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Posted to commits@systemml.apache.org by du...@apache.org on 2016/01/22 17:33:59 UTC
[23/51] [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/wfundInputGenerator.The0thReportAttempt.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/wfundInputGenerator.The0thReportAttempt.dml b/src/test/scripts/applications/impute/wfundInputGenerator.The0thReportAttempt.dml
index 18c8eb9..6514b63 100644
--- a/src/test/scripts/applications/impute/wfundInputGenerator.The0thReportAttempt.dml
+++ b/src/test/scripts/applications/impute/wfundInputGenerator.The0thReportAttempt.dml
@@ -1,501 +1,501 @@
-#-------------------------------------------------------------
-#
-# 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/initial_reports_preprocessed
-# 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
-
-initial_reports = read ($1);
-
-is_GROUP_4_ENABLED = 0; # = 1 or 0 ("0" if Group-4 = all 0s)
-num_EXTRA_MISSING_FREES = 0; # = 0 ("3" or "6" for Uganda)
-
-num_known_terms = 5; # The number of known term reports
-num_predicted_terms = 1; # The number of predicted (future) term reports
-
-num_terms = num_known_terms + num_predicted_terms + 1; # We predict the "0-th" report, too
-num_attrs = 19;
-
-num_frees_per_term = 13;
-if (is_GROUP_4_ENABLED == 1) {
- num_frees_per_term = 15;
-}
-num_regular_frees = (num_predicted_terms + 1) * num_frees_per_term;
-num_frees = num_regular_frees + num_EXTRA_MISSING_FREES;
-
-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 (dt in 0:num_predicted_terms)
-{
- ta_shift = 0;
- if (dt > 0) {
- ta_shift = (num_known_terms + dt) * num_attrs;
- }
- fv_shift = dt * num_frees_per_term;
-
-# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
-# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
- for (i in 1:6) {
- CReps [ta_shift + 1, fv_shift + i] = 1.0 + zero;
- CReps [ta_shift + 1 + i, fv_shift + i] = 1.0 + zero;
- }
-# row 8 is free variable not appearing in any non-free variable
- CReps [ta_shift + 8, fv_shift + 7] = 1.0 + zero;
-
-# constraint that row9 = row10 + row11 + row12 + row13 + row14 + row15
-# translated to free vars: row9 = free8 + free9 + free10 + free11 + free12 + free13
- for (i in 1:6) {
- CReps [ta_shift + 9, fv_shift + 7 + i] = 1.0 + zero;
- CReps [ta_shift + 9 + i, fv_shift + 7 + i] = 1.0 + zero;
- }
-# constraint that row16 = row14 + row15
-# translated to free vars: row16 = free14 + free15
-if (is_GROUP_4_ENABLED == 1) {
- for (i in 1:2) {
- CReps [ta_shift + 16, fv_shift + 13 + i] = 1.0 + zero;
- CReps [ta_shift + 16 + i, fv_shift + 13 + i] = 1.0 + zero;
- }
-}
-# constraint that row19 = total cost (all free variables)
-# translated to free vars: row19 = all free variables
- for (i in 1:num_frees_per_term) {
- CReps [ta_shift + 19, fv_shift + i] = 1.0 + zero;
- }
-}
-
-# ---------------------------------------------------------
-# SPECIAL FREE VARIABLES TO HANDLE UGANDA'S MISSING VALUES
-# ---------------------------------------------------------
-
-if (num_EXTRA_MISSING_FREES == 3 | num_EXTRA_MISSING_FREES == 6)
-{
- ta_shift = 3 * num_attrs;
- CReps [ta_shift + 4, num_regular_frees + 1] = 1.0 + zero;
- CReps [ta_shift + 5, num_regular_frees + 2] = 1.0 + zero;
- CReps [ta_shift + 6, num_regular_frees + 3] = 1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 1] = -1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 2] = -1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 3] = -1.0 + zero;
-}
-
-if (num_EXTRA_MISSING_FREES == 6)
-{
- ta_shift = 7 * num_attrs;
- CReps [ta_shift + 4, num_regular_frees + 4] = 1.0 + zero;
- CReps [ta_shift + 5, num_regular_frees + 5] = 1.0 + zero;
- CReps [ta_shift + 6, num_regular_frees + 6] = 1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 4] = -1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 5] = -1.0 + zero;
- CReps [ta_shift + 7, num_regular_frees + 6] = -1.0 + zero;
-}
-
-
-# ---------------------------------------------------------------------------------------
-#
-# In all regressions, except the last few "special" ones, there are 4 factors:
-# x[t] ~ aggregate[t], x[t-1], (x[t-1] - x[t-2])
-# 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 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 * 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] total[t] self[t-1] self[t-1]-
-# self[t-2]
-# PARAMS:
-# Group 1: 1.0 prm#01 prm#08 prm#09 Row #01 = free#01 + ... + free#06
-# Group 1: " prm#02 prm#10 prm#11 Row #02 = free#01
-# Group 1: " prm#03 " " Row #03 = free#02
-# Group 1: " prm#04 " " Row #04 = free#03
-# Group 1: " prm#05 " " Row #05 = free#04
-# Group 1: " prm#06 " " Row #06 = free#05
-# Group 1: " prm#07 " " 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#22 prm#23 Row #09 = free#08 + ... + free#13
-# Group 3: " prm#16 prm#24 prm#25 Row #10 = free#08
-# Group 3: " prm#17 " " Row #11 = free#09
-# Group 3: " prm#18 " " Row #12 = free#10
-# Group 3: " prm#19 " " Row #13 = free#11
-# Group 3: " prm#20 " " Row #14 = free#12
-# Group 3: " prm#21 " " Row #15 = free#13
-# --------------------------------------------------------------------
-# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
-# Group 4: 1.0 prm#29 prm#32 prm#33 Row #16 = free#14 + free#15
-# Group 4: " prm#30 prm#34 prm#35 Row #17 = free#14
-# Group 4: " prm#31 " " 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#27 1.0 0.0 0.0 # self[t-1]
-# prm#28 0.0 0.0 0.0 # trend
-# prm#08 0.0 0.0 0.0 # self[t-1]
-# prm#09 0.0 0.0 0.0 # trend
-# prm#10 0.0 0.0 0.0 # self[t-1]
-# prm#11 0.0 0.0 0.0 # trend
-# prm#13 0.0 0.0 0.0 # self[t-1]
-# prm#14 0.0 0.0 0.0 # trend
-# prm#22 0.0 0.0 0.0 # self[t-1]
-# prm#23 0.0 0.0 0.0 # trend
-# prm#24 0.0 0.0 0.0 # self[t-1]
-# prm#25 0.0 0.0 0.0 # trend
-### GROUP-4 ZEROS: THESE EQUATIONS USE REVOKED PARAMETERS AND DO NOT APPEAR
-# prm#32 0.0 0.0 0.0 # self[t-1]
-# prm#33 0.0 0.0 0.0 # trend
-# prm#34 0.0 0.0 0.0 # self[t-1]
-# prm#35 0.0 0.0 0.0 # trend
-#
-# ---------------------------------------------------------------------------------------
-
-
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
-# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
-# ---------------------------------------------------------
-
-
-for (t in 1 : num_terms) {
- for (i in 1 : num_attrs) {
-
-reg_index = ((t-1) * num_attrs + i - 1) * num_factors;
-
-# -------------------------------
-# SETTING FACTORS #1, #3, and #4:
-# -------------------------------
-
-if (t == 1 & i != 19) { # THESE "REGRESSIONS" ARE DIFFERENT (MORE LIKE REGULARIZATIONS):
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
- RegresValueMap [reg_index + 3, (t-1) * num_attrs + i ] = 1.5 + zero; # 3rd factor is approximated as:
- RegresValueMap [reg_index + 3, t * num_attrs + i ] = -0.3 + zero; # 1.5 x[t] - 0.3 x[t+1] - 0.2 x[t+2] =
- RegresValueMap [reg_index + 3, (t+1) * num_attrs + i ] = -0.2 + zero; # x[t] - 0.5 (x[t+1] - x[t]) - 0.2 (x[t+2] - x[t+1])
-}
-if (t == 2) {
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i ] = 1.0 + zero; # 3rd factor: x[t-1]
- w = 0.5;
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + i ] = (- 1 - w) + zero; # 4th factor is approximated as:
- RegresValueMap [reg_index + 4, (t-1) * num_attrs + i ] = (1 + 2*w) + zero; # - (1+w)x[t-1] + (1+2w)x[t] - w x[t+1] =
- RegresValueMap [reg_index + 4, t * num_attrs + i ] = (- w) + zero; # (x[t]-x[t-1]) - w * ((x[t+1]-x[t]) - (x[t]-x[t-1]))
-}
-if (t >= 3) {
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i ] = 1.0 + zero; # 3rd factor: x[t-1]
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + i ] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + i ] = -1.0 + zero; # x[t-1] - x[t-2]
-}
-
-# -------------------------------------------
