├── glad_module ├── __init__.py ├── glad │ ├── __init__.py │ └── __pycache__ │ │ ├── model.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ └── admm_unrolled_NN.cpython-37.pyc ├── direct │ ├── __init__.py │ └── __pycache__ │ │ ├── admm.cpython-37.pyc │ │ ├── gista.cpython-37.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── compare.cpython-37.pyc │ │ └── block_glasso.cpython-37.pyc ├── syntren │ ├── __init__.py │ ├── README.md │ ├── syntren_gui.bat │ ├── syntren_gui.sh │ ├── README.TXT │ ├── SynTReN.jar │ ├── data │ │ ├── sourceNetworks │ │ │ ├── test.sif │ │ │ ├── ecoli_sub_30.sif │ │ │ └── ecoli_sub_50.sif │ │ ├── samples │ │ │ ├── externalsFile.txt │ │ │ ├── sampleIniFile2_generateDataRandomExternals.ini │ │ │ ├── sampleIniFile1_createGeneNetwork.ini │ │ │ ├── sampleIniFile3_generateDataPredefinedExternals.ini │ │ │ ├── sampleIniFile.ini │ │ │ └── sampleIniFile1.ini │ │ ├── results │ │ │ ├── nn20_nbgr0_hop0.3_bionoise0.1_expnoise0.1_corrnoise0.1_neighAdd_external.txt │ │ │ ├── nn20_nbgr0_hop0.3_bionoise0.1_expnoise0.1_corrnoise0.1_neighAdd_correlatedExternal.txt │ │ │ └── nn20_nbgr0_hop0.3_bionoise0.1_expnoise0.1_corrnoise0.1_neighAdd_network.sif │ │ ├── nmse_samples │ │ │ ├── sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_5_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_5_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_5_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_5_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.10_expnoise_0.10_inputnoise_0.10_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.01_expnoise_0.01_inputnoise_0.01_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.04_expnoise_0.04_inputnoise_0.04_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.08_expnoise_0.08_inputnoise_0.08_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.06_expnoise_0.06_inputnoise_0.06_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.03_expnoise_0.03_inputnoise_0.03_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.02_expnoise_0.02_inputnoise_0.02_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.05_expnoise_0.05_inputnoise_0.05_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt │ │ │ ├── sampled_bionoise_0.08_expnoise_0.08_inputnoise_0.08_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.10_expnoise_0.10_inputnoise_0.10_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.06_expnoise_0.06_inputnoise_0.06_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.01_expnoise_0.01_inputnoise_0.01_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.04_expnoise_0.04_inputnoise_0.04_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.02_expnoise_0.02_inputnoise_0.02_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ ├── sampled_bionoise_0.05_expnoise_0.05_inputnoise_0.05_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ │ └── sampled_bionoise_0.03_expnoise_0.03_inputnoise_0.03_burnin_10_experiments_10_samples_1_normalized_dataset.txt │ │ └── myNMSEnetworks │ │ │ └── nmse_expts.sif │ ├── lib │ │ ├── colt.jar │ │ ├── xom-1.0a5.jar │ │ ├── xercesImpl.jar │ │ ├── commons-math-1.1.jar │ │ ├── xmlpull-1.1.3.1.jar │ │ ├── xstream-1.4.11.1.jar │ │ ├── cglib-nodep-2.1_3.jar │ │ ├── xpp3_min-1.1.3.4.O.jar │ │ ├── COLT-LICENSE-AGREEMENT.TXT │ │ ├── xom-license.txt │ │ └── xstream-license.txt │ ├── xstream-1.2.1.jar │ ├── syntren_cli.bat │ ├── SynTReN │ │ ├── islab │ │ │ ├── lib │ │ │ │ ├── Print.class │ │ │ │ ├── Utils.class │ │ │ │ ├── Sorting.class │ │ │ │ ├── XmlHelper.class │ │ │ │ ├── RandomElement.class │ │ │ │ ├── XmlDomReader.class │ │ │ │ ├── XmlSerializer.class │ │ │ │ ├── XmlXomReader.class │ │ │ │ ├── XmlDomReader$1.class │ │ │ │ ├── XmlDomReader$2.class │ │ │ │ └── DelimitedFileReader.class │ │ │ ├── util │ │ │ │ ├── SeqUtil.class │ │ │ │ ├── DataSetUtil.class │ │ │ │ ├── IniFileParser.class │ │ │ │ └── DiscreteUniform.class │ │ │ └── bayesian │ │ │ │ ├── AddInfo.class │ │ │ │ ├── DataSet.class │ │ │ │ ├── DelInfo.class │ │ │ │ ├── IXMLable.class │ │ │ │ ├── Network.class │ │ │ │ ├── Variable.class │ │ │ │ ├── WhiteNoise.class │ │ │ │ ├── ConstantModel.class │ │ │ │ ├── IProvideMean.class │ │ │ │ ├── LinearModel.class │ │ │ │ ├── TestGenerator.class │ │ │ │ ├── GenerateSubsets.class │ │ │ │ ├── IncidenceMatrix.class │ │ │ │ ├── LognormalNoise.class │ │ │ │ ├── WhiteNoiseModel.class │ │ │ │ ├── ProbabilityModel.class │ │ │ │ ├── genenetwork │ │ │ │ ├── Edge.class │ │ │ │ ├── Node.class │ │ │ │ ├── Sample.class │ │ │ │ ├── EdgeType.class │ │ │ │ ├── ITParser.class │ │ │ │ ├── NodeType.class │ │ │ │ ├── GeneNetwork.class │ │ │ │ ├── RandomModel.class │ │ │ │ ├── ITParser$Flag.class │ │ │ │ ├── NActivatorsMM.class │ │ │ │ ├── NRegulatorsMM.class │ │ │ │ ├── NRepressorsMM.class │ │ │ │ ├── GroupOfNetworks.class │ │ │ │ ├── InteractionType.class │ │ │ │ ├── NRegulatorsCoop.class │ │ │ │ ├── OneActivatorCoop.class │ │ │ │ ├── OneRepressorCoop.class │ │ │ │ ├── Sample$Examples.class │ │ │ │ ├── ITParser$Attribute.class │ │ │ │ ├── ITParser$Parameter.class │ │ │ │ ├── NRegulatorsMMHill.class │ │ │ │ ├── SimpleLinearModel.class │ │ │ │ ├── CreateNetworkException.class │ │ │ │ ├── TwoActivatorsSynergism.class │ │ │ │ ├── generation │ │ │ │ │ ├── IniSettings.class │ │ │ │ │ ├── DataGenerator.class │ │ │ │ │ ├── GeneNetworkGenerator.class │ │ │ │ │ ├── InteractionGenerator.class │ │ │ │ │ ├── NetworkGeneratorCLI.class │ │ │ │ │ ├── NetworkGeneratorGUI.class │ │ │ │ │ ├── ExpressionDataGeneration.class │ │ │ │ │ ├── random │ │ │ │ │ │ ├── ErdosRenyiGenerator.class │ │ │ │ │ │ ├── SmallWorldGenerator.class │ │ │ │ │ │ ├── AlbertBarabasiGenerator.class │ │ │ │ │ │ └── DirectedScaleFreeGenerator.class │ │ │ │ │ ├── ExternalNodeNotFoundException.class │ │ │ │ │ ├── IniSettings$ExternalInputValues.class │ │ │ │ │ ├── NetworkGeneratorGUI$SimpleAboutDialog.class │ │ │ │ │ ├── InteractionGenerator$InteractionCategory.class │ │ │ │ │ └── NetworkGeneratorGUI$SimpleAboutDialog$1.class │ │ │ │ ├── OneActivatorOneRepressorComp.class │ │ │ │ ├── OneActivatorOneRepressorNoComp.class │ │ │ │ └── Sample$SampleNormalizationType.class │ │ │ │ ├── IProvideMeanFactory.class │ │ │ │ ├── ProbabilityModelFactory.class │ │ │ │ ├── GenerateSubsets$CreateExampleNetworks.class │ │ │ │ └── LognormalNoise$TransformationFunction.class │ │ └── META-INF │ │ │ └── MANIFEST.MF │ ├── syntren_cli.sh │ ├── doc │ │ ├── RELEASE NOTES.txt │ │ └── additional documentation.html │ └── LICENSE-AGREEMENT.TXT ├── README.md ├── new_metrics.py ├── metrics.py ├── torch_sqrtm.py └── torch_sqrtm_faster.py ├── notebooks ├── README.md ├── metrics.py ├── glad.py ├── glad_model.py ├── torch_sqrtm_scipy.py └── torch_sqrtm.py ├── setup.sh ├── README.md ├── LICENSE └── environment.yml /glad_module/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /glad_module/glad/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /glad_module/direct/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /glad_module/syntren/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /glad_module/syntren/README.md: -------------------------------------------------------------------------------- 1 | # Syntren generator 2 | -------------------------------------------------------------------------------- /glad_module/syntren/syntren_gui.bat: -------------------------------------------------------------------------------- 1 | java -jar SynTReN.jar 2 | -------------------------------------------------------------------------------- /glad_module/syntren/syntren_gui.sh: -------------------------------------------------------------------------------- 1 | java -Xmx512M -jar SynTReN.jar 2 | 3 | -------------------------------------------------------------------------------- /glad_module/syntren/README.TXT: -------------------------------------------------------------------------------- 1 | please review the release notes and the additional documentation in the doc folder! -------------------------------------------------------------------------------- /glad_module/syntren/SynTReN.jar: -------------------------------------------------------------------------------- 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This folder contains all the files needed to run GLAD. 2 | Note: use the torch_sqrtm.py file for calculating the matrix gradients. 3 | -------------------------------------------------------------------------------- /glad_module/syntren/SynTReN/islab/bayesian/genenetwork/ITParser$Flag.class: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Harshs27/GLAD/HEAD/glad_module/syntren/SynTReN/islab/bayesian/genenetwork/ITParser$Flag.class -------------------------------------------------------------------------------- /glad_module/syntren/SynTReN/islab/bayesian/genenetwork/NActivatorsMM.class: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Harshs27/GLAD/HEAD/glad_module/syntren/SynTReN/islab/bayesian/genenetwork/NActivatorsMM.class -------------------------------------------------------------------------------- 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Created-By: Tim Van den Bulcke, Koen Van Leemput 5 | Main-Class: islab.bayesian.genenetwork.generation.NetworkGeneratorGUI 6 | 7 | -------------------------------------------------------------------------------- /glad_module/syntren/data/results/nn20_nbgr0_hop0.3_bionoise0.1_expnoise0.1_corrnoise0.1_neighAdd_network.sif: -------------------------------------------------------------------------------- 1 | rpoH ac lon 2 | rpoH ac mopA 3 | rpoH ac htpG 4 | rpoH ac ibpAB 5 | rpoE_rseABC ac rpoH 6 | rpoE_rseABC ac rpoE_rseABC 7 | rpoE_rseABC ac ecfABC 8 | rpoE_rseABC ac ecfH 9 | rpoE_rseABC ac fkpA 10 | rpoE_rseABC ac xprB_dsbC_recJ 11 | rpoE_rseABC ac lpxDA_fabZ 12 | crp du rpoH 13 | crp du crp 14 | crp ac araE 15 | crp du ppiA 16 | crp du ptsHI_crr 17 | crp ac cirA 18 | crp re cyaA 19 | crp re speC 20 | crp ac araBAD 21 | dnaA re rpoH 22 | -------------------------------------------------------------------------------- /glad_module/README.md: -------------------------------------------------------------------------------- 1 | This folder contains glad as a python module. Some variants of this code was used in the experiments for the ICLR paper. 2 | It also contains implementation of some other baselines that we compared against: ADMM, GISTA, Graphical Lasso (BCD) 3 | 4 | # matrix squareroot 5 | Thanks to the folks at pytorch discussion forum, I have updated the code to calculate the matrix square root and its backprop implementation. Replace the torch_sqrtm.py by torch_sqrtm_faster.py 6 | 7 | I have included both the versions as results in the paper were reported using the slower one. 8 | -------------------------------------------------------------------------------- /setup.sh: -------------------------------------------------------------------------------- 1 | # Create conda environment. 2 | conda create -n glad python=3.8 -y; 3 | conda activate glad; 4 | conda install -c conda-forge notebook -y; 5 | python -m ipykernel install --user --name glad; 6 | 7 | # install pytorch 8 | conda install numpy -y; 9 | conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch -y; 10 | 11 | # Install packages from conda-forge. 12 | conda install -c conda-forge scikit-learn matplotlib -y; 13 | 14 | # Install packages from anaconda. 15 | conda install -c anaconda pandas networkx scipy -y; 16 | 17 | # Create environment.yml. 18 | conda env export > environment.yml; -------------------------------------------------------------------------------- /glad_module/syntren/data/sourceNetworks/ecoli_sub_30.sif: -------------------------------------------------------------------------------- 1 | argR du argR 2 | fur du fecIR 3 | nlpD_rpoS du dps 4 | crp du caiTABCDE 5 | glpR du glpACB 6 | nlpD_rpoS du alkA 7 | fnr du arcA 8 | crp du uhpT 9 | malI du malI 10 | rob du fumC 11 | cysB du cysJIH 12 | crp du tnaLAB 13 | rpiR_alsBACEK du rpiR_alsBACEK 14 | crp du caiF 15 | fur du fepB 16 | nlpD_rpoS du acs 17 | himA du ompR_envZ 18 | fhlA du fdhF 19 | himA du narGHJI 20 | glnALG du nac 21 | metR du metH 22 | nlpD_rpoS du osmY 23 | rpoE_rseABC du ecfI 24 | nlpD_rpoS du cpxAR 25 | glnALG du glnALG 26 | rpoH du htpG 27 | deoR du nupG 28 | oxyR du katG 29 | nlpD_rpoS du katG 30 | caiF du fixABCX 31 | -------------------------------------------------------------------------------- /glad_module/syntren/syntren_cli.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # Run SynTReN from a jar file 4 | # this is a linux-only version 5 | #------------------------------------------------------------------------------- 6 | 7 | #java -Xmx512M -cp SynTReN.jar islab.bayesian.genenetwork.generation.NetworkGeneratorCLI $* 8 | java -Xmx512M -cp SynTReN.jar:lib/xmlpull-1.1.3.1.jar:lib/xpp3_min-1.1.3.4.O.jar:lib/xstream-1.4.11.1.jar:lib/cglib-nodep-2.1_3.jar:lib/colt.jar:lib/commons-math-1.1.jar:lib/xercesImpl.jar:lib/xpp3_min-1.1.3.4.O.jar islab.bayesian.genenetwork.generation.NetworkGeneratorCLI $* 9 | #java -Xmx512M -cp SynTReN.jar:lib/xmlpull-1.1.3.1.jar:lib/xpp3_min-1.1.3.4.O.jar:lib/xstream-1.4.11.1.jar:lib/cglib-nodep-2.1_3.jar:lib/colt.jar:lib/commons-math-1.1.jar:lib/xercesImpl.jar:lib/xpp3_min-1.1.3.4.O.jar expts_gene/syntren/islab.bayesian.genenetwork.generation.NetworkGeneratorCLI $* 10 | -------------------------------------------------------------------------------- /glad_module/syntren/data/sourceNetworks/ecoli_sub_50.sif: -------------------------------------------------------------------------------- 1 | argR du argR 2 | fur du fecIR 3 | nlpD_rpoS du dps 4 | crp du caiTABCDE 5 | glpR du glpACB 6 | nlpD_rpoS du alkA 7 | fnr du arcA 8 | crp du uhpT 9 | malI du malI 10 | rob du fumC 11 | cysB du cysJIH 12 | crp du tnaLAB 13 | rpiR_alsBACEK du rpiR_alsBACEK 14 | crp du caiF 15 | fur du fepB 16 | nlpD_rpoS du acs 17 | himA du ompR_envZ 18 | fhlA du fdhF 19 | himA du narGHJI 20 | glnALG du nac 21 | metR du metH 22 | nlpD_rpoS du osmY 23 | rpoE_rseABC du ecfI 24 | nlpD_rpoS du cpxAR 25 | glnALG du glnALG 26 | rpoH du htpG 27 | deoR du nupG 28 | oxyR du katG 29 | nlpD_rpoS du katG 30 | caiF du fixABCX 31 | crp du araE 32 | fnr du hypABCDE 33 | rpoE_rseABC du ecfLM 34 | rpoH du hflB 35 | fur du sodA 36 | rpoE_rseABC du ostA_surA_pdxA 37 | metJ du metF 38 | crp du flhDC 39 | cynR du cynTSX 40 | rpoH du hflB 41 | nlpD_rpoS du alkA 42 | araC du araBAD 43 | lrp du ilvIH 44 | lrp du gcvTHP 45 | arcA du fumC 46 | lexA_dinF du uvrA 47 | ada_alkB du ada_alkB 48 | narL du narK 49 | lexA_dinF du uvrD 50 | fnr du dmsABC 51 | -------------------------------------------------------------------------------- /glad_module/syntren/lib/xom-license.txt: -------------------------------------------------------------------------------- 1 | XOM 2 | 3 | Copyright 2002-2004 Elliotte Rusty Harold 4 | 5 | This library is free software; you can redistribute 6 | it and/or modify it under the terms of version 2.1 of 7 | the GNU Lesser General Public License as published by 8 | the Free Software Foundation. 9 | 10 | This library is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU Lesser General Public License for more details. 14 | 15 | You should have received a copy of the GNU Lesser General 16 | Public License along with this library; if not, write to the 17 | Free Software Foundation, Inc., 59 Temple Place, Suite 330, 18 | Boston, MA 02111-1307 USA 19 | 20 | You can contact Elliotte Rusty Harold by sending e-mail to 21 | elharo@metalab.unc.edu. Please include the word "XOM" in the 22 | subject line. The XOM home page is temporarily located at 23 | http://www.cafeconleche.org/XOM/ but will eventually move 24 | to http://www.xom.nu/ 25 | -------------------------------------------------------------------------------- /glad_module/syntren/doc/RELEASE NOTES.txt: -------------------------------------------------------------------------------- 1 | 1.2 (2007-06-08): 2 | IMPORTANT: some significant changes have occured which are _not_ downwards compatible: 3 | - file naming conventions have changed: the gene expression dataset now has the suffix "_unnormalized_dataset.txt". For backwards compatibility, the (old) normalized dataset with suffix "_normalized_dataset.txt" is also given. 4 | - dataset format is changed to the more conventional genes by conditions format. 5 | - More realistic datasets (introduction of a maximum expression value per gene, improving the realism of the data and removing some artifacts from MA-plots) 6 | OTHER: 7 | - command line interface is extended: users can now specifiy their own external input file (an example is provided in the data/samples folder) and for example generate a concentration series experiment. 8 | 9 | 1.1.3 (2006-03-23): 10 | A command line interface has been added, which can be launched with syntren_cli.bat or syntren_cli.sh for Windows and Linux users respectively. The command line interface requires an .ini file. Sample ini files can be found in ./data/samples/*.ini. 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## UPDATE 2 | We have developed an unsupervised version called `uGLAD`: [paper](). The code is available here: [github](https://github.com/Harshs27/uGLAD). This code is user-friendly and follows the function signature similar to sklearn glasso package. We will appreciate any pull-requests or improvement suggestions that will help with wider adoption. Thanks! 3 | 4 | ------------------- 5 | # GLAD 6 | GLAD: Learning Sparse Graph Recovery (ICLR 2020 - https://openreview.net/forum?id=BkxpMTEtPB) 7 | 8 | ### Installation 9 | Setup the environment - `setup.sh` or use the `environment.yml` file. 10 | 11 | ### Start with a simple example - notebooks 12 | Self contained GLAD code with a minimalist working example. 13 | 14 | ### glad module 15 | This folder contains glad as a python module. Some variants of this code was used in the experiments for the ICLR paper. It also contains implementation of some other baselines that we compared against. Please don't hesitate to contact me if you need assistance with the implementation or have constructive criticisms. 16 | 17 | ### matrix squareroot 18 | Thanks to the folks at pytorch discussion forum, I have updated the code to calculate the matrix square root and its backprop implementation. Kindly check the notebooks folder for the latest implementation. 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | uGLAD Non-Commercial License 2 | Copyright © 2019 Harsh Shrivastava 3 | 4 | Permission is hereby granted, free of charge, to any person obtaining a copy 5 | of this software and associated documentation files (the “Software”), to use, 6 | copy, modify, merge, publish, and distribute the Software **for non-commercial 7 | purposes only**, subject to the following conditions: 8 | 9 | 1. **Non-Commercial Use** — Use of the Software, in whole or in part, for 10 | commercial purposes is prohibited without prior written permission from the 11 | copyright holder. 12 | 13 | 2. **Attribution** — You must give appropriate credit to the original author(s) 14 | in any public use of this Software. 15 | 16 | 3. **Commercial Licensing** — To obtain a commercial license, please contact: 17 | harshshrivastava111@gmail.com 18 | 19 | 4. **No Warranty** — The Software is provided “as is,” without warranty of any 20 | kind, express or implied, including but not limited to the warranties of 21 | merchantability, fitness for a particular purpose, and noninfringement. In 22 | no event shall the authors or copyright holders be liable for any claim, 23 | damages, or other liability, whether in an action of contract, tort, or 24 | otherwise, arising from, out of, or in connection with the Software or the 25 | use or other dealings in the Software. 26 | -------------------------------------------------------------------------------- /glad_module/syntren/LICENSE-AGREEMENT.TXT: -------------------------------------------------------------------------------- 1 | The users of this software agrees to all the license agreements of the libraries that are used, as specified in the lib folder. 2 | 3 | This software is free to use for academic purposes. If used in your academic work, please include all relevant references by its authors. For any non-academic purpose, please contact the authors. 