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<div class="title">LogSumExp<div class="ingroups"><a class="el" href="group__groupStats.html">Statistics Functions</a></div></div> </div>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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Functions</h2></td></tr>
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<tr class="memitem:ga9fb5b24efffc928fcb601dca7f1abcc3"><td class="memItemLeft" align="right" valign="top">float16_t </td><td class="memItemRight" valign="bottom"><a class="el" href="group__LogSumExp.html#ga9fb5b24efffc928fcb601dca7f1abcc3">arm_logsumexp_dot_prod_f16</a> (const float16_t *pSrcA, const float16_t *pSrcB, uint32_t <a class="el" href="arm__variance__example__f32_8c.html#ab6558f40a619c2502fbc24c880fd4fb0">blockSize</a>, float16_t *pTmpBuffer)</td></tr>
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<tr class="memdesc:ga9fb5b24efffc928fcb601dca7f1abcc3"><td class="mdescLeft"> </td><td class="mdescRight">Dot product with log arithmetic. <a href="#ga9fb5b24efffc928fcb601dca7f1abcc3">More...</a><br/></td></tr>
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<tr class="separator:ga9fb5b24efffc928fcb601dca7f1abcc3"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:gafc0c0beeb5962c95ad3afd3739602254"><td class="memItemLeft" align="right" valign="top"><a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__LogSumExp.html#gafc0c0beeb5962c95ad3afd3739602254">arm_logsumexp_dot_prod_f32</a> (const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> *pSrcA, const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> *pSrcB, uint32_t <a class="el" href="arm__variance__example__f32_8c.html#ab6558f40a619c2502fbc24c880fd4fb0">blockSize</a>, <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> *pTmpBuffer)</td></tr>
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<tr class="memdesc:gafc0c0beeb5962c95ad3afd3739602254"><td class="mdescLeft"> </td><td class="mdescRight">Dot product with log arithmetic. <a href="#gafc0c0beeb5962c95ad3afd3739602254">More...</a><br/></td></tr>
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<tr class="separator:gafc0c0beeb5962c95ad3afd3739602254"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:gaa4da6151e57549ed9797c4093d662472"><td class="memItemLeft" align="right" valign="top">float16_t </td><td class="memItemRight" valign="bottom"><a class="el" href="group__LogSumExp.html#gaa4da6151e57549ed9797c4093d662472">arm_logsumexp_f16</a> (const float16_t *in, uint32_t <a class="el" href="arm__variance__example__f32_8c.html#ab6558f40a619c2502fbc24c880fd4fb0">blockSize</a>)</td></tr>
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<tr class="memdesc:gaa4da6151e57549ed9797c4093d662472"><td class="mdescLeft"> </td><td class="mdescRight">Computation of the LogSumExp. <a href="#gaa4da6151e57549ed9797c4093d662472">More...</a><br/></td></tr>
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<tr class="separator:gaa4da6151e57549ed9797c4093d662472"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:gaa66d25e7fc685c86e400567b4585857e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__LogSumExp.html#gaa66d25e7fc685c86e400567b4585857e">arm_logsumexp_f32</a> (const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> *in, uint32_t <a class="el" href="arm__variance__example__f32_8c.html#ab6558f40a619c2502fbc24c880fd4fb0">blockSize</a>)</td></tr>
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<tr class="memdesc:gaa66d25e7fc685c86e400567b4585857e"><td class="mdescLeft"> </td><td class="mdescRight">Computation of the LogSumExp. <a href="#gaa66d25e7fc685c86e400567b4585857e">More...</a><br/></td></tr>
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<tr class="separator:gaa66d25e7fc685c86e400567b4585857e"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Description</h2>
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<p>LogSumExp optimizations to compute sum of probabilities with Gaussian distributions </p>
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<h2 class="groupheader">Function Documentation</h2>
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<a class="anchor" id="ga9fb5b24efffc928fcb601dca7f1abcc3"></a>
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<td class="memname">float16_t arm_logsumexp_dot_prod_f16 </td>
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<td>(</td>
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<td class="paramtype">const float16_t * </td>
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<td class="paramname"><em>pSrcA</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const float16_t * </td>
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<td class="paramname"><em>pSrcB</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">uint32_t </td>
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<td class="paramname"><em>blockSize</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">float16_t * </td>
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<td class="paramname"><em>pTmpBuffer</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Vectors are containing the log of the samples</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">*pSrcA</td><td>points to the first input vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">*pSrcB</td><td>points to the second input vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">blockSize</td><td>number of samples in each vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">*pTmpBuffer</td><td>temporary buffer of length blockSize </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>The log of the dot product. </dd></dl>
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</div>