-# SETTING FACTOR #2 DEPENDS ON THE ATTRIBUTE:
-# -------------------------------------------
-
-if (i == 1) { # GROUP 1 SUBTOTAL
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
-}
-if (2 <= i & i <= 7) { # GROUP 1 ATTRIBUTES
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 1] = 1.0 + zero; # 2nd factor: Row#01[t]
-}
-
-if (i == 8) { # GROUP 2 SUBTOTAL
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
-}
-
-if (i == 9) { # GROUP 3 SUBTOTAL
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
-}
-if (10 <= i & i <= 15) { # GROUP 3 ATTRIBUTES:
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 9] = 1.0 + zero; # 2nd factor: Row#09[t]
-}
-
-if (i == 16) { # GROUP 4 SUBTOTAL
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
-}
-if (17 <= i & i <= 18) { # GROUP 4 ATTRIBUTES:
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 16] = 1.0 + zero; # 2nd factor: Row#16[t]
-}
-
-if (i == 19 & t >= 2) { # THE TOTAL, ONLY FOR t >= 2
- RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
-}
-
-###
-### SPECIAL REGULARIZATION EQUATIONS FOR PARAMETERS ARE HANDLED SEPARATELY!
-###
-
-}}
-
-
-# ----------------------------------------------------------
-# 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) {
- ta_shift = (t-1) * num_attrs - 1;
-
-# Group 1 attributes:
- reg_index = (ta_shift + 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, 8] = 1.0 + zero; # Param #08
- RegresParamMap [reg_index + 4, 9] = 1.0 + zero; # Param #09
- for (i in 2 : 7) {
- reg_index = (ta_shift + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, i] = 1.0 + zero; # Param #02-#07
- RegresParamMap [reg_index + 3, 10] = 1.0 + zero; # Param #10
- RegresParamMap [reg_index + 4, 11] = 1.0 + zero; # Param #11
- }
-
-# Group 2 attribute:
- reg_index = (ta_shift + 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 = (ta_shift + 9) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 15] = 1.0 + zero; # Param #17
- RegresParamMap [reg_index + 3, 22] = 1.0 + zero; # Param #22
- RegresParamMap [reg_index + 4, 23] = 1.0 + zero; # Param #23
- for (i in 10 : 15) {
- reg_index = (ta_shift + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 6 + i] = 1.0 + zero; # Param #16-#21
- RegresParamMap [reg_index + 3, 24] = 1.0 + zero; # Param #24
- RegresParamMap [reg_index + 4, 25] = 1.0 + zero; # Param #25
- }
-
-# Group 4 attributes:
-if (is_GROUP_4_ENABLED == 1) {
- reg_index = (ta_shift + 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, 32] = 1.0 + zero; # Param #32
- RegresParamMap [reg_index + 4, 33] = 1.0 + zero; # Param #33
- for (i in 17 : 18) {
- reg_index = (ta_shift + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 13 + i] = 1.0 + zero; # Param #30-#31
- RegresParamMap [reg_index + 3, 34] = 1.0 + zero; # Param #34
- RegresParamMap [reg_index + 4, 35] = 1.0 + zero; # Param #35
- }
-}
-
-# Group 5 attribute:
- reg_index = (ta_shift + 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
-}
-
-
-# ----------------------------------------------------------------------
-# 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;
-
-attribute_size = rowMeans (abs (initial_reports [, 1:num_known_terms]));
-max_attr_size = max (attribute_size);
-
-for (t in 1 : num_terms) {
- for (i in 1 : num_attrs) {
-
- scale_down = sqrt (attribute_size [i, 1] / max_attr_size) * 0.999 + 0.001;
- acceptable_drift = scale_down * max_attr_size * 0.002;
- if (t == 1) {
- acceptable_drift = acceptable_drift * 10;
- }
-
- regeqn = (t-1) * num_attrs + i;
- RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift ^ 2);
-
-}}
-
-
-
-
-# ----------------------------------------------------------------
-# SPECIAL REGULARIZATION EQUATIONS FOR PARAMETERS
-# GENERATE ALL THEIR AFFINE MAPS AND SCALE MULTIPLIERS ("WEIGHTS")
-# ----------------------------------------------------------------
-
-acceptable_drift = 0.02;
-
-# DO WHAT (ALMOST) ALL REGULARIZATIONS NEED
-for (i in 1:num_special_regs) {
- reg_index = (num_reg_eqs - num_special_regs + i - 1) * num_factors;
- RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
- RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
- regeqn = num_reg_eqs - num_special_regs + i;
- RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift ^ 2);
-}
-
-reg_index = (num_reg_eqs - num_special_regs) * num_factors;
-
-# PARAMETER #27, TOTAL's "self[t-1]"
- RegresParamMap [reg_index + 1, 27] = 1.0 + zero;
- RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
-
- regeqn = num_reg_eqs - num_special_regs + 1;
- drift_acceptable_here = acceptable_drift / 4;
- RegresScaleMult [regeqn, 1] = global_weight / (drift_acceptable_here ^ 2);
-
-reg_index = reg_index + num_factors;
-
-# PARAMETER #28, TOTAL's "trend"
- RegresParamMap [reg_index + 1, 28] = 1.0 + zero;
- RegresCoeffDefault [reg_index + 2, 1] = 0.7 + zero;
-### RegresParamMap [reg_index + 2, 27] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #08, GROUP-1 SUBTOTAL's "self[t-1]"
- RegresParamMap [reg_index + 1, 08] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #09, GROUP-1 SUBTOTAL's "trend"
- RegresParamMap [reg_index + 1, 09] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 08] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #10, GROUP-1 VALUE's "self[t-1]"
- RegresParamMap [reg_index + 1, 10] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #11, GROUP-1 VALUE's "trend"
- RegresParamMap [reg_index + 1, 11] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 10] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #13, GROUP-2 SUBTOTAL's "self[t-1]"
- RegresParamMap [reg_index + 1, 13] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #14, GROUP-2 SUBTOTAL's "trend"
- RegresParamMap [reg_index + 1, 14] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 13] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #22, GROUP-3 SUBTOTAL's "self[t-1]"
- RegresParamMap [reg_index + 1, 22] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #23, GROUP-3 SUBTOTAL's "trend"
- RegresParamMap [reg_index + 1, 23] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 22] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #24, GROUP-3 VALUE's "self[t-1]"
- RegresParamMap [reg_index + 1, 24] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #25, GROUP-3 VALUE's "trend"
- RegresParamMap [reg_index + 1, 25] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 24] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-if (is_GROUP_4_ENABLED == 1) {
-
-# PARAMETER #32, GROUP-4 SUBTOTAL's "self[t-1]"
- RegresParamMap [reg_index + 1, 32] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #33, GROUP-4 SUBTOTAL's "trend"
- RegresParamMap [reg_index + 1, 33] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 32] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #34, GROUP-4 VALUE's "self[t-1]"
- RegresParamMap [reg_index + 1, 34] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-
-# PARAMETER #35, GROUP-4 VALUE's "trend"
- RegresParamMap [reg_index + 1, 35] = 1.0 + zero;
- RegresParamMap [reg_index + 2, 34] = 1.0 + zero;
-reg_index = reg_index + num_factors;
-}
-
-
-
-# --------------------------------
-# WRITE OUT ALL GENERATED MATRICES
-# --------------------------------
-
-initial_reports_preprocessed = matrix (0.0, rows = num_attrs, cols = num_terms);
-initial_reports_preprocessed [, 2:(num_known_terms+1)] = initial_reports [, 1:num_known_terms];
-
-write (initial_reports_preprocessed, $2, format="text");
-write (CReps, $3, format="text");
-write (RegresValueMap, $4, format="text");
-write (RegresFactorDefault,$5, format="text");
-write (RegresParamMap, $6, format="text");
-write (RegresCoeffDefault, $7, format="text");
-write (RegresScaleMult, $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.