4 | 5 | "SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms" 6 | Tim Van den Bulcke, Koenraad Van Leemput, Bart Naudts, Piet van Remortel, Hongwu 7 | Ma, Alain Verschoren, Bart De Moor and Kathleen Marchal 8 | BMC Bioinformatics 2006, 7:43 9 | 10 | 11 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY 12 | EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 13 | OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT 14 | SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 15 | INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 16 | TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR 17 | BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 18 | CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY 19 | WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH 20 | DAMAGE. 21 | -------------------------------------------------------------------------------- /glad_module/syntren/lib/xstream-license.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2003-2006, Joe Walnes 2 | Copyright (c) 2006-2007, XStream Committers 3 | All rights reserved. 4 | 5 | Redistribution and use in source and binary forms, with or without 6 | modification, are permitted provided that the following conditions are met: 7 | 8 | Redistributions of source code must retain the above copyright notice, this list of 9 | conditions and the following disclaimer. Redistributions in binary form must reproduce 10 | the above copyright notice, this list of conditions and the following disclaimer in 11 | the documentation and/or other materials provided with the distribution. 12 | 13 | Neither the name of XStream nor the names of its contributors may be used to endorse 14 | or promote products derived from this software without specific prior written 15 | permission. 16 | 17 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY 18 | EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 19 | OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT 20 | SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 21 | INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 22 | TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR 23 | BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 24 | CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY 25 | WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH 26 | DAMAGE. 27 | -------------------------------------------------------------------------------- /notebooks/metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sklearn 3 | from sklearn import metrics 4 | 5 | def get_auc(y, scores): 6 | y = np.array(y).astype(int) 7 | fpr, tpr, thresholds = metrics.roc_curve(y, scores) 8 | roc_auc = metrics.auc(fpr, tpr) 9 | aupr = metrics.average_precision_score(y, scores) 10 | return roc_auc, aupr 11 | 12 | def report_metrics(G_true, G, beta=1): 13 | G_true = G_true.real 14 | G =G.real 15 | #print('Check report metrics: ', G_true, G) 16 | # G_true and G are numpy arrays 17 | # convert all non-zeros in G to 1 18 | d = G.shape[-1] 19 | 20 | # changing to 1/0 for TP and FP calculations 21 | G_binary = np.where(G!=0, 1, 0) 22 | G_true_binary = np.where(G_true!=0, 1, 0) 23 | # extract the upper diagonal matrix 24 | indices_triu = np.triu_indices(d, 1) 25 | edges_true = G_true_binary[indices_triu] #np.triu(G_true_binary, 1) 26 | edges_pred = G_binary[indices_triu] #np.triu(G_binary, 1) 27 | # Getting AUROC value 28 | edges_pred_auc = G[indices_triu] #np.triu(G_true_binary, 1) 29 | auc, aupr = get_auc(edges_true, np.absolute(edges_pred_auc)) 30 | # Now, we have the edge array for comparison 31 | # true pos = pred is 1 and true is 1 32 | TP = np.sum(edges_true * edges_pred) # true_pos 33 | # False pos = pred is 1 and true is 0 34 | mismatches = np.logical_xor(edges_true, edges_pred) 35 | FP = np.sum(mismatches * edges_pred) 36 | # Find all mismatches with Xor and then just select the ones with pred as 1 37 | # P = Number of pred edges : nnz_pred 38 | P = np.sum(edges_pred) 39 | # T = Number of True edges : nnz_true 40 | T = np.sum(edges_true) 41 | # F = Number of non-edges in true graph 42 | F = len(edges_true) - T 43 | # SHD = total number of mismatches 44 | SHD = np.sum(mismatches) 45 | # FDR = False discovery rate 46 | FDR = FP/P 47 | # TPR = True positive rate 48 | TPR = TP/T 49 | # FPR = False positive rate 50 | FPR = FP/F 51 | # False negative = pred is 0 and true is 1 52 | FN = np.sum(mismatches * edges_true) 53 | # F beta score 54 | num = (1+beta**2)*TP 55 | den = ((1+beta**2)*TP + beta**2 * FN + FP) 56 | F_beta = num/den 57 | # precision 58 | precision = TP/(TP+FP) 59 | # recall 60 | recall = TP/(TP+FN) 61 | # print('FDR, TPR, FPR, SHD, nnz_true, nnz_pred, F1, auc') 62 | return FDR, TPR, FPR, SHD, T, P, precision, recall, F_beta, aupr, auc 63 | -------------------------------------------------------------------------------- /glad_module/new_metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sklearn 3 | from sklearn import metrics 4 | 5 | def get_auc(y, scores): 6 | y = np.array(y).astype(int) 7 | fpr, tpr, thresholds = metrics.roc_curve(y, scores) 8 | roc_auc = metrics.auc(fpr, tpr) 9 | aupr = metrics.average_precision_score(y, scores) 10 | return roc_auc, aupr 11 | 12 | def report_metrics(G_true, G, beta=1): 13 | G_true = G_true.real 14 | G =G.real 15 | #print('Check report metrics: ', G_true, G) 16 | # G_true and G are numpy arrays 17 | # convert all non-zeros in G to 1 18 | d = G.shape[-1] 19 | 20 | # changing to 1/0 for TP and FP calculations 21 | G_binary = np.where(G!=0, 1, 0) 22 | G_true_binary = np.where(G_true!=0, 1, 0) 23 | # extract the upper diagonal matrix 24 | indices_triu = np.triu_indices(d, 1) 25 | edges_true = G_true_binary[indices_triu] #np.triu(G_true_binary, 1) 26 | edges_pred = G_binary[indices_triu] #np.triu(G_binary, 1) 27 | # Getting AUROC value 28 | edges_pred_auc = G[indices_triu] #np.triu(G_true_binary, 1) 29 | auc, aupr = get_auc(edges_true, np.absolute(edges_pred_auc)) 30 | # Now, we have the edge array for comparison 31 | # true pos = pred is 1 and true is 1 32 | TP = np.sum(edges_true * edges_pred) # true_pos 33 | # False pos = pred is 1 and true is 0 34 | mismatches = np.logical_xor(edges_true, edges_pred) 35 | FP = np.sum(mismatches * edges_pred) 36 | # Find all mismatches with Xor and then just select the ones with pred as 1 37 | # P = Number of pred edges : nnz_pred 38 | P = np.sum(edges_pred) 39 | # T = Number of True edges : nnz_true 40 | T = np.sum(edges_true) 41 | # F = Number of non-edges in true graph 42 | F = len(edges_true) - T 43 | # SHD = total number of mismatches 44 | SHD = np.sum(mismatches) 45 | # FDR = False discovery rate 46 | FDR = FP/P 47 | # TPR = True positive rate 48 | TPR = TP/T 49 | # FPR = False positive rate 50 | FPR = FP/F 51 | # False negative = pred is 0 and true is 1 52 | FN = np.sum(mismatches * edges_true) 53 | # F beta score 54 | num = (1+beta**2)*TP 55 | den = ((1+beta**2)*TP + beta**2 * FN + FP) 56 | F_beta = num/den 57 | # precision 58 | precision = TP/(TP+FP) 59 | # recall 60 | recall = TP/(TP+FN) 61 | # print('FDR, TPR, FPR, SHD, nnz_true, nnz_pred, F1, auc') 62 | return FDR, TPR, FPR, SHD, T, P, precision, recall, F_beta, aupr, auc 63 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_5_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 2 | 0 755.2187098575613 180.65557104638245 34.03557500373512 225.14037290558193 610.6445197376049 3 | 11 1433.8759622085877 1468.2683818470282 1094.4206861221335 1846.1276307163578 665.2699680950366 4 | 12 83.47909760559801 1301.8052964047001 1357.2990590858412 1266.9574074153704 167.26088769636294 5 | 1 1114.7907482570633 186.7930307627734 8192.82256427225 5320.133169976992 8854.378352735344 6 | 24 6830.8982640853155 9072.605782336406 1525.6498647192266 3954.0517533095385 904.6599105042661 7 | 2 1009.7190174171093 325.02068459729725 762.0813490941799 779.5881363808196 432.7502150804083 8 | 21 24.9466601351944 949.1799533035555 157.17284897941835 100.03156065481484 55.148743428198074 9 | 3 2121.465458286356 589.7259643661755 2263.2602945114686 2377.272683497964 2490.083072572397 10 | 4 631.855710354038 3852.9212070429985 669.6193588159756 631.5257655172583 488.497161958096 11 | 19 2451.0177797530278 314.45644671624086 223.8402328435983 1250.109561392973 209.5121266367561 12 | 13 2866.079973337437 1461.7072242067247 459.41752647962363 733.3109268665459 11214.87449156687 13 | 5 1228.7312406416531 341.9103151994361 2578.8591164234927 1627.2658455117685 709.23913601603 14 | 23 177.460326044149 538.6404549794452 2.9540029613987944 18.806436881201627 461.44818710568586 15 | 6 29.369825430213474 35.79017555438889 40.80918239771209 9.812556210120368 17.027753207536566 16 | 8 5415.9268899346325 4637.7176603493845 4177.850552158362 6131.042407520863 5988.4585898788155 17 | 7 652.9217124532104 218.08136042295268 935.5439269642815 894.2584314823094 106.83443821175285 18 | 9 215.46895141130827 545.7765668191781 33.45331428811535 63.17385267372279 593.8289549591498 19 | 15 1886.2691908992608 40.50685812649819 4325.383548393381 3926.1687586614403 2.16471387707265 20 | 18 453.410923932384 55.9868939613106 697.8679315494659 692.723028818683 10.83870091694399 21 | 10 3024.9531110071316 4195.229221382746 4521.599012040994 4033.802741582739 3950.3025615027823 22 | 14 8135.6196344795935 9136.404589311143 1296.8682525180213 7275.717991864149 3543.9214514460346 23 | 16 95.91819823042118 84.56177784079803 729.5173967339306 215.24146762668107 700.9195554917748 24 | 17 121.37975106753213 7488.317484540513 5646.013758548816 4030.5406729890296 6725.32066299044 25 | 20 494.93352315865945 37.643423406769635 504.08175295359496 1525.7532278577462 1634.920792162512 26 | 22 535.9333630554304 674.7684255025322 604.2192406110809 33.26012461053041 14.0295432630895 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_5_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 2 | 0 6685.346971153184 575.403255425487 2025.5373868337674 466.108942454051 3196.1409276455997 3 | 1 358.7710059895301 3223.9044629954774 3098.5589404572615 3223.8131891218136 2137.470191598928 4 | 9 1853.2967849874308 1782.324364128867 1755.1095085994232 1690.4722578112314 1946.2662959206752 5 | 2 378.7717586450981 1806.1440389243044 1642.6233395037111 2217.5199164212704 2038.4554139628804 6 | 5 38.468979345878374 7.058828672798779 11.346610280236346 2.323251637705104 6.228777537786524 7 | 3 6706.379800145535 4544.726292444738 4031.1215143394984 2734.932748349265 1407.7224395097505 8 | 7 3.469574065734084 384.84855843331883 476.21532166815035 1784.3092559853978 3888.877841515047 9 | 4 284.9215675010442 336.16859812037933 295.1510096691265 316.4912099572194 29.699699215276237 10 | 20 39.88040301775038 18.138698970967678 54.983625558582155 27.023165439214505 48.90345211585147 11 | 6 268.3009420506542 11662.129228329028 11475.991050653347 12181.478513006716 11907.088524674378 12 | 18 116.74968514711252 5799.0815213138885 4583.549082571381 6609.092996638698 6462.81813881371 13 | 8 377.3819583613779 377.25315999597314 377.12076802535483 218.4102345222923 2.544605097575771 14 | 15 13.30285527015118 9.749672273770614 3.6741005272475795 0.5924881199947543 0.010094086450099134 15 | 11 1599.5184130292755 1604.2584086316604 1602.7191421077316 1229.5960559738303 67.21845038967183 16 | 10 32.16788953182497 9.919471677126255 14.594444896967259 9.124892558472943 28.422468897419385 17 | 16 4073.8232200902885 4102.980250585615 4093.266691879938 4106.528265480941 4086.213523365237 18 | 13 5666.316534849737 5666.775014437841 5666.775014437841 5666.775014437841 5666.594280148776 19 | 23 11.23194034886501 50.50628734294927 34.47709262224759 1.5249764883633898 4.924647893726228 20 | 12 3572.495001234169 8063.1726758113255 249.71345693222486 10455.950381408553 2236.722446783331 21 | 17 1914.1534722795172 3761.6886544852455 7.6909004619653185 5276.896847709978 743.7030620243344 22 | 14 338.5785814529931 47.97965543804625 436.62421039068124 554.515641082751 917.8643941417407 23 | 24 19.459501480092804 23.913163768014314 60.14012266426104 53.33113826173368 1.5351389541046687 24 | 19 2568.664425497353 1791.0499049289708 4334.296412015051 3927.836479805677 479.3724725285109 25 | 21 1383.7772092371792 1184.11936495775 438.63485100708976 796.3088849331972 2016.6334538311637 26 | 22 5435.444105569044 3326.513060700124 2581.6109133097902 5659.131765407356 2580.322543426258 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_5_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 2 | 0 0.6964493316841426 0.05994291910638636 0.21101136043478785 0.048557129923823675 0.3329595640483488 3 | 1 0.11118735164554816 0.9991258859117256 0.960279835202549 0.9990975990654707 0.6624271355755063 4 | 9 0.14793320979909405 0.14226807396663174 0.1400957392573172 0.13493628716138584 0.15535418968597176 5 | 2 0.1254416448296841 0.5981588486228231 0.5440040574303804 0.7343983267218903 0.6750957382730085 6 | 5 0.9365859741721231 0.17185795000136111 0.27625053285967116 0.05656310448954366 0.15164908914471292 7 | 3 0.9896048474920242 0.6706275671160418 0.5948391696117757 0.40357134340533213 0.20772592539896342 8 | 7 8.618002021964261E-4 0.09559172370706111 0.11828612179109543 0.44320082190371796 0.9659501848466173 9 | 4 0.7625148686443899 0.8996635694740459 0.7898911807274409 0.8470024066772297 0.07948314493893571 10 | 20 0.07419384204738007 0.033745390331134766 0.10229200612817309 0.050274127553949645 0.09098039957720994 11 | 6 0.02200366598740629 0.9564245070510162 0.9411591029947497 0.9990169336877022 0.9765138980762461 12 | 18 0.01764006700027967 0.8762009631731394 0.6925424493757557 0.9985880743479606 0.9764869587069391 13 | 8 0.9999995 0.9996582057273093 0.9993073889977625 0.5787508397739229 0.006742780805744111 14 | 15 0.11544790871025053 0.08461185600828827 0.031885427124333544 0.005141867140548467 8.760083059906717E-5 15 | 11 0.9955211389684466 0.998471255567514 0.9975132344840458 0.765285886141139 0.041835959965529544 16 | 10 0.007562990712129608 0.002332166432277674 0.0034312991250249644 0.0021453529800466286 0.006682405075853924 17 | 16 0.9897666891635996 0.9968506140125925 0.9944906302035991 0.9977126315244794 0.9927770086565272 18 | 13 0.9999185934247969 0.9999995 0.9999995 0.9999995 0.9999676063394547 19 | 23 0.006676822799834932 0.030023443892354222 0.02049489499964313 9.065217113395534E-4 0.0029274551250009963 20 | 12 0.3105046291823027 0.7008134205564315 0.021703930815798224 0.9087825160864405 0.1944054991489874 21 | 17 0.34281200421511815 0.6736931210336353 0.0013773885113013053 0.9450567107587374 0.13319221312482768 22 | 14 0.3600671461867601 0.05102477993280829 0.46433543644947634 0.5897090818437027 0.9761184879644859 23 | 24 0.03481810876749136 0.0427868688157979 0.10760631942995319 0.09542327559576863 0.0027467628156820786 24 | 19 0.5577614314922386 0.38890971858022877 0.9411518870586233 0.8528929181629713 0.10409124439978412 25 | 21 0.6063621185482388 0.5188733576162768 0.19220692156967195 0.34893734284947203 0.8836755839290072 26 | 22 0.919249471516293 0.5625842734557014 0.43660544044400057 0.9570798233877079 0.4363875496311154 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_5_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 2 | 0 0.7961050878636277 0.19043598547506796 0.03587820862376156 0.2373291260057011 0.6437038737173726 3 | 11 0.1351474895547307 0.13838908736120253 0.10315272181447997 0.17400355488534489 0.06270386590881473 4 | 12 0.061493981019148186 0.9589608954082528 0.9998397799059977 0.9332905721113762 0.12321093720778653 5 | 1 0.1205407166522712 0.020197661156587183 0.8858780940246451 0.5752583307649304 0.957411167806857 6 | 24 0.7528780683940063 0.9999513464603423 0.16815187092369824 0.4358019591674069 0.0997084980104221 7 | 2 0.9867254075940796 0.317619220747323 0.7447270149752644 0.761835132702175 0.42289550359656924 8 | 21 0.005972681948944148 0.22725086014210927 0.03763002473582147 0.023949366103915477 0.013203607320398686 9 | 3 0.6949593525592884 0.1931851271876799 0.7414091531885445 0.7787578527510605 0.8157127956877497 10 | 4 0.14984882687208625 0.9137461503394105 0.15880472982223998 0.14977057823728918 0.11585038395672449 11 | 19 0.33120070080272807 0.04249181559786522 0.030247043737526767 0.16892458562877635 0.028310917914171778 12 | 13 0.25322566617636033 0.12914565854683074 0.04059074076025496 0.06478993946353598 0.990863510659246 13 | 5 0.38638162321057634 0.10751566999322826 0.8109371182869587 0.5117031275737989 0.2230243356352168 14 | 23 0.16186980101866377 0.4913189624504244 0.0026944832246685244 0.017154224066244672 0.4209083116901172 15 | 6 0.2715761197221405 0.3309436422879173 0.3773532611195767 0.09073448347290748 0.15745177494081553 16 | 8 0.8794479707911316 0.7530809533408762 0.6784069033857704 0.9955697175582949 0.9724167002371056 17 | 7 0.6192071034398765 0.206820396574576 0.8872356886091968 0.8480820326901435 0.10131787896005459 18 | 9 0.35459992671620255 0.8981912676043412 0.05505453438781261 0.10396599315935875 0.9772716789646121 19 | 15 0.41367315322261716 0.008883461495939056 0.9485894484170062 0.8610386143800124 4.7473818672004415E-4 20 | 18 0.6477294687033781 0.07998122490167461 0.9969535374385268 0.9896036811901636 0.015483848349921768 21 | 10 0.6450739316506644 0.8946363492927356 0.9642349963811282 0.8602120094184402 0.8424055220181172 22 | 14 0.6931904597975356 0.7784617254375883 0.11049886064607026 0.619923069997375 0.301957644381855 23 | 16 0.13141751468207302 0.11585808413789338 0.9995117189941406 0.2949023152422918 0.9603298193061558 24 | 17 0.01608315477147717 0.9922229039242035 0.7481125337792834 0.5340578546775092 0.8911237019303921 25 | 20 0.29297551876085237 0.02228299556301693 0.29839080635909315 0.9031684509464354 0.9677901067597852 26 | 22 0.7942106354023369 0.9999531600580215 0.8954048769649212 0.049288860372288794 0.02079066771633408 27 | -------------------------------------------------------------------------------- /glad_module/metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sklearn 3 | from sklearn import metrics 4 | 5 | def report_metrics(G_true, G): 6 | """Compute FDR, TPR, and FPR for B 7 | Args: 8 | B_true: ground truth adj matrix 9 | B: predicted adj mat 10 | Returns: 11 | fdr: (false positive) / prediction positive = FP/P 12 | tpr: (true positive) / condition positive = TP/T 13 | fpr: (false positive) / condition negative = FP/F 14 | shd: undirected extra + undirected missing = E+M 15 | nnz: prediction positive 16 | ps : probability of success, sign match 17 | """ 18 | B_true = G_true != 0 19 | B = G != 0 20 | d = B.shape[-1] 21 | 22 | # Probability of success : 1 = perfect match 23 | ps = int(np.all(np.sign(G)==np.sign(G_true))) 24 | 25 | # AUC 26 | # print('G , G_true', G, G_true, np.where(G_true>0, 1, 0).reshape(-1), G.reshape(-1)) 27 | G_true_binary = np.where(G_true>0, 1, 0).reshape(-1) 28 | sk_fpr, sk_tpr, sk_th = metrics.roc_curve(G_true_binary.reshape(-1), G.reshape(-1)) 29 | auc = metrics.auc(sk_fpr, sk_tpr) 30 | #print('auc = ', auc) 31 | #br 32 | 33 | # linear index of nonzeros 34 | pred = np.flatnonzero(B) 35 | cond = np.flatnonzero(B_true) 36 | # true pos 37 | true_pos = np.intersect1d(pred, cond, assume_unique=True) 38 | TP = (len(true_pos) - d)/2 + d 39 | # false pos 40 | false_pos = np.setdiff1d(pred, cond, assume_unique=True) 41 | FP = len(false_pos)/2 42 | # P = set of estimated edges 43 | P = max((len(pred)-d)/2+d, 1) 44 | # T = set of true edges 45 | T = max((len(cond)-d)/2+d, 1) 46 | # F = set of non-edges in ground truth graph 47 | F = max((d**2 - len(cond))/2, 1) 48 | # extra 49 | E = len(set(pred)-set(cond))/2 50 | # missing 51 | M = len(set(cond)-set(pred))/2 52 | # compute ratio 53 | fdr = float(FP) / P 54 | tpr = float(TP) / T 55 | fpr = float(FP) / F 56 | # structural hamming distance 57 | shd = E+M 58 | # print('FP=', FP, ' TP=',TP, ' P=', P, ' T=', T, ' F=',F, ' E=', E, ' M=', M) 59 | return fdr, tpr, fpr, shd, (len(pred)-d)/2 +d, (len(cond)-d)/2+d, ps#, auc 60 | 61 | 62 | def main(): 63 | a_pred = np.array([[0.74, 0.02, 0.01, 0, 0], [0.02, 1.25, 0,0,0], [0.01,0,0.79,0,0], [0,0,0,0.81,0], [0,0,0,0,0.78]]) 64 | # sign match check 65 | a_pred2 = np.array([[1.33, 0.32, 0,0,0], [0.32,1.33,0.02,0,0.08], [0, 0.02,1.33,0,0], [0,0,0,1.33,0], [0,0.08,0,0,1.33]]) 66 | 67 | a_true = np.array([[1.33, 0.32, 0,0,0], [0.32,1.33,-0.02,0,-0.08], [0, -0.02,1.33,0,0], [0,0,0,1.33,0], [0,-0.08,0,0,1.33]]) 68 | print(a_pred, a_true) 69 | print(report_metrics(a_true, a_pred)) 70 | print(report_metrics(a_true, a_pred2)) 71 | print(report_metrics(a_true, a_true)) 72 | 73 | if __name__=="__main__": 74 | main() 75 | 76 | -------------------------------------------------------------------------------- /notebooks/glad.