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Author: hosseinmoein

docs/HTML/DataFrame.html

@@ -2418,7 +2418,8 @@ <H4>2. DataFrame Internal Multithreading</H4>
 
   <H2 ID="4"><font color="blue">Views <font size="+4">&#128269;</font></font></H2>
   <P>
-    Views have useful and practical use-cases. A view is a slice of a DataFrame that is a reference to the original DataFrame. It appears exactly the same as a DataFrame, but if you modify any data in the view, the corresponding data point(s) in the original DataFrame will also be modified and vice versa. There are certain things you cannot do in views. For example, you cannot add or delete columns, extend the index column, ...<BR><BR>
+    Views have useful and practical use-cases. A view is a slice of a DataFrame that is a reference to the original DataFrame. It appears exactly the same as a DataFrame, but if you modify any data in the view, the corresponding data point(s) in the original DataFrame will also be modified and vice versa. Conceptually, views are very similar to <I>std::span</I> or <I>std::mdspan</I> in C++. If the original DataFrame goes out of scope, access to all its views will be UB.<BR>
+	There are certain things you cannot do in views. For example, you cannot add or delete columns, extend the index column, ...<BR><BR>
 
     In general there are two kinds of views
     <OL>

docs/HTML/PolyFitVisitor.html

@@ -104,6 +104,7 @@ <h1></h1>
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         This is a "single action visitor", meaning it is passed the whole data vector in one call and you must use the single_act_visit() interface.<BR><BR>
+
         This functor fits a <I>y = b<sub>0</sub> + b<sub>1</sub> * log(x)</I> through the given x-y coordinates by the way of calling PolyFit above on log(x).<BR><BR>
 
         This works with both scalar and multidimensional (i.e. vectors or arrays) datasets.<BR><BR>
@@ -121,6 +122,7 @@ <h1></h1>
               weight_func w_func = [](const I &, size_t) -> data_t { return (1); });
         </PRE>
         </I>
+        <B>degree</B>: The polynomial degree<BR>
         <B>w_func</B>: A functor that provides weights to be applied to sigma values. w_func is passed two parameters: (1) The value of the index column corresponding to the given y value, (2) The corresponding index into the y vector. The default is no weights.
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