+#
+#-------------------------------------------------------------
+
+# hadoop jar SystemML.jar -f test/scripts/applications/impute/wfundInputGenerator.dml -exec singlenode
+# -args
+# test/scripts/applications/impute/initial_reports
+# test/scripts/applications/impute/initial_reports_preprocessed
+# 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
+
+initial_reports = read ($1);
+
+is_GROUP_4_ENABLED = 0; # = 1 or 0 ("0" if Group-4 = all 0s)
+num_EXTRA_MISSING_FREES = 0; # = 0 ("3" or "6" for Uganda)
+
+num_known_terms = 5; # The number of known term reports
+num_predicted_terms = 1; # The number of predicted (future) term reports
+
+num_terms = num_known_terms + num_predicted_terms + 1; # We predict the "0-th" report, too
+num_attrs = 19;
+
+num_frees_per_term = 13;
+if (is_GROUP_4_ENABLED == 1) {
+ num_frees_per_term = 15;
+}
+num_regular_frees = (num_predicted_terms + 1) * num_frees_per_term;
+num_frees = num_regular_frees + num_EXTRA_MISSING_FREES;
+
+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 (dt in 0:num_predicted_terms)
+{
+ ta_shift = 0;
+ if (dt > 0) {
+ ta_shift = (num_known_terms + dt) * num_attrs;
+ }
+ fv_shift = dt * num_frees_per_term;
+
+# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
+# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
+ for (i in 1:6) {
+ CReps [ta_shift + 1, fv_shift + i] = 1.0 + zero;
+ CReps [ta_shift + 1 + i, fv_shift + i] = 1.0 + zero;
+ }
+# row 8 is free variable not appearing in any non-free variable
+ CReps [ta_shift + 8, fv_shift + 7] = 1.0 + zero;
+
+# constraint that row9 = row10 + row11 + row12 + row13 + row14 + row15
+# translated to free vars: row9 = free8 + free9 + free10 + free11 + free12 + free13
+ for (i in 1:6) {
+ CReps [ta_shift + 9, fv_shift + 7 + i] = 1.0 + zero;
+ CReps [ta_shift + 9 + i, fv_shift + 7 + i] = 1.0 + zero;
+ }
+# constraint that row16 = row14 + row15
+# translated to free vars: row16 = free14 + free15
+if (is_GROUP_4_ENABLED == 1) {
+ for (i in 1:2) {
+ CReps [ta_shift + 16, fv_shift + 13 + i] = 1.0 + zero;
+ CReps [ta_shift + 16 + i, fv_shift + 13 + i] = 1.0 + zero;
+ }
+}
+# constraint that row19 = total cost (all free variables)
+# translated to free vars: row19 = all free variables
+ for (i in 1:num_frees_per_term) {
+ CReps [ta_shift + 19, fv_shift + i] = 1.0 + zero;
+ }
+}
+
+# ---------------------------------------------------------
+# SPECIAL FREE VARIABLES TO HANDLE UGANDA'S MISSING VALUES
+# ---------------------------------------------------------
+
+if (num_EXTRA_MISSING_FREES == 3 | num_EXTRA_MISSING_FREES == 6)
+{
+ ta_shift = 3 * num_attrs;
+ CReps [ta_shift + 4, num_regular_frees + 1] = 1.0 + zero;
+ CReps [ta_shift + 5, num_regular_frees + 2] = 1.0 + zero;
+ CReps [ta_shift + 6, num_regular_frees + 3] = 1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 1] = -1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 2] = -1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 3] = -1.0 + zero;
+}
+
+if (num_EXTRA_MISSING_FREES == 6)
+{
+ ta_shift = 7 * num_attrs;
+ CReps [ta_shift + 4, num_regular_frees + 4] = 1.0 + zero;
+ CReps [ta_shift + 5, num_regular_frees + 5] = 1.0 + zero;
+ CReps [ta_shift + 6, num_regular_frees + 6] = 1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 4] = -1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 5] = -1.0 + zero;
+ CReps [ta_shift + 7, num_regular_frees + 6] = -1.0 + zero;
+}
+
+
+# ---------------------------------------------------------------------------------------
+#
+# In all regressions, except the last few "special" ones, there are 4 factors:
+# x[t] ~ aggregate[t], x[t-1], (x[t-1] - x[t-2])
+# 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 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 * 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] total[t] self[t-1] self[t-1]-
+# self[t-2]
+# PARAMS:
+# Group 1: 1.0 prm#01 prm#08 prm#09 Row #01 = free#01 + ... + free#06
+# Group 1: " prm#02 prm#10 prm#11 Row #02 = free#01
+# Group 1: " prm#03 " " Row #03 = free#02
+# Group 1: " prm#04 " " Row #04 = free#03
+# Group 1: " prm#05 " " Row #05 = free#04
+# Group 1: " prm#06 " " Row #06 = free#05
+# Group 1: " prm#07 " " 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#22 prm#23 Row #09 = free#08 + ... + free#13
+# Group 3: " prm#16 prm#24 prm#25 Row #10 = free#08
+# Group 3: " prm#17 " " Row #11 = free#09
+# Group 3: " prm#18 " " Row #12 = free#10
+# Group 3: " prm#19 " " Row #13 = free#11
+# Group 3: " prm#20 " " Row #14 = free#12
+# Group 3: " prm#21 " " Row #15 = free#13
+# --------------------------------------------------------------------
+# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
+# Group 4: 1.0 prm#29 prm#32 prm#33 Row #16 = free#14 + free#15
+# Group 4: " prm#30 prm#34 prm#35 Row #17 = free#14
+# Group 4: " prm#31 " " 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#27 1.0 0.0 0.0 # self[t-1]
+# prm#28 0.0 0.0 0.0 # trend
+# prm#08 0.0 0.0 0.0 # self[t-1]
+# prm#09 0.0 0.0 0.0 # trend
+# prm#10 0.0 0.0 0.0 # self[t-1]
+# prm#11 0.0 0.0 0.0 # trend
+# prm#13 0.0 0.0 0.0 # self[t-1]
+# prm#14 0.0 0.0 0.0 # trend
+# prm#22 0.0 0.0 0.0 # self[t-1]
+# prm#23 0.0 0.0 0.0 # trend
+# prm#24 0.0 0.0 0.0 # self[t-1]
+# prm#25 0.0 0.0 0.0 # trend
+### GROUP-4 ZEROS: THESE EQUATIONS USE REVOKED PARAMETERS AND DO NOT APPEAR
+# prm#32 0.0 0.0 0.0 # self[t-1]
+# prm#33 0.0 0.0 0.0 # trend
+# prm#34 0.0 0.0 0.0 # self[t-1]
+# prm#35 0.0 0.0 0.0 # trend
+#
+# ---------------------------------------------------------------------------------------
+
+
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
+# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
+# ---------------------------------------------------------
+
+
+for (t in 1 : num_terms) {
+ for (i in 1 : num_attrs) {
+
+reg_index = ((t-1) * num_attrs + i - 1) * num_factors;
+
+# -------------------------------
+# SETTING FACTORS #1, #3, and #4:
+# -------------------------------
+
+if (t == 1 & i != 19) { # THESE "REGRESSIONS" ARE DIFFERENT (MORE LIKE REGULARIZATIONS):
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
+ RegresValueMap [reg_index + 3, (t-1) * num_attrs + i ] = 1.5 + zero; # 3rd factor is approximated as:
+ RegresValueMap [reg_index + 3, t * num_attrs + i ] = -0.3 + zero; # 1.5 x[t] - 0.3 x[t+1] - 0.2 x[t+2] =
+ RegresValueMap [reg_index + 3, (t+1) * num_attrs + i ] = -0.2 + zero; # x[t] - 0.5 (x[t+1] - x[t]) - 0.2 (x[t+2] - x[t+1])
+}
+if (t == 2) {
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i ] = 1.0 + zero; # 3rd factor: x[t-1]
+ w = 0.5;