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch_sqrtm import MatrixSquareRoot 3 | 4 | torch_sqrtm = MatrixSquareRoot.apply 5 | def batch_matrix_sqrt(A): 6 | # A should be PSD 7 | # if shape of A is 2D, i.e. a single matrix 8 | if len(A.shape)==2: 9 | return torch_sqrtm(A) 10 | else: 11 | n = A.shape[0] 12 | sqrtm_torch = torch.zeros(A.shape).type_as(A) 13 | for i in range(n): 14 | sqrtm_torch[i] = torch_sqrtm(A[i]) 15 | return sqrtm_torch 16 | 17 | 18 | def get_frobenius_norm(A, single=False): 19 | if single: 20 | return torch.sum(A**2) 21 | return torch.mean(torch.sum(A**2, (1,2))) 22 | 23 | 24 | def glad(Sb, theta_true, model, my_args, criterion_graph): 25 | D, INIT_DIAG, lambda_init, L= my_args # NOTE: replace my_args with args parser 26 | USE_CUDA = False # NOTE : Please remove this flag when running on GPUs 27 | 28 | # if batch is 1, then reshaping Sb 29 | if len(Sb.shape)==2: 30 | Sb = Sb.reshape(1, Sb.shape[0], Sb.shape[1]) 31 | # Initializing the theta 32 | if INIT_DIAG == 1: 33 | #print(' extract batchwise diagonals, add offset and take inverse') 34 | batch_diags = 1/(torch.diagonal(Sb, offset=0, dim1=-2, dim2=-1) + model.theta_init_offset) 35 | theta_init = torch.diag_embed(batch_diags) 36 | else: 37 | #print('***************** (S+theta_offset*I)^-1 is used') 38 | theta_init = torch.inverse(Sb+model.theta_init_offset * torch.eye(D).expand_as(Sb).type_as(Sb)) 39 | 40 | theta_pred = theta_init#[ridx] 41 | identity_mat = torch.eye(Sb.shape[-1]).expand_as(Sb) 42 | # diagonal mask 43 | # mask = torch.eye(Sb.shape[-1], Sb.shape[-1]).byte() 44 | # dim = Sb.shape[-1] 45 | # mask1 = torch.ones(dim, dim) - torch.eye(dim, dim) 46 | if USE_CUDA == True: 47 | identity_mat = identity_mat.cuda() 48 | # mask = mask.cuda() 49 | # mask1 = mask1.cuda() 50 | 51 | zero = torch.Tensor([0]) 52 | dtype = torch.FloatTensor 53 | if USE_CUDA == True: 54 | zero = zero.cuda() 55 | dtype = torch.cuda.FloatTensor 56 | 57 | lambda_k = model.lambda_forward(zero + lambda_init, zero, k=0) 58 | loss_glad = torch.Tensor([0]).type(dtype) 59 | for k in range(L): 60 | # GLAD CELL 61 | b = 1.0/lambda_k * Sb - theta_pred 62 | b2_4ac = torch.matmul(b.transpose(-1, -2), b) + 4.0/lambda_k * identity_mat 63 | sqrt_term = batch_matrix_sqrt(b2_4ac) 64 | theta_k1 = 1.0/2*(-1*b+sqrt_term) 65 | 66 | theta_pred = model.eta_forward(theta_k1, Sb, k, theta_pred) 67 | # update the lambda 68 | lambda_k = model.lambda_forward(torch.Tensor([get_frobenius_norm(theta_pred-theta_k1)]).type(dtype), lambda_k, k) 69 | 70 | #if criterion_graph!= None: 71 | #loss_glad += criterion_graph(theta_pred*mask1, theta_true*mask1)/L 72 | #loss_glad += criterion_graph(theta_pred.masked_fill_(mask, 0), theta_true.masked_fill_(mask, 0))/L 73 | loss_glad += criterion_graph(theta_pred, theta_true)/L 74 | 75 | return theta_pred, loss_glad 76 | 77 | -------------------------------------------------------------------------------- /notebooks/glad_model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | class glad_model(torch.nn.Module): # entrywise thresholding 5 | def __init__(self, L, theta_init_offset, nF, H, USE_CUDA=False): # initializing all the weights here 6 | super(glad_model, self).__init__() # initializing the nn.module 7 | self.USE_CUDA = USE_CUDA 8 | if USE_CUDA == False: 9 | self.dtype = torch.FloatTensor 10 | else: # shift to GPU 11 | print('shifting to cuda') 12 | self.dtype = torch.cuda.FloatTensor 13 | self.L = L # number of unrolled iterations 14 | # self.rho_init = torch.Tensor([rho_init]).type(self.dtype) 15 | self.theta_init_offset = nn.Parameter(torch.Tensor([theta_init_offset]).type(self.dtype)) 16 | self.nF = nF # number of input features 17 | self.H = H # hidden layer size 18 | self.rho_l1 = self.rhoNN()#nn.Sequential(l1, nn.ReLU(), l2, nn.ReLU()).cuda() # NOTE: just testing 19 | # print('CHECK RHO INITIAL: ', self.rho_l1[0].weight) 20 | self.lambda_f = self.lambdaNN() 21 | self.zero = torch.Tensor([0]).type(self.dtype) 22 | 23 | def rhoNN(self):# per iteration NN 24 | l1 = nn.Linear(self.nF, self.H).type(self.dtype) 25 | lH1 = nn.Linear(self.H, self.H).type(self.dtype) 26 | # lH2 = nn.Linear(self.H, self.H).type(self.dtype) 27 | # lH3 = nn.Linear(self.H, self.H).type(self.dtype) 28 | # lH4 = nn.Linear(self.H, self.H).type(self.dtype) 29 | l2 = nn.Linear(self.H, 1).type(self.dtype) 30 | return nn.Sequential(l1, nn.Tanh(), 31 | lH1, nn.Tanh(), 32 | # lH2, nn.Tanh(), 33 | # lH3, nn.Tanh(), 34 | # lH4, nn.Tanh(), 35 | l2, nn.Sigmoid()).type(self.dtype) 36 | 37 | 38 | def lambdaNN(self): 39 | l1 = nn.Linear(2, self.H).type(self.dtype) 40 | # lH1 = nn.Linear(self.H, self.H).type(self.dtype) 41 | # lH2 = nn.Linear(self.H, self.H).type(self.dtype) 42 | l2 = nn.Linear(self.H, 1).type(self.dtype) 43 | return nn.Sequential(l1, nn.Tanh(), 44 | # lH1, nn.Tanh(), 45 | # lH2, nn.Tanh(), 46 | l2, nn.Sigmoid()).type(self.dtype) 47 | 48 | def eta_forward(self, X, S, k, F3=[]):# step_size):#=1): 49 | batch_size, shape1, shape2 = X.shape 50 | Xr = X.reshape(batch_size, -1, 1) 51 | Sr = S.reshape(batch_size, -1, 1) 52 | feature_vector = torch.cat((Xr, Sr), -1) 53 | if len(F3)>0: 54 | F3r = F3.reshape(batch_size, -1, 1) 55 | feature_vector = torch.cat((feature_vector, F3r), -1) 56 | rho_val = self.rho_l1(feature_vector).reshape(X.shape) # elementwise thresholding done 57 | return torch.sign(X)*torch.max(self.zero, torch.abs(X)-rho_val) 58 | 59 | def lambda_forward(self, normF, prev_lambda, k=0): 60 | feature_vector = torch.Tensor([normF, prev_lambda]).type(self.dtype) 61 | return self.lambda_f(feature_vector) 62 | 63 | 64 | -------------------------------------------------------------------------------- /glad_module/syntren/doc/additional documentation.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |

Additional SynTReN documentation

5 | date: 2007-05-15 6 |

7 | 8 |

Important classes

9 | The most relevant classes are:
10 | 11 | 16 | 17 |

NetworkGeneratorCLI

18 | The command line takes one argument: an '.ini' file where all the settings are saved and generates a number of files in a folder which is specified in the ini file. The main() method is in fact a wrapper for the run() method, which you can use as an API call to the command line interface. 19 | 20 |

Generated files using both CLI and GUI

21 | all generated files are saved in the output folder which is specified in the ini file and apply to the following template:
22 | nn<v>_nbgr<w>_hop<x>_bionoise<y>_expnoise<z>_(neigh|clust)Add_((unnormalized|maxExpr1)_dataset|external|network).(txt|sif|xml) 23 |

24 | Note: one exception to this rule: if the ini-field 'createGeneNetwork' is true, then the resulting xml file is saved to the location defined in 'GeneNetworkXMLFile'. When the field is false, the xml file is read from this location. 25 | 26 |

36 | 37 | 38 |

Ini-files

39 | The ini-files have a somewhat complex structure due to historical reasons. Several examples of ini files are included in the folder 'data/samples'. 40 | 41 | 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /glad_module/torch_sqrtm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Variable 3 | from torch.autograd import Function 4 | import numpy as np 5 | import scipy.linalg 6 | 7 | class MatrixSquareRoot(Function): 8 | """Square root of a positive definite matrix. 9 | Given a positive semi-definite matrix X, 10 | X = X^{1/2}X^{1/2}, compute the gradient: dX^{1/2} by solving the Sylvester equation, 11 | dX = (d(X^{1/2})X^{1/2} + X^{1/2}(dX^{1/2}). 12 | """ 13 | @staticmethod 14 | def forward(ctx, input): 15 | #m = input.numpy().astype(np.float_) 16 | m = input.detach().cpu().numpy().astype(np.float_) 17 | sqrtm = torch.from_numpy(scipy.linalg.sqrtm(m).real)#.type_as(input) 18 | ctx.save_for_backward(sqrtm) # save in cpu 19 | sqrtm = sqrtm.type_as(input) 20 | return sqrtm 21 | 22 | @staticmethod 23 | def backward(ctx, grad_output): 24 | grad_input = None 25 | if ctx.needs_input_grad[0]: 26 | #sqrtm, = ctx.saved_variables 27 | sqrtm, = ctx.saved_tensors 28 | #sqrtm = sqrtm.data.numpy().astype(np.float_) 29 | sqrtm = sqrtm.data.numpy().astype(np.float_) 30 | #gm = grad_output.data.numpy().astype(np.float_) 31 | gm = grad_output.data.cpu().numpy().astype(np.float_) 32 | # gm = np.eye(grad_output.shape[-1]) 33 | grad_sqrtm = scipy.linalg.solve_sylvester(sqrtm, sqrtm, gm) 34 | 35 | grad_input = torch.from_numpy(grad_sqrtm).type_as(grad_output.data) 36 | return Variable(grad_input) 37 | 38 | 39 | sqrtm = MatrixSquareRoot.apply 40 | 41 | def original_main(): 42 | from torch.autograd import gradcheck 43 | k = torch.randn(20, 10).double() 44 | # Create a positive definite matrix 45 | pd_mat = k.t().matmul(k) 46 | pd_mat = Variable(pd_mat, requires_grad=True) 47 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 48 | print(test) 49 | 50 | def single_main(): 51 | from torch.autograd import gradcheck 52 | n = 1 53 | A = torch.randn( 20, 10).double() 54 | # Create a positive definite matrix 55 | pd_mat = A.t().matmul(A) 56 | pd_mat = Variable(pd_mat, requires_grad=True) 57 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 58 | print(test) 59 | 60 | #sqrtm_scipy = np.zeros_like(A) 61 | print('err: ', pd_mat) 62 | sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 63 | # for i in range(n): 64 | # sqrtm_scipy[i] = sqrtm(pd_mat[i].detach().numpy()) 65 | sqrtm_torch = sqrtm(pd_mat) 66 | print('sqrtm torch: ', sqrtm_torch) 67 | print('scipy', sqrtm_scipy) 68 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 69 | 70 | def main():# batch 71 | from torch.autograd import gradcheck 72 | n = 2 73 | A = torch.randn(n, 4, 5).double() 74 | A.requires_grad = True 75 | # Create a positive definite matrix 76 | #pd_mat = A.t().matmul(A) 77 | pd_mat = torch.matmul(A.transpose(-1, -2), A) 78 | pd_mat = Variable(pd_mat, requires_grad=True) 79 | print('err: ', pd_mat.shape) 80 | #test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 81 | #print(test) 82 | 83 | sqrtm_scipy = np.zeros_like(pd_mat.detach().numpy()) 84 | #sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 85 | for i in range(n): 86 | sqrtm_scipy[i] = scipy.linalg.sqrtm(pd_mat[i].detach().numpy()) 87 | # batch implementation 88 | sqrtm_torch = torch.zeros(pd_mat.shape) 89 | for i in range(n): 90 | sqrtm_torch[i] = sqrtm(pd_mat[i]) 91 | #sqrtm_torch = sqrtm(pd_mat) 92 | print('sqrtm torch: ', sqrtm_torch) 93 | print('scipy', sqrtm_scipy) 94 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 95 | 96 | if __name__ == '__main__': 97 | main() 98 | -------------------------------------------------------------------------------- /glad_module/syntren/data/samples/sampleIniFile2_generateDataRandomExternals.ini: -------------------------------------------------------------------------------- 1 | # EXAMPLE INI FILE 2: 2 | # 3 | # GOAL: generate a dataset using the ecoli subnetwork generated with the example ini file 1 4 | # with all noise levels = 0.1, 10 different experiments and 1 sample per experiment 5 | # 6 | # results are saved in ./data/samples/sample2 7 | # 8 | 9 | 10 | 11 | 12 | ####################################### 13 | # Tasks to be performed 14 | ####################################### 15 | 16 | # IF TRUE 17 | # new gene network will created starting form the topology in the SIF file 18 | # and saved in xml file 19 | createGeneNetwork = false 20 | 21 | # Disregarded if createGeneNetwork = false 22 | # IF TRUE 23 | # select a subnetwork from the SIF file and use this to create the new gene network 24 | # ELSE 25 | # use the complete network specified in the SIF file to create the new gene network 26 | selectSubnetwork = false 27 | 28 | # Disregarded if createGeneNetwork = false 29 | # IF TRUE 30 | # !! only possible if selectSubnetwork = false 31 | # use a fixed set of external inputs, specified by the file 32 | # ELSE 33 | # choose 'nrExternals' inputs from the network (complete or selected subnetwork) 34 | fixedExternals = false 35 | 36 | # IF TRUE 37 | # create expression data file in the output folder 38 | generateExpressionData = true 39 | 40 | ####################################### 41 | # random seed 42 | ####################################### 43 | 44 | randomSeed = 13 45 | 46 | 47 | ####################################### 48 | # expression data 49 | ####################################### 50 | 51 | # this group of parameters will be disregarded if createExpressionData = false 52 | 53 | # externalInputValues is one of the following: 54 | # RANDOMIZED: randomize the external input value for each experiment (uniform distribution) 55 | # FROM_EXTERNALS_FILE: specify the values of the external inputs in the externalsFile 56 | # (tab-delimited with header, rows=externals, cols=experiments, 57 | # first column are external-names, other columns are experiments) 58 | # when using this setting, 'fixedExternals' must be true 59 | # FIXED: external input values are kept at a fixed value, as specified in the genenetwork.xml file if applicable 60 | externalInputValues = RANDOMIZED 61 | 62 | ## the different noise levels: 63 | bioNoise = 0.1 64 | inputNoise = 0.1 65 | expNoise = 0.1 66 | 67 | # number of burnIn cycles before actual sampling. 68 | # only required to be >0 if there are feedback cycles in the network 69 | burnIn = 0 70 | # the number of different experiments (in every experiment the external nodes are randomized) 71 | # disregarded if randomizeInputs = false, only 1 experiment will be performed 72 | nrExperiments = 10 73 | # the number of samples to be taken in each experiment 74 | nrSamplesPerExp = 1 75 | 76 | 77 | ####################################### 78 | # files & directories 79 | # 80 | # WARNING: existing files will be overwritten without any warning!! 81 | ####################################### 82 | 83 | # used to save generated expression data 84 | outputdir = ./data/samples/sample2 85 | 86 | # this file is an OUTPUT file for gene network generation 87 | # and an INPUT file for expression data generation 88 | # IF generateNetworkFile = true 89 | # GeneNetworkXMLFile = full path for gene network OUTPUT file (in xml format) 90 | # IF generateExpressionData = true 91 | # this file (possibly generated during the same run of the program) is an INPUT file for exression data generation 92 | GeneNetworkXMLFile = ./data/samples/genenetwork.xml 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | -------------------------------------------------------------------------------- /glad_module/syntren/data/myNMSEnetworks/nmse_expts.sif: -------------------------------------------------------------------------------- 1 | 0 du 36 2 | 0 du 41 3 | 1 du 15 4 | 1 du 20 5 | 1 du 27 6 | 1 du 28 7 | 1 du 35 8 | 1 du 44 9 | 1 du 72 10 | 1 du 75 11 | 1 du 89 12 | 2 du 5 13 | 2 du 31 14 | 2 du 54 15 | 2 du 55 16 | 2 du 61 17 | 2 du 72 18 | 2 du 79 19 | 2 du 88 20 | 3 du 20 21 | 3 du 43 22 | 3 du 70 23 | 3 du 81 24 | 3 du 83 25 | 3 du 84 26 | 4 du 17 27 | 4 du 33 28 | 4 du 44 29 | 4 du 47 30 | 4 du 81 31 | 5 du 11 32 | 5 du 14 33 | 5 du 21 34 | 5 du 23 35 | 5 du 33 36 | 5 du 35 37 | 5 du 63 38 | 5 du 65 39 | 6 du 11 40 | 6 du 14 41 | 6 du 30 42 | 6 du 41 43 | 6 du 81 44 | 6 du 84 45 | 6 du 91 46 | 6 du 92 47 | 7 du 30 48 | 7 du 57 49 | 7 du 59 50 | 7 du 75 51 | 7 du 76 52 | 7 du 89 53 | 7 du 94 54 | 8 du 18 55 | 8 du 68 56 | 8 du 92 57 | 9 du 18 58 | 9 du 32 59 | 9 du 34 60 | 9 du 35 61 | 9 du 57 62 | 9 du 61 63 | 9 du 71 64 | 9 du 87 65 | 9 du 89 66 | 10 du 23 67 | 10 du 25 68 | 10 du 49 69 | 10 du 59 70 | 10 du 61 71 | 11 du 20 72 | 11 du 51 73 | 11 du 52 74 | 11 du 60 75 | 11 du 66 76 | 11 du 69 77 | 11 du 72 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| 88 du 93 280 | 91 du 94 281 | 95 du 98 282 | -------------------------------------------------------------------------------- /notebooks/torch_sqrtm_scipy.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Variable 3 | from torch.autograd import Function 4 | import numpy as np 5 | import scipy.linalg 6 | 7 | class MatrixSquareRoot(Function): 8 | """Square root of a positive definite matrix. 9 | 10 | NOTE: matrix square root is not differentiable for matrices with 11 | zero eigenvalues. 12 | """ 13 | @staticmethod 14 | def forward(ctx, input): 15 | #m = input.numpy().astype(np.float_) 16 | m = input.detach().cpu().numpy().astype(np.float_) 17 | sqrtm = torch.from_numpy(scipy.linalg.sqrtm(m).real)#.type_as(input) 18 | ctx.save_for_backward(sqrtm) # save in cpu 19 | sqrtm = sqrtm.type_as(input) 20 | return sqrtm 21 | 22 | @staticmethod 23 | def backward(ctx, grad_output): 24 | grad_input = None 25 | if ctx.needs_input_grad[0]: 26 | #sqrtm, = ctx.saved_variables 27 | sqrtm, = ctx.saved_tensors 28 | #sqrtm = sqrtm.data.numpy().astype(np.float_) 29 | sqrtm = sqrtm.data.numpy().astype(np.float_) 30 | #gm = grad_output.data.numpy().astype(np.float_) 31 | gm = grad_output.data.cpu().numpy().astype(np.float_) 32 | # gm = np.eye(grad_output.shape[-1]) 33 | # Given a positive semi-definite matrix X, 34 | # since X = X^{1/2}X^{1/2}, we can compute the gradient of the 35 | # matrix square root dX^{1/2} by solving the Sylvester equation: 36 | # dX = (d(X^{1/2})X^{1/2} + X^{1/2}(dX^{1/2}). 37 | grad_sqrtm = scipy.linalg.solve_sylvester(sqrtm, sqrtm, gm) 38 | 39 | grad_input = torch.from_numpy(grad_sqrtm).type_as(grad_output.data) 40 | return Variable(grad_input) 41 | 42 | 43 | sqrtm = MatrixSquareRoot.apply 44 | 45 | 46 | def original_main(): 47 | from torch.autograd import gradcheck 48 | k = torch.randn(20, 10).double() 49 | # Create a positive definite matrix 50 | pd_mat = k.t().matmul(k) 51 | pd_mat = Variable(pd_mat, requires_grad=True) 52 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 53 | print(test) 54 | 55 | def single_main(): 56 | from torch.autograd import gradcheck 57 | n = 1 58 | A = torch.randn( 20, 10).double() 59 | # Create a positive definite matrix 60 | pd_mat = A.t().matmul(A) 61 | pd_mat = Variable(pd_mat, requires_grad=True) 62 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 63 | print(test) 64 | 65 | #sqrtm_scipy = np.zeros_like(A) 66 | print('err: ', pd_mat) 67 | sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 68 | # for i in range(n): 69 | # sqrtm_scipy[i] = sqrtm(pd_mat[i].detach().numpy()) 70 | sqrtm_torch = sqrtm(pd_mat) 71 | print('sqrtm torch: ', sqrtm_torch) 72 | print('scipy', sqrtm_scipy) 73 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 74 | 75 | def main():# batch 76 | from torch.autograd import gradcheck 77 | n = 2 78 | A = torch.randn(n, 4, 5).double() 79 | A.requires_grad = True 80 | # Create a positive definite matrix 81 | #pd_mat = A.t().matmul(A) 82 | pd_mat = torch.matmul(A.transpose(-1, -2), A) 83 | pd_mat = Variable(pd_mat, requires_grad=True) 84 | print('err: ', pd_mat.shape) 85 | #test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 86 | #print(test) 87 | 88 | sqrtm_scipy = np.zeros_like(pd_mat.detach().numpy()) 89 | #sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 90 | for i in range(n): 91 | sqrtm_scipy[i] = scipy.linalg.sqrtm(pd_mat[i].detach().numpy()) 92 | # batch implementation 93 | sqrtm_torch = torch.zeros(pd_mat.shape) 94 | for i in range(n): 95 | sqrtm_torch[i] = sqrtm(pd_mat[i]) 96 | #sqrtm_torch = sqrtm(pd_mat) 97 | print('sqrtm torch: ', sqrtm_torch) 98 | print('scipy', sqrtm_scipy) 99 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 100 | 101 | if __name__ == '__main__': 102 | main() 103 | 104 | -------------------------------------------------------------------------------- /notebooks/torch_sqrtm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Variable 3 | from torch.autograd import Function 4 | import numpy as np 5 | import scipy.linalg 6 | 7 | class MatrixSquareRoot(Function): 8 | """Square root of a positive definite matrix. 9 | NOTE: matrix square root is not differentiable for matrices with 10 | zero eigenvalues. 