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Posted to commits@systemml.apache.org by du...@apache.org on 2016/01/22 17:34:04 UTC
[28/51] [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.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.R
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.R b/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.R
index fa3b66a..1375435 100644
--- a/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.R
+++ b/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.R
@@ -1,45 +1,45 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# JUnit test class: dml.test.integration.descriptivestats.CategoricalCategoricalTest.java
-# command line invocation assuming $SS_HOME is set to the home of the R script
-# Rscript $SS_HOME/ScaleScalePearsonRWithWeightsTest.R $SS_HOME/in/ $SS_HOME/expected/
-args <- commandArgs(TRUE)
-options(digits=22)
-
-library("Matrix")
-library("boot")
-# Usage: R --vanilla -args Xfile X < ScaleScaleTest.R
-
-#parseCommandArgs()
-######################
-
-X = readMM(paste(args[1], "X.mtx", sep=""))
-Y = readMM(paste(args[1], "Y.mtx", sep=""))
-WM = readMM(paste(args[1],"WM.mtx", sep=""))
-
-# create a matrix from X and Y vectors
-mat = cbind(X[,1], Y[,1]);
-
-# corr is a function in "boot" package
-R = corr(mat, WM[,1]);
-
-write(R, paste(args[2], "PearsonR", 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.
+#
+#-------------------------------------------------------------
+
+# JUnit test class: dml.test.integration.descriptivestats.CategoricalCategoricalTest.java
+# command line invocation assuming $SS_HOME is set to the home of the R script
+# Rscript $SS_HOME/ScaleScalePearsonRWithWeightsTest.R $SS_HOME/in/ $SS_HOME/expected/
+args <- commandArgs(TRUE)
+options(digits=22)
+
+library("Matrix")
+library("boot")
+# Usage: R --vanilla -args Xfile X < ScaleScaleTest.R
+
+#parseCommandArgs()
+######################
+
+X = readMM(paste(args[1], "X.mtx", sep=""))
+Y = readMM(paste(args[1], "Y.mtx", sep=""))
+WM = readMM(paste(args[1],"WM.mtx", sep=""))
+
+# create a matrix from X and Y vectors
+mat = cbind(X[,1], Y[,1]);
+
+# corr is a function in "boot" package
+R = corr(mat, WM[,1]);
+
+write(R, paste(args[2], "PearsonR", sep=""));
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.dml b/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.dml
index 82ec837..a3bcf33 100644
--- a/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.dml
+++ b/src/test/scripts/applications/descriptivestats/ScaleScalePearsonRWithWeightsTest.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.
-#
-#-------------------------------------------------------------
-
-# Note this script is externalized to customers, please do not change w/o consulting component owner.
-# How to invoke this dml script ScaleScale.dml?
-# Assume $SS_HOME is set to the home of the dml script
-# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
-# Assume X and Y are scale variables and both have 100000 rows
-# hadoop jar SystemML.jar -f $SS_HOME/ScaleScale.dml -args "$INPUT_DIR/X" 100000 "$INPUT_DIR/Y" "$INPUT_DIR/WM" "$OUPUT_DIR/PearsonR"
-
-#X <- scale variable
-#Y <- scale variable
-#WM <- weights
-
-X = read($1, rows=$2, cols=1, format="text");
-Y = read($3, rows=$2, cols=1, format="text");
-WM = read($4, rows=$2, cols=1, format="text");
-
-W = sum(WM);
-
-# weighted co-variance
-covXY = cov(X,Y,WM);
-
-# compute standard deviations for both X and Y by computing 2^nd central moment
-m2X = moment(X,WM,2);
-m2Y = moment(Y,WM,2);
-sigmaX = sqrt(m2X * (W/(W-1.0)) );
-sigmaY = sqrt(m2Y * (W/(W-1.0)) );
-
-# Pearson's R
-R = covXY / (sigmaX*sigmaY);
-
-write(R, $5);
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# Note this script is externalized to customers, please do not change w/o consulting component owner.
+# How to invoke this dml script ScaleScale.dml?
