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Posted to commits@systemml.apache.org by du...@apache.org on 2016/01/22 17:34:00 UTC
[24/51] [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/impute/old/wfundInputGenerator.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/old/wfundInputGenerator.dml b/src/test/scripts/applications/impute/old/wfundInputGenerator.dml
index 978dbe7..8f6836c 100644
--- a/src/test/scripts/applications/impute/old/wfundInputGenerator.dml
+++ b/src/test/scripts/applications/impute/old/wfundInputGenerator.dml
@@ -1,403 +1,403 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# hadoop jar SystemML.jar -f test/scripts/applications/impute/wfundInputGenerator.dml -exec singlenode
-# -args
-# test/scripts/applications/impute/initial_reports
-# test/scripts/applications/impute/CReps
-# test/scripts/applications/impute/RegresValueMap
-# test/scripts/applications/impute/RegresFactorDefault
-# test/scripts/applications/impute/RegresParamMap
-# test/scripts/applications/impute/RegresCoeffDefault
-# test/scripts/applications/impute/RegresScaleMult
-
-is_GROUP_4_ENABLED = 0; # = 1 or 0
-
-num_terms = 6; # The number of term reports, feel free to change
-num_attrs = 19;
-
-num_frees = 13;
-if (is_GROUP_4_ENABLED == 1) {
- num_frees = 15; # The estimated last report had 15 degrees of freedom
-}
-
-zero = matrix (0.0, rows = 1, cols = 1);
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM FREE VARIABLES TO THE REPORTS
-# AFFINE MAP = LINEAR MAP + INITIAL (DEFAULT) REPORTS
-# ---------------------------------------------------------
-
-CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
-
-# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
-# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
-CReps [(num_terms-1) * num_attrs + 1, 1] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 1, 2] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 1, 3] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 1, 4] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 1, 5] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 1, 6] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 2, 1] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 3, 2] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 4, 3] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 5, 4] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 6, 5] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 7, 6] = 1.0 + zero;
-
-# row 8 is free variable not appearing in any non-free variable
-CReps [(num_terms-1) * num_attrs + 8, 7] = 1.0 + zero;
-
-# constraint that row9 = row10 + row11 + row12 + row13 + row14 + row15
-# translated to free vars: row9 = free8 + free9 + free10 + free11 + free12 + free13
-CReps [(num_terms-1) * num_attrs + 9, 8] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 9, 9] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 9, 10] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 9, 11] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 9, 12] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 9, 13] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 10, 8] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 11, 9] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 12, 10] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 13, 11] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 14, 12] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 15, 13] = 1.0 + zero;
-
-# constraint that row16 = row14 + row15
-# translated to free vars: row16 = free14 + free15
-if (is_GROUP_4_ENABLED == 1) {
- CReps [(num_terms-1) * num_attrs + 16, 14] = 1.0 + zero;
- CReps [(num_terms-1) * num_attrs + 16, 15] = 1.0 + zero;
- CReps [(num_terms-1) * num_attrs + 17, 14] = 1.0 + zero;
- CReps [(num_terms-1) * num_attrs + 18, 15] = 1.0 + zero;
-}
-
-# constraint that row19 = total cost (all free variables)
-# translated to free vars: row19 = all free variables
-CReps [(num_terms-1) * num_attrs + 19, 1] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 2] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 3] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 4] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 5] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 6] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 7] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 8] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 9] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 10] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 11] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 12] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 19, 13] = 1.0 + zero;
-if (is_GROUP_4_ENABLED == 1) {
- CReps [(num_terms-1) * num_attrs + 19, 14] = 1.0 + zero;
- CReps [(num_terms-1) * num_attrs + 19, 15] = 1.0 + zero;
-}
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
-# AFFINE MAP = LINER MAP + A VECTOR OF DEFAULTS
-# ---------------------------------------------------------
-
-# In all regressions, except the last few "special" ones, there are 4 factors:
-# x[t] ~ x[t-1], (x[t-1] - x[t-2]), aggregate[t]
-# The last regressions are for regularization, but they also follow the 4-factor pattern.
-num_factors = 4;
-
-# We have one regression equation per time-term, except the first two terms, for each
-# attribute, plus a few "special" regularization regression equations:
-num_special_regs = 12;
-if (is_GROUP_4_ENABLED == 1) {
- num_special_regs = 16;
-}
-
-num_reg_eqs = (num_terms - 2) * num_attrs + num_special_regs;
-
-RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
-RegresFactorDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
-
-# All regression equations for the same attribute share the same parameters, regardless
-# of the term; some parameters are shared across multiple attributes, (those attributes
-# whose behavior is believed to be similar) as specified in the table below:
-
-num_params = 28;
-if (is_GROUP_4_ENABLED == 1) {
- num_params = 35;
-}
-
-# Factors: -self[t] self[t-1] self[t-1]- total[t]
-# self[t-2]
-# PARAMS:
-# Group 1: 1.0 prm#01 prm#02 prm#03 Row #01 = free#01 + ... + free#06
-# Group 1: " prm#04 prm#05 prm#06 Row #02 = free#01
-# Group 1: " " " prm#07 Row #03 = free#02
-# Group 1: " " " prm#08 Row #04 = free#03
-# Group 1: " " " prm#09 Row #05 = free#04
-# Group 1: " " " prm#10 Row #06 = free#05
-# Group 1: " " " prm#11 Row #07 = free#06
-# Group 2: 1.0 prm#12 prm#13 prm#14 Row #08 = free#07
-# Group 3: 1.0 prm#15 prm#16 prm#17 Row #09 = free#08 + ... + free#13
-# Group 3: " prm#18 prm#19 prm#20 Row #10 = free#08
-# Group 3: " " " prm#21 Row #11 = free#09
-# Group 3: " " " prm#22 Row #12 = free#10
-# Group 3: " " " prm#23 Row #13 = free#11
-# Group 3: " " " prm#24 Row #14 = free#12
-# Group 3: " " " prm#25 Row #15 = free#13
-
-# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
-# Group 4: 1.0 prm#29 prm#30 prm#31 Row #16 = free#14 + free#15
-# Group 4: " prm#32 prm#33 prm#34 Row #17 = free#14
-# Group 4: " " " prm#35 Row #18 = free#15
-
-# Group 5: 1.0 prm#26 prm#27 prm#28 Row #19 = free#01 + ... + free#15
-#
-# (The aggregates in Groups 1..4 regress on the total cost in Group 5;
-# the total cost in Group 5 regresses on the intercept.)