11 | """ 12 | @staticmethod 13 | def forward(ctx, input): 14 | itr_TH = 10 # number of iterations threshold 15 | dim = input.shape[0] 16 | norm = torch.norm(input)#.double()) 17 | #Y = input.double()/norm 18 | Y = input/norm 19 | I = torch.eye(dim,dim,device=input.device)#.double() 20 | Z = torch.eye(dim,dim,device=input.device)#.double() 21 | #print('Check: ', Y.type(), I.type(), Z.type()) 22 | for i in range(itr_TH): 23 | T = 0.5*(3.0*I - Z.mm(Y)) 24 | Y = Y.mm(T) 25 | Z = T.mm(Z) 26 | sqrtm = Y*torch.sqrt(norm) 27 | ctx.mark_dirty(Y,I,Z) 28 | ctx.save_for_backward(sqrtm) 29 | return sqrtm 30 | 31 | @staticmethod 32 | def backward(ctx, grad_output): 33 | itr_TH = 10 # number of iterations threshold 34 | grad_input = None 35 | sqrtm, = ctx.saved_tensors 36 | dim = sqrtm.shape[0] 37 | norm = torch.norm(sqrtm) 38 | A = sqrtm/norm 39 | I = torch.eye(dim, dim, device=sqrtm.device)#.double() 40 | #Q = grad_output.double()/norm 41 | Q = grad_output/norm 42 | for i in range(itr_TH): 43 | Q = 0.5*(Q.mm(3.0*I-A.mm(A))-A.t().mm(A.t().mm(Q)-Q.mm(A))) 44 | A = 0.5*A.mm(3.0*I-A.mm(A)) 45 | grad_input = 0.5*Q 46 | return grad_input 47 | sqrtm = MatrixSquareRoot.apply 48 | 49 | 50 | def original_main(): 51 | from torch.autograd import gradcheck 52 | k = torch.randn(20, 10).double() 53 | # Create a positive definite matrix 54 | pd_mat = k.t().matmul(k) 55 | pd_mat = Variable(pd_mat, requires_grad=True) 56 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 57 | print(test) 58 | 59 | def single_main(): 60 | from torch.autograd import gradcheck 61 | n = 1 62 | A = torch.randn( 20, 10).double() 63 | # Create a positive definite matrix 64 | pd_mat = A.t().matmul(A) 65 | pd_mat = Variable(pd_mat, requires_grad=True) 66 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 67 | print(test) 68 | 69 | #sqrtm_scipy = np.zeros_like(A) 70 | print('err: ', pd_mat) 71 | sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 72 | # for i in range(n): 73 | # sqrtm_scipy[i] = sqrtm(pd_mat[i].detach().numpy()) 74 | sqrtm_torch = sqrtm(pd_mat) 75 | print('sqrtm torch: ', sqrtm_torch) 76 | print('scipy', sqrtm_scipy) 77 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 78 | 79 | def main():# batch 80 | from torch.autograd import gradcheck 81 | n = 2 82 | A = torch.randn(n, 4, 5).double() 83 | A.requires_grad = True 84 | # Create a positive definite matrix 85 | #pd_mat = A.t().matmul(A) 86 | pd_mat = torch.matmul(A.transpose(-1, -2), A) 87 | pd_mat = Variable(pd_mat, requires_grad=True) 88 | pd_mat.type = torch.FloatTensor 89 | print('err: ', pd_mat.shape, pd_mat.type) 90 | #test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 91 | #print(test) 92 | 93 | sqrtm_scipy = np.zeros_like(pd_mat.detach().numpy()) 94 | #sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 95 | for i in range(n): 96 | sqrtm_scipy[i] = scipy.linalg.sqrtm(pd_mat[i].detach().numpy().astype(np.float)) 97 | # batch implementation 98 | sqrtm_torch = torch.zeros(pd_mat.shape) 99 | for i in range(n): 100 | print('custom implementation', pd_mat[i].type()) 101 | sqrtm_torch[i] = sqrtm(pd_mat[i].type(torch.FloatTensor)) 102 | #sqrtm_torch = sqrtm(pd_mat) 103 | print('sqrtm torch: ', sqrtm_torch) 104 | print('scipy', sqrtm_scipy) 105 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 106 | 107 | if __name__ == '__main__': 108 | main() 109 | 110 | -------------------------------------------------------------------------------- /glad_module/torch_sqrtm_faster.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.autograd import Variable 3 | from torch.autograd import Function 4 | import numpy as np 5 | import scipy.linalg 6 | 7 | class MatrixSquareRoot(Function): 8 | """Square root of a positive definite matrix. 9 | NOTE: matrix square root is not differentiable for matrices with 10 | zero eigenvalues. 11 | """ 12 | @staticmethod 13 | def forward(ctx, input): 14 | itr_TH = 10 # number of iterations threshold 15 | dim = input.shape[0] 16 | norm = torch.norm(input)#.double()) 17 | #Y = input.double()/norm 18 | Y = input/norm 19 | I = torch.eye(dim,dim,device=input.device)#.double() 20 | Z = torch.eye(dim,dim,device=input.device)#.double() 21 | #print('Check: ', Y.type(), I.type(), Z.type()) 22 | for i in range(itr_TH): 23 | T = 0.5*(3.0*I - Z.mm(Y)) 24 | Y = Y.mm(T) 25 | Z = T.mm(Z) 26 | sqrtm = Y*torch.sqrt(norm) 27 | ctx.mark_dirty(Y,I,Z) 28 | ctx.save_for_backward(sqrtm) 29 | return sqrtm 30 | 31 | @staticmethod 32 | def backward(ctx, grad_output): 33 | itr_TH = 10 # number of iterations threshold 34 | grad_input = None 35 | sqrtm, = ctx.saved_tensors 36 | dim = sqrtm.shape[0] 37 | norm = torch.norm(sqrtm) 38 | A = sqrtm/norm 39 | I = torch.eye(dim, dim, device=sqrtm.device)#.double() 40 | #Q = grad_output.double()/norm 41 | Q = grad_output/norm 42 | for i in range(itr_TH): 43 | Q = 0.5*(Q.mm(3.0*I-A.mm(A))-A.t().mm(A.t().mm(Q)-Q.mm(A))) 44 | A = 0.5*A.mm(3.0*I-A.mm(A)) 45 | grad_input = 0.5*Q 46 | return grad_input 47 | sqrtm = MatrixSquareRoot.apply 48 | 49 | 50 | def original_main(): 51 | from torch.autograd import gradcheck 52 | k = torch.randn(20, 10).double() 53 | # Create a positive definite matrix 54 | pd_mat = k.t().matmul(k) 55 | pd_mat = Variable(pd_mat, requires_grad=True) 56 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 57 | print(test) 58 | 59 | def single_main(): 60 | from torch.autograd import gradcheck 61 | n = 1 62 | A = torch.randn( 20, 10).double() 63 | # Create a positive definite matrix 64 | pd_mat = A.t().matmul(A) 65 | pd_mat = Variable(pd_mat, requires_grad=True) 66 | test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 67 | print(test) 68 | 69 | #sqrtm_scipy = np.zeros_like(A) 70 | print('err: ', pd_mat) 71 | sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 72 | # for i in range(n): 73 | # sqrtm_scipy[i] = sqrtm(pd_mat[i].detach().numpy()) 74 | sqrtm_torch = sqrtm(pd_mat) 75 | print('sqrtm torch: ', sqrtm_torch) 76 | print('scipy', sqrtm_scipy) 77 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 78 | 79 | def main():# batch 80 | from torch.autograd import gradcheck 81 | n = 2 82 | A = torch.randn(n, 4, 5).double() 83 | A.requires_grad = True 84 | # Create a positive definite matrix 85 | #pd_mat = A.t().matmul(A) 86 | pd_mat = torch.matmul(A.transpose(-1, -2), A) 87 | pd_mat = Variable(pd_mat, requires_grad=True) 88 | pd_mat.type = torch.FloatTensor 89 | print('err: ', pd_mat.shape, pd_mat.type) 90 | #test = gradcheck(MatrixSquareRoot.apply, (pd_mat,)) 91 | #print(test) 92 | 93 | sqrtm_scipy = np.zeros_like(pd_mat.detach().numpy()) 94 | #sqrtm_scipy = scipy.linalg.sqrtm(pd_mat.detach().numpy().astype(np.float_)) 95 | for i in range(n): 96 | sqrtm_scipy[i] = scipy.linalg.sqrtm(pd_mat[i].detach().numpy().astype(np.float)) 97 | # batch implementation 98 | sqrtm_torch = torch.zeros(pd_mat.shape) 99 | for i in range(n): 100 | print('custom implementation', pd_mat[i].type()) 101 | sqrtm_torch[i] = sqrtm(pd_mat[i].type(torch.FloatTensor)) 102 | #sqrtm_torch = sqrtm(pd_mat) 103 | print('sqrtm torch: ', sqrtm_torch) 104 | print('scipy', sqrtm_scipy) 105 | print('Difference: ', np.linalg.norm(sqrtm_scipy - sqrtm_torch.detach().numpy())) 106 | 107 | if __name__ == '__main__': 108 | main() 109 | -------------------------------------------------------------------------------- /glad_module/syntren/data/samples/sampleIniFile1_createGeneNetwork.ini: -------------------------------------------------------------------------------- 1 | # EXAMPLE INI FILE 1: 2 | # 3 | # GOAL: create a subnetwork from the ecoli network with 100 nodes and 100 background nodes, 4 | # using cluster addition 5 | # with sigmoidal interactions and some bio+exp noise 6 | # 7 | # all top nodes will be considered as external inputs 8 | # 9 | # ecoli source network: ./data/ecoli/EColi_full.sif 10 | # file will be saved as ./data/samples/sample1/genenetwork.xml 11 | # 12 | 13 | 14 | 15 | 16 | ####################################### 17 | # Tasks to be performed 18 | ####################################### 19 | 20 | # IF TRUE 21 | # new gene network will created starting form the topology in the SIF file 22 | # and saved in xml file 23 | createGeneNetwork = true 24 | 25 | # Disregarded if createGeneNetwork = false 26 | # IF TRUE 27 | # select a subnetwork from the SIF file and use this to create the new gene network 28 | # ELSE 29 | # use the complete network specified in the SIF file to create the new gene network 30 | selectSubnetwork = true 31 | 32 | # Disregarded if createGeneNetwork = false 33 | # IF TRUE 34 | # !! only possible if selectSubnetwork = false 35 | # use a fixed set of external inputs, specified by the file 36 | # ELSE 37 | # choose 'nrExternals' inputs from the network (complete or selected subnetwork) 38 | fixedExternals = false 39 | 40 | # IF TRUE 41 | # create expression data file in the output folder 42 | generateExpressionData = false 43 | 44 | 45 | 46 | ####################################### 47 | # random seed 48 | ####################################### 49 | 50 | randomSeed = 13 51 | 52 | ####################################### 53 | # gene network topology 54 | ####################################### 55 | 56 | # this group of parameters will be disregarded if selectSubnetwork = false 57 | # (if selectSubnetwork = false, all nodes in the SIF file will become nodes in the genenetwork 58 | # and there will be no background nodes) 59 | 60 | # method of subnetwork selection, values: (clusterAddition, neighborAddition) 61 | subnetworkSelection = clusterAddition 62 | # nr of nodes in the foreground network 63 | nrNodes = 100 64 | # nr of nodes in the background network 65 | nrBackgroundNodes = 100 66 | 67 | ####################################### 68 | # gene network interaction types 69 | ####################################### 70 | 71 | # this group of parameters will be disregarded if createGeneNetwork = false 72 | 73 | # IF TRUE 74 | # will use the information from the sif file to set edge types 75 | # ac --> Activator; re --> Repressor; everything else --> unknown 76 | # unknown interactions will be assigned a type according to "percentActivators" 77 | # ELSE 78 | # all interactions will be assigned a type according to "percentActivators" 79 | useEdgeTypesFromSIF = false 80 | 81 | # the desired percentage of activators (value between 0 and 1) 82 | percentActivators = 0.2 83 | 84 | # category of desired interactions, values: (LINEARLIKE, SIGMOIDAL, STEP, STEEP, LINEAR, MIXED, DEFAULT, RANDOM) 85 | interactionCategory = SIGMOIDAL 86 | # nr of external nodes (other top nodes are fixed to a random value) 87 | # IF -1 THEN all top nodes are external nodes 88 | nrExternals = -1 89 | # nr of correlated nodes among the external nodes (must be <= nrExternals) 90 | nrCorrelatedExternals = 0 91 | 92 | # the noise on the correlated inputs (compared to the inputs from which they depend) 93 | correlationNoise = 0.1 94 | 95 | # probability of selecting a complex interaction (synergistic or antagonistic) for 2-input genes 96 | higherOrderProbability = 0 97 | 98 | ####################################### 99 | # files & directories 100 | # 101 | # WARNING: existing files will be overwritten without any warning!! 102 | ####################################### 103 | 104 | # full path for source network file in sif-format 105 | NetworkSIFFile = ./data/sourceNetworks/EColi_full.sif 106 | 107 | # this file is an OUTPUT file for gene network generation 108 | # and an INPUT file for expression data generation 109 | # IF generateNetworkFile = true 110 | # GeneNetworkXMLFile = full path for gene network OUTPUT file (in xml format) 111 | # IF generateExpressionData = true 112 | # this file (possibly generated during the same run of the program) is an INPUT file for exression data generation 113 | GeneNetworkXMLFile = ./data/samples/sample1/genenetwork.xml 114 | 115 | 116 | 117 | 118 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 1536.4672519627281 334.34826154530714 67.95339383990977 406.12948267097454 1164.4052489951043 1728.5779905128736 1575.2442883192464 678.7384944164488 996.4827147012702 482.7073295818177 3 | 2 578.2834326505599 4818.808946621519 5319.864006722449 4995.3075487609 1284.7360403692533 169.97351716946778 272.70802808296673 3555.7601572047593 1308.872216708014 3786.725420148698 4 | 6 43.33488096291339 13691.617674953144 3964.5367114880632 4062.0734454384196 1602.774878237429 93.8297482822297 50.13483301956306 10067.076493450671 1698.0755930424216 10734.08590722648 5 | 1 764.8701711395394 142.48429062838403 6187.819756770998 4039.965562948613 6626.73686871292 2126.241028777791 1651.6927500629183 475.0293712088364 2842.2377710816054 1287.0324880934784 6 | 14 545.9978514337789 1534.2004936360559 20.36939774191265 50.68382969260949 387.17792447400865 609.5738685890569 946.8087976026717 1581.7869680082397 644.6912265328206 231.71997502752419 7 | 3 872.1771309126738 57.84016880159538 1.3619935660622646 45.12576765673575 822.3443106864761 885.021866611368 881.8597244203575 272.87170330947674 780.1202904055432 150.08586888641022 8 | 9 43.01933240528957 7956.676530210659 7985.174301960668 7964.133678395476 691.8521421226944 2.5829253068119677 23.511768804308563 6928.50948783398 548.3628820725986 7565.755607518581 9 | 16 1326.6779689845598 7.767194086562157 66.12856327417927 72.43717340066294 1351.7057850748809 1403.4607766848687 1400.375808300898 106.38180506545298 1184.8063259917315 24.787686964666257 10 | 4 308.2952562856642 95.97362340311504 238.9177848629895 244.05166023939407 121.6253394617145 264.359790994392 279.95910054373076 16.09369056372126 160.19341868855693 131.35501253533545 11 | 19 1.5721186041483224 780.9666599020936 96.73512984476407 62.90782241654291 709.8390369967542 53.87474289686676 10.112491738368933 961.0118021263442 359.8405218679082 528.6135014041248 12 | 5 58.346132235734785 12.435825834408307 45.01339095850543 55.60395517982876 59.41694123877286 72.20971513238658 33.869900725079184 40.15905294519732 31.02377754369241 14.23120710326926 13 | 18 214.4882762301673 1740.9856688628463 221.2299869066485 228.98872720398342 125.08331017174604 16.080463673449117 897.1350040097651 690.9334491317696 973.7512324696936 1728.865644101198 14 | 7 151.0157584125153 40.05962977286921 274.925729125931 162.3715719291326 84.62073234560971 5.751969141705713 318.7972510809515 334.4974934500842 115.45350559429119 191.3967296396611 15 | 13 7465.255107698691 8380.733693576027 788.1117301814837 4825.921510183631 8147.372227813013 8388.89689996344 235.2824962759233 59.35499252131507 7064.708640669465 4929.160485644139 16 | 8 2731.7521531832253 3249.7299784186866 3678.8540352835776 958.4502256970303 1554.978097179393 860.7952368339317 1295.4714104773043 560.5485467188321 2689.289115090674 969.0010775835127 17 | 10 183.31652123360638 58.89892686793545 251.14499184463483 244.48031727314037 28.005484914619853 148.00359261507447 81.73217631736262 6.766701992670198 91.39319390176716 202.75891752141172 18 | 22 2721.574736734439 9259.142558381265 528.1749834209555 753.0037874067848 10109.90451449749 3850.628151090511 7461.683748809407 10382.156012172201 5035.598863313816 1868.693373424551 19 | 11 2969.517334287702 4631.811618848077 5454.53812042745 4821.60623961819 4025.6968812055356 1585.2711732619377 976.6838262727204 1903.3017362829144 3177.7237169403343 5084.99240488873 20 | 12 2433.1280457101 2945.3027546563185 3072.221899944303 2918.7090807271784 2892.188820553952 985.745380097246 540.1307780678468 1747.3672703960376 2340.14570453566 2804.6522063603575 21 | 15 11541.455115079427 10990.960376165041 1366.3156755840057 9125.250392182972 4540.337542412742 4522.544011513778 10350.156523856873 1813.6984633072213 3060.776880040973 8329.452626798937 22 | 23 896.6105816237443 1021.4094185434573 4662.315462479739 1317.6281576663887 3484.087573433579 3239.2023292575423 492.61747359515664 4388.109551118391 3768.988194974737 1648.4315572732871 23 | 17 5.051842807108177 320.12759932406874 274.9011209481782 133.41538079954353 263.6475924363631 191.46721631873064 238.8761098154253 187.1745790686369 68.6144228036578 61.388209852644536 24 | 21 22336.22258860647 38.425384294966165 1053.878900728043 11942.805100300822 1407.880950544231 3786.124540475206 2999.897373944673 3882.6345818547097 21474.101247359362 21982.927780600716 25 | 20 301.65687315151365 24.020933468437764 308.988576067084 933.6483332683476 1014.2665973794287 626.439266679641 790.1972943203494 72.85203979843519 583.2605021865153 261.13880546371917 26 | 24 471.1845656341778 0.0532066644418412 741.1676161094402 3871.4061152043655 4210.723791895972 2761.0648586054954 4124.158296034583 3.3795183984922987 2735.2406831943163 369.8301781592947 27 | -------------------------------------------------------------------------------- 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/glad_module/syntren/data/nmse_samples/sampled_bionoise_0.10_expnoise_0.10_inputnoise_0.10_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 2890.2618874451027 2596.1379932232794 141.58937321565656 50.44665567242749 2450.7306257034456 19.107789153885548 1530.32214281012 1724.7007990106297 778.7629101761763 2617.039352929569 3 | 24 6.02386923163463 227.34525076785533 36.41343003945897 25.345757395287027 4.062805969832656 276.2992575308329 9.400813143332819 19.414453189342595 390.7974205029203 84.28861222406843 4 | 1 1.8454036418287225 11.359251101728944 5.879607720621911 6.915393801938203 0.09787635519970907 14.02776112951318 9.960850877254956 2.6203384467565414 11.211085689804603 6.8638142568707545 5 | 7 1322.9898213708013 341.01567114654875 969.279006848067 760.2398859552073 1533.89718879295 28.308175695614274 509.18134105096306 1176.6884487748223 245.45317024016504 764.7748576126864 6 | 2 12338.15132504592 4625.408040218683 12383.619787325606 8030.213965147662 11289.75571042512 6145.047320374894 7699.726500214349 11782.836424146324 4195.118857919515 1891.6873258013054 7 | 3 8.836043781203601 2478.4449551657735 6.742260795520104 753.492621162298 258.6756657485807 1570.6337280921007 466.8190863843373 85.33223040590053 3336.7963537623755 3663.0153912468063 8 | 4 1483.1204598044555 267.3857204638648 1485.6914574832394 1316.415525304372 1294.4994905740298 704.369326008137 1133.4800789116448 1436.733991284053 188.24040138705638 15.99923996905595 9 | 9 5387.663926744594 297.25233857783445 5387.663926744594 5264.149703142209 5382.843547833686 1778.9085463245128 5360.056167649291 5387.663926744594 96.94252188313428 7.277363056831678 10 | 15 4595.50812255413 470.85844545857066 4595.50812255413 4456.8948210845465 4587.257721392484 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317.8058495267507 97.61956477822503 87.41042580587963 25 | 16 95.27005558254702 6.099985184142629 94.97886272132325 286.7927590182687 292.52931817065445 176.0287485202872 279.77011613213193 25.682352279293827 228.79911599352081 85.46743053119403 26 | 21 107.5560199157949 326.7993661440694 2568.2818658186548 2568.318486928921 2568.0417906378434 2041.687618502201 2567.8965940077455 0.5857001896438958 2563.3650067623526 673.9810742776652 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.04_expnoise_0.04_inputnoise_0.04_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 1541.6720414793506 827.6275178459731 364.33169703174707 1711.4909796384807 1825.9607739124056 1458.7853420964827 405.8230525166357 75.77634284158397 1533.384169470232 1282.125722624553 3 | 9 220.00463626321962 3031.271653646543 7328.599111817674 87.99392210532028 13.435437796812122 362.9967417644214 7237.1514394238175 7756.584662396387 164.90293004795726 513.7155836758398 4 | 1 13.916445915779333 49.47688600953297 66.00330516706518 7.061420678202593 24.748270400720475 18.529864516660226 44.67731465901873 105.0408970007522 35.343726770951314 1.9553937857879602 5 | 16 22700.290484109355 7246.207681327395 344.99526547160536 23551.73038989293 20564.893040870502 21227.952755217495 579.1858589887948 2.041191311147061 17062.9773090914 23669.93948774306 6 | 2 2197.166854564683 671.3034292353076 848.5510228503663 1304.7994529970824 893.1615411241803 1466.156180728677 2022.9405757663455 1932.32011906036 862.7866879765434 588.813431816347 7 | 22 9.640772703730239 14.4421450055135 13.39733213194997 108.00918471644353 4.142424328792658 7.689235375543669 3.3226646224336194 26.718851533254558 145.42463418831778 292.1928404364781 8 | 3 1489.997354435428 1443.7666412482558 858.4276951401331 329.7327447994782 44.010336127064086 1786.463562118386 656.1410416399746 1540.6251483764818 1.0815933794041965 764.463384323417 9 | 12 214.57237084949745 146.4054733979145 1416.6231293394533 2472.7826164956787 