make more expressive

notes/2024-10-17.md

@@ -109,7 +109,7 @@ X_train, X_test, y_train, y_test = train_test_split(iris_df[feature_vars],
 This function returns multiple values, the docs say that it returns [twice as many](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#:~:text=splittinglist%2C%20length%3D2%20*%20len(arrays)) as it is passed.  We passed two separate things, the features and the labels separated, so we get train and test each for both.  
 
 ```{note}
-If you get different numbers fort the index than I do here or run the train test split multipe times and see things change, you have a different ranomd seed above. 
+If you get different numbers for the index than I do here or run the train test split multipe times and see things change, you have a different ranomd seed above. 
 ```
 
 ```{code-cell} ipython3
@@ -298,7 +298,8 @@ Gaussian Naive Bayes is a very simple model, but it is a {term}`generative` mode
 
 ```{code-cell} ipython3
 N = 20
-gnb_df = pd.DataFrame(np.concatenate([np.random.multivariate_normal(th, sig*np.eye(4),N)
+n_features = len(feature_vars)
+gnb_df = pd.DataFrame(np.concatenate([np.random.multivariate_normal(th, sig*np.eye(n_features),N)
                  for th, sig in zip(gnb.theta_,gnb.var_)]),
                  columns = gnb.feature_names_in_)
 gnb_df['species'] = [ci for cl in [[c]*N for c in gnb.classes_] for ci in cl]