+# Assume $SS_HOME is set to the home of the dml script
+# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
+# Assume X and Y are scale variables and both have 100000 rows
+# hadoop jar SystemML.jar -f $SS_HOME/ScaleScale.dml -args "$INPUT_DIR/X" 100000 "$INPUT_DIR/Y" "$INPUT_DIR/WM" "$OUPUT_DIR/PearsonR"
+
+#X <- scale variable
+#Y <- scale variable
+#WM <- weights
+
+X = read($1, rows=$2, cols=1, format="text");
+Y = read($3, rows=$2, cols=1, format="text");
+WM = read($4, rows=$2, cols=1, format="text");
+
+W = sum(WM);
+
+# weighted co-variance
+covXY = cov(X,Y,WM);
+
+# compute standard deviations for both X and Y by computing 2^nd central moment
+m2X = moment(X,WM,2);
+m2Y = moment(Y,WM,2);
+sigmaX = sqrt(m2X * (W/(W-1.0)) );
+sigmaY = sqrt(m2Y * (W/(W-1.0)) );
+
+# Pearson's R
+R = covXY / (sigmaX*sigmaY);
+
+write(R, $5);
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/SimpleQuantileTest.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/SimpleQuantileTest.dml b/src/test/scripts/applications/descriptivestats/SimpleQuantileTest.dml
index 53aea26..9f7a9be 100644
--- a/src/test/scripts/applications/descriptivestats/SimpleQuantileTest.dml
+++ b/src/test/scripts/applications/descriptivestats/SimpleQuantileTest.dml
@@ -1,35 +1,35 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-$$readhelper$$
-
-V = read("$$indir$$vector", rows=$$rows$$, cols=1, format="text")
-W = read("$$indir$$weight", rows=$$rows$$, cols=1, format="text")
-
-# median
-md = median(V) #quantile(V, 0.5)
-medianHelper1 = md * Helper;
-write(medianHelper1, "$$outdir$$median", format="text");
-
-# weighted median
-wmd = median(V,W) #quantile(V, W, 0.5)
-medianHelper2 = wmd * Helper;
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+$$readhelper$$
+
+V = read("$$indir$$vector", rows=$$rows$$, cols=1, format="text")
+W = read("$$indir$$weight", rows=$$rows$$, cols=1, format="text")
+
+# median
+md = median(V) #quantile(V, 0.5)
+medianHelper1 = md * Helper;
+write(medianHelper1, "$$outdir$$median", format="text");
+
+# weighted median
+wmd = median(V,W) #quantile(V, W, 0.5)
+medianHelper2 = wmd * Helper;
write(medianHelper2, "$$outdir$$weighted_median", format="text");
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.R
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.R b/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.R
index a5986db..c7b8e8f 100644
--- a/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.R
+++ b/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.R
@@ -1,63 +1,63 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# JUnit test class: dml.test.integration.descriptivestats.UnivariateStatsTest.java
-# command line invocation assuming $C_HOME is set to the home of the R script
-# Rscript $C_HOME/Categorical.R $C_HOME/in/ $C_HOME/expected/
-args <- commandArgs(TRUE)
-options(digits=22)
-
-#library("batch")
-library("Matrix")
-# Usage: R --vanilla -args Xfile X < DescriptiveStatistics.R
-
-#parseCommandArgs()
-######################
-
-V = readMM(paste(args[1], "vector.mtx", sep=""))
-W = readMM(paste(args[1], "weight.mtx", sep=""))
-
-tab = table(rep(V[,1],W[,1]))
-cat = t(as.numeric(names(tab)))
-Nc = t(as.vector(tab))
-
-# the number of categories of a categorical variable
-R = length(Nc)
-
-# total count
-s = sum(Nc)
-
-# percentage values of each categorical compare to the total case number
-Pc = Nc / s
-
-# all categorical values of a categorical variable
-#C = t(as.matrix(as.numeric(Nc > 0)))
-C= (Nc > 0)
-
-# mode
-mx = max(Nc)
-Mode = (Nc == mx)
-
-writeMM(as(t(Nc),"CsparseMatrix"), paste(args[2], "Nc", sep=""), format="text");
-write(R, paste(args[2], "R", sep=""));
-writeMM(as(t(Pc),"CsparseMatrix"), paste(args[2], "Pc", sep=""), format="text");
-writeMM(as(t(C),"CsparseMatrix"), paste(args[2], "C", sep=""), format="text");
-writeMM(as(t(Mode),"CsparseMatrix"), paste(args[2], "Mode", sep=""), format="text");
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# JUnit test class: dml.test.integration.descriptivestats.UnivariateStatsTest.java
+# command line invocation assuming $C_HOME is set to the home of the R script
+# Rscript $C_HOME/Categorical.R $C_HOME/in/ $C_HOME/expected/
+args <- commandArgs(TRUE)
+options(digits=22)
+
+#library("batch")
+library("Matrix")
+# Usage: R --vanilla -args Xfile X < DescriptiveStatistics.R
+
+#parseCommandArgs()
+######################
+
+V = readMM(paste(args[1], "vector.mtx", sep=""))
+W = readMM(paste(args[1], "weight.mtx", sep=""))
+
+tab = table(rep(V[,1],W[,1]))
+cat = t(as.numeric(names(tab)))
+Nc = t(as.vector(tab))
+
+# the number of categories of a categorical variable
+R = length(Nc)
+
+# total count
+s = sum(Nc)
+
+# percentage values of each categorical compare to the total case number
+Pc = Nc / s
+
+# all categorical values of a categorical variable
+#C = t(as.matrix(as.numeric(Nc > 0)))
+C= (Nc > 0)
+
+# mode
+mx = max(Nc)
+Mode = (Nc == mx)
+
+writeMM(as(t(Nc),"CsparseMatrix"), paste(args[2], "Nc", sep=""), format="text");
+write(R, paste(args[2], "R", sep=""));
+writeMM(as(t(Pc),"CsparseMatrix"), paste(args[2], "Pc", sep=""), format="text");
+writeMM(as(t(C),"CsparseMatrix"), paste(args[2], "C", sep=""), format="text");
+writeMM(as(t(Mode),"CsparseMatrix"), paste(args[2], "Mode", sep=""), format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.dml b/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.dml
index d838cff..cacc311 100644
--- a/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.dml
+++ b/src/test/scripts/applications/descriptivestats/WeightedCategoricalTest.dml
@@ -1,56 +1,56 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# Note this script is externalized to customers, please do not change w/o consulting component owner.
-# How to invoke this dml script Categorical.dml?
-# Assume C_HOME is set to the home of the dml script
-# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
-# Assume rows = 10000 for vector
-# hadoop jar SystemML.jar -f $C_HOME/Categorical.dml -args "$INPUT_DIR/vector" 10000 "$INPUT_DIR/W" "$OUTPUT_DIR/Nc" "$OUPUT_DIR/R" "$OUTPUT_DIR/Pc" "$OUTPUT_DIR/C" "$OUTPUT_DIR/Mode"
-
-V = read($1, rows=$2, cols=1, format="text")
-W = read($3, rows=$2, cols=1, format="text")
-
-# a set of number of values specify the number of cases of each categorical
-Nc = table(V, 1, W);
-
-# the number of categories of a categorical variable
-R = nrow(Nc)
-
-# total count
-s = sum(Nc)
-
-# percentage values of each categorical compare to the total case number
-Pc = Nc / s
-
-# all categorical values of a categorical variable
-C = ppred(Nc, 0, ">")
-
-# mode
-mx = max(Nc)
-Mode = ppred(Nc, mx, "==")
-
-write(Nc, $4, format="text")
-write(R, $5)
-write(Pc, $6, format="text")
-write(C, $7, format="text")
-write(Mode, $8, format="text")
-
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# Note this script is externalized to customers, please do not change w/o consulting component owner.