2497.928225834602 2.5050749921627355 2149.6123747014194 80.12013430586819 2497.928225834602 1910.0482009597397 10 | 4 603.6552997995964 1152.9428303787065 479.87156546362627 1767.351741726765 854.7852023108007 100.59308715367774 1742.0999368820346 1204.1535883765503 850.4372222175965 1156.9169433973914 11 | 10 2462.2732897013425 404.7280737062043 2873.229529922083 2.8036080764696294 1128.8530404172436 3141.698744416624 7.9676181058806845 359.9833760270631 1275.4975916217445 449.31638394578374 12 | 5 867.1504921196896 1289.8418346735145 933.2736469666069 2356.139705031326 1443.2582328190945 626.5641356438421 3055.342689212518 2623.371819727223 2177.2549231191547 2818.3338527053106 13 | 19 3618.6074747075513 11014.11389908123 4505.852674131759 25752.3619138929 15022.342348526628 654.8875918994388 26815.385370642653 26431.315174359926 25193.27316507761 26566.103780767597 14 | 6 3189.24159158918 3516.4971725673195 5481.202440700546 920.6102685374777 4920.4941938616 4017.721782352831 5482.166638527166 1088.191640279981 3060.857978216817 862.9465888670562 15 | 7 12255.17375157362 14414.7285217148 16742.763593052674 15836.369533357547 3766.546077363101 16855.30060046125 5718.245714111435 14057.597579989131 21167.419150995338 4411.518160509327 16 | 11 23.340298281177546 26.177213873857077 29.07759819603765 28.450001498046674 3.013227862194497 30.273497582081887 5.775942025193746 23.981107139543454 33.90437773762687 4.479264188542041 17 | 21 2153.9293930476147 1359.8396932871694 24.97977801138571 507.72384423582673 886.8983716632567 1028.5232020417052 23.716923872951874 35.96834943211256 187.93345266929634 927.8979329417309 18 | 8 22175.154187530232 27001.52495933886 27986.680075625798 6216.0313348523905 10026.36038576922 23284.35395861524 5208.032914289885 8075.986851380078 3595.7191854958387 906.4024638758436 19 | 13 486.11534933398565 106.93198547923845 54.37323805926205 3022.4038284649514 2740.340997518443 405.54546301914104 3065.689981225805 2861.0294106450283 3101.504141711301 3110.7686852477777 20 | 18 6652.097658792185 6826.923909259728 6827.892485144582 75.62216942440652 636.5905126076524 6706.4035123762615 42.848218283327974 220.56519375387808 9.265219499527745 0.0015235013696948926 21 | 14 58.301128137566 666.8902846116046 2141.731058133148 2318.655378878386 3055.310007100443 1275.5256011166011 3457.805399287456 1728.2991763321527 2800.624816159326 3013.9170020073893 22 | 15 571.1983464414758 1753.3646906250644 932.0444782966398 598.84905323056 1773.3876509612855 1298.43503622527 667.1982630505939 673.0849237583518 240.84987047390567 213.76345280334644 23 | 17 944.3186945395087 7616.05476096837 1191.518430665146 7533.401455586231 2071.522694340575 1384.2832932711385 1348.3003645912106 7825.984123828243 5941.312264777815 1513.9043146232602 24 | 20 9900.814663651607 172.07477666902966 9603.036746083766 200.0614571403223 8544.31338854383 8972.99489038589 9187.217918373708 94.62340185758681 883.0125071927819 8744.203789341871 25 | 23 2125.126892880884 2808.424349755755 10843.957842136371 4332.841201728745 10957.372967579237 6259.347949045818 6978.3073351318835 2483.2136367718003 948.5917622580743 9867.22411461723 26 | 24 5732.765881062628 5350.790863415659 6.822768884982685 3353.367109087719 2.6082229710970726 986.0598407924474 614.3214852333903 5797.089962302056 6123.5070787812265 83.8105249932516 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 2190.1985412177382 951.1872606143891 449.85654936102594 2191.196918569283 2304.496129295427 1803.798462371252 429.79561144755996 101.68583239660639 1990.1359433480125 1618.333626295562 3 | 6 335.05159057643164 6880.568192012801 8716.81318892483 518.4795260398158 84.66457885886786 2196.3278657340916 10070.644736947095 10771.887442477762 629.0280362693711 2000.9380124953586 4 | 1 2812.6486070297065 8453.742716243753 12074.183448488866 1299.6623594285209 4468.009336944472 3182.658826516939 7156.600750674895 18738.657354433533 6875.619606307856 370.7742527230484 5 | 4 3781.8385040425505 1855.6615143957742 818.6166813077812 3794.3052710179895 3630.5707514056926 3742.6467484829054 1708.1433768775253 3.8466612083477707 3109.375945835006 3795.080395239392 6 | 2 966.1938446448156 302.33427606394565 396.8891373986123 550.6517232773467 399.70499066822316 637.7734713970523 896.1129968007976 874.1417319070749 352.5940282037025 279.5608386852185 7 | 20 66.78471264100072 471.6868419173618 23.904204864067438 684.6406869164985 492.29730301001473 30.815137090212232 53.77985870121661 31.789202498174475 558.7099558218928 975.4717010747293 8 | 3 1886.620768871545 1610.5470346214595 995.1539287767598 390.52286546234325 58.37442085745576 2135.215969530554 761.7754906910307 1741.2567962230075 1.2811787656258466 922.4472901974258 9 | 22 708.0510415165406 469.3125110892539 976.1984409711364 1567.0605478309053 6233.884046783891 207.7363281885125 4068.6892310680737 345.04331404260535 969.3477583493601 4812.602763034834 10 | 5 854.2901369040608 1688.2233217766945 761.7424779193026 2524.223817521085 1191.6843067799628 141.08919795719882 2485.9834221470132 1757.0273311026324 1240.5180586140352 1784.0737363042392 11 | 17 3106.1175186918276 1396.971373823998 329.7168418102721 3105.94017007711 3106.1175186918276 3037.2926100589502 88.40014871616168 8.306078558074683 3105.575090171552 3080.8186941843032 12 | 7 1054.558865481893 1707.232977707475 1207.495790770875 3383.0344595413508 2256.635259119296 870.3372462692934 4346.88000545358 3087.5036481466586 3185.2503260741278 3714.308158743119 13 | 10 5648.095291402466 4740.879918697715 6034.531544485441 363.0491008679371 2538.3363601569026 7976.497301236452 7.922808753934381 555.4156847286976 411.5159245786688 245.21944087407317 14 | 8 2705.7653363048053 4014.7212958145515 5423.127784963059 826.9104970778246 4535.485423556141 4336.546918767911 5529.737927076273 1188.958062782445 2459.3294365971965 841.5505122911461 15 | 19 207.78668838378357 259.2983345703648 763.538006538616 1769.3638452233824 1178.3528882707835 1120.6967684521871 657.017442358272 308.5664567686003 152.63398313452015 3677.009554775247 16 | 24 3255.321951220441 140.77948883489336 652.3272100252093 516.1202986045737 1121.0622921149277 1362.9254750725884 713.4806198530406 343.86985303936456 691.110369225316 6491.860176212782 17 | 9 241.1850331129427 269.2968337426572 343.7114128426325 354.10120621558934 74.1522254580186 297.94418262580893 107.14352197868234 272.6162764991646 424.76191124452095 103.87675253559144 18 | 18 1553.6807026294455 1003.6437356520252 687.2689704138264 701.0367693550662 4460.0293362613675 677.347284165194 3743.271474152805 1109.0563314757096 19.59365108203778 4740.792996499257 19 | 11 962.4676247470413 1321.8752125277067 1298.3771316606935 281.68519409410004 571.2417500988083 986.3145764423671 252.10942625059207 315.86677783993423 171.99133278160141 37.4744074536052 20 | 13 6.923266465940394 23.118415684059315 125.04153417429183 584.1420552842375 529.4036737871814 72.00448441012877 870.8888432556994 448.6189736733354 845.5260313845438 935.7472840569878 21 | 12 16.356596856700293 183.49107549600097 612.4350389544426 654.5581207139361 881.2084003924316 355.3203575358033 988.8328448492986 501.6150062718887 794.6220321998962 877.239577784673 22 | 21 20058.22259644925 20058.22259644925 19973.806328422546 5219.250659337608 4681.959656796095 20035.556057612976 591.5730031446426 10742.943070914365 1298.1195093867175 411.3648757546949 23 | 14 222.519953399882 830.360218619636 413.27758866655284 277.2995875439758 852.1804293818283 548.0145854254114 297.300740880538 327.034377187986 102.24778088289077 101.67353859713164 24 | 23 6376.263889286595 318.9729131462548 4213.894935781664 7571.077795923402 331.4176541451666 2023.6861352839824 5804.499557292897 6690.382987536911 8207.824399180272 8202.434056413726 25 | 15 1285.3311419110823 10145.323803608784 1562.4339488736314 10330.364390721666 2851.6328383860928 1834.0682296486625 1892.7840454261795 10709.573389391157 9433.141791352951 2373.873108726992 26 | 16 495.6685145229621 680.3232680162109 2665.194422931474 1078.4712475801869 2690.5618970004225 1757.5732141824994 1731.366783970329 609.0714153058875 184.98913233574712 2450.747485304361 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.08_expnoise_0.08_inputnoise_0.08_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 3928.9631985615038 1691.31124950144 804.155000332396 4001.5622608100593 4107.208002391531 3327.2422805398205 800.7824243863034 167.51465257593608 3567.732321567426 2870.2609093722117 3 | 17 1293.751130876448 2428.755892477712 9833.46324179098 6.217415830336937 1239.374308125522 2816.3423849199917 1020.4083056570923 252.3594567845137 900.4628796057449 106.11798966550768 4 | 1 2498.9534979713235 8810.56492899007 13265.525821382756 989.1976926091437 4489.333814873514 2643.158261146376 6638.452132292812 17457.119465530424 5217.165931120392 380.42444159369427 5 | 12 1617.7058997743757 1557.759636851555 123.39414358956707 5503.919600339283 344.5298654810651 1485.4693106341806 417.80602701617255 488.1281908860511 2428.026320146169 5515.952979634059 6 | 2 4018.7424440966493 1078.445704646912 1415.072414803939 2676.729868725641 1560.3002497850487 2621.3261090999313 3700.4053353439162 3563.1451233794232 1608.4716146591036 1068.6310342315755 7 | 20 101.36915828948864 953.5656086949156 851.2506079776012 36.83957861145443 23.479417963873622 415.7963748036019 51.21334319113843 213.66104504512455 128.04584560495977 1545.7715369039142 8 | 3 5470.36221841225 5443.280076162616 3143.0806892969003 1195.7837410375353 162.51141586319713 6838.438409482266 2375.9200843126887 5518.460684269802 3.808254870231105 3178.7147961342525 9 | 4 48.95019014199158 76.2075406991158 30.123757983909982 125.64991914646838 56.164358524947424 7.868631844624608 122.92393465784339 82.24746712298857 69.96071963629346 83.12861921005941 10 | 13 1186.444217764616 1306.6160409647248 768.5642211459952 227.47218697626118 83.11140824184636 1516.3109748938195 55.58449166436793 1599.1796513644606 323.55137371738357 425.1378821691055 11 | 5 661.0423112701822 1059.9557069213804 847.0325178303821 1977.1877500949706 1038.0180491711665 460.6691561872973 2410.0232050100913 2082.185694326162 1965.447222439749 2096.536250255486 12 | 8 11065.806691929 7849.671431465285 10099.753335196718 953.1330773883574 8991.461395121643 11672.197610322108 27.99386962255315 858.652538662576 1151.8024880492835 557.7778566517931 13 | 16 4458.837309498205 2847.9209434593845 5075.395889124815 43.44981340272595 1090.8555733187675 6377.512135952765 9.002779811143176 50.88234024741469 21.125130726476755 5.485664318942436 14 | 6 56.67410265633942 62.319632980614 97.0413966549193 16.647327238324365 92.29380053944622 65.29177086997106 89.01755378887857 20.477800204088876 46.485463708748746 16.058162506609097 15 | 7 6178.880256205799 8485.672721222185 10235.022144751261 7985.207864442463 1993.5869510593682 9442.313408119297 3350.3975050984 8097.435043185168 11614.590375341171 2621.590389765159 16 | 15 31.09696538860914 16.71651807865565 7.496436391142892 15.508771335217896 80.78128636745552 12.388870026859077 63.74142652040821 10.180183009312575 0.2003665694329062 72.8334634133769 17 | 21 794.020553695644 135.48516876613593 935.0844639209112 1207.6784712693286 15.612327476740992 474.86248302933893 352.8857628044972 1208.8176665538665 1965.4449300038902 535.5857202477994 18 | 9 3127.3344606933415 3625.9099860312963 3785.6515428216985 870.5476077037242 1364.472511454832 3375.0136455622737 697.2165327208531 1004.0802713659205 507.76944281726054 126.73369708699788 19 | 10 6.518803284313151 90.62173908901504 235.37982923590175 302.16518218087293 428.3567177161451 135.96397301246878 445.7879001596887 173.89928863826051 403.9871783662502 316.42415230379896 20 | 11 6269.533077450796 14234.715362496883 8160.684442895894 4440.695351325994 14453.12938882389 8584.364388129525 5853.367532212622 5667.993567019199 2521.0208578119455 2042.7230416009295 21 | 18 158.75995823298504 155.42102580897532 166.3006908655526 168.48356904606706 168.63919300929157 151.11410337976784 168.64806559283596 145.4475062082053 168.26161783111098 167.99014781468247 22 | 14 135.33687301344972 1287.5482194203212 218.15264329164967 1279.8718752465816 315.8838898596988 297.73034079805075 236.80738279570795 1298.816819923382 956.8599974782674 226.7420841442385 23 | 23 1796.9563476723542 154.52871366747593 366.4482048838958 169.76562078671907 420.4295680225087 823.0372071957422 881.2637110659384 183.74688651411444 693.9713270627207 472.3156307852274 24 | 19 859.1125506556804 5068.0978833378285 548.3865620198322 6972.774225933663 6926.859053215699 195.67164155883864 6972.774225933663 6972.774225933663 6972.774225933663 6972.774225933663 25 | 24 3861.7913045823893 3503.9911359528355 3642.408022838618 3862.05238724194 3862.05238724194 3838.8316102661215 3862.05238724194 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7.525661350475179 73.92861910373242 256.7432924079828 44.00676585345506 85.14348343912435 4 | 1 1191.3737530506994 4457.202712295356 6328.915466297216 524.0045711569928 2216.9586140680535 1425.6251645264256 3621.967193202018 8883.595566071532 2670.519843016075 180.9718450947143 5 | 24 641.2262182755571 1057.8969453586142 7.80934388070239 1660.5855035738384 147.54949053580577 56.963131211850374 475.9769520844203 190.50413938557395 1204.2793164880086 300.7769328343448 6 | 2 652.1395364829474 199.62790140993638 244.2020457696443 419.82751938401555 233.8509332230223 461.5887372153542 607.398948588518 550.5347124550302 296.74476826204875 163.62618806107488 7 | 7 18.411855053756902 578.8531914530527 2547.3004265819386 1319.7896520682805 5007.277873685518 42.83726400851945 312.485501572807 44.63696263628105 3670.7959152226545 4556.264610782852 8 | 3 3780.4895764372804 3559.442768969295 2053.826529993821 791.0336890677213 108.60771892310845 4470.618831871813 1703.5852500842323 3346.4130184801343 2.6788640406274333 1839.3352110142507 9 | 21 2791.181247473832 2252.843814960012 942.5812456608807 492.26406328152876 230.9400635378465 3386.1865463352656 2303.2576985374913 7834.809351126387 505.9058300064039 3680.4454909466367 10 | 4 880.35075822024 1399.00543102076 617.1297668282277 2265.8301077918377 1246.8866872700987 127.03725459825742 2225.165565790446 1489.6253917092865 1114.4440052964446 1430.0485213722775 11 | 9 625.0990408780029 253.62074133220503 861.7000330611247 2.3405736461226545 325.2641278849321 1067.505193396615 7.861636011062813 198.91232505387268 394.1682516139113 239.849053936037 12 | 5 85.79765537664537 162.22087983426078 117.31049278219669 254.51859094523897 169.21993320045183 72.81048419663396 357.5966498747381 294.54151804523474 245.9452619797962 340.43299706087294 13 | 6 405.8761660511544 371.9281465838967 621.92039727856 102.14127730630166 497.6982142925769 518.0523524582269 504.24543815439085 133.67294765014833 364.04085782089066 99.31227195146316 14 | 11 570.8408153861559 560.527944182846 399.0055584198347 497.51047143671786 122.17378707045958 570.781820742335 568.0037894446655 570.7693600001496 209.42705752187226 205.30725447839842 15 | 8 1386.0553086319217 1396.8764256740947 1702.5240137322746 1678.7944734443768 397.91805589357455 1665.6872184251931 576.3097590177653 1490.6586496279365 2171.117730371196 438.3677356658528 16 | 18 13.150639391160151 35.36034290462121 159.516643172474 179.00413843557448 1240.9358967364162 64.05290470174893 1189.5990853831358 114.91495245936656 67.11373467078722 1255.8493289547107 17 | 10 9189.711065434853 11232.684816382789 12470.672751129181 3129.8243856342797 4648.358783906965 8769.347961595588 2473.5427626646897 3426.545820973493 1647.7009730674833 389.7471285307176 18 | 16 0.015319767778250183 16.462227062758227 653.6326183207537 143.0891857774294 5494.149481402618 0.09588489118576436 3.5991398666078815 0.10866473888484954 3288.812527553182 4053.609186160547 19 | 12 15.511193847165915 232.01747290706126 610.8480043294836 692.3741572593749 946.4477673190177 327.7982460483056 1019.7813446493245 464.81522262192254 829.9120015702335 874.7588278852231 20 | 14 5807.0859312136345 18064.57534026095 9580.56793224328 5110.367272437156 16653.728690292708 13253.919593126553 7969.160488527923 6917.5963242857 2484.790847583386 2228.8674262443956 21 | 15 698.5779702602102 4256.7644253631 2234.2968361744156 567.8024463286871 4254.209129969762 3615.1014657007904 1594.907138623772 1249.2149517981236 47.03192757503446 34.97991519738086 22 | 17 653.6769076336016 653.6769076336016 647.5272473247607 653.62744459163 11.336617776013862 653.6769076336016 653.6769076336016 653.6769076336016 234.90856704334448 120.68736060698811 23 | 22 0.05039048527509047 0.6294418159243993 16.87126554126298 24.731466315542075 556.2428910343497 2.625631935601946 558.2219637611768 13.069947392192208 2.4633766088856133 553.1943813662358 24 | 19 1488.7418601253783 11578.463347984569 1701.6933352070948 11212.863794930872 3209.7992080256117 1972.7495316230666 1866.521877382025 11562.945378049344 9405.375920651468 2124.137588155818 25 | 20 2731.8985113017134 44.49619774457589 2725.283089674199 66.76448005756922 2552.8543050915164 2697.8443426473063 2680.348158967473 30.05135102767995 273.654450686337 2709.8312392896664 26 | 23 1302.471403682024 1763.5347541252297 6841.824484797913 2749.369020044202 6907.928966959255 4425.084782689794 4457.312906071191 1573.5892212907272 495.38952242173247 6238.446369921377 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.03_expnoise_0.03_inputnoise_0.03_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 1287.6104016058378 96.37790482462259 355.4323282065472 88.68984611297644 527.7158071331811 1084.7610480372277 1229.448788745044 138.37055990047628 1496.0905774336065 841.1566739362441 3 | 9 1135.872313155476 1364.4456789445373 5562.554361465737 728.6892183166049 4601.129050609678 1641.6413897693922 1505.307361928575 2004.3478829058893 760.1648103617168 1307.3283980751294 4 | 21 192.77114987439248 5546.0148698876 4786.178293591994 5546.184194968941 3927.0456348717894 654.573304533116 372.4788502962334 5533.048733461089 46.4805918789823 1613.8394203753821 5 | 1 39.75975892065867 205.21077849769776 182.77663032264405 244.4591877356753 220.49733424755402 257.2258881865124 310.83436789438366 95.88166465721854 321.3692107749975 73.95418339746547 6 | 11 7359.7800455983215 3173.388500028421 4194.1928237739885 584.9033715684028 190.31203530963387 1350.1567451237986 105.10033987419625 3707.99075151746 759.3217303452014 4112.122811841771 7 | 23 2919.5757856087525 283.4200241906919 731.5948344705183 13.491308166021785 27.83920853060578 16.906607720468777 3.202854108463485 1476.2624160600476 1.4679833413908194 1893.3273130607133 8 | 2 3773.479310817867 2483.1531974237714 2741.884035137665 1489.5345929122752 885.2490309677877 2154.97980283037 1021.507137484568 1667.4855783718135 2577.4622459117973 1696.3828780855943 9 | 10 37.05884082063446 1544.2209199815964 1272.8803053175077 4015.2524093844754 6298.984353171253 2278.3664669440846 5295.831559366263 3109.62808073012 1212.211262506906 3182.957648593392 10 | 3 11830.42339374032 14086.63263113471 11711.662585293721 12012.77806276986 1282.8444085915778 1199.9556781707413 1104.8984519601918 1509.0463819177683 2538.389343264294 2133.256828714619 11 | 7 474.53142347551875 207.54365761202303 535.7534462369609 414.35875986398855 5028.082284143804 4981.838553925703 5074.196954003872 5003.096145971018 4326.152834587217 4533.057342386033 12 | 18 891.0344880573418 345.3907141786657 882.2518201806878 777.3673208260833 6689.928470717664 6734.440649583886 6727.918293750075 6623.783246991352 6230.605428409432 6269.32813538006 13 | 24 9.132696311731905 2.8905250808582923 10.60076975961186 6.9711275288531045 1864.2039553534376 1866.8209914594283 1867.3188028031516 1854.3612288537431 1753.214106037985 1786.459086160627 14 | 4 59.83195938388595 139.07076998914812 3.7955087259539884 166.74892421959396 28.845553696223735 67.56643992146994 71.51746605926105 119.05406764716247 75.53442957415017 115.43367442386239 15 | 8 4364.23913891994 304.81805094090697 5713.795403008381 64.77244406208925 5562.5446565959255 3357.972114788264 3301.5658166176813 680.7344166612672 2498.4260362097625 964.0836533042768 16 | 5 277.71625977951646 57.92859249799929 592.5161290332445 707.1950616947521 1027.077260248183 594.2449318696708 832.9445153746905 