-
-# THE LAST FEW "SPECIAL" REGULARIZATION EQUATIONS:
-# Factors: 1.0 -1.0 0.0 0.0
-# PARAMS:
-# prm#26 1.0 0.0 0.0
-# prm#27 0.0 0.0 0.0
-# prm#01 0.0 0.0 0.0
-# prm#02 0.0 0.0 0.0
-# prm#04 0.0 0.0 0.0
-# prm#05 0.0 0.0 0.0
-# prm#12 0.0 0.0 0.0
-# prm#13 0.0 0.0 0.0
-# prm#15 0.0 0.0 0.0
-# prm#16 0.0 0.0 0.0
-# prm#18 0.0 0.0 0.0
-# prm#19 0.0 0.0 0.0
-# prm#29 0.0 0.0 0.0 # GROUP-4 ZEROS:
-# prm#30 0.0 0.0 0.0 # THESE EQUATIONS
-# prm#32 0.0 0.0 0.0 # USE REVOKED PARAMETERS
-# prm#33 0.0 0.0 0.0 # AND DO NOT APPEAR
-
-
-for (t in 3 : num_terms)
-{
-# Group 1 attributes:
- for (i in 1 : 7) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- if (i == 1) {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 1] = 1.0 + zero; # 4th factor: Row#01[t]
- }
- }
-
-# Group 2 attribute:
- reg_index = ((t-3) * num_attrs - 1 + 8) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 8] = -1.0 + zero; # First factor is -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + 8] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 8] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + 8] = -1.0 + zero; # x[t-1] - x[t-2]
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
-
-# Group 3 attributes:
- for (i in 9 : 15) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- if (i == 9) {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 9] = 1.0 + zero; # 4th factor: Row#09[t]
- }
- }
-
-# Group 4 attributes:
- for (i in 16 : 18) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- if (i == 16) {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 16] = 1.0 + zero; # 4th factor: Row#16[t]
- }
- }
-
-# Group 5 attribute:
- reg_index = ((t-3) * num_attrs - 1 + 19) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 19] = -1.0 + zero; # First factor is -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + 19] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 19] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + 19] = -1.0 + zero; # x[t-1] - x[t-2]
- RegresFactorDefault [reg_index + 4, 1] = 1.0 + zero; # The Intercept
-}
-
-for (i in 1:num_special_regs)
-{
- reg_index = ((num_terms - 2) * num_attrs - 1 + i) * num_factors;
- RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
- RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
-}
-
-# ----------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM PARAMETERS TO THE COEFFICIENTS
-# AT REGRESSION FACTORS: A LINER MAP + A VECTOR OF DEFAULTS
-# ----------------------------------------------------------
-
-RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
-RegresCoeffDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
-
-for (t in 3 : num_terms) {
-# Group 1 attributes:
- reg_index = ((t-3) * num_attrs - 1 + 1) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 1] = 1.0 + zero; # Param #01
- RegresParamMap [reg_index + 3, 2] = 1.0 + zero; # Param #02
- RegresParamMap [reg_index + 4, 3] = 1.0 + zero; # Param #03
- for (i in 2 : 7) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 4] = 1.0 + zero; # Param #04
- RegresParamMap [reg_index + 3, 5] = 1.0 + zero; # Param #05
- RegresParamMap [reg_index + 4, 4 + i] = 1.0 + zero; # Param #06-#11
- }
-# Group 2 attribute:
- reg_index = ((t-3) * num_attrs - 1 + 8) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 12] = 1.0 + zero; # Param #12
- RegresParamMap [reg_index + 3, 13] = 1.0 + zero; # Param #13
- RegresParamMap [reg_index + 4, 14] = 1.0 + zero; # Param #14
-# Group 3 attributes:
- reg_index = ((t-3) * num_attrs - 1 + 9) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 15] = 1.0 + zero; # Param #15
- RegresParamMap [reg_index + 3, 16] = 1.0 + zero; # Param #16
- RegresParamMap [reg_index + 4, 17] = 1.0 + zero; # Param #17
- for (i in 10 : 15) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 18] = 1.0 + zero; # Param #18
- RegresParamMap [reg_index + 3, 19] = 1.0 + zero; # Param #19
- RegresParamMap [reg_index + 4, 10 + i] = 1.0 + zero; # Param #20-#25
- }
-
-# Group 4 attributes:
-if (is_GROUP_4_ENABLED == 1) {
- reg_index = ((t-3) * num_attrs - 1 + 16) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 29] = 1.0 + zero; # Param #29
- RegresParamMap [reg_index + 3, 30] = 1.0 + zero; # Param #30
- RegresParamMap [reg_index + 4, 31] = 1.0 + zero; # Param #31
- for (i in 17 : 18) {
- reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 32] = 1.0 + zero; # Param #32
- RegresParamMap [reg_index + 3, 33] = 1.0 + zero; # Param #33
- RegresParamMap [reg_index + 4, 17 + i] = 1.0 + zero; # Param #34-#35
- }
-}
-
-# Group 5 attribute:
- reg_index = ((t-3) * num_attrs - 1 + 19) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 26] = 1.0 + zero; # Param #26
- RegresParamMap [reg_index + 3, 27] = 1.0 + zero; # Param #27
- RegresParamMap [reg_index + 4, 28] = 1.0 + zero; # Param #28
-}
-
-reg_index = ((num_terms - 2) * num_attrs) * num_factors;
- RegresParamMap [reg_index + 1, 26] = 1.0 + zero; # Param #26
- RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 27] = 1.0 + zero; # Param #27
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 01] = 1.0 + zero; # Param #01
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 02] = 1.0 + zero; # Param #02
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 04] = 1.0 + zero; # Param #04
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 05] = 1.0 + zero; # Param #05
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 12] = 1.0 + zero; # Param #12
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 13] = 1.0 + zero; # Param #13
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 15] = 1.0 + zero; # Param #15
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 16] = 1.0 + zero; # Param #16
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 18] = 1.0 + zero; # Param #18
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 19] = 1.0 + zero; # Param #19
-
-if (is_GROUP_4_ENABLED == 1) {
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 29] = 1.0 + zero; # Param #29
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 30] = 1.0 + zero; # Param #30
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 32] = 1.0 + zero; # Param #32
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 33] = 1.0 + zero; # Param #33
-}
-
-# ----------------------------------------------------------
-# GENERATE A VECTOR OF SCALE MULTIPLIERS, ONE PER REGRESSION
-# ----------------------------------------------------------
-
-RegresScaleMult = matrix (1.0, rows = num_reg_eqs, cols = 1);
-initial_reports = read ($1);
-
-global_weight = 0.5 + zero;
-
-attribute_size = rowMeans (abs (initial_reports [, 1:(num_terms-1)]));
-max_attr_size = max (attribute_size);
-
-for (t in 3 : num_terms) {
- for (i in 1 : num_attrs) {
- regeqn = (t-3) * num_attrs + i;
- scale_down = sqrt (attribute_size [i, 1] / max_attr_size) * 0.999 + 0.001;
- acceptable_drift = scale_down * max_attr_size * 0.001;
- RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift^2);
- }
-}
-
-regeqn = (num_terms - 2) * num_attrs + 1;
-for (i in 1 : num_special_regs) {
- acceptable_drift = 0.01;
- RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift^2);
- regeqn = regeqn + 1;
-}
-
-# --------------------------------
-# WRITE OUT ALL GENERATED MATRICES
-# --------------------------------
-
-# write (initial_reports, $1, format="text");
-write (CReps, $2, format="text");
-write (RegresValueMap, $3, format="text");
-write (RegresFactorDefault,$4, format="text");
-write (RegresParamMap, $5, format="text");
-write (RegresCoeffDefault, $6, format="text");
-write (RegresScaleMult, $7, 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.
+#
+#-------------------------------------------------------------
+
+# hadoop jar SystemML.jar -f test/scripts/applications/impute/wfundInputGenerator.dml -exec singlenode
+# -args
+# test/scripts/applications/impute/initial_reports
+# test/scripts/applications/impute/CReps
+# test/scripts/applications/impute/RegresValueMap
+# test/scripts/applications/impute/RegresFactorDefault
+# test/scripts/applications/impute/RegresParamMap
+# test/scripts/applications/impute/RegresCoeffDefault
+# test/scripts/applications/impute/RegresScaleMult
+
+is_GROUP_4_ENABLED = 0; # = 1 or 0
+
+num_terms = 6; # The number of term reports, feel free to change
+num_attrs = 19;
+
+num_frees = 13;
+if (is_GROUP_4_ENABLED == 1) {
+ num_frees = 15; # The estimated last report had 15 degrees of freedom
+}
+
+zero = matrix (0.0, rows = 1, cols = 1);
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM FREE VARIABLES TO THE REPORTS
+# AFFINE MAP = LINEAR MAP + INITIAL (DEFAULT) REPORTS
+# ---------------------------------------------------------
+
+CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
+
+# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
+# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
+CReps [(num_terms-1) * num_attrs + 1, 1] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 1, 2] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 1, 3] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 1, 4] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 1, 5] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 1, 6] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 2, 1] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 3, 2] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 4, 3] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 5, 4] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 6, 5] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 7, 6] = 1.0 + zero;
+
+# row 8 is free variable not appearing in any non-free variable
+CReps [(num_terms-1) * num_attrs + 8, 7] = 1.0 + zero;
+
+# constraint that row9 = row10 + row11 + row12 + row13 + row14 + row15
+# translated to free vars: row9 = free8 + free9 + free10 + free11 + free12 + free13
+CReps [(num_terms-1) * num_attrs + 9, 8] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 9, 9] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 9, 10] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 9, 11] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 9, 12] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 9, 13] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 10, 8] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 11, 9] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 12, 10] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 13, 11] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 14, 12] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 15, 13] = 1.0 + zero;
+
+# constraint that row16 = row14 + row15
+# translated to free vars: row16 = free14 + free15
+if (is_GROUP_4_ENABLED == 1) {
+ CReps [(num_terms-1) * num_attrs + 16, 14] = 1.0 + zero;
+ CReps [(num_terms-1) * num_attrs + 16, 15] = 1.0 + zero;
+ CReps [(num_terms-1) * num_attrs + 17, 14] = 1.0 + zero;
+ CReps [(num_terms-1) * num_attrs + 18, 15] = 1.0 + zero;
+}
+
+# constraint that row19 = total cost (all free variables)
+# translated to free vars: row19 = all free variables
+CReps [(num_terms-1) * num_attrs + 19, 1] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 2] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 3] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 4] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 5] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 6] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 7] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 8] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 9] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 10] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 11] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 12] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 19, 13] = 1.0 + zero;
+if (is_GROUP_4_ENABLED == 1) {
+ CReps [(num_terms-1) * num_attrs + 19, 14] = 1.0 + zero;
+ CReps [(num_terms-1) * num_attrs + 19, 15] = 1.0 + zero;
+}
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
+# AFFINE MAP = LINER MAP + A VECTOR OF DEFAULTS
+# ---------------------------------------------------------
+
+# In all regressions, except the last few "special" ones, there are 4 factors:
+# x[t] ~ x[t-1], (x[t-1] - x[t-2]), aggregate[t]
+# The last regressions are for regularization, but they also follow the 4-factor pattern.
