Simple overview of use/purpose.
An in-depth paragraph about your project and overview of use.
| env=~/.ssh/agent.env | |
| agent_load_env () { test -f "$env" && . "$env" >| /dev/null ; } | |
| agent_start () { | |
| (umask 077; ssh-agent >| "$env") | |
| . "$env" >| /dev/null ; } | |
| agent_load_env |
| #sklearn.metrics has a mean_squared_error function. The RMSE is just the square root of whatever it returns. | |
| #source:https://intellipaat.com/community/1269/is-there-a-library-function-for-root-mean-square-error-rmse-in-python | |
| from sklearn.metrics import mean_squared_error | |
| from math import sqrt | |
| rms = sqrt(mean_squared_error(y_actual, y_predicted)) |