+# How to invoke this dml script Categorical.dml?
+# Assume C_HOME is set to the home of the dml script
+# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
+# Assume rows = 10000 for vector
+# hadoop jar SystemML.jar -f $C_HOME/Categorical.dml -args "$INPUT_DIR/vector" 10000 "$INPUT_DIR/W" "$OUTPUT_DIR/Nc" "$OUPUT_DIR/R" "$OUTPUT_DIR/Pc" "$OUTPUT_DIR/C" "$OUTPUT_DIR/Mode"
+
+V = read($1, rows=$2, cols=1, format="text")
+W = read($3, rows=$2, cols=1, format="text")
+
+# a set of number of values specify the number of cases of each categorical
+Nc = table(V, 1, W);
+
+# the number of categories of a categorical variable
+R = nrow(Nc)
+
+# total count
+s = sum(Nc)
+
+# percentage values of each categorical compare to the total case number
+Pc = Nc / s
+
+# all categorical values of a categorical variable
+C = ppred(Nc, 0, ">")
+
+# mode
+mx = max(Nc)
+Mode = ppred(Nc, mx, "==")
+
+write(Nc, $4, format="text")
+write(R, $5)
+write(Pc, $6, format="text")
+write(C, $7, format="text")
+write(Mode, $8, format="text")
+
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/WeightedScaleTest.R
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/WeightedScaleTest.R b/src/test/scripts/applications/descriptivestats/WeightedScaleTest.R
index 866ca02..eba3d1c 100644
--- a/src/test/scripts/applications/descriptivestats/WeightedScaleTest.R
+++ b/src/test/scripts/applications/descriptivestats/WeightedScaleTest.R
@@ -1,155 +1,155 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# JUnit test class: dml.test.integration.descriptivestats.UnivariateStatsTest.java
-# command line invocation assuming $S_HOME is set to the home of the R script
-# Rscript $S_HOME/WeightedScaleTest.R $S_HOME/in/ $S_HOME/expected/
-args <- commandArgs(TRUE)
-options(digits=22)
-
-options(repos="http://cran.stat.ucla.edu/")
-is.installed <- function(mypkg) is.element(mypkg, installed.packages()[,1])
-
-is_plotrix = is.installed("plotrix");
-if ( !is_plotrix ) {
-install.packages("plotrix");
-}
-library("plotrix");
-
-is_psych = is.installed("psych");
-if ( !is_psych ) {
-install.packages("psych");
-}
-library("psych")
-
-is_moments = is.installed("moments");
-if( !is_moments){
-install.packages("moments");
-}
-library("moments")
-
-#library("batch")
-library("Matrix")
-
-# Usage: R --vanilla -args Xfile X < DescriptiveStatistics.R
-
-#parseCommandArgs()
-######################
-
-Temp = readMM(paste(args[1], "vector.mtx", sep=""))
-W = readMM(paste(args[1], "weight.mtx", sep=""))
-P = readMM(paste(args[1], "prob.mtx", sep=""))
-
-W = round(W)
-
-V=rep(Temp[,1],W[,1])
-
-n = sum(W)
-
-# sum
-s1 = sum(V)
-
-# mean
-mu = s1/n
-
-# variances
-var = var(V)
-
-# standard deviations
-std_dev = sd(V, na.rm = FALSE)
-
-# standard errors of mean
-SE = std.error(V, na.rm)
-
-# coefficients of variation
-cv = std_dev/mu
-
-# harmonic means (note: may generate out of memory for large sparse matrices becauses of NaNs)
-#har_mu = harmonic.mean(V)
-
-# geometric means is not currently supported.
