Add license

LICENSE.md

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 GenoPhenoEnvo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ML/README.md

@@ -52,14 +52,14 @@ The following models predict gcc_90 of next 35 days. For gcc_sd, last years' gcc
 <b>Notes</b>:
   - Since gcc_90 is not getting updated daily, the models using immediate past gcc_90 values (PEG_RFR0 and PEG_RFR) use forward fill strategy to fill in immediate past gcc_90 values while submitting the forecast for next 35 days
 
-  - Forecasted weather parameters used for submission of forecast for next 35 days are the median of 30 forecasted ensenle weather parameter files extrated from NOAA. 
+  - Forecasted weather parameters used for submission of forecast for next 35 days are the median of 30 forecasted ensemble weather parameter files extrated from NOAA. 
 
 ### Fall 2021
 
 * <b>PEG_FUSION_0 (Prediction based on last year gcc_90 values and current weather data using ensemble approach):</b> 
   - <b>Input:</b> gcc_90/rcc_90 data of 20 days from last year i.e., (t-10)th to (t+9)th day of last year and weather variables of tth day (Max. temp, Min. temp, Radiation, Precipitation). Weather data extracted from Daymet is used to train the model. To forecast gcc_90 for future days, NOAA forecasted weather parameters are being used.
 
-  - <b>Output:</b> gcc_90/rcc_90 of next 35 days
+  - <b>Output:</b> gcc_90/rcc_90 and gcc_sd/rcc_sd of next 35 days
 
   - <b>Model Description:</b> Ensemble machine learning approach is taken to predict gcc90/rcc_90, as part of which four models (Random Forest Regressor (RFR), ElasticNet Regressor, Extreme Gradient Boosting (XgBoost), K-Nearest Neighbor Regressor (KNN)) are trained individually for each of the sites to predict rcc_90 and three models (Random Forest Regressor (RFR), ElasticNet Regressor, Extreme Gradient Boosting (XgBoost)) are trained individually for each of the sites to predict gcc_90. Finally, rcc_90 and rcc_sd is predicted by taking the average and standard deviation of the predicted outputs by the four models and gcc_90 and gcc_sd are predicted by taking the average and standard deviation of the predicted outputs by the three models. 
 
@@ -98,4 +98,4 @@ Can log into server using `ssh -i [ssh key path] [username]@128.196.65.173`. It
  00 22 * * * cd /home/kristinariemer/neon-datasets/models/RFR && bash forecast_gcc.sh && echo `date` >> /home/kristinariemer/cron.txt
  ```
 
-Confirmation of submissions are in `cron.txt`. 
+Confirmation of submissions are in `cron.txt`.