These code files were used to train Gaussian process (GP) regressions (and linear regressions) on the various datasets generated by Teresa Spix. If any other code is requested or there are any questions, we can be contacted at: Noelle Toong ([email protected]), Dr. Irene Kaplow ([email protected]), and Dr. Andreas Pfenning ([email protected]).
"Regressions_LeaveOutCopies_first2DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb":
This file was used to train linear regressions with a linear kernel and interacting terms on the first set of PV and Lhx6 data. This file also generated output files used in "gpeTest_leaveAllOut_rawpred_compareavg_FINAL-NoDupes-afterlooxv.Rmd".
"Regressions_LeaveOutCopies_all3DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb":
This file was used to train linear regressions with a linear kernel and interacting terms on all the PV and Lhx6 data. This file also generated output files used in "gpeTest_leaveAllOut_rawpred_compareavg_allThreeDataSet_FINAL-NoDupes-afterlooxv.Rmd".
This R file was used to train GP regressions on separated PV and Lhx6 data in the first round of data. The input files came from "Regressions_LeaveOutCopies_first2DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb". The output files are evaluated in "calculateCoeffs_GPs.ipynb".
This R file was used to train GP regressions on separated all the PV and Lhx6 data. The input files came from "Regressions_LeaveOutCopies_first2DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb". The output files are evaluated in "calculateCoeffs_GPs.ipynb".
This file was used to calculate the correlation coefficients between the GP regressions and the averaged actual responses. The input files are generated from "gpeTest_leaveAllOut_rawpred_compareavg_FINAL-NoDupes-afterlooxv.Rmd" and "gpeTest_leaveAllOut_rawpred_compareavg_allThreeDataSet_FINAL-NoDupes-afterlooxv.Rmd".
This file was used to generate the artificial data points to test the GP regressions and see which points they predicted have the largest differences, for the first iteration of data.
This file was used to generate the artificial data points to test the GP regressions and see which points they predicted have the largest differences, for both iterations of data.
For Python 3, these packages are needed:
For R, these packages are needed (I ran this on R 3.6):
The raw starting data files are included here. When starting with "Regressions_LeaveOutCopies_first2DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb" or Regressions_LeaveOutCopies_all3DataSets_PredictOnRaw_CompareToAvg-Testing-NoDupes-afterlooxv.ipynb", replace where the file names are read in with the path to where you downloaded the files or where your data are.