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + i ] = (- 1 - w) + zero; # 4th factor is approximated as:
+ RegresValueMap [reg_index + 4, (t-1) * num_attrs + i ] = (1 + 2*w) + zero; # - (1+w)x[t-1] + (1+2w)x[t] - w x[t+1] =
+ RegresValueMap [reg_index + 4, t * num_attrs + i ] = (- w) + zero; # (x[t]-x[t-1]) - w * ((x[t+1]-x[t]) - (x[t]-x[t-1]))
+}
+if (t >= 3) {
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i ] = -1.0 + zero; # 1st factor: -x[t]
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i ] = 1.0 + zero; # 3rd factor: x[t-1]
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + i ] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + i ] = -1.0 + zero; # x[t-1] - x[t-2]
+}
+
+# -------------------------------------------
+# SETTING FACTOR #2 DEPENDS ON THE ATTRIBUTE:
+# -------------------------------------------
+
+if (i == 1) { # GROUP 1 SUBTOTAL
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+}
+if (2 <= i & i <= 7) { # GROUP 1 ATTRIBUTES
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 1] = 1.0 + zero; # 2nd factor: Row#01[t]
+}
+
+if (i == 8) { # GROUP 2 SUBTOTAL
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+}
+
+if (i == 9) { # GROUP 3 SUBTOTAL
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+}
+if (10 <= i & i <= 15) { # GROUP 3 ATTRIBUTES:
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 9] = 1.0 + zero; # 2nd factor: Row#09[t]
+}
+
+if (i == 16) { # GROUP 4 SUBTOTAL
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+}
+if (17 <= i & i <= 18) { # GROUP 4 ATTRIBUTES:
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 16] = 1.0 + zero; # 2nd factor: Row#16[t]
+}
+
+if (i == 19 & t >= 2) { # THE TOTAL, ONLY FOR t >= 2
+ RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
+}
+
+###
+### SPECIAL REGULARIZATION EQUATIONS FOR PARAMETERS ARE HANDLED SEPARATELY!
+###
+
+}}
+
+
+# ----------------------------------------------------------
+# 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) {
+ ta_shift = (t-1) * num_attrs - 1;
+
+# Group 1 attributes:
+ reg_index = (ta_shift + 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, 8] = 1.0 + zero; # Param #08
+ RegresParamMap [reg_index + 4, 9] = 1.0 + zero; # Param #09
+ for (i in 2 : 7) {
+ reg_index = (ta_shift + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, i] = 1.0 + zero; # Param #02-#07
+ RegresParamMap [reg_index + 3, 10] = 1.0 + zero; # Param #10
+ RegresParamMap [reg_index + 4, 11] = 1.0 + zero; # Param #11
+ }
+
+# Group 2 attribute:
+ reg_index = (ta_shift + 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 = (ta_shift + 9) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 15] = 1.0 + zero; # Param #17
+ RegresParamMap [reg_index + 3, 22] = 1.0 + zero; # Param #22
+ RegresParamMap [reg_index + 4, 23] = 1.0 + zero; # Param #23
+ for (i in 10 : 15) {
+ reg_index = (ta_shift + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 6 + i] = 1.0 + zero; # Param #16-#21
+ RegresParamMap [reg_index + 3, 24] = 1.0 + zero; # Param #24
+ RegresParamMap [reg_index + 4, 25] = 1.0 + zero; # Param #25
+ }
+
+# Group 4 attributes:
+if (is_GROUP_4_ENABLED == 1) {
+ reg_index = (ta_shift + 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, 32] = 1.0 + zero; # Param #32
+ RegresParamMap [reg_index + 4, 33] = 1.0 + zero; # Param #33
+ for (i in 17 : 18) {
+ reg_index = (ta_shift + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 13 + i] = 1.0 + zero; # Param #30-#31
+ RegresParamMap [reg_index + 3, 34] = 1.0 + zero; # Param #34
+ RegresParamMap [reg_index + 4, 35] = 1.0 + zero; # Param #35
+ }
+}
+
+# Group 5 attribute:
+ reg_index = (ta_shift + 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
+}
+
+
+# ----------------------------------------------------------------------
+# 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;
+
+attribute_size = rowMeans (abs (initial_reports [, 1:num_known_terms]));
+max_attr_size = max (attribute_size);
+
+for (t in 1 : num_terms) {
+ for (i in 1 : num_attrs) {
+
+ scale_down = sqrt (attribute_size [i, 1] / max_attr_size) * 0.999 + 0.001;
+ acceptable_drift = scale_down * max_attr_size * 0.002;
+ if (t == 1) {
+ acceptable_drift = acceptable_drift * 10;
+ }
+
+ regeqn = (t-1) * num_attrs + i;
+ RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift ^ 2);
+
+}}
+
+
+
+
+# ----------------------------------------------------------------
+# SPECIAL REGULARIZATION EQUATIONS FOR PARAMETERS
+# GENERATE ALL THEIR AFFINE MAPS AND SCALE MULTIPLIERS ("WEIGHTS")
+# ----------------------------------------------------------------
+
+acceptable_drift = 0.02;
+
+# DO WHAT (ALMOST) ALL REGULARIZATIONS NEED
+for (i in 1:num_special_regs) {
+ reg_index = (num_reg_eqs - num_special_regs + i - 1) * num_factors;
+ RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
+ RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
+ regeqn = num_reg_eqs - num_special_regs + i;
+ RegresScaleMult [regeqn, 1] = global_weight / (acceptable_drift ^ 2);
+}
+
+reg_index = (num_reg_eqs - num_special_regs) * num_factors;
+
+# PARAMETER #27, TOTAL's "self[t-1]"
+ RegresParamMap [reg_index + 1, 27] = 1.0 + zero;
+ RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
+
+ regeqn = num_reg_eqs - num_special_regs + 1;
+ drift_acceptable_here = acceptable_drift / 4;
+ RegresScaleMult [regeqn, 1] = global_weight / (drift_acceptable_here ^ 2);
+
+reg_index = reg_index + num_factors;
+
+# PARAMETER #28, TOTAL's "trend"
+ RegresParamMap [reg_index + 1, 28] = 1.0 + zero;
+ RegresCoeffDefault [reg_index + 2, 1] = 0.7 + zero;
+### RegresParamMap [reg_index + 2, 27] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #08, GROUP-1 SUBTOTAL's "self[t-1]"
+ RegresParamMap [reg_index + 1, 08] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #09, GROUP-1 SUBTOTAL's "trend"
+ RegresParamMap [reg_index + 1, 09] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 08] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #10, GROUP-1 VALUE's "self[t-1]"
+ RegresParamMap [reg_index + 1, 10] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #11, GROUP-1 VALUE's "trend"
+ RegresParamMap [reg_index + 1, 11] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 10] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #13, GROUP-2 SUBTOTAL's "self[t-1]"
+ RegresParamMap [reg_index + 1, 13] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #14, GROUP-2 SUBTOTAL's "trend"
+ RegresParamMap [reg_index + 1, 14] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 13] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #22, GROUP-3 SUBTOTAL's "self[t-1]"
