rXY; 
-      
-   }
-}   
-
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+V = read($1,rows=$2,cols=$3);
+m = $2;
+n = $3;
+W = m;
+
+R = matrix(0, rows=n,cols=n); 
+
+parfor( i in 1:(n-1), log=debug )
+{
+   X = V[,i];                 
+   m2X = moment(X,2);
+   sigmaX = sqrt(m2X * (W/(W-1.0)) );
+      
+   parfor( j in (i+1):n )  
+   {  
+      Y = V[,j];
+
+      #corr computation    
+      m2Y = moment(Y,2);
+      sigmaY = sqrt(m2Y * (W/(W-1.0)) );      
+      covXY = cov(X,Y);      
+      rXY = covXY / (sigmaX*sigmaY); 
+      
+      print("R[{"+i+","+j+"}]="+rXY); 
+      R[i,j] = rXY; 
+      
+   }
+}   
+
 write(R, $4); 
\ No newline at end of file