450.73319221242696 684.449943026502 354.9985495448372 17 | 13 4.318848731210289 9.973314618560487 85.33259439583824 304.9729863627084 353.58887013095335 245.05676937044478 322.14883005891824 74.16081139441813 287.2753223316903 42.5248353504874 18 | 6 420.65267763701667 358.08459453920284 739.6181511558462 710.8306115865777 83.87583340061354 182.98001416247533 704.9848817183597 301.49060568501045 0.28059926460519874 76.91937772129403 19 | 12 642.9572937553082 829.6337864930381 105.86284662557786 144.82446954877403 1347.9336247686715 1202.1832823243692 126.32246825148854 896.1343051051443 1379.0102064604716 1351.7731586532323 20 | 14 3793.6662394652744 3813.3330909377364 3788.5969878047254 3802.220394603988 45.44829696465909 40.74214546742323 29.43375682675686 46.84472835968144 272.6293443468923 111.6660471294048 21 | 22 13329.431505556693 64.32567649802061 16859.923660851135 1.8502919468704162 16700.736912243534 12012.491504232285 10506.676202003957 470.3862879128701 7453.196493160632 1067.7735262629365 22 | 19 1675.3030860812928 1055.0474611242748 2246.8602407773997 2246.114860090279 19.76572156395839 160.12821519234163 2246.6203584555556 634.3934965195654 4.5175270177704564E-4 18.108829991589285 23 | 15 1675.859024805089 1648.6825117578508 570.8110855468249 1111.6771614906372 2708.945239178837 348.16131382819134 1690.6730100005911 612.071568540194 452.606373714301 1544.355953842098 24 | 17 2280.5289725748567 2362.690803902378 5740.306825669346 4369.986006372951 314.31216505192583 5971.864829288638 2424.3566588540575 5696.457570997462 5797.822318039305 2544.254760543711 25 | 16 1510.0324396675633 1091.2913441650867 1086.0068882179912 1842.2845193976132 902.5532959447205 1402.349533780614 826.1147780624954 1614.6709542031372 52.94522236382313 1558.6942845576061 26 | 20 1540.701523835147 2302.1042088209656 141.96756044180935 142.63835977646102 3863.107822519337 44.08289367191324 3336.464047367286 36.52707953243385 285.67944346004566 1089.178089075357 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.02_expnoise_0.02_inputnoise_0.02_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 293.6501222172601 119.7604025074077 188.33570832103845 277.05994841090995 113.02659895815233 215.70953492897672 285.6096630233948 174.57517713611415 200.3233115259963 59.57600446809441 3 | 22 0.33826164508741674 202.29920807431355 39.454175144322505 83.97449557304778 2243.836240738973 10.168521434591172 174.58358764719202 643.4383490214656 3.8782958779847614 310.39192516132175 4 | 1 411.2514197839601 967.3857840068617 3420.140615245427 84.93947051363804 2699.5408354859314 3319.268803968359 207.92438947481043 413.3298109403342 3428.6491844941374 1473.2372316244027 5 | 21 2939.0091281244363 2583.5897502083562 32.39659932806451 2953.7254683672477 224.43415131026404 44.24897440481161 2952.906973122122 2939.1390821965247 29.630182953767612 1610.4634320905425 6 | 2 1235.9605258428703 151.83745527362325 902.3879885217726 510.3987639009736 291.31399953863973 1114.1917218616054 726.927510293512 249.83623659997153 1149.6629349800596 409.2931222656846 7 | 10 2.6941621969916687 1963.4744712278498 156.87812318369177 1147.150165365631 1871.8324429511545 32.18077871024477 353.0774461122155 1919.7504036600126 23.595413590860737 1595.3446730309674 8 | 3 526.0832270425874 313.8027315844063 230.90563553178023 443.142925611658 455.7396814401694 612.8219728330974 138.36845834960067 286.385421728342 227.24739919878465 579.3809118354944 9 | 9 154.38747053171807 536.4104291228847 765.0303361327914 251.23355472256262 248.1100542031172 55.43150937056336 978.931497114721 604.6330095743863 738.5069058868568 105.47123527510666 10 | 23 434.60133910210277 204.5225049762232 67.65274168933026 380.4153454296221 385.5643422190723 455.5596711323097 6.649979467285658 149.80574348407436 65.45428301109355 444.39544079079616 11 | 4 260.7223103152335 359.77841853089313 91.38499938533904 326.32319657681575 556.2554522695078 315.73493104548913 771.550582497677 213.53032467110668 592.9221818492498 702.0564698468708 12 | 7 1022.5415102250518 1714.246489294048 60.189243279030975 1381.5156896203605 3020.127398126045 1462.5349098213132 3775.5305095217836 492.7156103284233 3257.5648425506047 3558.1712625319806 13 | 5 6038.574876980642 14564.152668703204 4983.229939926906 14160.553163045815 20733.853151344054 19249.33967472523 22027.62595120785 18725.126386726468 6495.577604254875 10790.521234426304 14 | 14 2191.5210855359073 541.6083048071432 2274.095793319321 644.4023466478056 362.4105935858375 48.46906346546821 257.13329304185754 17.278809457098845 2106.308607569188 1129.3534105188908 15 | 6 8132.454668930606 5178.034250493024 6641.449539236509 3685.2903031248197 1826.3423387698056 7595.6009440509515 10680.673893226953 4877.071657961403 7985.144770872336 12425.381531118674 16 | 15 2565.480263322896 3762.371784274133 3172.709253287278 4288.279150612574 4514.8266572008515 2720.4618107999695 1314.1035376628643 3968.9352199194973 2338.573050344497 733.1629048085451 17 | 16 4089.9705231056914 1978.0731797041512 5491.572968450682 2991.3929725168678 319.52630897135583 2936.92587241687 6.543201429016864 5272.121839537421 209.73274953362917 83.12260438420677 18 | 8 74.7501991434137 66.97974482463016 88.42391300685298 97.92315309549213 46.60669567689017 103.45683399070477 106.29531733734214 0.03420838092588532 36.16256104105393 92.05942130169603 19 | 12 946.7617335559858 1661.497412676762 469.6930513921325 271.19330401767394 5349.405233700758 128.90455100516675 183.54610635732527 8624.466284586713 6966.311302344462 368.3242084242096 20 | 20 997.2338200163684 319.58431993555354 95.51908717924029 880.9697908118849 877.5335698262204 1030.2677060366573 15.797562078140613 208.23613707058908 102.0006131293303 1021.3314520522159 21 | 11 19652.64898860872 12317.42147850729 9811.912869586136 15318.62878455387 233.6444336261135 21396.579086632133 785.541631220742 11258.64711003661 7879.358278036426 18691.414662918276 22 | 19 1754.7552115363733 3267.195140225857 4113.748909579094 2600.448101330946 1942.9381025902708 1568.7404530087701 6004.37705207704 171.26106464089318 567.5376864733954 1997.240963702117 23 | 13 142.38102840795597 3818.008369561095 64.68455289164713 3518.0146725548993 473.69254282817576 6381.982757543581 2303.656407031257 7806.290449818101 917.4927098620491 3085.0224074510475 24 | 17 6910.315153228818 7792.24775201889 8100.8255018838445 14308.168503537376 17841.75025818826 16527.650703614723 21903.700311940505 14042.453736581545 1436.1392809543695 13534.716568191838 25 | 18 185.45912040427584 690.7800734940244 217.93335336231016 292.65064574721936 268.7285428059458 452.1893993215543 903.8058659021145 965.976370285298 529.3575901443265 315.7919540930904 26 | 24 377.6165600202035 352.538317814521 3563.3969444774307 361.4324699181604 3474.043205776957 2988.515439240475 120.07356830356525 99.20206767508337 2809.2196960471088 1715.1893558012691 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.05_expnoise_0.05_inputnoise_0.05_burnin_10_experiments_10_samples_1_unnormalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 1793.154159815084 420.5952840588154 75.83222114545357 528.8402735253202 1433.6241838236685 2071.575366426481 1964.4828734509156 927.9875973526018 1262.4345195247818 626.989378789617 3 | 21 726.8455163554463 2819.422479673382 705.5132618159746 1561.4655720522533 967.9370620029152 602.9323744057248 115.26309445759493 171.08607101910172 997.5288593217534 1196.2371666277986 4 | 22 30.414766857747065 557.1370089854065 584.099583769164 535.6428908345891 71.87534153729878 8.748081270379126 13.695482759351354 283.4243751976314 110.05834394061581 447.17358426381236 5 | 1 2075.9312743050064 378.10906596480766 14869.919686136907 11385.91456460215 16387.037323856475 5031.700433343958 4235.620775147834 1367.471770022732 7258.312036148252 2930.058928421034 6 | 7 1951.9931118478787 1954.6109432931426 156.82165691219924 547.2669692501473 31.82444635478579 1867.9694157625431 1869.7269591193344 1953.8882830952798 1367.1949023435309 1941.439667969198 7 | 2 3120.8375074495175 1029.2087875405637 2453.9064205840004 2168.071206712344 1330.7165516910788 2426.2180733393884 2989.7273133130393 200.20652455920288 1716.3913759159288 1030.790786567276 8 | 16 100.04092307419542 1343.064375277826 61.43006995054092 174.65490203048375 1313.427583280666 1365.9056622662622 705.2396909494889 12736.412305183869 1242.4194086357504 1549.179769017022 9 | 3 512.1509173668862 130.67428499928914 506.5457501339992 536.7567825815089 503.04837861165265 645.3411211793098 325.18886240845757 355.91806981581294 276.56171212758454 145.31817324170916 10 | 12 22.681996641634278 200.12458747191886 32.222526078983044 24.095908171136386 38.405229290512835 3.5184374411856107 103.29797538028603 87.25132876046902 163.17612970798342 199.23934106056427 11 | 4 99.92398912013306 36.29591146191182 244.6464005795526 172.8837581314097 88.24988722672006 6.094636171216966 246.67284147050833 308.1426112331126 114.84574121027687 184.84513723483187 12 | 15 233.88674790495187 677.4737492868971 14.099430973360924 51.491416875561974 124.88987039133126 453.5094687275946 152.1707388910577 59.33192103014171 157.2522449666714 59.78562222613498 13 | 23 3642.3341259600297 4814.9704287751065 635.8816990045167 235.81287110903284 510.49000620304315 2566.907948435836 83.03663124481065 274.76475858294515 1044.1746860343853 1386.5894622813064 14 | 5 44.10988900474561 53.53671465659033 54.57823754239759 12.011920426818401 25.995704257535966 12.992159811860859 19.233787683012174 9.261711916722538 39.06632873257673 15.908509174052368 15 | 11 2538.762067499886 1942.9828661305366 1700.194756853129 2983.536925574105 2888.329968242537 2981.571433827054 2959.0757332078533 2987.4901031288837 2591.844664382495 2976.5114229886767 16 | 14 5428.580492314123 4267.7259717459 3934.9744308594863 5890.080276719083 5704.621129624153 5883.6311841696515 5821.426336913034 5901.611413768137 4952.225095654312 5870.46580533524 17 | 20 1047.06696060253 105.31057754686455 607.487592070157 177.96062081386162 82.97256917146045 102.50670194977013 30.986891432242263 137.75772485937688 1552.2163822876983 293.5787674450241 18 | 6 1104.8552829442217 427.6147820188439 1923.4985463373796 1639.5838751572708 249.49399942414897 1050.142326761684 852.0937182093273 56.44508264963414 701.2367181892909 1559.8883602497497 19 | 18 631.4677263713554 90.11919121457156 1051.2939808921094 951.6666054004166 24.12192154934456 561.1672499688304 340.62956893264356 0.73472438867736 312.94559694770743 872.0802097203037 20 | 9 1.5083494668091213 5.814366816572673E-4 2735.068814242027 2646.1956295267228 2735.3470416406453 49.1658789972041 46.33993050418034 0.47419764207305876 761.0899622286437 6.641281819270262 21 | 13 237.0076767076808 626.5576451627288 62.879849227377946 16.896956337192982 1.0948402371465866 107.88812249015139 105.05444761433534 99.6743079420669 152.68634642703122 102.09886264715459 22 | 8 2440.2932182560817 3433.2905348644176 3839.1315976407755 3402.62303255647 3102.6747155159405 1188.7785787378161 704.5680331397998 1335.9847073488434 2506.685895893321 3215.07104641025 23 | 10 1530.6465302147474 1356.9001479214905 210.99828179859747 1129.518644755364 553.0875378447788 660.413131673198 1683.9372296887268 224.89219119921066 551.7376851085685 1345.5528672682808 24 | 17 57.77864834272499 3184.7743013485942 2382.8615222713174 1615.357489299818 2895.5206863635512 2311.8799036310475 2508.1242616825625 2111.580104986952 665.7815322050318 643.8309751256235 25 | 19 4396.651892250427 321.1295607172262 4126.0684196319635 14113.473363930447 14402.080891238753 8317.004011848414 12535.408385616369 1310.2872842590698 9823.227240174516 4131.926965553058 26 | 24 3327.495503317307 3340.652684130652 3337.700659494077 3340.5927244735026 3340.652684130652 3340.652684130652 3340.652684130652 3340.652684130652 3256.175870333362 3340.369764044684 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.08_expnoise_0.08_inputnoise_0.08_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.9405338827742606 0.40487412482147594 0.19250244570676542 0.9579129913206079 0.9832029710192134 0.7964910697541154 0.19169510244870414 0.04010045364296383 0.8540607186984092 0.6870966973310956 3 | 17 0.11669841564691463 0.21907765557139802 0.8869940696104173 5.608208251975296E-4 0.11179353949917568 0.25403857542274333 0.09204245680733422 0.02276322553652029 0.08122318805446065 0.009572011934946898 4 | 1 0.14209368712574572 0.5009799731917018 0.754294738638507 0.05624704403392072 0.25526925371421527 0.15029335411325576 0.37747086572480065 0.9926341060230127 0.2966547173052789 0.02163141956130199 5 | 12 0.2915822637688264 0.28077729170934684 0.022241090749981016 0.9920501228884515 0.0620995436358051 0.26774737263073756 0.07530715390889417 0.08798232294744524 0.4376378988401136 0.9942190708882388 6 | 2 0.9877747091333663 0.26507331759679015 0.34781346711802247 0.6579187654568381 0.38350930591636573 0.644300965035323 0.9095299971671905 0.875792536298561 0.39534944162770586 0.26266095020833263 7 | 20 0.024095444611395313 0.22666250460543014 0.20234223328919332 0.00875676626814301 0.005581056650787851 0.09883478059556941 0.01217340949702267 0.05078722131435001 0.030436492050990156 0.36742982853791595 8 | 3 0.7966873780863474 0.7927432149687387 0.457748977762677 0.17415040821227104 0.023667682073661465 0.9959299492669951 0.3460219756824743 0.8036922964307036 5.546229786095564E-4 0.46293862371539757 9 | 4 0.3868521134002545 0.6022662647677591 0.23806729669138801 0.9930081298843497 0.4438655034867147 0.06218559825471037 0.9714647434856063 0.6499996503803478 0.5528977960615878 0.6569633730159824 10 | 13 0.15481347451089622 0.17049412532393676 0.10028629722225856 0.02968176598031501 0.010844813172637895 0.1978562219339744 0.00725295648513159 0.20866936218991566 0.04221868301767831 0.05547422432451937 11 | 5 0.2725602883108087 0.4370398507775672 0.3492475796668312 0.8152320267896184 0.4279945382169037 0.18994263436034067 0.993698297966906 0.8585246715474193 0.8103911844591487 0.864441678060968 12 | 8 0.922989203773208 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0.3947819605584661 0.06736236272870076 0.4649180379982457 0.6004500417437648 0.007762349754627556 0.2360986011929381 0.1754525530160637 0.601016442381375 0.9772066972639749 0.26628980786760753 18 | 9 0.788998817508759 0.9147850117501136 0.955086393325621 0.2196314598570432 0.3442443434151876 0.8514860846812622 0.1759015630654197 0.2533205695613214 0.1281057382845706 0.031973790586500246 19 | 10 0.014543534826743929 0.20217827736460953 0.5251354573381738 0.674134447506952 0.955669402904965 0.30333739038386376 0.9945585974215496 0.38797157244222846 0.9013006439795688 0.7059463951193772 20 | 11 0.3769245771913156 0.8557916519725024 0.490620672262403 0.26697478058678087 0.8689227118949797 0.516092327367947 0.35190468811143016 0.340759997974279 0.15156387392530077 0.12280859025110252 21 | 18 0.9413274171531661 0.9215300534493438 0.9860383030179788 0.9989811325733267 0.9999038658933638 0.8959932354047383 0.9999564736557005 0.8623945664524173 0.9976651284231995 0.9960555149397786 22 | 14 0.10067855097449428 0.9578209260690227 0.1622860904705277 0.9521104113291352 0.23498941270869084 0.22148479291161985 0.17616355121168575 0.96620375881 0.7118184119858928 0.16867586762042405 23 | 23 0.8733139567931804 0.0751003676555142 0.1780924351231176 0.08250544661741153 0.2043271724870701 0.39999295527415796 0.42829081490273596 0.08930028863412746 0.3372674279612846 0.22954360183017802 24 | 19 0.12320951364008476 0.7268405924343927 0.07864678678207478 0.9999995 0.9934145815338884 0.02806222278003232 0.9999995 0.9999995 0.9999995 0.9999995 25 | 24 0.999931897983551 0.9072868600986584 0.9431270828088916 0.9999995 0.9999995 0.9939869545870641 0.9999995 0.9990697380613525 0.9997995937754964 0.8975866220148744 26 | 22 0.1791256782317242 0.248997972179091 0.9843560017673398 0.40091282811157497 0.9935951863315797 0.655094233185392 0.6377567823517961 0.22361338705477674 0.06568068662658633 0.9117520556383399 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.10_expnoise_0.10_inputnoise_0.10_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.937602316872392 0.8421883871285775 0.04593165932408294 0.016364918847801193 0.7950181686549059 0.006198575796475308 0.49643722352379577 0.559493751098618 0.2526310546622873 0.8489687980565724 3 | 24 0.0021750626564859855 0.0820884627570874 0.013147943427699574 0.009151694416162265 0.0014669736685391788 0.0997644825877206 0.0033943893571403575 0.00701005458526122 0.14110679413873917 0.030434427735054 4 | 1 0.12788232227225807 0.7871705556640644 0.40744359245868106 0.47922123852680526 0.006782611302359674 0.9720922994088017 0.6902645649537791 0.18158356151201815 0.7769030258251539 0.4756468920502613 5 | 7 0.8623515284708622 0.22228091289549018 0.6317956643113728 0.49553973663886586 0.9998252170241169 0.018451841567489063 0.3318946983803546 0.7669893342668245 0.1599913416607459 0.4984957229036332 6 | 2 0.9887619485011434 0.3706736403188392 0.9924057265822043 0.6435299582476911 0.9047450108414281 0.49245537698922637 0.6170451615245929 0.9442597999231302 0.3361908755106757 0.15159713938808048 7 | 3 0.0023866657741580515 0.6694421048718588 0.0018211230590941643 0.20352265046877518 0.06986977128417876 0.4242371196201868 0.12609049522448904 0.02304872166608825 0.9012877086237402 0.9894013294841406 8 | 4 0.9955776487689527 0.17948862153038128 0.9973034882360797 0.8836732477268029 0.8689616211809391 0.4728236016274841 0.7608737540024813 0.9644396984029209 0.126360562945075 0.010739846251317369 9 | 9 0.9999995 0.05517274165452165 0.9999995 0.9770741350320508 0.9991047937662295 0.3301816299342032 0.994875248438135 0.9999995 0.01799341509232726 0.0013507448714506905 10 | 15 0.9999995 0.10246053264892294 0.9999995 0.9698367348679708 0.9982041823079361 0.6445728063085856 0.9920210055507254 0.999945530667305 0.04180621572572451 0.002991566072969994 11 | 10 0.042582551431781905 0.2601727252880867 0.06007621655091559 0.03406923883904542 0.09684381242598464 0.6662892280236882 0.03286466870504767 0.037551634695036834 0.8726678881763513 0.4214530318718739 12 | 11 0.05477146656630534 0.6976991439890151 0.0666200990137219 0.15034952852562575 0.09261946699155113 0.18676099281593941 0.12668988109049303 0.049829418431458196 0.31166809097802506 0.501789258854903 13 | 5 0.7534694946573985 0.6172900564956938 0.7217564056560658 0.7411070227570925 0.6195084228519877 0.658248333779047 0.40049679160569474 0.6102358489532231 3.348752465838792E-4 0.33026636992267255 14 | 6 0.23829226694645383 0.45413218898748187 0.16964124593238458 0.22809731495512678 0.4507625779781059 0.2965204801061746 0.6014093915597803 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0.34224608066580137 0.5110710405476148 0.5233294651420919 25 | 21 0.7211037937674275 0.3141890529260205 0.11227970097240751 0.3343986972458947 0.392017084712542 0.39680441127502414 0.0641540938859563 0.49299799004723893 0.7614924544996696 0.09130385316633746 26 | 23 0.021846670028809995 0.32972172446150555 0.14005593612243203 0.46926132145721683 0.4759773826071356 0.5386023932043479 0.7082249655183772 0.09178645588531961 0.7173454736409814 0.5516110253751698 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.07_expnoise_0.07_inputnoise_0.07_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.933459844916271 0.4053948060278478 0.19172829170438455 0.9338853520792985 0.9821733322250439 0.7687766205930847 0.1831783453679363 0.0433383730071993 0.8481934190345386 0.6897317423034758 3 | 6 0.03090245518208636 0.6346080906977829 0.8039685125443995 0.047820367868451584 0.007808777595101559 0.20257156015659109 0.9288350104615579 0.9935119792893871 0.058016470435635015 0.1845503766953922 4 | 1 0.14894912420794038 0.44768392707026233 0.6394112103742902 0.06882607721386756 0.23661188106735676 0.16854371487343156 0.3789913220779576 0.99234102503942 0.36411143439208954 0.019635051489874336 5 | 4 0.9965102762690596 0.4889647631425112 0.2157045929847298 0.999795255622249 0.9566513902247128 0.9861832918887208 0.45009389654789855 0.0010135909873852675 0.8193171335836471 0.9999995 6 | 2 0.9875065516231077 0.3090032916770742 0.40564388359693165 0.5627982287538082 0.4085218501329038 0.6518417447458754 0.9158798311954791 0.8934239149730218 0.3603716944008658 