+ RegresParamMap [reg_index + 1, 22] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #23, GROUP-3 SUBTOTAL's "trend"
+ RegresParamMap [reg_index + 1, 23] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 22] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #24, GROUP-3 VALUE's "self[t-1]"
+ RegresParamMap [reg_index + 1, 24] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #25, GROUP-3 VALUE's "trend"
+ RegresParamMap [reg_index + 1, 25] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 24] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+if (is_GROUP_4_ENABLED == 1) {
+
+# PARAMETER #32, GROUP-4 SUBTOTAL's "self[t-1]"
+ RegresParamMap [reg_index + 1, 32] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #33, GROUP-4 SUBTOTAL's "trend"
+ RegresParamMap [reg_index + 1, 33] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 32] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #34, GROUP-4 VALUE's "self[t-1]"
+ RegresParamMap [reg_index + 1, 34] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+
+# PARAMETER #35, GROUP-4 VALUE's "trend"
+ RegresParamMap [reg_index + 1, 35] = 1.0 + zero;
+ RegresParamMap [reg_index + 2, 34] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+}
+
+
+
+# --------------------------------
+# WRITE OUT ALL GENERATED MATRICES
+# --------------------------------
+
+initial_reports_preprocessed = matrix (0.0, rows = num_attrs, cols = num_terms);
+initial_reports_preprocessed [, 2:(num_known_terms+1)] = initial_reports [, 1:num_known_terms];
+
+write (initial_reports_preprocessed, $2, format="text");
+write (CReps, $3, format="text");
+write (RegresValueMap, $4, format="text");
+write (RegresFactorDefault,$5, format="text");
+write (RegresParamMap, $6, format="text");
+write (RegresCoeffDefault, $7, format="text");
+write (RegresScaleMult, $8, format="text");
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/05d2c0a8/src/test/scripts/applications/impute/wfundInputGenerator.pre2013-08-26.dml
----------------------------------------------------------------------
diff --git a/src/test/scripts/applications/impute/wfundInputGenerator.pre2013-08-26.dml b/src/test/scripts/applications/impute/wfundInputGenerator.pre2013-08-26.dml
index 735efe7..82bf551 100644
--- a/src/test/scripts/applications/impute/wfundInputGenerator.pre2013-08-26.dml
+++ b/src/test/scripts/applications/impute/wfundInputGenerator.pre2013-08-26.dml
@@ -1,442 +1,442 @@
-#-------------------------------------------------------------
-#
-# 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 = 1; # = 1 or 0
-num_known_terms = 6; # The number of known term reports, feel free to change
-num_predicted_terms = 1; # The number of predicted term reports, feel free to change
-
-num_terms = num_known_terms + num_predicted_terms;
-num_attrs = 19;
-
-num_frees_per_term = 13;
-if (is_GROUP_4_ENABLED == 1) {
- num_frees_per_term = 15;
-}
-num_frees = num_predicted_terms * num_frees_per_term;
-
-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 (dt in 1:num_predicted_terms)
-{
- ta_shift = (num_known_terms + dt - 1) * num_attrs;
- fv_shift = (dt - 1) * num_frees_per_term;
-# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
-# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
- CReps [ta_shift + 1, fv_shift + 1] = 1.0 + zero;
- CReps [ta_shift + 1, fv_shift + 2] = 1.0 + zero;
- CReps [ta_shift + 1, fv_shift + 3] = 1.0 + zero;
- CReps [ta_shift + 1, fv_shift + 4] = 1.0 + zero;
- CReps [ta_shift + 1, fv_shift + 5] = 1.0 + zero;
- CReps [ta_shift + 1, fv_shift + 6] = 1.0 + zero;
- CReps [ta_shift + 2, fv_shift + 1] = 1.0 + zero;
- CReps [ta_shift + 3, fv_shift + 2] = 1.0 + zero;
- CReps [ta_shift + 4, fv_shift + 3] = 1.0 + zero;
- CReps [ta_shift + 5, fv_shift + 4] = 1.0 + zero;
- CReps [ta_shift + 6, fv_shift + 5] = 1.0 + zero;
- CReps [ta_shift + 7, fv_shift + 6] = 1.0 + zero;
-
-# row 8 is free variable not appearing in any non-free variable
- CReps [ta_shift + 8, fv_shift + 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 [ta_shift + 9, fv_shift + 8] = 1.0 + zero;
- CReps [ta_shift + 9, fv_shift + 9] = 1.0 + zero;
- CReps [ta_shift + 9, fv_shift + 10] = 1.0 + zero;
- CReps [ta_shift + 9, fv_shift + 11] = 1.0 + zero;
- CReps [ta_shift + 9, fv_shift + 12] = 1.0 + zero;
- CReps [ta_shift + 9, fv_shift + 13] = 1.0 + zero;
- CReps [ta_shift + 10, fv_shift + 8] = 1.0 + zero;
- CReps [ta_shift + 11, fv_shift + 9] = 1.0 + zero;
- CReps [ta_shift + 12, fv_shift + 10] = 1.0 + zero;
- CReps [ta_shift + 13, fv_shift + 11] = 1.0 + zero;
- CReps [ta_shift + 14, fv_shift + 12] = 1.0 + zero;
- CReps [ta_shift + 15, fv_shift + 13] = 1.0 + zero;
-
-# constraint that row16 = row14 + row15
-# translated to free vars: row16 = free14 + free15
- if (is_GROUP_4_ENABLED == 1) {
- CReps [ta_shift + 16, fv_shift + 14] = 1.0 + zero;
- CReps [ta_shift + 16, fv_shift + 15] = 1.0 + zero;
- CReps [ta_shift + 17, fv_shift + 14] = 1.0 + zero;
- CReps [ta_shift + 18, fv_shift + 15] = 1.0 + zero;
- }
-
-# constraint that row19 = total cost (all free variables)
-# translated to free vars: row19 = all free variables
- CReps [ta_shift + 19, fv_shift + 1] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 2] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 3] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 4] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 5] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 6] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 7] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 8] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 9] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 10] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 11] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 12] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 13] = 1.0 + zero;
- if (is_GROUP_4_ENABLED == 1) {
- CReps [ta_shift + 19, fv_shift + 14] = 1.0 + zero;
- CReps [ta_shift + 19, fv_shift + 15] = 1.0 + zero;
- }
-}
-
-# ---------------------------------------------------------
-# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
-# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
-# ---------------------------------------------------------
-
-# In all regressions, except the last few "special" ones, there are 4 factors:
-# x[t] ~ aggregate[t], x[t-1], (x[t-1] - x[t-2])
-# 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 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 * 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] total[t] self[t-1] self[t-1]-
-# self[t-2]
-# PARAMS:
-# Group 1: 1.0 prm#01 prm#08 prm#09 Row #01 = free#01 + ... + free#06
