solutions for GP indent correction

GP_Solutions.qmd

@@ -108,13 +108,7 @@ df <- df[, c("datetime", "observation")]
 cutoff = as.Date('2020-12-31')
 df_train <- subset(df, df$datetime <= cutoff)
 df_test <- subset(df, df$datetime > cutoff)
-```
-
-## GP Model
 
-Now we will setup our X's. We already have the functions to do this and can simply pass in the datetime. We then combine $X_1$ and $X_2$ to create out input matrix $X$. Remember, everything is ordered as in our dataset. 
-
-```{r}
 # Setting up iso-week and sin wave predictors by calling the functions
 X1 <- fx.iso_week(df_train$datetime) # range is 1-53
 X2 <- fx.sin(df_train$datetime) # range is 0 to 1
@@ -199,7 +193,7 @@ rmse <- sqrt(mean((yt_true - yt_pred)^2))
 rmse
 ```
 
-### Use an environmental predictor in your model. Following is a function `fx.green` that creates the variable given the `datetime` and the `location`.
+###### Use an environmental predictor in your model. Following is a function `fx.green` that creates the variable given the `datetime` and the `location`.
 
 Here is a snippet of the supporting file that you will use; You can look into the data.frame and try to plot `ker` for one site at a time and see what it yields. 
 
@@ -342,7 +336,7 @@ rmse <- sqrt(mean((yt_true - yt_pred)^2))
 rmse
 ```
 
-### Fit a GP Model for all the locations (*More advanced*).
+###### Fit a GP Model for all the locations (*More advanced*).
 
 ```{r}
 # GP function. This can be varied but easiest way is to just take in X, y, XX and return the predicted means and bounds.