-#geom_mu = geometric.mean(V)
-
-# min and max
-mn=min(V)
-mx=max(V)
-
-# range
-rng = mx - mn
-
-# Skewness
-g1 = n^2*moment(V, order=3, central=TRUE)/((n-1)*(n-2)*std_dev^3)
-
-# standard error of skewness (not sure how it is defined without the weight)
-se_g1=sqrt( 6*n*(n-1.0) / ((n-2.0)*(n+1.0)*(n+3.0)) )
-
-m2 = moment(V, order=2, central=TRUE)
-m4 = moment(V, order=4, central=TRUE)
-
-# Kurtosis (using binomial formula)
-g2 = (n^2*(n+1)*m4-3*m2^2*n^2*(n-1))/((n-1)*(n-2)*(n-3)*var^2)
-
-# Standard error of Kurtosis (not sure how it is defined without the weight)
-se_g2= sqrt( (4*(n^2-1)*se_g1^2)/((n+5)*(n-3)) )
-
-# median
-md = median(V) #quantile(V, 0.5, type = 1)
-
-# quantile
-Q = t(quantile(V, P[,1], type = 1))
-
-# inter-quartile mean
-S=c(sort(V))
-
-q25d=n*0.25
-q75d=n*0.75
-q25i=ceiling(q25d)
-q75i=ceiling(q75d)
-
-iqm = sum(S[(q25i+1):q75i])
-iqm = iqm + (q25i-q25d)*S[q25i] - (q75i-q75d)*S[q75i]
-iqm = iqm/(n*0.5)
-
-#print(paste("IQM ", iqm));
-
-# outliers use ppred to describe it
-out_minus = t(as.numeric(Temp < mu-5*std_dev)*Temp)
-out_plus = t(as.numeric(Temp > mu+5*std_dev)*Temp)
-
-write(mu, paste(args[2], "mean", sep=""));
-write(std_dev, paste(args[2], "std", sep=""));
-write(SE, paste(args[2], "se", sep=""));
-write(var, paste(args[2], "var", sep=""));
-write(cv, paste(args[2], "cv", sep=""));
-# write(har_mu),paste(args[2], "har", sep=""));
-# write(geom_mu, paste(args[2], "geom", sep=""));
-write(mn, paste(args[2], "min", sep=""));
-write(mx, paste(args[2], "max", sep=""));
-write(rng, paste(args[2], "rng", sep=""));
-write(g1, paste(args[2], "g1", sep=""));
-write(se_g1, paste(args[2], "se_g1", sep=""));
-write(g2, paste(args[2], "g2", sep=""));
-write(se_g2, paste(args[2], "se_g2", sep=""));
-write(md, paste(args[2], "median", sep=""));
-write(iqm, paste(args[2], "iqm", sep=""));
-writeMM(as(t(out_minus),"CsparseMatrix"), paste(args[2], "out_minus", sep=""), format="text");
-writeMM(as(t(out_plus),"CsparseMatrix"), paste(args[2], "out_plus", sep=""), format="text");
-writeMM(as(t(Q),"CsparseMatrix"), paste(args[2], "quantile", sep=""), format="text");
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# JUnit test class: dml.test.integration.descriptivestats.UnivariateStatsTest.java
+# command line invocation assuming $S_HOME is set to the home of the R script
+# Rscript $S_HOME/WeightedScaleTest.R $S_HOME/in/ $S_HOME/expected/
+args <- commandArgs(TRUE)
+options(digits=22)
+
+options(repos="http://cran.stat.ucla.edu/")
+is.installed <- function(mypkg) is.element(mypkg, installed.packages()[,1])
+
+is_plotrix = is.installed("plotrix");
+if ( !is_plotrix ) {
+install.packages("plotrix");
+}
+library("plotrix");
+
+is_psych = is.installed("psych");
+if ( !is_psych ) {
+install.packages("psych");
+}
+library("psych")
+
+is_moments = is.installed("moments");
+if( !is_moments){
+install.packages("moments");
+}
+library("moments")
+
+#library("batch")
+library("Matrix")
+
+# Usage: R --vanilla -args Xfile X < DescriptiveStatistics.R
+
+#parseCommandArgs()
+######################
+
+Temp = readMM(paste(args[1], "vector.mtx", sep=""))
+W = readMM(paste(args[1], "weight.mtx", sep=""))
+P = readMM(paste(args[1], "prob.mtx", sep=""))
+
+W = round(W)
+
+V=rep(Temp[,1],W[,1])
+
+n = sum(W)
+
+# sum
+s1 = sum(V)
+
+# mean
+mu = s1/n
+
+# variances
+var = var(V)
+
+# standard deviations
+std_dev = sd(V, na.rm = FALSE)
+
+# standard errors of mean
+SE = std.error(V, na.rm)
+
+# coefficients of variation
+cv = std_dev/mu
+
+# harmonic means (note: may generate out of memory for large sparse matrices becauses of NaNs)
+#har_mu = harmonic.mean(V)
+
+# geometric means is not currently supported.
+#geom_mu = geometric.mean(V)
+
+# min and max
+mn=min(V)
+mx=max(V)
+
+# range
+rng = mx - mn
+
+# Skewness
+g1 = n^2*moment(V, order=3, central=TRUE)/((n-1)*(n-2)*std_dev^3)
+
+# standard error of skewness (not sure how it is defined without the weight)
+se_g1=sqrt( 6*n*(n-1.0) / ((n-2.0)*(n+1.0)*(n+3.0)) )
+
+m2 = moment(V, order=2, central=TRUE)
+m4 = moment(V, order=4, central=TRUE)
+
+# Kurtosis (using binomial formula)
+g2 = (n^2*(n+1)*m4-3*m2^2*n^2*(n-1))/((n-1)*(n-2)*(n-3)*var^2)
+
+# Standard error of Kurtosis (not sure how it is defined without the weight)
+se_g2= sqrt( (4*(n^2-1)*se_g1^2)/((n+5)*(n-3)) )
+
+# median
+md = median(V) #quantile(V, 0.5, type = 1)
+
+# quantile
+Q = t(quantile(V, P[,1], type = 1))
+
+# inter-quartile mean
+S=c(sort(V))
+
+q25d=n*0.25
+q75d=n*0.75
+q25i=ceiling(q25d)
+q75i=ceiling(q75d)
+
+iqm = sum(S[(q25i+1):q75i])
+iqm = iqm + (q25i-q25d)*S[q25i] - (q75i-q75d)*S[q75i]
+iqm = iqm/(n*0.5)
+
+#print(paste("IQM ", iqm));
+
+# outliers use ppred to describe it
+out_minus = t(as.numeric(Temp < mu-5*std_dev)*Temp)
+out_plus = t(as.numeric(Temp > mu+5*std_dev)*Temp)
+
+write(mu, paste(args[2], "mean", sep=""));
+write(std_dev, paste(args[2], "std", sep=""));
+write(SE, paste(args[2], "se", sep=""));
+write(var, paste(args[2], "var", sep=""));
+write(cv, paste(args[2], "cv", sep=""));
+# write(har_mu),paste(args[2], "har", sep=""));
+# write(geom_mu, paste(args[2], "geom", sep=""));
+write(mn, paste(args[2], "min", sep=""));
+write(mx, paste(args[2], "max", sep=""));
+write(rng, paste(args[2], "rng", sep=""));
+write(g1, paste(args[2], "g1", sep=""));
+write(se_g1, paste(args[2], "se_g1", sep=""));
+write(g2, paste(args[2], "g2", sep=""));
+write(se_g2, paste(args[2], "se_g2", sep=""));
+write(md, paste(args[2], "median", sep=""));
+write(iqm, paste(args[2], "iqm", sep=""));
+writeMM(as(t(out_minus),"CsparseMatrix"), paste(args[2], "out_minus", sep=""), format="text");
+writeMM(as(t(out_plus),"CsparseMatrix"), paste(args[2], "out_plus", sep=""), format="text");
+writeMM(as(t(Q),"CsparseMatrix"), paste(args[2], "quantile", sep=""), format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/descriptivestats/WeightedScaleTest.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/descriptivestats/WeightedScaleTest.dml b/src/test/scripts/applications/descriptivestats/WeightedScaleTest.dml
index 6968c1e..aac58de 100644
--- a/src/test/scripts/applications/descriptivestats/WeightedScaleTest.dml
+++ b/src/test/scripts/applications/descriptivestats/WeightedScaleTest.dml
@@ -1,125 +1,125 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# Note this script is externalized to customers, please do not change w/o consulting component owner.