-# Group 1: " prm#02 prm#10 prm#11 Row #02 = free#01
-# Group 1: " prm#03 " " Row #03 = free#02
-# Group 1: " prm#04 " " Row #04 = free#03
-# Group 1: " prm#05 " " Row #05 = free#04
-# Group 1: " prm#06 " " Row #06 = free#05
-# Group 1: " prm#07 " " 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#22 prm#23 Row #09 = free#08 + ... + free#13
-# Group 3: " prm#16 prm#24 prm#25 Row #10 = free#08
-# Group 3: " prm#17 " " Row #11 = free#09
-# Group 3: " prm#18 " " Row #12 = free#10
-# Group 3: " prm#19 " " Row #13 = free#11
-# Group 3: " prm#20 " " Row #14 = free#12
-# Group 3: " prm#21 " " Row #15 = free#13
-# --------------------------------------------------------------------
-# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
-# Group 4: 1.0 prm#29 prm#32 prm#33 Row #16 = free#14 + free#15
-# Group 4: " prm#30 prm#34 prm#35 Row #17 = free#14
-# Group 4: " prm#31 " " 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#27 1.0 0.0 0.0
-# prm#28 0.0 0.0 0.0
-# prm#08 0.0 0.0 0.0
-# prm#09 0.0 0.0 0.0
-# prm#10 0.0 0.0 0.0
-# prm#11 0.0 0.0 0.0
-# prm#13 0.0 0.0 0.0
-# prm#14 0.0 0.0 0.0
-# prm#22 0.0 0.0 0.0
-# prm#23 0.0 0.0 0.0
-# prm#24 0.0 0.0 0.0
-# prm#25 0.0 0.0 0.0
-# prm#32 0.0 0.0 0.0 # GROUP-4 ZEROS:
-# prm#33 0.0 0.0 0.0 # THESE EQUATIONS
-# prm#34 0.0 0.0 0.0 # USE REVOKED PARAMETERS
-# prm#35 0.0 0.0 0.0 # AND DO NOT APPEAR
-
-
-
-for (t in 1 : num_terms)
-{
-# Group 1 attributes:
- for (i in 1 : 7) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
- if (i == 1) {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 1] = 1.0 + zero; # 2nd factor: Row#01[t]
- }
- if (t == 1) {
- RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
- } else {
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
- }
- if (t >= 3) {
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- }
- }
-
-# Group 2 attribute:
- reg_index = ((t-1) * num_attrs - 1 + 8) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 8] = -1.0 + zero; # 1st factor is -x[t]
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
- if (t == 1) {
- RegresValueMap [reg_index + 3, 8] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
- } else {
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 8] = 1.0 + zero; # 3rd factor: x[t-1]
- }
- if (t >= 3) {
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + 8] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + 8] = -1.0 + zero; # x[t-1] - x[t-2]
- }
-
-# Group 3 attributes:
- for (i in 9 : 15) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
- if (i == 9) {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 9] = 1.0 + zero; # 2nd factor: Row#09[t]
- }
- if (t == 1) {
- RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
- } else {
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
- }
- if (t >= 3) {
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- }
- }
-
-# Group 4 attributes:
- for (i in 16 : 18) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
- if (i == 16) {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
- } else {
- RegresValueMap [reg_index + 2, (t-1) * num_attrs + 16] = 1.0 + zero; # 2nd factor: Row#16[t]
- }
- if (t == 1) {
- RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
- } else {
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
- }
- if (t >= 3) {
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
- }
- }
-
-# Group 5 attribute:
- reg_index = ((t-1) * num_attrs - 1 + 19) * num_factors;
- if (t >= 2) {
- RegresValueMap [reg_index + 1, (t-1) * num_attrs + 19] = -1.0 + zero; # 1st factor: -x[t]
- RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
- RegresValueMap [reg_index + 3, (t-2) * num_attrs + 19] = 1.0 + zero; # 3rd factor: x[t-1]
- }
- if (t >= 3) {
- RegresValueMap [reg_index + 4, (t-2) * num_attrs + 19] = 1.0 + zero; # 4th factor is
- RegresValueMap [reg_index + 4, (t-3) * num_attrs + 19] = -1.0 + zero; # x[t-1] - x[t-2]
- }
-}
-
-reg_index = num_terms * num_attrs * num_factors;
-for (i in 1:num_special_regs)
-{
- RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
- RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
- reg_index = reg_index + num_factors;
-}
-
-# ----------------------------------------------------------
-# 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) {
-# Group 1 attributes:
- reg_index = ((t-1) * 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, 8] = 1.0 + zero; # Param #08
- RegresParamMap [reg_index + 4, 9] = 1.0 + zero; # Param #09
- for (i in 2 : 7) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, i] = 1.0 + zero; # Param #02-#07
- RegresParamMap [reg_index + 3, 10] = 1.0 + zero; # Param #10
- RegresParamMap [reg_index + 4, 11] = 1.0 + zero; # Param #11
- }
-# Group 2 attribute:
- reg_index = ((t-1) * 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-1) * 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 #17
- RegresParamMap [reg_index + 3, 22] = 1.0 + zero; # Param #22
- RegresParamMap [reg_index + 4, 23] = 1.0 + zero; # Param #23
- for (i in 10 : 15) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 6 + i] = 1.0 + zero; # Param #16-#21
- RegresParamMap [reg_index + 3, 24] = 1.0 + zero; # Param #24
- RegresParamMap [reg_index + 4, 25] = 1.0 + zero; # Param #25
- }
-
-# Group 4 attributes:
-if (is_GROUP_4_ENABLED == 1) {
- reg_index = ((t-1) * 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, 32] = 1.0 + zero; # Param #32
- RegresParamMap [reg_index + 4, 33] = 1.0 + zero; # Param #33
- for (i in 17 : 18) {
- reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
- RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
- RegresParamMap [reg_index + 2, 13 + i] = 1.0 + zero; # Param #30-#31
- RegresParamMap [reg_index + 3, 34] = 1.0 + zero; # Param #34
- RegresParamMap [reg_index + 4, 35] = 1.0 + zero; # Param #35
- }
-}
-
-# Group 5 attribute:
- reg_index = ((t-1) * 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 * num_attrs * num_factors;
- RegresParamMap [reg_index + 1, 27] = 1.0 + zero; # Param #27
- RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 28] = 1.0 + zero; # Param #28
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 08] = 1.0 + zero; # Param #08
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 09] = 1.0 + zero; # Param #09
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 10] = 1.0 + zero; # Param #10
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 11] = 1.0 + zero; # Param #11
-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, 14] = 1.0 + zero; # Param #14
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 22] = 1.0 + zero; # Param #22