+num_factors = 4;
+
+# We have one regression equation per time-term, except the first two terms, for each
+# attribute, plus a few "special" regularization regression equations:
+num_special_regs = 12;
+if (is_GROUP_4_ENABLED == 1) {
+ num_special_regs = 16;
+}
+
+num_reg_eqs = (num_terms - 2) * num_attrs + num_special_regs;
+
+RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
+RegresFactorDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
+
+# All regression equations for the same attribute share the same parameters, regardless
+# of the term; some parameters are shared across multiple attributes, (those attributes
+# whose behavior is believed to be similar) as specified in the table below:
+
+num_params = 28;
+if (is_GROUP_4_ENABLED == 1) {
+ num_params = 35;
+}
+
+# Factors: -self[t] self[t-1] self[t-1]- total[t]
+# self[t-2]
+# PARAMS:
+# Group 1: 1.0 prm#01 prm#02 prm#03 Row #01 = free#01 + ... + free#06
+# Group 1: " prm#04 prm#05 prm#06 Row #02 = free#01
+# Group 1: " " " prm#07 Row #03 = free#02
+# Group 1: " " " prm#08 Row #04 = free#03
+# Group 1: " " " prm#09 Row #05 = free#04
+# Group 1: " " " prm#10 Row #06 = free#05
+# Group 1: " " " prm#11 Row #07 = free#06
+# Group 2: 1.0 prm#12 prm#13 prm#14 Row #08 = free#07
+# Group 3: 1.0 prm#15 prm#16 prm#17 Row #09 = free#08 + ... + free#13
+# Group 3: " prm#18 prm#19 prm#20 Row #10 = free#08
+# Group 3: " " " prm#21 Row #11 = free#09
+# Group 3: " " " prm#22 Row #12 = free#10
+# Group 3: " " " prm#23 Row #13 = free#11
+# Group 3: " " " prm#24 Row #14 = free#12
+# Group 3: " " " prm#25 Row #15 = free#13
+
+# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
+# Group 4: 1.0 prm#29 prm#30 prm#31 Row #16 = free#14 + free#15
+# Group 4: " prm#32 prm#33 prm#34 Row #17 = free#14
+# Group 4: " " " prm#35 Row #18 = free#15
+
+# Group 5: 1.0 prm#26 prm#27 prm#28 Row #19 = free#01 + ... + free#15
+#
+# (The aggregates in Groups 1..4 regress on the total cost in Group 5;
+# the total cost in Group 5 regresses on the intercept.)
+
+# THE LAST FEW "SPECIAL" REGULARIZATION EQUATIONS:
+# Factors: 1.0 -1.0 0.0 0.0
+# PARAMS:
+# prm#26 1.0 0.0 0.0
+# prm#27 0.0 0.0 0.0
+# prm#01 0.0 0.0 0.0
+# prm#02 0.0 0.0 0.0
+# prm#04 0.0 0.0 0.0
+# prm#05 0.0 0.0 0.0
+# prm#12 0.0 0.0 0.0
+# prm#13 0.0 0.0 0.0
+# prm#15 0.0 0.0 0.0
+# prm#16 0.0 0.0 0.0
+# prm#18 0.0 0.0 0.0
+# prm#19 0.0 0.0 0.0
+# prm#29 0.0 0.0 0.0 # GROUP-4 ZEROS:
+# prm#30 0.0 0.0 0.0 # THESE EQUATIONS
+# prm#32 0.0 0.0 0.0 # USE REVOKED PARAMETERS
+# prm#33 0.0 0.0 0.0 # AND DO NOT APPEAR
+
+
+for (t in 3 : num_terms)
+{
+# Group 1 attributes:
+ for (i in 1 : 7) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ if (i == 1) {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 1] = 1.0 + zero; # 4th factor: Row#01[t]
+ }
+ }
+
+# Group 2 attribute:
+ reg_index = ((t-3) * num_attrs - 1 + 8) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 8] = -1.0 + zero; # First factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + 8] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 8] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + 8] = -1.0 + zero; # x[t-1] - x[t-2]
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
+
+# Group 3 attributes:
+ for (i in 9 : 15) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ if (i == 9) {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 9] = 1.0 + zero; # 4th factor: Row#09[t]
+ }
+ }
+
+# Group 4 attributes:
+ for (i in 16 : 18) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # First factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ if (i == 16) {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 19] = 1.0 + zero; # 4th factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 16] = 1.0 + zero; # 4th factor: Row#16[t]
+ }
+ }
+
+# Group 5 attribute:
+ reg_index = ((t-3) * num_attrs - 1 + 19) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 19] = -1.0 + zero; # First factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + 19] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 19] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + 19] = -1.0 + zero; # x[t-1] - x[t-2]
+ RegresFactorDefault [reg_index + 4, 1] = 1.0 + zero; # The Intercept
+}
+
+for (i in 1:num_special_regs)
+{
+ reg_index = ((num_terms - 2) * num_attrs - 1 + i) * num_factors;
+ RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
+ RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
+}
+
+# ----------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM PARAMETERS TO THE COEFFICIENTS
+# AT REGRESSION FACTORS: A LINER MAP + A VECTOR OF DEFAULTS
+# ----------------------------------------------------------
+
+RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
+RegresCoeffDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
+
+for (t in 3 : num_terms) {
+# Group 1 attributes:
+ reg_index = ((t-3) * num_attrs - 1 + 1) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 1] = 1.0 + zero; # Param #01
+ RegresParamMap [reg_index + 3, 2] = 1.0 + zero; # Param #02
+ RegresParamMap [reg_index + 4, 3] = 1.0 + zero; # Param #03
+ for (i in 2 : 7) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 4] = 1.0 + zero; # Param #04
+ RegresParamMap [reg_index + 3, 5] = 1.0 + zero; # Param #05
+ RegresParamMap [reg_index + 4, 4 + i] = 1.0 + zero; # Param #06-#11
+ }
+# Group 2 attribute:
+ reg_index = ((t-3) * num_attrs - 1 + 8) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 12] = 1.0 + zero; # Param #12
+ RegresParamMap [reg_index + 3, 13] = 1.0 + zero; # Param #13
+ RegresParamMap [reg_index + 4, 14] = 1.0 + zero; # Param #14
+# Group 3 attributes:
+ reg_index = ((t-3) * num_attrs - 1 + 9) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 15] = 1.0 + zero; # Param #15
+ RegresParamMap [reg_index + 3, 16] = 1.0 + zero; # Param #16
