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-rw-r--r--vignettes/compiled_models.html42
1 files changed, 21 insertions, 21 deletions
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index e6f21b09..5fcd88fb 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -94,20 +94,20 @@ mb.1 <- microbenchmark(
smb.1 <- summary(mb.1)[-1]
rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
print(smb.1)</code></pre>
-<pre><code>## min lq mean median uq
-## deSolve, not compiled 4969.585 5033.7311 5092.7389 5097.8773 5154.3160
-## Eigenvalue based 868.731 891.7239 909.6449 914.7169 930.1018
-## deSolve, compiled 4935.049 4935.4796 4968.2150 4935.9097 4984.7978
+<pre><code>## min lq mean median uq
+## deSolve, not compiled 6980.8002 6996.4739 7024.5611 7012.1476 7046.4415
+## Eigenvalue based 925.3350 928.9405 951.8405 932.5460 965.0932
+## deSolve, compiled 747.2635 761.9405 771.4339 776.6174 783.5191
## max neval
-## deSolve, not compiled 5210.7547 3
-## Eigenvalue based 945.4867 3
-## deSolve, compiled 5033.6858 3</code></pre>
-<p>We see that using the compiled model is almost a factor of 8 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p>
+## deSolve, not compiled 7080.7354 3
+## Eigenvalue based 997.6404 3
+## deSolve, compiled 790.4207 3</code></pre>
+<p>We see that using the compiled model is by a factor of 9 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p>
<pre class="r"><code>smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
-<pre><code>## median
-## deSolve, not compiled 1.0328141
-## Eigenvalue based 0.1853188
-## deSolve, compiled 1.0000000</code></pre>
+<pre><code>## median
+## deSolve, not compiled 9.029089
+## Eigenvalue based 1.200779
+## deSolve, compiled 1.000000</code></pre>
</div>
<div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level1">
<h1>Benchmark for a model that can not be solved with Eigenvalues</h1>
@@ -124,16 +124,16 @@ smb.2 &lt;- summary(mb.2)[-1]
rownames(smb.2) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;deSolve, compiled&quot;)
print(smb.2)</code></pre>
<pre><code>## min lq mean median uq
-## deSolve, not compiled 11.745276 11.754288 11.820726 11.763300 11.858451
-## deSolve, compiled 1.385829 1.386407 1.400841 1.386985 1.408347
-## max neval
-## deSolve, not compiled 11.95360 3
-## deSolve, compiled 1.42971 3</code></pre>
+## deSolve, not compiled 14.127630 14.245064 14.298201 14.362497 14.383486
+## deSolve, compiled 1.354744 1.362167 1.366362 1.369589 1.372171
+## max neval
+## deSolve, not compiled 14.404474 3
+## deSolve, compiled 1.374752 3</code></pre>
<pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
-<pre><code>## median
-## deSolve, not compiled 8.4812
-## deSolve, compiled 1.0000</code></pre>
-<p>Here we get a performance benefit of more than a factor of 10 using the version of the differential equation model compiled from C code using the inline package!</p>
+<pre><code>## median
+## deSolve, not compiled 10.48672
+## deSolve, compiled 1.00000</code></pre>
+<p>Here we get a performance benefit of a factor of 10.5 using the version of the differential equation model compiled from C code using the inline package!</p>
</div>

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