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mkazhdan
2019-07-19 00:40:01 -04:00
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README.md
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@@ -753,7 +753,7 @@ 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: 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> <blockquote><code>% AdaptiveTreeVisualization --in quadratic.2D.tree --samples quadratic.2D.evaluation.samples</code></blockquote>
This will output the evaluation positions and values: This will output the evaluation positions and values:
<blockquote><CODE>0 0 1.33836e-05<br> <blockquote><CODE>0 0 1.33836e-05</CODE></blockquote><br>
<blockquote><CODE>0.5 0 0.25001</CODE></blockquote><br> <blockquote><CODE>0.5 0 0.25001</CODE></blockquote><br>
<blockquote><CODE>0.5 0.5 0.500006</CODE></blockquote><br> <blockquote><CODE>0.5 0.5 0.500006</CODE></blockquote><br>
<blockquote><CODE>2 2 nan</CODE></blockquote> <blockquote><CODE>2 2 nan</CODE></blockquote>