-# How to invoke this dml script Scale.dml?
-# Assume S_HOME is set to the home of the dml script
-# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
-# Assume rows = 10000 for V and rows = 5 for P
-# hadoop jar SystemML.jar -f $S_HOME/Scale.dml -args "$INPUT_DIR/vector" 10000 "$INPUT_DIR/weight "$INPUT_DIR/prob 5
-# "$OUTPUT_DIR/mean" "$OUTPUT_DIR/std" "$OUTPUT_DIR/se" "$OUTPUT_DIR/var" "$OUTPUT_DIR/cv"
-# "$OUTPUT_DIR/min" "$OUTPUT_DIR/max" "$OUTPUT_DIR/rng"
-# "$OUTPUT_DIR/g1" "$OUTPUT_DIR/se_g1" "$OUTPUT_DIR/g2" "$OUTPUT_DIR/se_g2"
-# "$OUTPUT_DIR/median" "$OUTPUT_DIR/iqm"
-# "OUTPUT_DIR/out_minus" "$OUTPUT_DIR/out_plus" "$OUTPUT_DIR/quantile"
-
-V = read($1, rows=$2, cols=1, format="text")
-W = read($3, rows=$2, cols=1, format="text")
-P = read($4, rows=$5, cols=1, format="text")
-
-W = round(W)
-
-n = nrow(V)
-
-wt = sum(W)
-
-# sum
-s1 = sum(V*W)
-
-# 2nd central moment
-m2 = moment(V, W, 2)
-
-# 3rd central moment
-m3 = moment(V, W, 3)
-
-# 4th central moment
-m4 = moment(V, W, 4)
-
-# mean
-mu = mean(V, W)
-
-# variances
-var = m2*wt/(wt-1.0)
-
-# standard deviations
-std_dev = sqrt(var)
-
-# standard errors of mean
-SE = std_dev/sqrt(wt)
-
-# coefficients of variation
-cv = std_dev/mu
-
-# harmonic means (note: may generate out of memory for large sparse matrices becauses of NaNs)
-#har_mu = wt/(sum((1.0/V)*W))
-
-# geometric means is not currently supported.
-#geom_mu = wt*exp(sum(log(V)*W)/wt)
-
-# min and max
-mn=min(V)
-mx=max(V)
-
-# range
-rng = mx - mn
-
-# Skewness
-g1 = wt^2*m3/((wt-1)*(wt-2)*std_dev^3)
-
-# standard error of skewness
-se_g1=sqrt( 6*wt*(wt-1) / ((wt-2)*(wt+1)*(wt+3)) )
-
-# Kurtosis (using binomial formula)
-g2 = (wt^2*(wt+1)*m4-3*m2^2*wt^2*(wt-1))/((wt-1)*(wt-2)*(wt-3)*std_dev^4)
-
-# Standard error of Kurtosis
-se_g2= sqrt( (4*(wt^2-1)*se_g1^2)/((wt+5)*(wt-3)) )
-
-# outliers use ppred to describe it
-out_minus = ppred(V, mu-5*std_dev, "<")*V
-out_plus = ppred(V, mu+5*std_dev, ">")*V
-
-# median
-md = median(V,W); #quantile(V, W, 0.5)
-
-# quantile
-Q = quantile(V, W, P)
-
-# inter-quartile mean
-iqm = interQuartileMean(V, W)
-
-write(mu, $6);
-write(std_dev, $7);
-write(SE, $8);
-write(var, $9);
-write(cv, $10);
-write(mn, $11);
-write(mx, $12);
-write(rng, $13);
-write(g1, $14);
-write(se_g1, $15);
-write(g2, $16);
-write(se_g2, $17);
-write(md, $18);
-write(iqm, $19);
-write(out_minus, $20, format="text");
-write(out_plus, $21, format="text");
-write(Q, $22, format="text");
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# Note this script is externalized to customers, please do not change w/o consulting component owner.
+# How to invoke this dml script Scale.dml?