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 23] = 1.0 + zero; # Param #23
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 24] = 1.0 + zero; # Param #24
-reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 25] = 1.0 + zero; # Param #25
-
-if (is_GROUP_4_ENABLED == 1) {
- 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
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 34] = 1.0 + zero; # Param #34
- reg_index = reg_index + num_factors;
- RegresParamMap [reg_index + 1, 35] = 1.0 + zero; # Param #35
-}
-
-# ----------------------------------------------------------
-# 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_known_terms]));
-max_attr_size = max (attribute_size);
-
-for (t in 1 : num_terms) {
- for (i in 1 : num_attrs) {
- regeqn = (t-1) * 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);
- }
-}
-
-for (i in 1 : num_special_regs) {
- regeqn = num_terms * num_attrs + i;
- acceptable_drift = 0.01;
- RegresScaleMult [regeqn, 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/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 = 1; # = 1 or 0
+num_known_terms = 6; # The number of known term reports, feel free to change
+num_predicted_terms = 1; # The number of predicted term reports, feel free to change
+
+num_terms = num_known_terms + num_predicted_terms;
+num_attrs = 19;
+
+num_frees_per_term = 13;
+if (is_GROUP_4_ENABLED == 1) {
+ num_frees_per_term = 15;
+}
+num_frees = num_predicted_terms * num_frees_per_term;
+
+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 (dt in 1:num_predicted_terms)
+{
+ ta_shift = (num_known_terms + dt - 1) * num_attrs;
+ fv_shift = (dt - 1) * num_frees_per_term;
+# constraint that row1 = row2 + row3 + row4 + row5 + row6 + row7
+# translated to free vars: row1 = free1 + free2 + free3 + free4 + free5 + free6
+ CReps [ta_shift + 1, fv_shift + 1] = 1.0 + zero;
+ CReps [ta_shift + 1, fv_shift + 2] = 1.0 + zero;
+ CReps [ta_shift + 1, fv_shift + 3] = 1.0 + zero;
+ CReps [ta_shift + 1, fv_shift + 4] = 1.0 + zero;
+ CReps [ta_shift + 1, fv_shift + 5] = 1.0 + zero;
+ CReps [ta_shift + 1, fv_shift + 6] = 1.0 + zero;
+ CReps [ta_shift + 2, fv_shift + 1] = 1.0 + zero;
+ CReps [ta_shift + 3, fv_shift + 2] = 1.0 + zero;
+ CReps [ta_shift + 4, fv_shift + 3] = 1.0 + zero;
+ CReps [ta_shift + 5, fv_shift + 4] = 1.0 + zero;
+ CReps [ta_shift + 6, fv_shift + 5] = 1.0 + zero;
+ CReps [ta_shift + 7, fv_shift + 6] = 1.0 + zero;
+
+# row 8 is free variable not appearing in any non-free variable
+ CReps [ta_shift + 8, fv_shift + 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 [ta_shift + 9, fv_shift + 8] = 1.0 + zero;
+ CReps [ta_shift + 9, fv_shift + 9] = 1.0 + zero;
+ CReps [ta_shift + 9, fv_shift + 10] = 1.0 + zero;
+ CReps [ta_shift + 9, fv_shift + 11] = 1.0 + zero;
+ CReps [ta_shift + 9, fv_shift + 12] = 1.0 + zero;
+ CReps [ta_shift + 9, fv_shift + 13] = 1.0 + zero;
+ CReps [ta_shift + 10, fv_shift + 8] = 1.0 + zero;
+ CReps [ta_shift + 11, fv_shift + 9] = 1.0 + zero;
+ CReps [ta_shift + 12, fv_shift + 10] = 1.0 + zero;
+ CReps [ta_shift + 13, fv_shift + 11] = 1.0 + zero;
+ CReps [ta_shift + 14, fv_shift + 12] = 1.0 + zero;
+ CReps [ta_shift + 15, fv_shift + 13] = 1.0 + zero;
+
+# constraint that row16 = row14 + row15
+# translated to free vars: row16 = free14 + free15
+ if (is_GROUP_4_ENABLED == 1) {
+ CReps [ta_shift + 16, fv_shift + 14] = 1.0 + zero;
+ CReps [ta_shift + 16, fv_shift + 15] = 1.0 + zero;
+ CReps [ta_shift + 17, fv_shift + 14] = 1.0 + zero;
+ CReps [ta_shift + 18, fv_shift + 15] = 1.0 + zero;
+ }
+
+# constraint that row19 = total cost (all free variables)
+# translated to free vars: row19 = all free variables
+ CReps [ta_shift + 19, fv_shift + 1] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 2] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 3] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 4] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 5] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 6] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 7] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 8] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 9] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 10] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 11] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 12] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 13] = 1.0 + zero;
+ if (is_GROUP_4_ENABLED == 1) {
+ CReps [ta_shift + 19, fv_shift + 14] = 1.0 + zero;
+ CReps [ta_shift + 19, fv_shift + 15] = 1.0 + zero;
+ }
+}
+
+# ---------------------------------------------------------
+# GENERATE AN AFFINE MAP FROM REPORTS TO REGRESSION FACTORS
+# AFFINE MAP = LINEAR MAP + A VECTOR OF DEFAULTS
+# ---------------------------------------------------------
+
+# In all regressions, except the last few "special" ones, there are 4 factors:
+# x[t] ~ aggregate[t], x[t-1], (x[t-1] - x[t-2])
+# 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 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 * 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] total[t] self[t-1] self[t-1]-
+# self[t-2]
+# PARAMS:
+# Group 1: 1.0 prm#01 prm#08 prm#09 Row #01 = free#01 + ... + free#06
+# Group 1: " prm#02 prm#10 prm#11 Row #02 = free#01
+# Group 1: " prm#03 " " Row #03 = free#02
+# Group 1: " prm#04 " " Row #04 = free#03
+# Group 1: " prm#05 " " Row #05 = free#04
+# Group 1: " prm#06 " " Row #06 = free#05
+# Group 1: " prm#07 " " 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#22 prm#23 Row #09 = free#08 + ... + free#13
+# Group 3: " prm#16 prm#24 prm#25 Row #10 = free#08
+# Group 3: " prm#17 " " Row #11 = free#09
+# Group 3: " prm#18 " " Row #12 = free#10
+# Group 3: " prm#19 " " Row #13 = free#11
+# Group 3: " prm#20 " " Row #14 = free#12
+# Group 3: " prm#21 " " Row #15 = free#13
+# --------------------------------------------------------------------
+# GROUP-4 ZEROS: FIVE PARAMETERS REVOKED
+# Group 4: 1.0 prm#29 prm#32 prm#33 Row #16 = free#14 + free#15
+# Group 4: " prm#30 prm#34 prm#35 Row #17 = free#14
+# Group 4: " prm#31 " " 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#27 1.0 0.0 0.0
+# prm#28 0.0 0.0 0.0
+# prm#08 0.0 0.0 0.0
+# prm#09 0.0 0.0 0.0
+# prm#10 0.0 0.0 0.0
+# prm#11 0.0 0.0 0.0
+# prm#13 0.0 0.0 0.0
+# prm#14 0.0 0.0 0.0
+# prm#22 0.0 0.0 0.0
+# prm#23 0.0 0.0 0.0
+# prm#24 0.0 0.0 0.0
+# prm#25 0.0 0.0 0.0
+# prm#32 0.0 0.0 0.0 # GROUP-4 ZEROS:
+# prm#33 0.0 0.0 0.0 # THESE EQUATIONS
+# prm#34 0.0 0.0 0.0 # USE REVOKED PARAMETERS
+# prm#35 0.0 0.0 0.0 # AND DO NOT APPEAR
+
+
+
