# Wrapping text in enumeration environment around a table

I would like to wrap the text in an enumerate environment around a table, this is my actual situation (sorry for including so much code, but I wanted to give a precise idea of the number of lines in the enumerate environment):

\subsection{Given the following data set where “Target 2” represent the class attribute,
compute the naive Bayesian classification for the instance $<L,white>$ and $<XS,?>$.}
\begin{table}[h!t]
\centering
\begin{tabular}{ccc}
\toprule
Size        &   Color   &   Target2 \\
\midrule
XS      &   green   &   Yes     \\
L       &   green   &   Yes     \\
XS      &   white   &   No      \\
M       &   black   &   No      \\
XL      &   green   &   Yes     \\
XS      &   white   &   Yes     \\
L       &   black   &   No      \\
M       &   green   &   Yes     \\
\bottomrule
\end{tabular}
\end{table}
\begin{enumerate}
\item In this case the experience $E$ is made up of $e_{1} = L$ and $e_{2} = white$,
which in Naive Bayes are to be considered as independent, therefore we have:
\begin{align*}
P(yes|E) &= P(L|yes)\cdot P(white|yes) \cdot P(yes)     \\
&= \sfrac{1}{5}\times \sfrac{1}{5} \times \sfrac{5}{8} \\
&= 0.025
\end{align*}
\begin{align*}
P(no|E) &= P(L|no) \cdot P(white|no) \cdot P(no) \\
&= \sfrac{1}{3} \times \sfrac{1}{3} times \sfrac{3}{8}  \\
&= 0.041
\end{align*}
Then we normalize:
\begin{align*}
P(yes) &= \frac{0.025}{0.066}
\simeq 0.38
\end{align*}
\begin{align*}
P(no) &= \frac{0.041}{0.066}
\simeq 0.62
\end{align*}
As $P(no) > P(yes)$, we label $<L,white>$ as no''.
\item Now we have to classify a sample with a missing value. During the testing phase
we simply omit the attribute\footnote{Classification Other Methods, slide 24}:
\begin{align*}
P(yes|XS) &= P(XS|yes) \cdot P(yes) = \sfrac{2}{5} \times \sfrac{5}{8}
= 0.25
\end{align*}
\begin{align*}
P(no|XS) &= P(XS|no) \cdot P(no)
= \sfrac{1}{3} \times \sfrac{3}{8}
= 0.125
\end{align*}
Let us normalize
\begin{align*}
P(yes) &= \sfrac{0.25}{0.375} \simeq 0.7 \\
P(no)  &= \sfrac{0.125}{0.375} \simeq 0.3
\end{align*}
Bottom line this sample is classified as yes''.
\end{enumerate}


I have tried using the wrapfig and the floatftl package unsuccessfully, the table was moved to the end of the list in both cases. I have considered using two minipage environments, but I would like the text to actually wrap around the table.

It helps when you post questions to make complete documents including loading all the packages you need, I guessed

\usepackage{booktabs,xfrac,amsmath}


in this case. Also I fixed a few font issues (for multi-letter identifiers and angle brackets)

Changing margins within a LaTeX list is a bit delicate, but this is I think the layout you want \documentclass{article}

\usepackage{booktabs,xfrac,amsmath}

\begin{document}

\subsection{Given the following data set where “Target 2” represent the class attribute,
compute the naive Bayesian classification for the instance $\langle L,white\rangle$ and $\langle \mathit{XS},?\rangle$.}

\savebox0{%
\begin{tabular}{ccc}
\toprule
Size        &   Color   &   Target2 \\
\midrule
XS      &   green   &   Yes     \\
L       &   green   &   Yes     \\
XS      &   white   &   No      \\
M       &   black   &   No      \\
XL      &   green   &   Yes     \\
XS      &   white   &   Yes     \\
L       &   black   &   No      \\
M       &   green   &   Yes     \\
\bottomrule
\end{tabular}}

\begin{enumerate}
\makeatletter
\dimen@\wd0
\parshape \@ne \@totalleftmargin \linewidth
\hbox to \textwidth{\hfill\vtop to \z@{\vskip1em \box\z@\vss}}
\item

In this case the experience $E$ is made up of $e_{1} = L$ and $e_{2} = \mathit{white}$,
which in Naive Bayes are to be considered as independent, therefore we have:
\begin{align*}
P(\mathit{yes}|E) &= P(L|\mathit{yes})\cdot P(\mathit{white}|\mathit{yes}) \cdot P(\mathit{yes})     \\
&= \sfrac{1}{5}\times \sfrac{1}{5} \times \sfrac{5}{8} \\
&= 0.025
\end{align*}
\begin{align*}
P(\mathit{no}|E) &= P(L|\mathit{no}) \cdot P(\mathit{white}|\mathit{no}) \cdot P(\mathit{no}) \\
&= \sfrac{1}{3} \times \sfrac{1}{3} \times \sfrac{3}{8}  \\
&= 0.041
\end{align*}

\parshape \@ne \@totalleftmargin \linewidth

Then we normalize:
\begin{align*}
P(\mathit{yes}) &= \frac{0.025}{0.066}
\simeq 0.38
\end{align*}
\begin{align*}
P(\mathit{no}) &= \frac{0.041}{0.066}
\simeq 0.62
\end{align*}
As $P(\mathit{no}) > P(\mathit{yes})$, we label $\langle L,\mathit{white}\rangle$ as no''.

\item Now we have to classify a sample with a missing value. During the testing phase
we simply omit the attribute\footnote{Classification Other Methods, slide 24}:
\begin{align*}
P(\mathit{yes}|\mathit{XS}) &= P(\mathit{XS}|\mathit{yes}) \cdot P(\mathit{yes}) \\
&= \sfrac{2}{5} \times \sfrac{5}{8}\\
&= 0.25
\end{align*}
\begin{align*}
P(\mathit{no}|\mathit{XS}) &= P(\mathit{XS}|\mathit{no}) \cdot P(\mathit{no})\\
&= \sfrac{1}{3} \times \sfrac{3}{8}\\
&= 0.125
\end{align*}
Let us normalize
\begin{align*}
P(\mathit{yes}) &= \sfrac{0.25}{0.375} \simeq 0.7 \\
P(\mathit{no})  &= \sfrac{0.125}{0.375} \simeq 0.3
\end{align*}
Bottom line this sample is classified as yes''.
\end{enumerate}

\end{document}

• Not quite right: note the "therefore we have:" is stretched out. That's probably fixable but a workaround is to put a blank line after that line, before the following align. – David Carlisle Apr 29 '12 at 13:27
• I had noticed that, but I guess that a \hfill should do the job. Still it is a great answer, even though I hoped there was some package to manage these situations, thank you very much again. – gcedo Apr 29 '12 at 13:30
• @DavidCarlisle I have faced the same problem, but with documentclass tikzposter. Using your solution, I get a bunch of errors. Is there any short modification of your solution? – Antoine Oct 10 '16 at 14:32
• @Antoine See the first line of this answer, no way I can address that and in anycase best not to add new questions on comments of 4 year old answers. Ask a new question with a complete example that demonstrates the problem, you can link to this answer in your question. – David Carlisle Oct 10 '16 at 14:47
• @DavidCarlisle I did not know, how to link the answer, but nevertheless, here is a new question. – Antoine Oct 11 '16 at 11:30