+# Assume S_HOME is set to the home of the dml script
+# Assume input and output directories are on hdfs as INPUT_DIR and OUTPUT_DIR
+# Assume rows = 10000 for V and rows = 5 for P
+# hadoop jar SystemML.jar -f $S_HOME/Scale.dml -args "$INPUT_DIR/vector" 10000 "$INPUT_DIR/weight "$INPUT_DIR/prob 5
+# "$OUTPUT_DIR/mean" "$OUTPUT_DIR/std" "$OUTPUT_DIR/se" "$OUTPUT_DIR/var" "$OUTPUT_DIR/cv"
+# "$OUTPUT_DIR/min" "$OUTPUT_DIR/max" "$OUTPUT_DIR/rng"
+# "$OUTPUT_DIR/g1" "$OUTPUT_DIR/se_g1" "$OUTPUT_DIR/g2" "$OUTPUT_DIR/se_g2"
+# "$OUTPUT_DIR/median" "$OUTPUT_DIR/iqm"
+# "OUTPUT_DIR/out_minus" "$OUTPUT_DIR/out_plus" "$OUTPUT_DIR/quantile"
+
+V = read($1, rows=$2, cols=1, format="text")
+W = read($3, rows=$2, cols=1, format="text")
+P = read($4, rows=$5, cols=1, format="text")
+
+W = round(W)
+
+n = nrow(V)
+
+wt = sum(W)
+
+# sum
+s1 = sum(V*W)
+
+# 2nd central moment
+m2 = moment(V, W, 2)
+
+# 3rd central moment
+m3 = moment(V, W, 3)
+
+# 4th central moment
+m4 = moment(V, W, 4)
+
+# mean
+mu = mean(V, W)
+
+# variances
+var = m2*wt/(wt-1.0)
+
+# standard deviations
+std_dev = sqrt(var)
+
+# standard errors of mean
+SE = std_dev/sqrt(wt)
+
+# coefficients of variation
+cv = std_dev/mu
+
+# harmonic means (note: may generate out of memory for large sparse matrices becauses of NaNs)
+#har_mu = wt/(sum((1.0/V)*W))
+
+# geometric means is not currently supported.
+#geom_mu = wt*exp(sum(log(V)*W)/wt)
+
+# min and max
+mn=min(V)
+mx=max(V)
+
+# range
+rng = mx - mn
+
+# Skewness
+g1 = wt^2*m3/((wt-1)*(wt-2)*std_dev^3)
+
+# standard error of skewness
+se_g1=sqrt( 6*wt*(wt-1) / ((wt-2)*(wt+1)*(wt+3)) )
+
+# Kurtosis (using binomial formula)
+g2 = (wt^2*(wt+1)*m4-3*m2^2*wt^2*(wt-1))/((wt-1)*(wt-2)*(wt-3)*std_dev^4)
+
+# Standard error of Kurtosis
+se_g2= sqrt( (4*(wt^2-1)*se_g1^2)/((wt+5)*(wt-3)) )
+
+# outliers use ppred to describe it
+out_minus = ppred(V, mu-5*std_dev, "<")*V
+out_plus = ppred(V, mu+5*std_dev, ">")*V
+
+# median
+md = median(V,W); #quantile(V, W, 0.5)
+
+# quantile
+Q = quantile(V, W, P)
+
+# inter-quartile mean
+iqm = interQuartileMean(V, W)
+
+write(mu, $6);
+write(std_dev, $7);
+write(SE, $8);
+write(var, $9);
+write(cv, $10);
+write(mn, $11);
+write(mx, $12);
+write(rng, $13);
+write(g1, $14);
+write(se_g1, $15);
+write(g2, $16);
+write(se_g2, $17);
+write(md, $18);
+write(iqm, $19);
+write(out_minus, $20, format="text");
+write(out_plus, $21, format="text");
+write(Q, $22, format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/glm/GLM.R
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/glm/GLM.R b/src/test/scripts/applications/glm/GLM.R
index d569319..32b51f9 100644
--- a/src/test/scripts/applications/glm/GLM.R
+++ b/src/test/scripts/applications/glm/GLM.R
@@ -1,141 +1,141 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# JUnit test class: dml.test.integration.applications.GLMTest.java
-# Intended to solve GLM Regression using R, in order to compare against the DML implementation
-# INPUT 1: Matrix X [rows, columns]
-# INPUT 2: Matrix y [rows, 1]
-# INPUT 3-6: Distribution family and link, see below:
-# ---------------------------------------------
-# Dst Var Lnk Lnk Distribution Cano-
-# typ pow typ pow Family.link nical?