+for (t in 1 : num_terms)
+{
+# Group 1 attributes:
+ for (i in 1 : 7) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
+ if (i == 1) {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 1] = 1.0 + zero; # 2nd factor: Row#01[t]
+ }
+ if (t == 1) {
+ RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
+ } else {
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
+ }
+ if (t >= 3) {
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ }
+ }
+
+# Group 2 attribute:
+ reg_index = ((t-1) * num_attrs - 1 + 8) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 8] = -1.0 + zero; # 1st factor is -x[t]
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+ if (t == 1) {
+ RegresValueMap [reg_index + 3, 8] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
+ } else {
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 8] = 1.0 + zero; # 3rd factor: x[t-1]
+ }
+ if (t >= 3) {
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + 8] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + 8] = -1.0 + zero; # x[t-1] - x[t-2]
+ }
+
+# Group 3 attributes:
+ for (i in 9 : 15) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
+ if (i == 9) {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 9] = 1.0 + zero; # 2nd factor: Row#09[t]
+ }
+ if (t == 1) {
+ RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
+ } else {
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
+ }
+ if (t >= 3) {
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ }
+ }
+
+# Group 4 attributes:
+ for (i in 16 : 18) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + i] = -1.0 + zero; # 1st factor is -x[t]
+ if (i == 16) {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 19] = 1.0 + zero; # 2nd factor: Row#19[t]
+ } else {
+ RegresValueMap [reg_index + 2, (t-1) * num_attrs + 16] = 1.0 + zero; # 2nd factor: Row#16[t]
+ }
+ if (t == 1) {
+ RegresValueMap [reg_index + 3, i] = 1.0 + zero; # For t = 1 the 3rd factor is x[t] = x[1]
+ } else {
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + i] = 1.0 + zero; # 3rd factor: x[t-1]
+ }
+ if (t >= 3) {
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + i] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + i] = -1.0 + zero; # x[t-1] - x[t-2]
+ }
+ }
+
+# Group 5 attribute:
+ reg_index = ((t-1) * num_attrs - 1 + 19) * num_factors;
+ if (t >= 2) {
+ RegresValueMap [reg_index + 1, (t-1) * num_attrs + 19] = -1.0 + zero; # 1st factor: -x[t]
+ RegresFactorDefault [reg_index + 2, 1] = 1.0 + zero; # 2nd factor: Intercept
+ RegresValueMap [reg_index + 3, (t-2) * num_attrs + 19] = 1.0 + zero; # 3rd factor: x[t-1]
+ }
+ if (t >= 3) {
+ RegresValueMap [reg_index + 4, (t-2) * num_attrs + 19] = 1.0 + zero; # 4th factor is
+ RegresValueMap [reg_index + 4, (t-3) * num_attrs + 19] = -1.0 + zero; # x[t-1] - x[t-2]
+ }
+}
+
+reg_index = num_terms * num_attrs * num_factors;
+for (i in 1:num_special_regs)
+{
+ RegresFactorDefault [reg_index + 1, 1] = 1.0 + zero;
+ RegresFactorDefault [reg_index + 2, 1] = -1.0 + zero;
+ reg_index = reg_index + num_factors;
+}
+
+# ----------------------------------------------------------
+# 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) {
+# Group 1 attributes:
+ reg_index = ((t-1) * 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, 8] = 1.0 + zero; # Param #08
+ RegresParamMap [reg_index + 4, 9] = 1.0 + zero; # Param #09
+ for (i in 2 : 7) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, i] = 1.0 + zero; # Param #02-#07
+ RegresParamMap [reg_index + 3, 10] = 1.0 + zero; # Param #10
+ RegresParamMap [reg_index + 4, 11] = 1.0 + zero; # Param #11
+ }
+# Group 2 attribute:
+ reg_index = ((t-1) * 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-1) * 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 #17
+ RegresParamMap [reg_index + 3, 22] = 1.0 + zero; # Param #22
+ RegresParamMap [reg_index + 4, 23] = 1.0 + zero; # Param #23
+ for (i in 10 : 15) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 6 + i] = 1.0 + zero; # Param #16-#21
+ RegresParamMap [reg_index + 3, 24] = 1.0 + zero; # Param #24
+ RegresParamMap [reg_index + 4, 25] = 1.0 + zero; # Param #25
+ }
+
+# Group 4 attributes:
+if (is_GROUP_4_ENABLED == 1) {
+ reg_index = ((t-1) * 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, 32] = 1.0 + zero; # Param #32
+ RegresParamMap [reg_index + 4, 33] = 1.0 + zero; # Param #33
+ for (i in 17 : 18) {
+ reg_index = ((t-1) * num_attrs - 1 + i) * num_factors;
+ RegresCoeffDefault [reg_index + 1, 1] = 1.0 + zero; # Default coefficient = 1.0
+ RegresParamMap [reg_index + 2, 13 + i] = 1.0 + zero; # Param #30-#31
+ RegresParamMap [reg_index + 3, 34] = 1.0 + zero; # Param #34
+ RegresParamMap [reg_index + 4, 35] = 1.0 + zero; # Param #35
+ }
+}
+
+# Group 5 attribute:
+ reg_index = ((t-1) * 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 * num_attrs * num_factors;
+ RegresParamMap [reg_index + 1, 27] = 1.0 + zero; # Param #27
+ RegresCoeffDefault [reg_index + 2, 1] = 1.0 + zero;
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 28] = 1.0 + zero; # Param #28
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 08] = 1.0 + zero; # Param #08
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 09] = 1.0 + zero; # Param #09
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 10] = 1.0 + zero; # Param #10
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 11] = 1.0 + zero; # Param #11
+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, 14] = 1.0 + zero; # Param #14
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 22] = 1.0 + zero; # Param #22
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 23] = 1.0 + zero; # Param #23
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 24] = 1.0 + zero; # Param #24
+reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 25] = 1.0 + zero; # Param #25
+
+if (is_GROUP_4_ENABLED == 1) {
+ 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
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 34] = 1.0 + zero; # Param #34
+ reg_index = reg_index + num_factors;
+ RegresParamMap [reg_index + 1, 35] = 1.0 + zero; # Param #35
+}
+
+# ----------------------------------------------------------
+# 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_known_terms]));
+max_attr_size = max (attribute_size);
+
+for (t in 1 : num_terms) {
+ for (i in 1 : num_attrs) {
+ regeqn = (t-1) * 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);
+ }
+}
+
+for (i in 1 : num_special_regs) {
+ regeqn = num_terms * num_attrs + i;
+ acceptable_drift = 0.01;
+ RegresScaleMult [regeqn, 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");