+ RegresParamMap [reg_index + 4, 17] = 1.0 + zero; # Param #17
+ for (i in 10 : 15) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 18] = 1.0 + zero; # Param #18
+ RegresParamMap [reg_index + 3, 19] = 1.0 + zero; # Param #19
+ RegresParamMap [reg_index + 4, 10 + i] = 1.0 + zero; # Param #20-#25
+ }
+
+# Group 4 attributes:
+if (is_GROUP_4_ENABLED == 1) {
+ reg_index = ((t-3) * num_attrs - 1 + 16) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 29] = 1.0 + zero; # Param #29
+ RegresParamMap [reg_index + 3, 30] = 1.0 + zero; # Param #30
+ RegresParamMap [reg_index + 4, 31] = 1.0 + zero; # Param #31
+ for (i in 17 : 18) {
+ reg_index = ((t-3) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 32] = 1.0 + zero; # Param #32
+ RegresParamMap [reg_index + 3, 33] = 1.0 + zero; # Param #33
+ RegresParamMap [reg_index + 4, 17 + i] = 1.0 + zero; # Param #34-#35
+ }
+}
+
+# Group 5 attribute:
+ reg_index = ((t-3) * num_attrs - 1 + 19) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 26] = 1.0 + zero; # Param #26
+ RegresParamMap [reg_index + 3, 27] = 1.0 + zero; # Param #27
+ RegresParamMap [reg_index + 4, 28] = 1.0 + zero; # Param #28
+}
+
+reg_index = ((num_terms - 2) * num_attrs) * num_factors;
+ RegresParamMap [reg_index + 1, 26] = 1.0 + zero; # Param #26
+ RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 27] = 1.0 + zero; # Param #27
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 01] = 1.0 + zero; # Param #01
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 02] = 1.0 + zero; # Param #02
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 04] = 1.0 + zero; # Param #04
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 05] = 1.0 + zero; # Param #05
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 12] = 1.0 + zero; # Param #12
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 13] = 1.0 + zero; # Param #13
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 15] = 1.0 + zero; # Param #15
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 16] = 1.0 + zero; # Param #16
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 18] = 1.0 + zero; # Param #18
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 19] = 1.0 + zero; # Param #19
+
+if (is_GROUP_4_ENABLED == 1) {
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 29] = 1.0 + zero; # Param #29
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 30] = 1.0 + zero; # Param #30
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 32] = 1.0 + zero; # Param #32
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 33] = 1.0 + zero; # Param #33
+}
+
+# ----------------------------------------------------------
+# GENERATE A VECTOR OF SCALE MULTIPLIERS, ONE PER REGRESSION
+# ----------------------------------------------------------
+
+RegresScaleMult = matrix (1.0, rows = num_reg_eqs, cols = 1);
+initial_reports = read ($1);
+
+global_weight = 0.5 + zero;
+
+attribute_size = rowMeans (abs (initial_reports [, 1:(num_terms-1)]));
+max_attr_size = max (attribute_size);
+
+for (t in 3 : num_terms) {
+ for (i in 1 : num_attrs) {
+ regeqn = (t-3) * num_attrs + i;
+ scale_down = sqrt (attribute_size [i, 1] / max_attr_size) * 0.999 + 0.001;
+ acceptable_drift = scale_down * max_attr_size * 0.001;
+ RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift^2);
+ }
+}
+
+regeqn = (num_terms - 2) * num_attrs + 1;
+for (i in 1 : num_special_regs) {
+ acceptable_drift = 0.01;
+ RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift^2);
+ regeqn = regeqn + 1;
+}
+
+# --------------------------------
+# WRITE OUT ALL GENERATED MATRICES
+# --------------------------------
+
+# write (initial_reports, $1, format="text");
+write (CReps, $2, format="text");
+write (RegresValueMap, $3, format="text");
+write (RegresFactorDefault,$4, format="text");
+write (RegresParamMap, $5, format="text");
+write (RegresCoeffDefault, $6, format="text");
+write (RegresScaleMult, $7, format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/impute/test/testInputGenerator.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/test/testInputGenerator.dml b/src/test/scripts/applications/impute/test/testInputGenerator.dml
index 9eaf034..2040756 100644
--- a/src/test/scripts/applications/impute/test/testInputGenerator.dml
+++ b/src/test/scripts/applications/impute/test/testInputGenerator.dml
@@ -1,152 +1,152 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# Generates synthetic data to try inputeGaussMCMC.dml
-# How To Run:
-#
-#
-# hadoop jar SystemML.jar -f test/scripts/applications/impute/test/testInputGenerator.dml -exec singlenode
-# -args test/scripts/applications/impute/test/initial_reports test/scripts/applications/impute/test/CReps
-# test/scripts/applications/impute/test/RegresValueMap test/scripts/applications/impute/test/RegresParamMap
-
-num_terms = 40; # The number of term reports, feel free to change
-num_attrs = 6; # 4 regular attributes, 1 total cost, 1 auxiliary ("macroeconomic")
-num_frees = 4; # We estimate the last report, which has 4 degrees of freedom
-num_factors = 4; # In regressions: x[t] ~ x[t-1], (x[t-1] - x[t-2]), total_cost[t]
-
-# We have one regression equation per term, except the first two terms,
-# for each attribute except the auxiliary attribute:
-num_reg_eqs = (num_terms - 2) * (num_attrs - 1);
-
-# All regression equations for the same attribute share the same parameters,
-# regardless of the term:
-num_params = num_factors * (num_attrs - 1);
-
-# GENERATE THE INITIAL REPORTS MATRIX (with the last term report set to 0.0)
-
-initial_reports_matrix = matrix (0.0, rows = num_attrs, cols = num_terms);
-
-# We assume that the terms are quarterly.
-# Auxiliary attribute is = sqrt(1.1)^t, a steady exponential growth of 21% a year.
-# The total cost regresses on the auxiliary attribute and shows a combination of
-# exponential and cyclic behavior year after year.
