Update app.py

app.py

@@ -37,6 +37,8 @@ data = data_utils(grid_info = grid_info,
                   input_max = input_max,
                   input_min = input_min,
                   output_scale = output_scale)
+                  
+# set variables to V1 subset
 data.set_to_v1_vars()''',language='python')
 
 grid_info = xr.open_dataset('ClimSim_low-res_grid-info.nc')
@@ -71,7 +73,7 @@ data.target_val = data.load_npy_file('val_target_small.npy')
 
 st.header('**Step 4:**  Train models')
 st.subheader('Train constant prediction model')
-st.latex(r'''\hat{y}=E[y_{limit}]''')
+st.latex(r'''\hat{y}=E[y_{train}]''')
 st.code('''const_model = data.target_train.mean(axis = 0)''',language='python')
 
 const_model = data.target_train.mean(axis = 0)
@@ -80,7 +82,7 @@ const_model = data.target_train.mean(axis = 0)
 
 st.subheader('Train multiple linear regression model')
 st.latex(r'''\beta=(X^{T}_{train} X_{train})^{-1} X^{T}_{train} y_{train} \\
-\hat{y}=X^{T}_{train} \beta \\
+\hat{y}=X^{T}_{input} \beta \\
 \text{where } X_{train} \text{ and } X_{input} \text{ correspond to the training data and the input data you would like to inference on, respectively.} \\ 
 X_{train} \text{ and } X_{input} \text{ both have a column of ones concatenated to the feature space for the bias.}''')
 st.text('adding bias unit')