-# ---------------------------------------------
-# 1 0.0 1 -1.0 Gaussian.inverse
-# 1 0.0 1 0.0 Gaussian.log
-# 1 0.0 1 1.0 Gaussian.id Yes
-# 1 1.0 1 0.0 Poisson.log Yes
-# 1 1.0 1 0.5 Poisson.sqrt
-# 1 1.0 1 1.0 Poisson.id
-# 1 2.0 1 -1.0 Gamma.inverse Yes
-# 1 2.0 1 0.0 Gamma.log
-# 1 2.0 1 1.0 Gamma.id
-# 1 3.0 1 -2.0 InvGaussian.1/mu^2 Yes
-# 1 3.0 1 -1.0 InvGaussian.inverse
-# 1 3.0 1 0.0 InvGaussian.log
-# 1 3.0 1 1.0 InvGaussian.id
-# 1 * 1 * AnyVariance.AnyLink
-# ---------------------------------------------
-# 2 -1.0 * * Binomial {-1, 1}
-# 2 0.0 * * Binomial { 0, 1}
-# 2 1.0 * * Binomial two-column
-# 2 * 1 0.0 Binomial.log
-# 2 * 2 * Binomial.logit Yes
-# 2 * 3 * Binomial.probit
-# 2 * 4 * Binomial.cloglog
-# 2 * 5 * Binomial.cauchit
-# ---------------------------------------------
-# INPUT 3: (int) Distribution type
-# INPUT 4: (double) For Power families: Variance power of the mean
-# INPUT 5: (int) Link function type
-# INPUT 6: (double) Link as power of the mean
-# INPUT 7: (int) Intercept: 0 = no, 1 = yes
-# INPUT 8: (double) tolerance (epsilon)
-# INPUT 9: the regression coefficients output file
-# OUTPUT : Matrix beta [columns, 1]
-#
-# Assume that $GLMR_HOME is set to the home of the R script
-# Assume input and output directories are $GLMR_HOME/in/ and $GLMR_HOME/expected/
-# Rscript $GLMR_HOME/GLM.R $GLMR_HOME/in/X.mtx $GLMR_HOME/in/y.mtx 2 0.0 2 0.0 1 0.00000001 $GLMR_HOME/expected/w.mtx
-
-args <- commandArgs (TRUE);
-
-library ("Matrix");
-# library ("batch");
-
-options (warn = -1);
-
-X_here <- readMM (args[1]); # (paste (args[1], "X.mtx", sep=""));
-y_here <- readMM (args[2]); # (paste (args[1], "y.mtx", sep=""));
-
-num_records <- nrow (X_here);
-num_features <- ncol (X_here);
-dist_type <- as.integer (args[3]);
-dist_param <- as.numeric (args[4]);
-link_type <- as.integer (args[5]);
-link_power <- as.numeric (args[6]);
-icept <- as.integer (args[7]);
-eps_n <- as.numeric (args[8]);
-
-f_ly <- gaussian ();
-var_power <- dist_param;
-
-if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == 1.0) { f_ly <- gaussian (link = "identity"); } else
-if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == -1.0) { f_ly <- gaussian (link = "inverse"); } else
-if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == 0.0) { f_ly <- gaussian (link = "log"); } else
-if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 1.0) { f_ly <- poisson (link = "identity"); } else
-if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 0.0) { f_ly <- poisson (link = "log"); } else
-if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 0.5) { f_ly <- poisson (link = "sqrt"); } else
-if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == 1.0) { f_ly <- Gamma (link = "identity"); } else
-if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == -1.0) { f_ly <- Gamma (link = "inverse"); } else
-if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == 0.0) { f_ly <- Gamma (link = "log"); } else
-if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == 1.0) { f_ly <- inverse.gaussian (link = "identity"); } else
-if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == -1.0) { f_ly <- inverse.gaussian (link = "inverse"); } else
-if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == 0.0) { f_ly <- inverse.gaussian (link = "log"); } else
-if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == -2.0) { f_ly <- inverse.gaussian (link = "1/mu^2"); } else
-if (dist_type == 2 & link_type == 1 & link_power == 0.0) { f_ly <- binomial (link = "log"); } else
-if (dist_type == 2 & link_type == 1 & link_power == 1.0) { f_ly <- binomial (link = "identity"); } else
-if (dist_type == 2 & link_type == 1 & link_power == 0.5) { f_ly <- binomial (link = "sqrt"); } else
-if (dist_type == 2 & link_type == 2 ) { f_ly <- binomial (link = "logit"); } else
-if (dist_type == 2 & link_type == 3 ) { f_ly <- binomial (link = "probit"); } else
-if (dist_type == 2 & link_type == 4 ) { f_ly <- binomial (link = "cloglog"); } else
-if (dist_type == 2 & link_type == 5 ) { f_ly <- binomial (link = "cauchit"); }
-
-# quasi(link = "identity", variance = "constant")
-# quasibinomial(link = "logit")
-# quasipoisson(link = "log")
-
-if (dist_type == 2 & dist_param != 1.0) {
- y_here <- (y_here - dist_param) / (1.0 - dist_param);
-}
-
-# epsilon tolerance: the iterations converge when |dev - devold|/(|dev| + 0.1) < epsilon.
-# maxit integer giving the maximal number of IWLS iterations.
-# trace logical indicating if output should be produced for each iteration.
-#
-c_rol <- glm.control (epsilon = eps_n, maxit = 100, trace = FALSE);
-
-X_matrix = as.matrix (X_here);
-y_matrix = as.matrix (y_here);
-
-if (icept == 0) {
- glmOut <- glm (y_matrix ~ X_matrix - 1, family = f_ly, control = c_rol);
- betas <- coef (glmOut);
-} else {
- glmOut <- glm (y_matrix ~ X_matrix , family = f_ly, control = c_rol);
- betas <- coef (glmOut);
- beta_intercept = betas [1];
- betas [1 : num_features] = betas [2 : (num_features + 1)];
- betas [num_features + 1] = beta_intercept;
-}
-
-print (c("Deviance", glmOut$deviance));
-writeMM (as (betas, "CsparseMatrix"), args[9], format = "text");
-
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+# JUnit test class: dml.test.integration.applications.GLMTest.java
+# Intended to solve GLM Regression using R, in order to compare against the DML implementation
+# INPUT 1: Matrix X [rows, columns]
+# INPUT 2: Matrix y [rows, 1]
+# INPUT 3-6: Distribution family and link, see below:
+# ---------------------------------------------
+# Dst Var Lnk Lnk Distribution Cano-
+# typ pow typ pow Family.link nical?