-
-zero = matrix (0.0, rows = 1, cols = 1);
-
-initial_reports_matrix [6, 1] = zero + 1; # auxiliary attribute
-for (t in 2 : num_terms) {
- initial_reports_matrix [6, t] = initial_reports_matrix [6, t-1] * sqrt (1.1);
-}
-
-initial_reports_matrix [1, 1] = zero + 1 * 0.4615107865026;
-initial_reports_matrix [2, 1] = zero + 1 * 0.0270996863066;
-initial_reports_matrix [3, 1] = zero + 1 * 0.3772761445953;
-initial_reports_matrix [4, 1] = zero + 1 * 0.1341133825954;
-initial_reports_matrix [5, 1] = zero + 1; # total cost attribute
-
-initial_reports_matrix [1, 2] = zero + 2 * 0.3281440348352;
-initial_reports_matrix [2, 2] = zero + 2 * 0.0345738029588;
-initial_reports_matrix [3, 2] = zero + 2 * 0.4052452565031;
-initial_reports_matrix [4, 2] = zero + 2 * 0.2320369057028;
-initial_reports_matrix [5, 2] = zero + 2; # total cost attribute
-
-for (t in 3 : (num_terms - 1))
-{
- initial_reports_matrix [5, t] =
- - 1.1 * initial_reports_matrix [5, t-1]
- + 1.1 * (initial_reports_matrix [5, t-1] - initial_reports_matrix [5, t-2])
- + 3.0 * (initial_reports_matrix [6, t]);
-
- initial_reports_matrix [1, t] =
- 0.45 * initial_reports_matrix [1, t-1]
- + 0.00 * (initial_reports_matrix [1, t-1] - initial_reports_matrix [1, t-2])
- + 0.2243041078721 * (initial_reports_matrix [5, t]);
-
- initial_reports_matrix [2, t] =
- 0.00 * initial_reports_matrix [2, t-1]
- + 0.45 * (initial_reports_matrix [2, t-1] - initial_reports_matrix [2, t-2])
- + 0.0417492985298 * (initial_reports_matrix [5, t]);
-
- initial_reports_matrix [3, t] =
- - 0.40 * initial_reports_matrix [3, t-1]
- + 0.00 * (initial_reports_matrix [3, t-1] - initial_reports_matrix [3, t-2])
- + 0.4807004854222 * (initial_reports_matrix [5, t]);
-
- initial_reports_matrix [4, t] =
- - 0.20 * initial_reports_matrix [4, t-1]
- + 0.30 * (initial_reports_matrix [4, t-1] - initial_reports_matrix [4, t-2])
- + 0.2549604916594 * (initial_reports_matrix [5, t]);
-}
-
-# GENERATE A LINEAR MAP FROM FREE VARIABLES TO THE REPORTS
-
-CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
-CReps [(num_terms-1) * num_attrs + 1, 1] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 2, 2] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 3, 3] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 4, 4] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 5, 1] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 5, 2] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 5, 3] = 1.0 + zero;
-CReps [(num_terms-1) * num_attrs + 5, 4] = 1.0 + zero;
-
-# GENERATE A LINEAR MAP FROM REPORTS TO REGRESSION FACTORS
-
-RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
-
-for (t in 3 : num_terms) {
- for (i in 1 : (num_attrs - 2)) {
- reg_index = ((t-3)*(num_attrs-1)-1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = 1.0 + zero; # First factor is x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 5] = 1.0 + zero; # 4th factor = total_cost[t]
- }
- # For the total cost itself, the regression is almost the same, except the last line:
- reg_index = ((t-3)*(num_attrs-1)-1 + 5) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 5] = 1.0 + zero; # First factor is x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + 5] = 1.0 + zero; # Second factor is x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 5] = 1.0 + zero; # Third factor is
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + 5] = -1.0 + zero; # x[t-1] - x[t-2]
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + 6] = 1.0 + zero; # 4th factor = auxiliary[t]
-}
-
-# GENERATE A LINEAR MAP FROM PARAMETERS TO REGRESSION FACTORS
-
-RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
-
-for (t in 3 : num_terms) {
- for (i in 1 : (num_attrs - 1)) {
- reg_index = ((t-3)*(num_attrs-1)-1 + i) * num_factors;
- RegresParamMap [reg_index + 1, 0 * (num_attrs-1) + i] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 1 * (num_attrs-1) + i] = 1.0 + zero;
- RegresParamMap [reg_index + 3, 2 * (num_attrs-1) + i] = 1.0 + zero;
- RegresParamMap [reg_index + 4, 3 * (num_attrs-1) + i] = 1.0 + zero;
- }
-}
-
-# WRITE OUT ALL GENERATED MATRICES
-
-write (initial_reports_matrix, $1, format="text");
-write (CReps, $2, format="text");
-write (RegresValueMap, $3, format="text");
-write (RegresParamMap, $4, 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.
+#
+#-------------------------------------------------------------
+
+# Generates synthetic data to try inputeGaussMCMC.dml
+# How To Run:
+#
+#
+# hadoop jar SystemML.jar -f test/scripts/applications/impute/test/testInputGenerator.dml -exec singlenode
+# -args test/scripts/applications/impute/test/initial_reports test/scripts/applications/impute/test/CReps
+# test/scripts/applications/impute/test/RegresValueMap test/scripts/applications/impute/test/RegresParamMap
+
+num_terms = 40; # The number of term reports, feel free to change
+num_attrs = 6; # 4 regular attributes, 1 total cost, 1 auxiliary ("macroeconomic")
+num_frees = 4; # We estimate the last report, which has 4 degrees of freedom
+num_factors = 4; # In regressions: x[t] ~ x[t-1], (x[t-1] - x[t-2]), total_cost[t]
+
+# We have one regression equation per term, except the first two terms,
+# for each attribute except the auxiliary attribute:
+num_reg_eqs = (num_terms - 2) * (num_attrs - 1);
+
+# All regression equations for the same attribute share the same parameters,
+# regardless of the term:
+num_params = num_factors * (num_attrs - 1);
+
+# GENERATE THE INITIAL REPORTS MATRIX (with the last term report set to 0.0)
+
+initial_reports_matrix = matrix (0.0, rows = num_attrs, cols = num_terms);
+
+# We assume that the terms are quarterly.
+# Auxiliary attribute is = sqrt(1.1)^t, a steady exponential growth of 21% a year.
+# The total cost regresses on the auxiliary attribute and shows a combination of
+# exponential and cyclic behavior year after year.
+
+zero = matrix (0.0, rows = 1, cols = 1);
+
+initial_reports_matrix [6, 1] = zero + 1; # auxiliary attribute
+for (t in 2 : num_terms) {
+ initial_reports_matrix [6, t] = initial_reports_matrix [6, t-1] * sqrt (1.1);
+}
+
+initial_reports_matrix [1, 1] = zero + 1 * 0.4615107865026;
+initial_reports_matrix [2, 1] = zero + 1 * 0.0270996863066;
+initial_reports_matrix [3, 1] = zero + 1 * 0.3772761445953;
+initial_reports_matrix [4, 1] = zero + 1 * 0.1341133825954;
+initial_reports_matrix [5, 1] = zero + 1; # total cost attribute
+
+initial_reports_matrix [1, 2] = zero + 2 * 0.3281440348352;
+initial_reports_matrix [2, 2] = zero + 2 * 0.0345738029588;
+initial_reports_matrix [3, 2] = zero + 2 * 0.4052452565031;
+initial_reports_matrix [4, 2] = zero + 2 * 0.2320369057028;
+initial_reports_matrix [5, 2] = zero + 2; # total cost attribute
+
+for (t in 3 : (num_terms - 1))
+{
+ initial_reports_matrix [5, t] =
+ - 1.1 * initial_reports_matrix [5, t-1]
+ + 1.1 * (initial_reports_matrix [5, t-1] - initial_reports_matrix [5, t-2])
+ + 3.0 * (initial_reports_matrix [6, t]);
+
+ initial_reports_matrix [1, t] =
+ 0.45 * initial_reports_matrix [1, t-1]
+ + 0.00 * (initial_reports_matrix [1, t-1] - initial_reports_matrix [1, t-2])
+ + 0.2243041078721 * (initial_reports_matrix [5, t]);
+
+ initial_reports_matrix [2, t] =
+ 0.00 * initial_reports_matrix [2, t-1]
+ + 0.45 * (initial_reports_matrix [2, t-1] - initial_reports_matrix [2, t-2])
+ + 0.0417492985298 * (initial_reports_matrix [5, t]);
+
+ initial_reports_matrix [3, t] =
+ - 0.40 * initial_reports_matrix [3, t-1]
+ + 0.00 * (initial_reports_matrix [3, t-1] - initial_reports_matrix [3, t-2])
+ + 0.4807004854222 * (initial_reports_matrix [5, t]);
+
+ initial_reports_matrix [4, t] =
+ - 0.20 * initial_reports_matrix [4, t-1]
+ + 0.30 * (initial_reports_matrix [4, t-1] - initial_reports_matrix [4, t-2])
+ + 0.2549604916594 * (initial_reports_matrix [5, t]);
+}
+
+# GENERATE A LINEAR MAP FROM FREE VARIABLES TO THE REPORTS