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</div>
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<a class="anchor" id="gafc0c0beeb5962c95ad3afd3739602254"></a>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname"><a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> arm_logsumexp_dot_prod_f32 </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> * </td>
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<td class="paramname"><em>pSrcA</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> * </td>
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<td class="paramname"><em>pSrcB</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">uint32_t </td>
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<td class="paramname"><em>blockSize</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> * </td>
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<td class="paramname"><em>pTmpBuffer</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Vectors are containing the log of the samples</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">*pSrcA</td><td>points to the first input vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">*pSrcB</td><td>points to the second input vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">blockSize</td><td>number of samples in each vector </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">*pTmpBuffer</td><td>temporary buffer of length blockSize </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>The log of the dot product. </dd></dl>
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</div>
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</div>
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<a class="anchor" id="gaa4da6151e57549ed9797c4093d662472"></a>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">float16_t arm_logsumexp_f16 </td>
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<td>(</td>
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<td class="paramtype">const float16_t * </td>
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<td class="paramname"><em>in</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">uint32_t </td>
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<td class="paramname"><em>blockSize</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>In probabilistic computations, the dynamic of the probability values can be very wide because they come from gaussian functions. To avoid underflow and overflow issues, the values are represented by their log. In this representation, multiplying the original exp values is easy : their logs are added. But adding the original exp values is requiring some special handling and it is the goal of the LogSumExp function.</p>
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<p>If the values are x1...xn, the function is computing:</p>
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<p>ln(exp(x1) + ... + exp(xn)) and the computation is done in such a way that rounding issues are minimised.</p>
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<p>The max xm of the values is extracted and the function is computing: xm + ln(exp(x1 - xm) + ... + exp(xn - xm))</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">*in</td><td>Pointer to an array of input values. </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">blockSize</td><td>Number of samples in the input array. </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>LogSumExp </dd></dl>
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</div>
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</div>
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<a class="anchor" id="gaa66d25e7fc685c86e400567b4585857e"></a>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname"><a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> arm_logsumexp_f32 </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="arm__math__types_8h.html#a4611b605e45ab401f02cab15c5e38715">float32_t</a> * </td>
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<td class="paramname"><em>in</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">uint32_t </td>
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<td class="paramname"><em>blockSize</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>In probabilistic computations, the dynamic of the probability values can be very wide because they come from gaussian functions. To avoid underflow and overflow issues, the values are represented by their log. In this representation, multiplying the original exp values is easy : their logs are added. But adding the original exp values is requiring some special handling and it is the goal of the LogSumExp function.</p>
|
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<p>If the values are x1...xn, the function is computing:</p>
|
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<p>ln(exp(x1) + ... + exp(xn)) and the computation is done in such a way that rounding issues are minimised.</p>
|
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<p>The max xm of the values is extracted and the function is computing: xm + ln(exp(x1 - xm) + ... + exp(xn - xm))</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">*in</td><td>Pointer to an array of input values. </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">blockSize</td><td>Number of samples in the input array. </td></tr>
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</table>
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</dd>
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<dl class="section return"><dt>Returns</dt><dd>LogSumExp </dd></dl>
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