0.2857275083142245 7 | 20 0.04748409372188046 0.3353704960801372 0.01699594801304218 0.48678119973518086 0.3500245842310515 0.021909637696725246 0.03823761082338308 0.022602200579664496 0.3972441425061572 0.6935627607752561 8 | 3 0.8800590884478416 0.7512779349074586 0.4642131974134936 0.1821686703907103 0.027230135730569306 0.9960222270362794 0.3553482793155353 0.8122506091959422 5.976362792241598E-4 0.4302974581573697 9 | 22 0.10782879444942682 0.0714714043529944 0.1486648488050682 0.2386469898270992 0.9493555719690558 0.03163607779453355 0.6196189667848782 0.05254650075277089 0.14762156123828546 0.732909244777806 10 | 5 0.33601084782700497 0.6640148647007204 0.2996098453847101 0.9928319997982493 0.4687152958529827 0.05549344300829088 0.9777912225308831 0.6910769745259469 0.48792267007809687 0.7017149125634842 11 | 17 0.9999995 0.4497481717712533 0.10615074122846282 0.9999424034719466 0.9999995 0.9778416538121941 0.02845999997878957 0.002674101786249733 0.9998248677635193 0.9918546659086077 12 | 7 0.24121056701398308 0.39049753224502337 0.27619202103596713 0.7738056991640021 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0.8260562744706306 0.17298419217545263 0.6950517456024342 0.2499471254144402 0.6359661638312739 0.9908953592381923 0.2423263228067495 18 | 18 0.28559037248848285 0.18448513122773597 0.12633059091747834 0.12886132379027568 0.8198218831557654 0.12450683262102442 0.688070781093932 0.20386158512544775 0.003601613961902395 0.8714305554993599 19 | 11 0.693276350096246 0.9521617133508938 0.935235779097229 0.20290104125739905 0.411471913806118 0.7104535799657723 0.1815972801181814 0.22752242384785168 0.12388730838645698 0.026993240866967496 20 | 13 0.006978119057647984 0.02330158139387023 0.126032230149132 0.5887701749464477 0.5335981047988444 0.07257497127552603 0.8777888391430182 0.45217335273937925 0.8522250793564574 0.9431611491691251 21 | 12 0.016436833684271075 0.18439118582545005 0.6154393218776818 0.6577690372344632 0.8855311435092096 0.357063371593664 0.9936835366627365 0.5040756646299669 0.7985200283136448 0.8815428519533545 22 | 21 0.9999995 0.9999995 0.9957909403725128 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-------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.06_expnoise_0.06_inputnoise_0.06_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.9237809542473192 0.4101450489156298 0.1938646372336072 0.9497086571647944 0.9834005226642237 0.7709218000494539 0.19744285156320998 0.0405239520948965 0.8580044343278248 0.700863349233625 3 | 13 0.03842594857900855 0.23766169006472834 0.01934956605686601 0.07479034496340276 0.04614601314797228 0.007152754923978302 0.07026535870947073 0.2440213242991124 0.04182617267607795 0.08092451175409429 4 | 1 0.133113585768479 0.49800848307372136 0.7071371428043843 0.05854764489075754 0.24770338431852104 0.15928677052516416 0.4046866395942807 0.9925745445446451 0.29838031202171167 0.020220196359043 5 | 24 0.3156973215937036 0.5208383915898372 0.0038448037155624547 0.8175623217114107 0.07264359696635476 0.02804487315183968 0.23433952734729283 0.09379162118211193 0.5929073762585617 0.14808264133106097 6 | 2 0.9855644247943732 0.3016933444751152 0.369057288062249 0.6344762807758019 0.35341387478598607 0.697589109132486 0.9179489386808605 0.8320112443056356 0.44846397202076876 0.2472847310982288 7 | 7 0.002364857002890551 0.0743491092807606 0.327180571141809 0.16951653116557094 0.6431451969630768 0.00550210739110439 0.040136288523813395 0.005733264430442808 0.4714846712050887 0.5852161143118286 8 | 3 0.8424188193987906 0.7931622385210991 0.4576608626118279 0.17626861626665175 0.02420141214594316 0.9962025716988336 0.37961545617422804 0.7456921245912995 5.969400091725596E-4 0.4098650626095588 9 | 21 0.2620236767169908 0.21148695377600646 0.08848533351822777 0.0462115600315851 0.021679625643876897 0.3178801268184908 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0.15444687403635318 0.9567853395844785 0.778255496323922 0.175763495991769 25 | 20 0.9702894728024334 0.01580373211987492 0.9679398708912451 0.023712766740416058 0.906698271396769 0.9581944402769881 0.9519803138119932 0.010673350208663059 0.09719395928802887 0.9624518310898375 26 | 23 0.1873419985215912 0.2536594080807636 0.9840992047061612 0.3954576549107681 0.9936073948778082 0.6364855492973067 0.6411211067658262 0.22633844300554753 0.07125474149741969 0.897311839098281 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.01_expnoise_0.01_inputnoise_0.01_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.8616053322875203 0.19702079568399133 0.032371467077895504 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-------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.09_expnoise_0.09_inputnoise_0.09_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.8484033809914465 0.18461974712529192 0.03752236763261734 0.22425575669003606 0.6429589363715238 0.9544827002236023 0.8698152065332359 0.3747844496759428 0.5502358108655615 0.2665403573837453 3 | 2 0.10833150826539745 0.9027214195599258 0.9965855133935511 0.9357854132642853 0.24067331885715051 0.031841630661533435 0.051087183775783626 0.6661108361594099 0.2451948185905132 0.7093782269904911 4 | 6 0.0030482172957598586 0.9630815840809852 0.2788693335508443 0.2857301614288267 0.11274072978841003 0.006600074933065142 0.003526532477637396 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0.07574643476609487 0.8436109448130943 0.017649436503885833 10 | 4 0.9900810096523519 0.3082164257204929 0.7672773318375953 0.7837646193078578 0.39059614586474534 0.8489835748127378 0.8990802916242935 0.05168440667668688 0.5144563806322944 0.4218427004059281 11 | 19 0.001635271888981666 0.8123387267347109 0.10062105880225926 0.06543488088200111 0.738353849283177 0.05603893520049527 0.010518718768214447 0.999616428970493 0.37429560872467504 0.5498483363159673 12 | 5 0.7852777298989699 0.16737316950517717 0.6058330194730654 0.7483709035005601 0.799689695711266 0.9718670116248489 0.4558533313885001 0.5404987224222977 0.417547499189337 0.19153711787827543 13 | 18 0.12085501623280662 0.980971337779392 0.12465368330945696 0.1290254032983589 0.07047912243482046 0.00906065698528102 0.5054973977051791 0.38931159631775064 0.5486673820836482 0.974142219586754 14 | 7 0.4383086556773077 0.11626920698377799 0.7979453800783212 0.47126780781422956 0.2456035040761657 0.01669453498430036 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0.13430632069033055 0.17382673480868333 0.9614020645219797 0.9841823836564978 25 | 20 0.2886901078846292 0.022988390093832682 0.29570665646700134 0.8935153217018322 0.9706681978175036 0.5995116822353302 0.7562305468601225 0.06972048410268428 0.5581889633142715 0.24991371532356546 26 | 24 0.11027575475200509 1.2452456016388481E-5 0.1734624268819308 0.9060615785049861 0.9854752852071599 0.6461979729581228 0.9652155481787452 7.909404706209526E-4 0.6401540983450333 0.08655483434966418 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.04_expnoise_0.04_inputnoise_0.04_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.8305289218535915 0.44585915265960074 0.19627262050008396 0.9220137096845273 0.983680829710845 0.7858762335889727 0.21862482629350413 0.04082219994197891 0.8260640828871998 0.6907062367624766 3 | 9 0.028360627045163397 0.39075887809377763 0.9447240281114208 0.011343228258537185 0.001731951867087004 0.046793628473689265 0.932935579581602 0.9998953135299681 0.021257508828811184 0.06622267750070418 4 | 1 0.1314767938248747 0.46743704393636515 0.6235717795856892 0.0667133660589344 0.23381136711840972 0.17506245426480832 0.42209268972996306 0.9923827133541804 0.33391283275840916 0.018473747332929412 5 | 16 0.9587481803861662 0.3060440320812695 0.014570896493341569 0.9947087977601866 0.8685595365650804 0.8965639048379251 0.024461950775459103 8.620984197326584E-5 0.7206559078401886 0.9997013663596165 6 | 2 0.9875253734782744 0.3017199937708815 0.3813846290347862 0.5864472989196273 0.4014350037379563 0.6589697213680332 0.9092186801642855 0.8684889558070151 0.3877829052927925 0.26464453664747584 7 | 22 0.03147569581932712 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0.29642495923820295 0.9099134480801425 0.48368705583355703 0.3107743698832521 0.9203044186396145 0.6738264476505542 0.3462443810642855 0.349299279894468 0.12498970538436051 0.1109331341356426 23 | 17 0.11630148189645574 0.9379867835160387 0.14674638974596524 0.9278072731927038 0.2551269614030371 0.170487145179782 0.16605551849204517 0.9638415041049262 0.73172693159506 0.1864511591883527 24 | 20 0.9571769860861965 0.01663560239322473 0.9283888328540172 0.01934125918755051 0.8260350703712662 0.8674785355671647 0.8881888871135306 0.009147867693734157 0.08536663689041094 0.8453597924151858 25 | 23 0.19273586180650437 0.2547067138352226 0.9834798887064403 0.39296189130327275 0.993765938926477 0.5676841346968646 0.63288930307781 0.22521211412654368 0.0860314042487666 0.8948961823124744 26 | 24 0.9355246029607478 0.8731904637094747 0.001113401154807536 0.5472327840343297 4.2563342199776016E-4 0.1609141660747361 0.10025053795790023 0.946021588501552 0.9992892867870373 0.01367695973373851 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.02_expnoise_0.02_inputnoise_0.02_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.850325279008142 0.34679126610713595 0.5453653910172628 0.8022849646906802 0.3272921310869564 0.6246320249698076 0.827042449579184 0.5055188980862504 0.5800780005776737 0.17251476770727212 3 | 22 1.1661004583954684E-4 0.06973926920018776 0.013601167140755752 0.028948803153828785 0.7735250232741849 0.0035054277297730816 0.06018477250978439 0.22181461145240133 0.0013369776522970977 0.10700242592367548 4 | 1 0.11236785446395768 0.2643226497427994 0.9344985680585803 0.023208347988041073 0.7376062358173651 0.9069369634930525 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0.10722273971564514 0.007205139665493055 0.8783155884580448 0.4709322754379033 15 | 6 0.4891509344416978 0.3114484365804452 0.3994699485477873 0.22166286427016485 0.10985085044896538 0.4568602532299548 0.6424212403296448 0.29334613667691595 0.48029053776921454 0.7473619262781009 16 | 15 0.5638775866541863 0.826947356462779 0.6973429741306361 0.9425387257569169 0.9923325453016681 0.5979416261311894 0.28883228689169677 0.8723487938653186 0.5140047057445648 0.16114492685761203 17 | 16 0.7446632121936284 0.36014888606924395 0.999853750431665 0.5446445853982241 0.058176333123864644 0.5347277301323486 0.001191324330245234 0.9598981611058091 0.03818615857683972 0.01513417889859057 18 | 8 0.6032259470328455 0.540519228936608 0.7135713252291535 0.7902291557664064 0.37611094632436015 0.834885356510272 0.8577916072565371 2.7605789973732405E-4 0.29182791993999535 0.7429094803007535 19 | 12 0.10977621442698794 0.19264920600332255 0.05446050816907038 0.0314446319890759 0.6202589682051828 0.014946372598529835 0.021282014275167134 0.9999995 0.807737845950967 0.04270687740067565 20 | 20 0.9666652639419394 0.30978799032020027 0.09259110728725371 0.8539651165722275 0.850634227266833 0.9986865507333423 0.015313313898293495 0.2018530021377402 0.09887395276193633 0.9900242228588421 21 | 11 0.7107252396745842 0.4454515183962849 0.3548414328238826 0.5539882242184588 0.008449598635980512 0.7737933348515673 0.02840860102102959 0.40716163354519935 0.28495185579742077 0.6759628268870885 22 | 19 0.29174760881731515 0.5432076015126217 0.6839565996180891 0.43235347610255814 0.3230351115552239 0.2608205822734572 0.9982945973685317 0.02847406052080703 0.09435946498620333 0.33206356736328435 23 | 13 0.01796275903205414 0.48167909089888017 0.008160588876768451 0.44383195247944557 0.059760946365155276 0.8051495322256993 0.290628782463815 0.98483987545403 0.11575067721590808 0.38920574414417 24 | 17 0.3053564806066073 0.34432776173159974 0.3579633505004045 0.6322565442076891 0.7884002315229853 0.730332139655454 0.9678917229109084 0.6205150064798582 0.0634608446603519 0.5980788611847767 25 | 18 0.11590621011359534 0.4317161656226493 0.13620160060346087 0.18289759577159367 0.1679470218455114 0.28260437887688894 0.5648506925234789 0.6037053335257518 0.33083210971100996 0.1973601972418969 26 | 24 0.10405208298328866 0.0971417839780727 0.9818925169736338 0.09959256381861879 0.9572711321656389 0.8234841619871015 0.0330862208282224 0.027335087680713282 0.7740792959814675 0.4726196996560175 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.05_expnoise_0.05_inputnoise_0.05_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.8162601316349556 0.19145881019303662 0.03451951884646784 0.2407329168654184 0.6525988067437768 0.9430000047878686 0.8942505250318363 0.4224284199015854 0.5746717098735539 0.2854112849490358 3 | 21 0.24084266404179094 0.9342249567262689 0.23377415099782867 0.5173967778851881 0.32072914449067647 0.19978363492842258 0.03819280728876679 0.056689935065284594 0.33053448433206367 0.39637713868149116 4 | 22 0.052063369043478344 0.9536956124718277 0.9998495905014699 0.9169024254392236 0.12303472353033267 0.014974784641036258 0.023443644216101733 0.48515998530736687 0.18839559756266344 0.765462495665711 5 | 1 0.12137343399399608 0.02210689550682512 0.8693993090525542 0.6657000484424395 0.9581005968755484 0.29418833271756234 0.24764395861231736 0.07995194574689046 0.42437159058755586 0.1713116991632056 6 | 7 0.9986601899212701 0.9999995 0.08023160774755342 0.2799875328103013 0.016281721204806407 0.955672783981588 0.9565719616333095 0.9996297794481878 0.6994712801733429 0.9932609371246096 7 | 2 0.9863999523200624 0.3253009797959893 0.7756037187082248 0.6852600719591133 0.4205982336492509 0.7668522908185276 0.9449600859584881 0.06327907358456329 0.5424980849931115 0.3258010006271907 8 | 16 0.0060236692650036385 0.08086866204025595 0.003698830567903303 0.010516330047884334 0.07908416997860038 0.0822439828007744 0.04246392896317512 0.7668855203610108 0.07480862207251635 0.09327929285171004 9 | 3 0.7514382367665368 0.19172796724730143 0.7432142214627725 0.7875404623879462 0.7380828068719695 0.946857611239094 0.47712370923916 0.5222102269986795 0.4057769658731524 0.21321377775187866 10 | 12 0.11226766340646559 0.9905441827116125 0.15948982662756786 0.11926601307508747 0.19009196690916913 0.017414990249962018 0.511287542882192 0.43186245742876644 0.8076627069240986 0.9861625337892548 11 | 4 0.30882456171782674 0.11217595542445691 0.7561028948112876 0.5343136448333207 0.27274464304736484 0.018836050892072183 0.7623658025024938 0.9523439536250368 0.35494165121633087 0.5712814209567705 12 | 15 0.025567001787865928 0.07405709265019718 0.0015412595203996115 0.005628712011730743 0.013652161006057182 0.049574751462551976 0.01663433088081422 0.0064857857259685995 0.017189808589936347 0.006535381435828274 13 | 23 0.7538256615187955 0.9965171077511037 0.13160350638436613 0.0488043620961214 0.10565215966377434 0.5312530414209266 0.01718544793358658 0.05686593232196889 0.21610474114139214 0.28697167324914086 14 | 5 0.2970419899271786 0.36052351557824674 0.3675372723011803 0.08088990534628619 0.17505860695741385 0.08749097064270259 0.12952294136546408 0.06236962730918558 0.2630779693103612 0.10713006376703633 15 | 11 0.8493100119632113 0.6499997862748572 0.5687781646685506 0.99810368777386 0.9662534316567578 0.9974461579326153 0.9899205122619955 0.999426172192279 0.8670681081513286 0.9957533967555474 16 | 14 0.9187235154415957 0.7222625165556658 0.6659482248463324 0.9968269358301036 0.9654401525207119 0.9957355026300598 0.9852080625322258 0.9987784453972446 0.8381059570875299 0.9935074168271513 17 | 20 0.0949310385826999 0.009547854030763171 0.055077115610770416 0.01613458087815163 0.0075226059666604825 0.009293644097202005 0.0028093884123890057 0.012489634747108326 0.14072978980337458 0.026616957986476387 18 | 6 0.4931582319524372 0.19086820972171267 0.858564154888251 0.7318372799518087 0.11136301879362723 0.4687368030536502 0.38033671741161834 0.025194573073610466 0.31300086577928665 0.696264748568695 19 | 18 0.5714610442079868 0.08155540649811809 0.9513923372496658 0.8612322836910417 0.021829680126494718 0.5078410332785918 0.30826045579388917 6.649055032017942E-4 0.28320721731843207 0.7892087694546847 20 | 9 5.514286449479877E-4 2.1256403011669928E-7 0.9998977844753265 0.9674071575363016 0.9999995 0.017974265665674075 0.016941143711848964 1.733591378919937E-4 0.27824242046711756 0.0024279473125450113 21 | 13 0.08465886933593884 0.22380566971544846 0.02246060977244122 0.006035573356089215 3.910756726021162E-4 0.038537513179620536 0.037525327775397854 0.035603548076736664 0.05453938720934698 0.03646959622685214 22 | 8 0.6222968417453403 0.8755201385869247 0.9790132802007769 0.8676996481279576 0.7912101144175198 0.3031492894168369 0.17967122087523382 0.3406881836099148 0.6392275750275465 0.8198721953576443 23 | 10 0.6934607950254159 0.6147448393691041 0.09559296242256782 0.5117294436869056 0.2505768093033729 0.29920076666627143 0.7629092850777299 0.10188761064393644 0.2499652573002541 0.6096039436052418 24 | 17 0.018009429603579156 0.9926844990074484 0.7427307157805142 0.5035020344469935 0.9025250237328796 0.7206059603437422 0.7817747320752004 0.6581731200686526 0.20752208609115572 0.2006801639655973 25 | 19 0.2940947567849796 0.02148055437867735 0.2759952614144169 0.9440589380461223 0.9633640735515412 0.5563295280109792 0.8385011983473756 0.08764592458680684 0.6570817287433679 0.27638714316444013 26 | 24 0.9960609959174727 0.9999995 0.999115833411556 0.9999815515232086 0.9999995 0.9999995 0.9999995 0.9999995 0.9747119949683698 0.9999148099794386 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/nmse_samples/sampled_bionoise_0.03_expnoise_0.03_inputnoise_0.03_burnin_10_experiments_10_samples_1_normalized_dataset.txt: -------------------------------------------------------------------------------- 1 | GENE exp_0_smpl_0 exp_1_smpl_0 exp_2_smpl_0 exp_3_smpl_0 exp_4_smpl_0 exp_5_smpl_0 exp_6_smpl_0 exp_7_smpl_0 exp_8_smpl_0 exp_9_smpl_0 2 | 0 0.814480200431289 0.06096401142830781 0.224829337784464 0.05610091651032662 0.33380755221342734 0.6861674888021584 0.7776899709945068 0.08752653847854615 0.946354698480064 0.5320751180071878 3 | 9 0.18381518215312775 0.22080459934488111 0.9001733129219643 0.11792182963399074 0.7445884231462029 0.26566244074766043 0.24359986921476384 0.32435826362478837 0.12301544061309544 0.21156146236064116 4 | 21 0.03471629513902975 0.9987858099820466 0.8619462940207825 0.9988163038578267 0.7072244751015917 0.11788257758024784 0.0670799842629785 0.9964507291398719 0.008370723248572022 0.29063750286938017 5 | 1 0.1177216013836449 0.6075927551306497 0.5411692173492605 0.7238003407066946 0.6528535381777626 0.7616002786906483 0.920325488557909 0.2838886203837779 0.9515172917251703 0.21896523356548497 6 | 11 0.9985982423590162 0.43057539203850936 0.5690813524298755 0.07936154004322366 0.025822139083304285 0.18319353896943624 0.014260346643440391 0.5031119169605293 0.10302717480819518 0.557945833547771 7 | 23 0.9969322813256843 0.09677795407215069 0.249813863688661 0.004606806473158746 0.009506109006494382 0.005773010958416012 0.0010936618493833938 0.5040916099978007 5.012645976496563E-4 0.6465045801550529 8 | 2 0.9877603211011697 0.6499996416037293 0.7177260114307468 0.38990625006698065 0.2317261590851866 0.564095723516477 0.26739360018120817 0.4364873780021133 0.6746863374470643 0.444051645271954 9 | 10 0.004842025383754288 0.20176445693120515 0.16631169816289906 0.524623913157987 0.8230112290221421 0.29768627464179176 0.6919415251720625 0.4062970607690095 0.1583848165122069 0.4158781383502596 10 | 3 0.7742973340000162 0.9219654891723019 0.7665244780080198 0.7862323873258961 0.08396174612270088 0.07853670588132974 0.0723152415784368 0.09876659114495813 0.16613675062412034 0.1396209603187187 11 | 7 0.09045508840868471 0.03956193198009754 0.10212521857811586 0.07898498687799481 0.9584520751170723 0.9496371041830317 0.9672424445905597 0.9536892222782497 0.8246504188405074 0.8640905162064996 12 | 18 0.12951448326630385 0.05020355605915758 0.12823789666164348 0.11299262622752938 0.9724007775081466 0.9788707536114117 0.9779227100094892 0.9627863747142381 0.9056368224938387 0.9112652818309263 13 | 24 0.004845787777720685 0.0015337059976498852 0.005624744191873459 0.0036988643247508244 0.9891423555194452 0.990530948892464 0.990795086453014 0.983919827347738 0.9302513952919625 0.9478911057181069 14 | 4 0.3333792060754681 