+
+CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
+CReps [(num_terms-1) * num_attrs + 1, 1] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 2, 2] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 3, 3] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 4, 4] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 5, 1] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 5, 2] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 5, 3] = 1.0 + zero;
+CReps [(num_terms-1) * num_attrs + 5, 4] = 1.0 + zero;
+
+# GENERATE A LINEAR MAP FROM REPORTS TO REGRESSION FACTORS
+
+RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
+
+for (t in 3 : num_terms) {
+ for (i in 1 : (num_attrs - 2)) {
+ reg_index = ((t-3)*(num_attrs-1)-1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = 1.0 + zero; # First factor is x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + i] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 5] = 1.0 + zero; # 4th factor = total_cost[t]
+ }
+ # For the total cost itself, the regression is almost the same, except the last line:
+ reg_index = ((t-3)*(num_attrs-1)-1 + 5) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 5] = 1.0 + zero; # First factor is x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + 5] = 1.0 + zero; # Second factor is x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 5] = 1.0 + zero; # Third factor is
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + 5] = -1.0 + zero; # x[t-1] - x[t-2]
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + 6] = 1.0 + zero; # 4th factor = auxiliary[t]
+}
+
+# GENERATE A LINEAR MAP FROM PARAMETERS TO REGRESSION FACTORS
+
+RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
+
+for (t in 3 : num_terms) {
+ for (i in 1 : (num_attrs - 1)) {
+ reg_index = ((t-3)*(num_attrs-1)-1 + i) * num_factors;
+ RegresParamMap [reg_index + 1, 0 * (num_attrs-1) + i] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 1 * (num_attrs-1) + i] = 1.0 + zero;
+ RegresParamMap [reg_index + 3, 2 * (num_attrs-1) + i] = 1.0 + zero;
+ RegresParamMap [reg_index + 4, 3 * (num_attrs-1) + i] = 1.0 + zero;
+ }
+}
+
+# WRITE OUT ALL GENERATED MATRICES
+
+write (initial_reports_matrix, $1, format="text");
+write (CReps, $2, format="text");
+write (RegresValueMap, $3, format="text");
+write (RegresParamMap, $4, format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml b/src/test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml
index 9a20214..fcbf47c 100644
--- a/src/test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml
+++ b/src/test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml
@@ -1,174 +1,174 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-# hadoop jar SystemML.jar -f test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml -exec singlenode
-# -args
-# test/scripts/applications/impute/initial_reports
-# test/scripts/applications/impute/CReps
-# test/scripts/applications/impute/RegresValueMap
-# test/scripts/applications/impute/RegresFactorDefault
-# test/scripts/applications/impute/RegresParamMap
-# test/scripts/applications/impute/RegresCoeffDefault
-# test/scripts/applications/impute/RegresScaleMult
-
-
-# GENERATE SYNTHETIC "INITIAL REPORTS"
-
-num_terms = 10;
-num_series = 10;
-num_attrs = 2 * num_series;
-num_frees = num_series * (num_terms + 1);
-
-initial_reports = Rand (rows = num_attrs, cols = num_terms, min = -50.0, max = 50.0);
-
-for (s in 1:num_series) {
- for (t in 1:(num_terms - 1)) {
- val = 400 - (t - 14.16) * (t - 5.5) * (t + 3.16) / 2.463552;
- initial_reports [2 * (s-1) + 1, t] = initial_reports [2 * (s-1) + 1, t] + val;
- }
-}
-
-zero = matrix (0.0, rows = 1, cols = 1);
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM FREE VARIABLES TO THE REPORTS
-# AFFINE MAP = LINEAR MAP + INITIAL (DEFAULT) REPORTS
-# ---------------------------------------------------------
-
-CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
-
-for (s in 1:num_series) {
- for (t in 0:num_terms) {
- ta_shift = (t - 1) * num_attrs + 2 * s;
- if (t == 0) {
- ta_shift = (num_terms - 1) * num_attrs + (2 * s - 1);
- }
- CReps [ta_shift, t * num_series + s] = 1.0 + zero;
-} }
-
-# In all regressions, except the last few "special" ones, there are 3 factors
-# (here "x" are the "states" and "y" are the "observations"):
-# Observation regression: y[t]-x[t] ~ a * 1 ### + b * (y[t-1]-x[t-1])
-# State-change regression: x[t] ~ c * x[t-1] + d * (x[t-1]-x[t-2])
-
-num_factors = 3;
-num_reg_eqs = num_terms * 2 * num_series;
-num_params = 3 * num_series;
-
-RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
-RegresFactorDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
-
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
-# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
-# ---------------------------------------------------------
-
-
-for (t in 1 : num_terms) {
- for (s in 1 : num_series) {
-
- reg_index = ((t-1) * num_series + (s-1)) * 2 * num_factors;
-
-# Observation regression:
-
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s - 1] = -1.0 + zero; # 1st factor:
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s ] = 1.0 + zero; # -(y[t]-x[t])
-
- RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
-
-# RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s - 1] = 1.0 + zero; # 3rd factor:
-# RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s ] = -1.0 + zero; # y[t-1]-x[t-1]
-
- reg_index = reg_index + num_factors;
-
-# State-change regression:
-
- if (t >= 3) {
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s] = -1.0 + zero; # 1st factor: -x[t]
- RegresValueMap [reg_index + 2, (t-2) * num_attrs + 2 * s] = 1.0 + zero; # 2nd factor: x[t-1]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s] = 1.0 + zero; # 3rd factor:
- RegresValueMap [reg_index + 3, (t-3) * num_attrs + 2 * s] = -1.0 + zero; # x[t-1]-x[t-2]
- }
- }
-}
-
-# ----------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM PARAMETERS TO THE COEFFICIENTS
-# AT REGRESSION FACTORS: A LINEAR MAP + A VECTOR OF DEFAULTS
-# ----------------------------------------------------------
-
-RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
-RegresCoeffDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
-
-for (t in 1 : num_terms) {
- for (s in 1 : num_series) {
-
- reg_index = ((t-1) * num_series + (s-1)) * 2 * num_factors;
-
-# Observation regression:
-
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 3 * (s-1) + 1] = 1.0 + zero;
-
-# RegresParamMap [reg_index + 3, 4 * (s-1) + 2] = 1.0 + zero;
-
- reg_index = reg_index + num_factors;
-
-# State-change regression:
-
- if (t >= 3) {
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 3 * (s-1) + 2] = 1.0 + zero;
- RegresParamMap [reg_index + 3, 3 * (s-1) + 3] = 1.0 + zero;
- }
- }
-}
-
-# ----------------------------------------------------------------------
-# GENERATE A VECTOR OF SCALE MULTIPLIERS ("WEIGHTS"), ONE PER REGRESSION
-# ----------------------------------------------------------------------
-
-RegresScaleMult = matrix (1.0, rows = num_reg_eqs, cols = 1);
-global_weight = 0.5 + zero;
-acceptable_drift = 1.0;
-
-for (t in 1 : num_terms) {
- for (s in 1 : num_series) {
- reg_id = ((t-1) * num_series + (s-1)) * 2 + 1;
- RegresScaleMult [reg_id , 1] = global_weight / (acceptable_drift ^ 2);
- RegresScaleMult [reg_id + 1, 1] = global_weight / (acceptable_drift ^ 2);
- }
-}
-
-
-# --------------------------------
-# WRITE OUT ALL GENERATED MATRICES
-# --------------------------------
-
-
-write (initial_reports, $1, format="text");
-write (CReps, $2, format="text");
-write (RegresValueMap, $3, format="text");
-write (RegresFactorDefault,$4, format="text");
-write (RegresParamMap, $5, format="text");
-write (RegresCoeffDefault, $6, format="text");
-write (RegresScaleMult, $7, 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.
