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mkazhdan
2019-07-19 00:39:27 -04:00
parent 1bdfeebc0f
commit 88446a24ef
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README.md
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@@ -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>