0.7748919367626941 0.021148290959226664 0.9291125435754723 0.16072526938912665 0.3764751535854265 0.39848997594063373 0.663360367284838 0.42087219643312396 0.6431878068189368 15 | 8 0.7638070054980527 0.05334770971522559 0.9999995 0.011336144735207665 0.9735283612701381 0.5876952531471805 0.577823308845896 0.11913868598368858 0.4372618567478447 0.16872903267674738 16 | 5 0.2637382436860335 0.05501293030791828 0.5626936044397969 0.671600516523184 0.9753824027544324 0.5643353931637286 0.7910207481090312 0.4280468870619319 0.6500002052747277 0.33713076087938276 17 | 13 0.012119958351186625 0.027988050826304397 0.23946832928006603 0.8558437961121214 0.9922742485656249 0.6877012887677175 0.9040442595227068 0.20811702407985763 0.8061789515392659 0.11933718113682197 18 | 6 0.5085826967192075 0.4329358599768627 0.8942222737540791 0.8594172070460927 0.10140859623212631 0.22122875710961915 0.8523495305074074 0.36451189645606297 3.392535891890408E-4 0.09299801625229585 19 | 12 0.46624526001656624 0.6016151061024984 0.07676723000178101 0.10502054042679092 0.9774640858254566 0.8717721417873986 0.09160367667944114 0.6498384514050214 0.9999995 0.9802483523572096 20 | 14 0.9944461846264192 0.9996015209622696 0.9931173650480647 0.9966885133932206 0.011913511274161051 0.01067987233354624 0.007715567297695617 0.012279562419732638 0.07146522498014575 0.029271387494489414 21 | 22 0.7905981728607682 0.003815298670927377 0.9999995 1.0974492286823528E-4 0.9905577805583006 0.712487537881302 0.6231742895172427 0.02789965495585048 0.44206562950630396 0.06333201821402965 22 | 19 0.7455366130645518 0.4695129599676378 0.9998886696119308 0.9995569633090314 0.008796061579543617 0.07125961159053054 0.9997819181499132 0.28231523159888994 2.0103716278194467E-7 0.008058718383949861 23 | 15 0.5629216956290536 0.553793094368804 0.19173566475691523 0.3734127891880315 0.9099357552358317 0.1169475201861916 0.5678977189948887 0.20559507697794163 0.15203065626203485 0.5187497631507091 24 | 17 0.37529862896851957 0.38881971246343633 0.944662097013383 0.7191532212597449 0.05172524709428694 0.9827687829314702 0.3989678452413361 0.937445979455767 0.9541272192242725 0.418699053974567 25 | 16 0.7759519664176474 0.5607758099725852 0.5580603160031319 0.9466844936404686 0.46379003946379826 0.720617550826589 0.42451100366121586 0.8297219775670605 0.027206666775100974 0.8009575578473601 26 | 20 0.22736840358649918 0.33973209654940895 0.02095080525290493 0.02104979819312542 0.5700965728276393 0.006505515185515422 0.492377330935138 0.005390468973051531 0.04215902820379126 0.1607350155129433 27 | -------------------------------------------------------------------------------- /glad_module/syntren/data/samples/sampleIniFile3_generateDataPredefinedExternals.ini: -------------------------------------------------------------------------------- 1 | # EXAMPLE INI FILE 2: 2 | # 3 | # GOAL: generate a dataset using the complete ecoli network 4 | # using a predefined set of external inputs, with the values specified in the externalsFile 5 | # with all noise levels = 0.1, 10 different experiments and 1 sample per experiment 6 | # 7 | # This setting can for example be used to generate a concentration series experiment 8 | # where the concentration of e.g. a toxic agent is increased over a series of microarray experiments 9 | # In SynTReN, you could assume that the expression level of one or more external genes is linked to the 10 | # concentration of the toxic agent and thus specify this concentration series as a series of increasing 11 | # expression values in the externalsFile. 12 | # 13 | # results are saved in ./data/samples/sample3 14 | # 15 | 16 | 17 | 18 | 19 | ####################################### 20 | # Tasks to be performed 21 | ####################################### 22 | 23 | # IF TRUE 24 | # new gene network will created starting form the topology in the SIF file 25 | # and saved in xml file 26 | createGeneNetwork = true 27 | 28 | # Disregarded if createGeneNetwork = false 29 | # IF TRUE 30 | # select a subnetwork from the SIF file and use this to create the new gene network 31 | # ELSE 32 | # use the complete network specified in the SIF file to create the new gene network 33 | selectSubnetwork = false 34 | 35 | # Disregarded if createGeneNetwork = false 36 | # IF TRUE 37 | # !! only possible if selectSubnetwork = false 38 | # use a fixed set of external inputs, specified by the file 39 | # ELSE 40 | # choose 'nrExternals' inputs from the network (complete or selected subnetwork) 41 | fixedExternals = true 42 | 43 | # IF TRUE 44 | # create expression data file in the output folder 45 | generateExpressionData = true 46 | 47 | ####################################### 48 | # random seed 49 | ####################################### 50 | 51 | randomSeed = 13 52 | 53 | # IF TRUE 54 | # will use the information from the sif file to set edge types 55 | # ac --> Activator; re --> Repressor; everything else --> unknown 56 | # unknown interactions will be assigned a type according to "percentActivators" 57 | # ELSE 58 | # all interactions will be assigned a type according to "percentActivators" 59 | useEdgeTypesFromSIF = false 60 | 61 | # the desired percentage of activators (value between 0 and 1) 62 | percentActivators = 0.2 63 | 64 | # category of desired interactions, values: (LINEARLIKE, SIGMOIDAL, STEP, STEEP, LINEAR, MIXED, DEFAULT, RANDOM) 65 | interactionCategory = SIGMOIDAL 66 | # nr of external nodes (other top nodes are fixed to a random value) 67 | # IF -1 THEN all top nodes are external nodes 68 | nrExternals = -1 69 | # nr of correlated nodes among the external nodes (must be < nrExternals) 70 | # IF -1 THEN half the external nodes will be independent, half correlated 71 | nrCorrelatedExternals = 0 72 | 73 | # the noise on the correlated inputs (compared to the inputs from which they depend) 74 | #correlationNoise = 0.1 75 | 76 | # probability of selecting a complex interaction (synergistic or antagonistic) for 2-input genes 77 | higherOrderProbability = 0 78 | 79 | 80 | 81 | ####################################### 82 | # expression data 83 | ####################################### 84 | 85 | # this group of parameters will be disregarded if createExpressionData = false 86 | 87 | # externalInputValues is one of the following: 88 | # RANDOMIZED: randomize the external input value for each experiment (uniform distribution) 89 | # FROM_EXTERNALS_FILE: specify the values of the external inputs in the externalsFile 90 | # (tab-delimited with header, rows=externals, cols=experiments, 91 | # first column are external-names, other columns are experiments) 92 | # when using this setting, 'fixedExternals' must be true 93 | # FIXED: external input values are kept at a fixed value, as specified in the genenetwork.xml file if applicable 94 | externalInputValues = FROM_EXTERNALS_FILE 95 | 96 | ## the different noise levels: 97 | bioNoise = 0.0001 98 | inputNoise = 0.0001 99 | expNoise = 0.0001 100 | 101 | # number of burnIn cycles before actual sampling. 102 | # only required to be >0 if there are feedback cycles in the network 103 | burnIn = 0 104 | # the number of different experiments (in every experiment the external nodes are randomized) 105 | # disregarded if randomizeInputs = false, only 1 experiment will be performed 106 | nrExperiments = 10 107 | # the number of samples to be taken in each experiment 108 | nrSamplesPerExp = 1 109 | 110 | 111 | ####################################### 112 | # files & directories 113 | # 114 | # WARNING: existing files will be overwritten without any warning!! 115 | ####################################### 116 | 117 | # used to save generated expression data 118 | outputdir = ./data/samples/sample3 119 | 120 | # this file is an OUTPUT file for gene network generation 121 | # and an INPUT file for expression data generation 122 | # IF generateNetworkFile = true 123 | # GeneNetworkXMLFile = full path for gene network OUTPUT file (in xml format) 124 | # IF generateExpressionData = true 125 | # this file (possibly generated during the same run of the program) is an INPUT file for exression data generation 126 | GeneNetworkXMLFile = ./data/samples/sample3/genenetwork.xml 127 | externalsFile = ./data/samples/externalsFile.txt 128 | NetworkSIFFile = ./data/sourceNetworks/EColi_full.sif 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | -------------------------------------------------------------------------------- /glad_module/syntren/data/samples/sampleIniFile.ini: -------------------------------------------------------------------------------- 1 | # 2 | # an example settings file with all available parameters 3 | # 4 | # 5 | 6 | 7 | 8 | 9 | 10 | ####################################### 11 | # Tasks to be performed 12 | ####################################### 13 | 14 | # IF TRUE 15 | # new gene network will created starting form the topology in the SIF file 16 | # and saved in xml file 17 | createGeneNetwork = true 18 | 19 | # Disregarded if createGeneNetwork = false 20 | # IF TRUE 21 | # select a subnetwork from the SIF file and use this to create the new gene network 22 | # ELSE 23 | # use the complete network specified in the SIF file to create the new gene network 24 | selectSubnetwork = false 25 | 26 | # Disregarded if createGeneNetwork = false 27 | # IF TRUE 28 | # !! only possible if selectSubnetwork = false 29 | # use a fixed set of external inputs, specified by the file 30 | # ELSE 31 | # choose 'nrExternals' inputs from the network (complete or selected subnetwork) 32 | fixedExternals = false 33 | 34 | # IF TRUE 35 | # create expression data file in the output folder 36 | generateExpressionData = true 37 | 38 | 39 | 40 | 41 | ####################################### 42 | # random seed 43 | ####################################### 44 | 45 | randomSeed = 13 46 | 47 | ####################################### 48 | # gene network topology 49 | ####################################### 50 | 51 | # this group of parameters will be disregarded if selectSubnetwork = false 52 | # (if selectSubnetwork = false, all nodes in the SIF file will become nodes in the genenetwork 53 | # and there will be no background nodes) 54 | 55 | # method of subnetwork selection, values: (clusterAddition, neighborAddition) 56 | subnetworkSelection = clusterAddition 57 | # nr of nodes in the foreground network 58 | nrNodes = 100 59 | # nr of nodes in the background network 60 | nrBackgroundNodes = 1 61 | 62 | ####################################### 63 | # gene network interaction types 64 | ####################################### 65 | 66 | # this group of parameters will be disregarded if createGeneNetwork = false 67 | 68 | # IF TRUE 69 | # will use the information from the sif file to set edge types 70 | # ac --> Activator; re --> Repressor; everything else --> unknown 71 | # unknown interactions will be assigned a type according to "percentActivators" 72 | # ELSE 73 | # all interactions will be assigned a type according to "percentActivators" 74 | useEdgeTypesFromSIF = false 75 | 76 | # the desired percentage of activators (value between 0 and 1) 77 | percentActivators = 0.2 78 | 79 | # category of desired interactions, values: (LINEARLIKE, SIGMOIDAL, STEP, STEEP, LINEAR, MIXED, DEFAULT, RANDOM) 80 | interactionCategory = SIGMOIDAL 81 | # nr of external nodes (other top nodes are fixed to a random value) 82 | # IF -1 THEN all top nodes are external nodes 83 | nrExternals = -1 84 | # nr of correlated nodes among the external nodes (must be < nrExternals) 85 | # IF -1 THEN half the external nodes will be independent, half correlated 86 | nrCorrelatedExternals = 0 87 | 88 | # the noise on the correlated inputs (compared to the inputs from which they depend) 89 | correlationNoise = 0.05 90 | 91 | # probability of selecting a complex interaction (synergistic or antagonistic) for 2-input genes 92 | higherOrderProbability = 0 93 | 94 | ####################################### 95 | # expression data 96 | ####################################### 97 | 98 | # this group of parameters will be disregarded if createExpressionData = false 99 | 100 | # externalInputValues is one of the following: 101 | # RANDOMIZED: randomize the external input value for each experiment (uniform distribution) 102 | # FROM_EXTERNALS_FILE: specify the values of the external inputs in the externalsFile (tab-delimited with header, rows=externals, cols=experiments, first column are external-names, other columns are experiments) 103 | # FIXED: external input values are fixed as specified in the genenetwork.xml file if applicable 104 | externalInputValues = RANDOMIZED 105 | 106 | 107 | ## the different noise levels: 108 | bioNoise = 0.05 109 | inputNoise = 0.05 110 | expNoise = 0.05 111 | 112 | # number of burnIn cycles before actual sampling. 113 | # only required to be > 0 if there are feedback cycles in the network 114 | burnIn = 10 115 | # the number of different experiments (in every experiment the external nodes are randomized) 116 | # disregarded if randomizeInputs = false, only 1 experiment will be performed 117 | nrExperiments = 20 118 | # the number of samples to be taken in each experiment 119 | nrSamplesPerExp = 1 120 | 121 | ####################################### 122 | # files & directories 123 | # 124 | # WARNING: existing files will be overwritten without any warning!! 125 | ####################################### 126 | 127 | # full path for source network file in sif-format 128 | NetworkSIFFile = ./data/sourceNetworks/ecoli_sub_30.sif 129 | 130 | # file with names of external inputs (one gene-name per line) 131 | # only genes without an incoming edge qualify (a single self-loop is allowed, but will be ignored) 132 | externalsFile = 133 | 134 | # used to save generated expression data 135 | outputdir = ./data/samples/sample0 136 | 137 | # this file is an OUTPUT file for gene network generation 138 | # and an INPUT file for expression data generation 139 | # IF generateNetworkFile = true 140 | # GeneNetworkXMLFile = full path for gene network OUTPUT file (in xml format) 141 | # IF generateExpressionData = true 142 | # this file (possible generated during the same run of the program) is an INPUT file for exression data generation 143 | GeneNetworkXMLFile = ./data/samples/genenetwork.xml 144 | -------------------------------------------------------------------------------- /glad_module/syntren/data/samples/sampleIniFile1.ini: -------------------------------------------------------------------------------- 1 | # 2 | # an example settings file with all available parameters 3 | # 4 | # 5 | 6 | 7 | 8 | 9 | 10 | ####################################### 11 | # Tasks to be performed 12 | ####################################### 13 | 14 | # IF TRUE 15 | # new gene network will created starting form the topology in the SIF file 16 | # and saved in xml file 17 | createGeneNetwork = true 18 | 19 | # Disregarded if createGeneNetwork = false 20 | # IF TRUE 21 | # select a subnetwork from the SIF file and use this to create the new gene network 22 | # ELSE 23 | # use the complete network specified in the SIF file to create the new gene network 24 | selectSubnetwork = false 25 | 26 | # Disregarded if createGeneNetwork = false 27 | # IF TRUE 28 | # !! only possible if selectSubnetwork = false 29 | # use a fixed set of external inputs, specified by the file 30 | # ELSE 31 | # choose 'nrExternals' inputs from the network (complete or selected subnetwork) 32 | fixedExternals = false 33 | 34 | # IF TRUE 35 | # create expression data file in the output folder 36 | generateExpressionData = true 37 | 38 | 39 | 40 | 41 | ####################################### 42 | # random seed 43 | ####################################### 44 | 45 | randomSeed = 13 46 | 47 | ####################################### 48 | # gene network topology 49 | ####################################### 50 | 51 | # this group of parameters will be disregarded if selectSubnetwork = false 52 | # (if selectSubnetwork = false, all nodes in the SIF file will become nodes in the genenetwork 53 | # and there will be no background nodes) 54 | 55 | # method of subnetwork selection, values: (clusterAddition, neighborAddition) 56 | subnetworkSelection = clusterAddition 57 | # nr of nodes in the foreground network 58 | nrNodes = 100 59 | # nr of nodes in the background network 60 | nrBackgroundNodes = 100 61 | 62 | ####################################### 63 | # gene network interaction types 64 | ####################################### 65 | 66 | # this group of parameters will be disregarded if createGeneNetwork = false 67 | 68 | # IF TRUE 69 | # will use the information from the sif file to set edge types 70 | # ac --> Activator; re --> Repressor; everything else --> unknown 71 | # unknown interactions will be assigned a type according to "percentActivators" 72 | # ELSE 73 | # all interactions will be assigned a type according to "percentActivators" 74 | useEdgeTypesFromSIF = false 75 | 76 | # the desired percentage of activators (value between 0 and 1) 77 | percentActivators = 0.2 78 | 79 | # category of desired interactions, values: (LINEARLIKE, SIGMOIDAL, STEP, STEEP, LINEAR, MIXED, DEFAULT, RANDOM) 80 | interactionCategory = SIGMOIDAL 81 | # nr of external nodes (other top nodes are fixed to a random value) 82 | # IF -1 THEN all top nodes are external nodes 83 | nrExternals = -1 84 | # nr of correlated nodes among the external nodes (must be < nrExternals) 85 | # IF -1 THEN half the external nodes will be independent, half correlated 86 | nrCorrelatedExternals = 0 87 | 88 | # the noise on the correlated inputs (compared to the inputs from which they depend) 89 | correlationNoise = 0.1 90 | 91 | # probability of selecting a complex interaction (synergistic or antagonistic) for 2-input genes 92 | higherOrderProbability = 0 93 | 94 | ####################################### 95 | # expression data 96 | ####################################### 97 | 98 | # this group of parameters will be disregarded if createExpressionData = false 99 | 100 | # externalInputValues is one of the following: 101 | # RANDOMIZED: randomize the external input value for each experiment (uniform distribution) 102 | # FROM_EXTERNALS_FILE: specify the values of the external inputs in the externalsFile (tab-delimited with header, rows=externals, cols=experiments, first column are external-names, other columns are experiments) 103 | # FIXED: external input values are fixed as specified in the genenetwork.xml file if applicable 104 | externalInputValues = RANDOMIZED 105 | 106 | 107 | ## the different noise levels: 108 | bioNoise = 0.1 109 | inputNoise = 0.1 110 | expNoise = 0.1 111 | 112 | # number of burnIn cycles before actual sampling. 113 | # only required to be > 0 if there are feedback cycles in the network 114 | burnIn = 1000 115 | # the number of different experiments (in every experiment the external nodes are randomized) 116 | # disregarded if randomizeInputs = false, only 1 experiment will be performed 117 | nrExperiments = 10 118 | # the number of samples to be taken in each experiment 119 | nrSamplesPerExp = 1 120 | 121 | ####################################### 122 | # files & directories 123 | # 124 | # WARNING: existing files will be overwritten without any warning!! 125 | ####################################### 126 | 127 | # full path for source network file in sif-format 128 | NetworkSIFFile = ./data/sourceNetworks/test.sif 129 | 130 | # file with names of external inputs (one gene-name per line) 131 | # only genes without an incoming edge qualify (a single self-loop is allowed, but will be ignored) 132 | externalsFile = 133 | 134 | # used to save generated expression data 135 | outputdir = ./data/samples/sample0 136 | 137 | # this file is an OUTPUT file for gene network generation 138 | # and an INPUT file for expression data generation 139 | # IF generateNetworkFile = true 140 | # GeneNetworkXMLFile = full path for gene network OUTPUT file (in xml format) 141 | # IF generateExpressionData = true 142 | # this file (possible generated during the same run of the program) is an INPUT file for exression data generation 143 | GeneNetworkXMLFile = ./data/samples/genenetwork.xml 144 | 145 | 146 | 147 | 148 | --------------------------------------------------------------------------------