+#
+#-------------------------------------------------------------
+
+# hadoop jar SystemML.jar -f test/scripts/applications/impute/testShadowRecurrenceInputGenerator.dml -exec singlenode
+# -args
+# test/scripts/applications/impute/initial_reports
+# test/scripts/applications/impute/CReps
+# test/scripts/applications/impute/RegresValueMap
+# test/scripts/applications/impute/RegresFactorDefault
+# test/scripts/applications/impute/RegresParamMap
+# test/scripts/applications/impute/RegresCoeffDefault
+# test/scripts/applications/impute/RegresScaleMult
+
+
+# GENERATE SYNTHETIC "INITIAL REPORTS"
+
+num_terms = 10;
+num_series = 10;
+num_attrs = 2 * num_series;
+num_frees = num_series * (num_terms + 1);
+
+initial_reports = Rand (rows = num_attrs, cols = num_terms, min = -50.0, max = 50.0);
+
+for (s in 1:num_series) {
+ for (t in 1:(num_terms - 1)) {
+ val = 400 - (t - 14.16) * (t - 5.5) * (t + 3.16) / 2.463552;
+ initial_reports [2 * (s-1) + 1, t] = initial_reports [2 * (s-1) + 1, t] + val;
+ }
+}
+
+zero = matrix (0.0, rows = 1, cols = 1);
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM FREE VARIABLES TO THE REPORTS
+# AFFINE MAP = LINEAR MAP + INITIAL (DEFAULT) REPORTS
+# ---------------------------------------------------------
+
+CReps = matrix (0.0, rows = (num_terms * num_attrs), cols = num_frees);
+
+for (s in 1:num_series) {
+ for (t in 0:num_terms) {
+ ta_shift = (t - 1) * num_attrs + 2 * s;
+ if (t == 0) {
+ ta_shift = (num_terms - 1) * num_attrs + (2 * s - 1);
+ }
+ CReps [ta_shift, t * num_series + s] = 1.0 + zero;
+} }
+
+# In all regressions, except the last few "special" ones, there are 3 factors
+# (here "x" are the "states" and "y" are the "observations"):
+# Observation regression: y[t]-x[t] ~ a * 1 ### + b * (y[t-1]-x[t-1])
+# State-change regression: x[t] ~ c * x[t-1] + d * (x[t-1]-x[t-2])
+
+num_factors = 3;
+num_reg_eqs = num_terms * 2 * num_series;
+num_params = 3 * num_series;
+
+RegresValueMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = (num_terms * num_attrs));
+RegresFactorDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
+
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
+# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
+# ---------------------------------------------------------
+
+
+for (t in 1 : num_terms) {
+ for (s in 1 : num_series) {
+
+ reg_index = ((t-1) * num_series + (s-1)) * 2 * num_factors;
+
+# Observation regression:
+
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s - 1] = -1.0 + zero; # 1st factor:
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s ] = 1.0 + zero; # -(y[t]-x[t])
+
+ RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
+
+# RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s - 1] = 1.0 + zero; # 3rd factor:
+# RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s ] = -1.0 + zero; # y[t-1]-x[t-1]
+
+ reg_index = reg_index + num_factors;
+
+# State-change regression:
+
+ if (t >= 3) {
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 2 * s] = -1.0 + zero; # 1st factor: -x[t]
+ RegresValueMap [reg_index + 2, (t-2) * num_attrs + 2 * s] = 1.0 + zero; # 2nd factor: x[t-1]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 2 * s] = 1.0 + zero; # 3rd factor:
+ RegresValueMap [reg_index + 3, (t-3) * num_attrs + 2 * s] = -1.0 + zero; # x[t-1]-x[t-2]
+ }
+ }
+}
+
+# ----------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM PARAMETERS TO THE COEFFICIENTS
+# AT REGRESSION FACTORS: A LINEAR MAP + A VECTOR OF DEFAULTS
+# ----------------------------------------------------------
+
+RegresParamMap = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = num_params);
+RegresCoeffDefault = matrix (0.0, rows = (num_reg_eqs * num_factors), cols = 1);
+
+for (t in 1 : num_terms) {
+ for (s in 1 : num_series) {
+
+ reg_index = ((t-1) * num_series + (s-1)) * 2 * num_factors;
+
+# Observation regression:
+
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 3 * (s-1) + 1] = 1.0 + zero;
+
+# RegresParamMap [reg_index + 3, 4 * (s-1) + 2] = 1.0 + zero;
+
+ reg_index = reg_index + num_factors;
+
+# State-change regression:
+
+ if (t >= 3) {
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 3 * (s-1) + 2] = 1.0 + zero;
+ RegresParamMap [reg_index + 3, 3 * (s-1) + 3] = 1.0 + zero;
+ }
+ }
+}
+
+# ----------------------------------------------------------------------
+# GENERATE A VECTOR OF SCALE MULTIPLIERS ("WEIGHTS"), ONE PER REGRESSION
+# ----------------------------------------------------------------------
+
+RegresScaleMult = matrix (1.0, rows = num_reg_eqs, cols = 1);
+global_weight = 0.5 + zero;
+acceptable_drift = 1.0;
+
+for (t in 1 : num_terms) {
+ for (s in 1 : num_series) {
+ reg_id = ((t-1) * num_series + (s-1)) * 2 + 1;
+ RegresScaleMult [reg_id , 1] = global_weight / (acceptable_drift ^ 2);
+ RegresScaleMult [reg_id + 1, 1] = global_weight / (acceptable_drift ^ 2);
+ }
+}
+
+
+# --------------------------------
+# WRITE OUT ALL GENERATED MATRICES
+# --------------------------------
+
+
+write (initial_reports, $1, format="text");
+write (CReps, $2, format="text");
+write (RegresValueMap, $3, format="text");
+write (RegresFactorDefault,$4, format="text");
+write (RegresParamMap, $5, format="text");
+write (RegresCoeffDefault, $6, format="text");
+write (RegresScaleMult, $7, format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/impute/tmp.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/tmp.dml b/src/test/scripts/applications/impute/tmp.dml
index b70a4ad..9e0417e 100644
--- a/src/test/scripts/applications/impute/tmp.dml
+++ b/src/test/scripts/applications/impute/tmp.dml
@@ -1,128 +1,128 @@
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-setwd ("test/scripts/applications/glm");
-source ("Misc.dml");
-
-blahblah = 0.0 / 0.0; # -0.00099999999;
-print (blahblah);
-x = matrix (0.0, rows = 55, cols = 1);
-x [55, 1] = blahblah;
-print (castAsScalar (x [55, 1]));
-for (i in 1:9) {
- x [i, 1] = -0.001 * i;
-}
-for (i in 1:5) {
- x [(9 * i + 1):(9 * i + 9), 1] = x [(9 * i - 8):(9 * i), 1] * 10;
-}
-y = atan_temporary (x);
-z = tan (y);
-for (i in 1:nrow(x)) {
- [x_m, x_e] = round_to_print (castAsScalar (x[i,1]));
- [a_m, a_e] = round_to_print (castAsScalar (y[i,1]));
- [t_m, t_e] = round_to_print (castAsScalar (z[i,1]));
- print ("x = " + x_m + "E" + x_e + "; atan(x) = " + a_m + "E" + a_e + "; tan(atan(x)) = " + t_m + "E" + t_e);
-}
-
-
-
-
-
-
-coeff_a = -3.14;
-coeff_b = 3.14 * (2 - 3 - 1.7);
-coeff_c = -3.14 * (2 * (-3) + 2 * (-1.7) + (-3) * (-1.7));
-coeff_d = 3.14 * (2 * (-3) * (-1.7));
-
-
- coeff_aa = coeff_b / coeff_a;
- coeff_bb = coeff_c / coeff_a;
- coeff_cc = coeff_d / coeff_a;
-
- coeff_Q = (coeff_aa * coeff_aa - 3.0 * coeff_bb) / 9.0;
- coeff_R = (2.0 * coeff_aa * coeff_aa * coeff_aa - 9.0 * coeff_aa * coeff_bb + 27.0 * coeff_cc) / 54.0;
-
- if (coeff_R * coeff_R < coeff_Q * coeff_Q * coeff_Q)
- {
- two_pi_third = 2.0943951023931954923084289221863;
- acos_argument = coeff_R / sqrt (coeff_Q * coeff_Q * coeff_Q);
-
- x = abs (acos_argument);
- acos_x = sqrt (1.0 - x) * (1.5707963050 + x * (-0.2145988016
- + x * ( 0.0889789874 + x * (-0.0501743046
- + x * ( 0.0308918810 + x * (-0.0170881256
- + x * ( 0.0066700901 + x * (-0.0012624911))))))));
- if (acos_argument >= 0.0) {
- coeff_theta = acos_x;
- } else {
- coeff_theta = 3.1415926535897932384626433832795 - acos_x;
- }
-
- root_1 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0);
- root_2 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0 + two_pi_third);
- root_3 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0 - two_pi_third);
-
- root_min = min (min (root_1, root_2), root_3);
- root_max = max (max (root_1, root_2), root_3);
- root_middle = root_1 + root_2 + root_3 - root_min - root_max;
-
- root_1 = root_min; root_2 = root_middle; root_3 = root_max;
-
- print ("Three roots: " + (round (root_1 * 10000) / 10000) + ", " + (round (root_2 * 10000) / 10000) + ", " + (round (root_3 * 10000) / 10000));
-
- } else {
- if (coeff_R >= 0.0) {
- sgn_coeff_R = 1.0;
- } else {
- sgn_coeff_R = -1.0;
- }
- coeff_bigA = - sgn_coeff_R * (abs (coeff_R) + sqrt (coeff_R * coeff_R - coeff_Q * coeff_Q * coeff_Q)) ^ (1.0 / 3.0);
- if (coeff_bigA != 0.0) {
- root = coeff_bigA + coeff_Q / coeff_bigA - coeff_aa / 3.0;
- } else {
- root = - coeff_aa / 3.0;
- }
- print ("One root: " + (round (root * 10000) / 10000));
- }
-
-/*
-atan_temporary =
- function (Matrix [double] Args) return (Matrix [double] AtanArgs)
-{
- AbsArgs = abs (Args);
- Eks = AbsArgs + ppred (AbsArgs, 0.0, "==") * 0.000000000001;
- Eks = ppred (AbsArgs, 1.0, "<=") * Eks + ppred (AbsArgs, 1.0, ">") / Eks;
- EksSq = Eks * Eks;
- AtanEks =
- Eks * ( 1.0000000000 +
- EksSq * (-0.3333314528 + # Milton Abramowitz and Irene A. Stegun, Eds.
