From 88446a24ef45aeb0855127acc71d2ecb97ef62fb Mon Sep 17 00:00:00 2001
From: mkazhdan <misha@cs.jhu.edu>
Date: Fri, 19 Jul 2019 00:39:27 -0400
Subject: [PATCH] no message

---
 README.md | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 041c40e..35d51b5 100644
--- a/README.md
+++ b/README.md
@@ -753,12 +753,10 @@ The function fitting the input samples can be by calling the point interpolant:
 Then, the reconstructed function can be evaluated at the evaluation samples by calling the adaptive tree visualization:
 <blockquote><code>% AdaptiveTreeVisualization --in quadratic.2D.tree --samples quadratic.2D.evaluation.samples</code></blockquote>
 This will output the evaluation positions and values:
-<blockquote><CODE>
-0 0 1.33836e-05<br>
-0.5 0 0.25001<br>
-0.5 0.5 0.500006<br>
-2 2 nan
-</CODE></blockquote>
+<blockquote><CODE>0 0 1.33836e-05<br>
+<blockquote><CODE>0.5 0 0.25001</CODE></blockquote><br>
+<blockquote><CODE>0.5 0.5 0.500006</CODE></blockquote><br>
+<blockquote><CODE>2 2 nan</CODE></blockquote>
 Note that because the last evaluation position, (2,2), is outside the bounding box of the fitting samples, the function cannot be evaluated at this point and a value of "nan" is output.
 </DETAILS>
 </dl>