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| author | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 08:23:38 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 08:23:38 +0200 |
| commit | 7faf98ac5475bb2041d7e434478c58c2f2cec0fd (patch) | |
| tree | 837a519b7fe4ad085a412cbb2e61d64605d8cfca /vignettes/compiled_models.html | |
| parent | cb338bea13b3b834bc3b09e6b1014959195f37bb (diff) | |
Static documentation rebuilt by staticdocs::build_site()
Diffstat (limited to 'vignettes/compiled_models.html')
| -rw-r--r-- | vignettes/compiled_models.html | 51 |
1 files changed, 23 insertions, 28 deletions
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index c3ffb035..cec76ef9 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -226,13 +226,8 @@ div.tocify { <pre><code>## gcc ## "/usr/bin/gcc"</code></pre> <p>First, we build a simple degradation model for a parent compound with one metabolite.</p> -<pre class="r"><code>library("mkin")</code></pre> -<pre><code>## Loading required package: minpack.lm</code></pre> -<pre><code>## Loading required package: rootSolve</code></pre> -<pre><code>## Loading required package: inline</code></pre> -<pre><code>## Loading required package: methods</code></pre> -<pre><code>## Loading required package: parallel</code></pre> -<pre class="r"><code>SFO_SFO <- mkinmod( +<pre class="r"><code>library("mkin") +SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> @@ -255,22 +250,22 @@ mb.1 <- microbenchmark( print(mb.1)</code></pre> <pre><code>## Unit: seconds ## expr min lq mean median uq -## deSolve, not compiled 13.694897 13.774112 13.820936 13.853327 13.883956 -## Eigenvalue based 2.087861 2.089503 2.116323 2.091145 2.130555 -## deSolve, compiled 1.794975 1.799892 1.814653 1.804808 1.824492 -## max neval cld -## 13.914585 3 c -## 2.169964 3 b -## 1.844177 3 a</code></pre> +## deSolve, not compiled 25.422123 25.889685 26.065978 26.357247 26.387905 +## Eigenvalue based 2.243667 2.254539 2.277770 2.265412 2.294821 +## deSolve, compiled 1.849468 1.865343 1.871339 1.881219 1.882274 +## max neval cld +## 26.41856 3 b +## 2.32423 3 a +## 1.88333 3 a</code></pre> <pre class="r"><code>autoplot(mb.1)</code></pre> -<p><img 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" title alt width="672" /></p> -<p>We see that using the compiled model is by a factor of 7.7 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> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>We see that using the compiled model is by a factor of 14 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>rownames(smb.1) <- smb.1$expr smb.1["median"]/smb.1["deSolve, compiled", "median"]</code></pre> -<pre><code>## median -## deSolve, not compiled 7.675788 -## Eigenvalue based 1.158652 -## deSolve, compiled 1.000000</code></pre> +<pre><code>## median +## deSolve, not compiled 14.010730 +## Eigenvalue based 1.204226 +## deSolve, compiled 1.000000</code></pre> </div> <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2"> <h2>Benchmark for a model that can not be solved with Eigenvalues</h2> @@ -290,20 +285,20 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" <pre class="r"><code>smb.2 <- summary(mb.2) print(mb.2)</code></pre> <pre><code>## Unit: seconds -## expr min lq mean median uq -## deSolve, not compiled 29.120048 29.170013 29.246607 29.21998 29.309886 -## deSolve, compiled 3.338458 3.343954 3.379437 3.34945 3.399926 +## expr min lq mean median uq +## deSolve, not compiled 54.386189 54.39423 54.477986 54.402271 54.523884 +## deSolve, compiled 3.424205 3.53522 3.574587 3.646236 3.649778 ## max neval cld -## 29.399796 3 b -## 3.450402 3 a</code></pre> +## 54.645498 3 b +## 3.653319 3 a</code></pre> <pre class="r"><code>smb.2["median"]/smb.2["deSolve, compiled", "median"]</code></pre> <pre><code>## median ## 1 NA ## 2 NA</code></pre> <pre class="r"><code>autoplot(mb.2)</code></pre> -<p><img 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" title alt width="672" /></p> -<p>Here we get a performance benefit of a factor of 8.7 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.43 on</p> +<p><img 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" title alt width="672" /></p> +<p>Here we get a performance benefit of a factor of 14.9 using the version of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 0.9.43.9000 on</p> <pre><code>## R version 3.3.1 (2016-06-21) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 8 (jessie)</code></pre> |