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diff --git a/output/docs/tdigest/tdigest.html b/output/docs/tdigest/tdigest.html
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+<h1 id="t-digest-overview">t-digest overview</h1>
+
+<p>This is an algorithm and data structure for estimating ranks and quantiles
of distributions of numerical values.</p>
+
+<p>The implementation in this library is based on the MergingDigest in
following <a href="https://github.com/tdunning/t-digest";>reference
implementation</a>.</p>
+
+<p>The implementation in this library has a few differences from the reference
implementation:</p>
+
+<ul>
+ <li>merge does not modify the input</li>
+ <li>serialization similar to other sketches in this library, although
reading the reference implementation format is supported</li>
+ <li>C++ template implementation for float and double types</li>
+</ul>
+
+<p>Unlike all other algorithms in the library, t-digest is empirical and has
no mathematical basis for estimating its error and its results are dependent on
the input data. However, for many common data distributions, it can produce
excellent results.</p>
+
+<p>The library contains a few different quantile sketches for estimating
distributions (ranks and quantiles). All other quantile skeches can handle
arbitrary comparable types and always retain a small set of items from the
input stream. All queries that return approximations in the input domain return
one of the retained items from the input. t-digest is different: it works on
numeric data only (floating point types), retains and returns values not
necessarilly seen in the input (interpo [...]
+
+<p>The closest alternative to t-digest in this library is <a
href="https://datasketches.apache.org/docs/REQ/ReqSketch.html";>REQ sketch</a>.
It prioritizes one chosen side of the rank domain: either low rank accuracy or
high rank accuracy. t-digest (in this implementation) prioritizes both ends of
the rank domain and has lower accuracy towards the middle of the rank domain
(median).</p>
+
+<p>Measurements show that t-digest is slightly biased (tends to underestimate
low ranks and overestimate high ranks), while still doing very well close to
the extremes. The effect seems to be more pronounced with more input values:</p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_err_vs_rank_k100_n2e10.png"
alt="ErrorVsRank2e10" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_err_vs_rank_k100_n2e15.png"
alt="ErrorVsRank2e15" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_err_vs_rank_k100_n2e20.png"
alt="ErrorVsRank2e20" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_err_vs_rank_k100_n2e25.png"
alt="ErrorVsRank2e25" /></p>
+
+<p>Rank error vs stream size:</p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_rank_error_k100.png" alt="RankErrorK100" /></p>
+
+<p>Comparisons with REQ sketch:</p>
+
+<p><img class="doc-img-full" src="/docs/img/tdigest/tdigest_vs_req_memory.png"
alt="TDigestVsReqMemory" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tgigest_vs_req_serialized_size.png"
alt="TDigestVsReqSeializedSize" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_vs_req_rank_error_0.99.png"
alt="TDigestVsReq0.99" /></p>
+
+<p><img class="doc-img-full"
src="/docs/img/tdigest/tdigest_vs_req_hra_exp_1.5.png"
alt="TDigestVsReqHRAexp1.5" /></p>
+
+ </div> <!-- End content -->
+ </div> <!-- End row -->
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+
+<!-- Start _include/page_footer.html -->
+<footer class="ds-footer">
+ <div class="container">
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+ Licensed under the Apache License, Version 2.0. All Rights Reserved.
+ | <a
href="https://privacy.apache.org/policies/privacy-policy-public.html";>Privacy
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+ Apache DataSketches, Apache, the Apache feather logo, and the Apache
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