- EksSq * ( 0.1999355085 + # "Handbook of Mathematical Functions"
- EksSq * (-0.1420889944 + # U.S. National Bureau of Standards, June 1964
- EksSq * ( 0.1065626393 + # Section 4.4, page 81, Equation 4.4.49
- EksSq * (-0.0752896400 +
- EksSq * ( 0.0429096138 +
- EksSq * (-0.0161657367 +
- EksSq * 0.0028662257 ))))))));
- pi_over_two = 1.5707963267948966192313216916398;
- AtanAbsArgs = ppred (AbsArgs, 1.0, "<=") * AtanEks + ppred (AbsArgs, 1.0, ">") * (pi_over_two - AtanEks);
- AtanArgs = (ppred (Args, 0.0, ">=") - ppred (Args, 0.0, "<")) * AtanAbsArgs;
-}
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+setwd ("test/scripts/applications/glm");
+source ("Misc.dml");
+
+blahblah = 0.0 / 0.0; # -0.00099999999;
+print (blahblah);
+x = matrix (0.0, rows = 55, cols = 1);
+x [55, 1] = blahblah;
+print (castAsScalar (x [55, 1]));
+for (i in 1:9) {
+ x [i, 1] = -0.001 * i;
+}
+for (i in 1:5) {
+ x [(9 * i + 1):(9 * i + 9), 1] = x [(9 * i - 8):(9 * i), 1] * 10;
+}
+y = atan_temporary (x);
+z = tan (y);
+for (i in 1:nrow(x)) {
+ [x_m, x_e] = round_to_print (castAsScalar (x[i,1]));
+ [a_m, a_e] = round_to_print (castAsScalar (y[i,1]));
+ [t_m, t_e] = round_to_print (castAsScalar (z[i,1]));
+ print ("x = " + x_m + "E" + x_e + "; atan(x) = " + a_m + "E" + a_e + "; tan(atan(x)) = " + t_m + "E" + t_e);
+}
+
+
+
+
+
+
+coeff_a = -3.14;
+coeff_b = 3.14 * (2 - 3 - 1.7);
+coeff_c = -3.14 * (2 * (-3) + 2 * (-1.7) + (-3) * (-1.7));
+coeff_d = 3.14 * (2 * (-3) * (-1.7));
+
+
+ coeff_aa = coeff_b / coeff_a;
+ coeff_bb = coeff_c / coeff_a;
+ coeff_cc = coeff_d / coeff_a;
+
+ coeff_Q = (coeff_aa * coeff_aa - 3.0 * coeff_bb) / 9.0;
+ coeff_R = (2.0 * coeff_aa * coeff_aa * coeff_aa - 9.0 * coeff_aa * coeff_bb + 27.0 * coeff_cc) / 54.0;
+
+ if (coeff_R * coeff_R < coeff_Q * coeff_Q * coeff_Q)
+ {
+ two_pi_third = 2.0943951023931954923084289221863;
+ acos_argument = coeff_R / sqrt (coeff_Q * coeff_Q * coeff_Q);
+
+ x = abs (acos_argument);
+ acos_x = sqrt (1.0 - x) * (1.5707963050 + x * (-0.2145988016
+ + x * ( 0.0889789874 + x * (-0.0501743046
+ + x * ( 0.0308918810 + x * (-0.0170881256
+ + x * ( 0.0066700901 + x * (-0.0012624911))))))));
+ if (acos_argument >= 0.0) {
+ coeff_theta = acos_x;
+ } else {
+ coeff_theta = 3.1415926535897932384626433832795 - acos_x;
+ }
+
+ root_1 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0);
+ root_2 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0 + two_pi_third);
+ root_3 = - coeff_aa / 3.0 - 2.0 * sqrt (coeff_Q) * cos (coeff_theta / 3.0 - two_pi_third);
+
+ root_min = min (min (root_1, root_2), root_3);
+ root_max = max (max (root_1, root_2), root_3);
+ root_middle = root_1 + root_2 + root_3 - root_min - root_max;
+
+ root_1 = root_min; root_2 = root_middle; root_3 = root_max;
+
+ print ("Three roots: " + (round (root_1 * 10000) / 10000) + ", " + (round (root_2 * 10000) / 10000) + ", " + (round (root_3 * 10000) / 10000));
+
+ } else {
+ if (coeff_R >= 0.0) {
+ sgn_coeff_R = 1.0;
+ } else {
+ sgn_coeff_R = -1.0;
+ }
+ coeff_bigA = - sgn_coeff_R * (abs (coeff_R) + sqrt (coeff_R * coeff_R - coeff_Q * coeff_Q * coeff_Q)) ^ (1.0 / 3.0);
+ if (coeff_bigA != 0.0) {
+ root = coeff_bigA + coeff_Q / coeff_bigA - coeff_aa / 3.0;
+ } else {
+ root = - coeff_aa / 3.0;
+ }
+ print ("One root: " + (round (root * 10000) / 10000));
+ }
+
+/*
+atan_temporary =
+ function (Matrix [double] Args) return (Matrix [double] AtanArgs)
+{
+ AbsArgs = abs (Args);
+ Eks = AbsArgs + ppred (AbsArgs, 0.0, "==") * 0.000000000001;
+ Eks = ppred (AbsArgs, 1.0, "<=") * Eks + ppred (AbsArgs, 1.0, ">") / Eks;
+ EksSq = Eks * Eks;
+ AtanEks =
+ Eks * ( 1.0000000000 +
+ EksSq * (-0.3333314528 + # Milton Abramowitz and Irene A. Stegun, Eds.
+ EksSq * ( 0.1999355085 + # "Handbook of Mathematical Functions"
+ EksSq * (-0.1420889944 + # U.S. National Bureau of Standards, June 1964
+ EksSq * ( 0.1065626393 + # Section 4.4, page 81, Equation 4.4.49
+ EksSq * (-0.0752896400 +
+ EksSq * ( 0.0429096138 +
+ EksSq * (-0.0161657367 +
+ EksSq * 0.0028662257 ))))))));
+ pi_over_two = 1.5707963267948966192313216916398;
+ AtanAbsArgs = ppred (AbsArgs, 1.0, "<=") * AtanEks + ppred (AbsArgs, 1.0, ">") * (pi_over_two - AtanEks);
+ AtanArgs = (ppred (Args, 0.0, ">=") - ppred (Args, 0.0, "<")) * AtanAbsArgs;
+}
*/
\ No newline at end of file