+# ---------------------------------------------
+# 1 0.0 1 -1.0 Gaussian.inverse
+# 1 0.0 1 0.0 Gaussian.log
+# 1 0.0 1 1.0 Gaussian.id Yes
+# 1 1.0 1 0.0 Poisson.log Yes
+# 1 1.0 1 0.5 Poisson.sqrt
+# 1 1.0 1 1.0 Poisson.id
+# 1 2.0 1 -1.0 Gamma.inverse Yes
+# 1 2.0 1 0.0 Gamma.log
+# 1 2.0 1 1.0 Gamma.id
+# 1 3.0 1 -2.0 InvGaussian.1/mu^2 Yes
+# 1 3.0 1 -1.0 InvGaussian.inverse
+# 1 3.0 1 0.0 InvGaussian.log
+# 1 3.0 1 1.0 InvGaussian.id
+# 1 * 1 * AnyVariance.AnyLink
+# ---------------------------------------------
+# 2 -1.0 * * Binomial {-1, 1}
+# 2 0.0 * * Binomial { 0, 1}
+# 2 1.0 * * Binomial two-column
+# 2 * 1 0.0 Binomial.log
+# 2 * 2 * Binomial.logit Yes
+# 2 * 3 * Binomial.probit
+# 2 * 4 * Binomial.cloglog
+# 2 * 5 * Binomial.cauchit
+# ---------------------------------------------
+# INPUT 3: (int) Distribution type
+# INPUT 4: (double) For Power families: Variance power of the mean
+# INPUT 5: (int) Link function type
+# INPUT 6: (double) Link as power of the mean
+# INPUT 7: (int) Intercept: 0 = no, 1 = yes
+# INPUT 8: (double) tolerance (epsilon)
+# INPUT 9: the regression coefficients output file
+# OUTPUT : Matrix beta [columns, 1]
+#
+# Assume that $GLMR_HOME is set to the home of the R script
+# Assume input and output directories are $GLMR_HOME/in/ and $GLMR_HOME/expected/
+# Rscript $GLMR_HOME/GLM.R $GLMR_HOME/in/X.mtx $GLMR_HOME/in/y.mtx 2 0.0 2 0.0 1 0.00000001 $GLMR_HOME/expected/w.mtx
+
+args <- commandArgs (TRUE);
+
+library ("Matrix");
+# library ("batch");
+
+options (warn = -1);
+
+X_here <- readMM (args[1]); # (paste (args[1], "X.mtx", sep=""));
+y_here <- readMM (args[2]); # (paste (args[1], "y.mtx", sep=""));
+
+num_records <- nrow (X_here);
+num_features <- ncol (X_here);
+dist_type <- as.integer (args[3]);
+dist_param <- as.numeric (args[4]);
+link_type <- as.integer (args[5]);
+link_power <- as.numeric (args[6]);
+icept <- as.integer (args[7]);
+eps_n <- as.numeric (args[8]);
+
+f_ly <- gaussian ();
+var_power <- dist_param;
+
+if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == 1.0) { f_ly <- gaussian (link = "identity"); } else
+if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == -1.0) { f_ly <- gaussian (link = "inverse"); } else
+if (dist_type == 1 & var_power == 0.0 & link_type == 1 & link_power == 0.0) { f_ly <- gaussian (link = "log"); } else
+if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 1.0) { f_ly <- poisson (link = "identity"); } else
+if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 0.0) { f_ly <- poisson (link = "log"); } else
+if (dist_type == 1 & var_power == 1.0 & link_type == 1 & link_power == 0.5) { f_ly <- poisson (link = "sqrt"); } else
+if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == 1.0) { f_ly <- Gamma (link = "identity"); } else
+if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == -1.0) { f_ly <- Gamma (link = "inverse"); } else
+if (dist_type == 1 & var_power == 2.0 & link_type == 1 & link_power == 0.0) { f_ly <- Gamma (link = "log"); } else
+if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == 1.0) { f_ly <- inverse.gaussian (link = "identity"); } else
+if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == -1.0) { f_ly <- inverse.gaussian (link = "inverse"); } else
+if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == 0.0) { f_ly <- inverse.gaussian (link = "log"); } else
+if (dist_type == 1 & var_power == 3.0 & link_type == 1 & link_power == -2.0) { f_ly <- inverse.gaussian (link = "1/mu^2"); } else
+if (dist_type == 2 & link_type == 1 & link_power == 0.0) { f_ly <- binomial (link = "log"); } else
+if (dist_type == 2 & link_type == 1 & link_power == 1.0) { f_ly <- binomial (link = "identity"); } else
+if (dist_type == 2 & link_type == 1 & link_power == 0.5) { f_ly <- binomial (link = "sqrt"); } else
+if (dist_type == 2 & link_type == 2 ) { f_ly <- binomial (link = "logit"); } else
+if (dist_type == 2 & link_type == 3 ) { f_ly <- binomial (link = "probit"); } else
+if (dist_type == 2 & link_type == 4 ) { f_ly <- binomial (link = "cloglog"); } else
+if (dist_type == 2 & link_type == 5 ) { f_ly <- binomial (link = "cauchit"); }
+
+# quasi(link = "identity", variance = "constant")
+# quasibinomial(link = "logit")
+# quasipoisson(link = "log")
+
+if (dist_type == 2 & dist_param != 1.0) {
+ y_here <- (y_here - dist_param) / (1.0 - dist_param);
+}
+
+# epsilon tolerance: the iterations converge when |dev - devold|/(|dev| + 0.1) < epsilon.
+# maxit integer giving the maximal number of IWLS iterations.
+# trace logical indicating if output should be produced for each iteration.
+#
+c_rol <- glm.control (epsilon = eps_n, maxit = 100, trace = FALSE);
+
+X_matrix = as.matrix (X_here);
+y_matrix = as.matrix (y_here);
+
+if (icept == 0) {
+ glmOut <- glm (y_matrix ~ X_matrix - 1, family = f_ly, control = c_rol);
+ betas <- coef (glmOut);
+} else {
+ glmOut <- glm (y_matrix ~ X_matrix , family = f_ly, control = c_rol);
+ betas <- coef (glmOut);
+ beta_intercept = betas [1];
+ betas [1 : num_features] = betas [2 : (num_features + 1)];
+ betas [num_features + 1] = beta_intercept;
+}
+
+print (c("Deviance", glmOut$deviance));
+writeMM (as (betas, "CsparseMatrix"), args[9], format = "text");
+