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Tips and tricks: Difference between revisions
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→Real matrix operations: add "Range"
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<tr><td>element multiplication</td><td><code>A.*B</code></td><td><code>product(A,B) </code></td></tr> | <tr><td>element multiplication</td><td><code>A.*B</code></td><td><code>product(A,B) </code></td></tr> | ||
<tr><td>element division</td><td><code>A./B</code></td><td><code>quotient(A,B) </code></td></tr> | <tr><td>element division</td><td><code>A./B</code></td><td><code>quotient(A,B) </code></td></tr> | ||
<tr><td>transpose*</td><td><code>A'</code></td><td><code>A.transpose()</code></td></tr> | <tr><td>transpose*</td><td><code>A.'</code></td><td><code>A.transpose()</code></td></tr> | ||
<tr><td>select element m,n of A**</td><td><code>A(m,n)</code></td><td><code>A(m-1,n-1)</code></td></tr> | <tr><td>select element m,n of A**</td><td><code>A(m,n)</code></td><td><code>A(m-1,n-1)</code></td></tr> | ||
<tr><td>select row N of A**</td><td><code>A(N,:)</code></td><td><code>A.row(N-1)</code></td></tr> | <tr><td>select row N of A**</td><td><code>A(N,:)</code></td><td><code>A.row(N-1)</code></td></tr> | ||
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<tr><td>number of rows</td><td><code>size(A,1)</code></td><td><code>A.rows()</code></td></tr> | <tr><td>number of rows</td><td><code>size(A,1)</code></td><td><code>A.rows()</code></td></tr> | ||
<tr><td>number of columns</td><td><code>size(A,2)</code></td><td><code>A.cols()</code></td></tr> | <tr><td>number of columns</td><td><code>size(A,2)</code></td><td><code>A.cols()</code></td></tr> | ||
<tr><td>range</td><td><code>0.1:0.2:0.9</code></td><td><code>Range (0.1, 0.9, 0.2).matrix_value ()</code></td></tr> | |||
</table> | </table> | ||
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===Changing BLAS=== | ===Changing BLAS=== | ||
Many Octave functions are wrappers to optimized numerical libraries, notably BLAS and ATLAS. It is possible to achieve impressive performance gains by simply using a library tuned to your platform. One example is using OpenBLAS to replace the default BLAS implementation ([http://www.stat.cmu.edu/~nmv/2013/07/09/for-faster-r-use-openblas-instead-better-than-atlas-trivial-to-switch-to-on-ubuntu/ further details]). | Many Octave functions are wrappers to optimized numerical libraries, notably BLAS and ATLAS. It is possible to achieve impressive performance gains by simply using a library tuned to your platform. One example is using OpenBLAS to replace the default BLAS implementation ([http://www.stat.cmu.edu/~nmv/2013/07/09/for-faster-r-use-openblas-instead-better-than-atlas-trivial-to-switch-to-on-ubuntu/ further details]). | ||
On Linux, this just takes a few commands | On some Linux distributions, this just takes a few commands. For instance, on Ubuntu, it usually suffices to run | ||
sudo apt-get install libopenblas-base libatlas3gf-base | sudo apt-get install libopenblas-base libatlas3gf-base | ||
followed by | |||
sudo update-alternatives --config libblas.so.3 | sudo update-alternatives --config libblas.so.3 | ||
and | and then selecting the openblas option. | ||