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52 lines
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</head><body><link href="../../../../../images/logo-icon.svg" rel="icon" type="image/svg"><script>var pathToRoot = "../../../";</script><script type="text/javascript" src="../../../scripts/sourceset_dependencies.js" async></script><link href="../../../styles/style.css" rel="Stylesheet"><link href="../../../styles/logo-styles.css" rel="Stylesheet"><link href="../../../styles/jetbrains-mono.css" rel="Stylesheet"><link href="../../../styles/main.css" rel="Stylesheet"><script type="text/javascript" src="../../../scripts/clipboard.js" async></script><script type="text/javascript" src="../../../scripts/navigation-loader.js" async></script><script type="text/javascript" src="../../../scripts/platform-content-handler.js" async></script><script type="text/javascript" src="../../../scripts/main.js" async></script>
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<div class="main-content" id="content" pageids="kmath-tensors::space.kscience.kmath.tensors.api/TensorAlgebra/dot/space.kscience.kmath.nd.MutableStructureND[TypeParam(bounds=[kotlin.Any?])]#space.kscience.kmath.nd.MutableStructureND[TypeParam(bounds=[kotlin.Any?])]/PointingToDeclaration//-1345790395">
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<div class="breadcrumbs"><a href="../../index.html">kmath-tensors</a>/<a href="../index.html">space.kscience.kmath.tensors.api</a>/<a href="index.html">TensorAlgebra</a>/<a href="dot.html">dot</a></div>
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<h1 class="cover"><span>dot</span></h1>
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<div class="divergent-group" data-filterable-current=":kmath-tensors:dokkaHtmlPartial/commonMain" data-filterable-set=":kmath-tensors:dokkaHtmlPartial/commonMain"><div class="with-platform-tags"><span class="pull-right"></span></div>
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<div class="platform-hinted " data-platform-hinted="data-platform-hinted"><div class="content sourceset-depenent-content" data-active="" data-togglable=":kmath-tensors:dokkaHtmlPartial/commonMain"><div class="symbol monospace">abstract infix fun <a href="../index.html#-1680022905%2FClasslikes%2F-1345790395">Tensor</a><<a href="index.html">T</a>>.<a href="dot.html">dot</a>(other: <a href="../index.html#-1680022905%2FClasslikes%2F-1345790395">Tensor</a><<a href="index.html">T</a>>): <a href="../index.html#-1680022905%2FClasslikes%2F-1345790395">Tensor</a><<a href="index.html">T</a>><span class="top-right-position"><span class="copy-icon"></span><div class="copy-popup-wrapper popup-to-left"><span class="copy-popup-icon"></span><span>Content copied to clipboard</span></div></span></div></div></div>
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<p class="paragraph">Matrix product of two tensors.</p><p class="paragraph">The behavior depends on the dimensionality of the tensors as follows:</p><ol><li><p class="paragraph">If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li><li><p class="paragraph">If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li><li><p class="paragraph">If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.</p></li><li><p class="paragraph">If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.</p></li><li><p class="paragraph">If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e., batch) dimensions are broadcast (and thus must be broadcastable). For example, if <code>input</code> is a (j &times; 1 &times; n &times; n) tensor and <code>other</code> is a (k &times; n &times; n) tensor, out will be a (j &times; k &times; n &times; n) tensor.</p></li></ol><p class="paragraph">For more information: https://pytorch.org/docs/stable/generated/torch.matmul.html</p><h4 class="">Return</h4><p class="paragraph">a mathematical product of two tensors.</p><h2 class="">Parameters</h2><div data-togglable="Parameters"><div class="platform-hinted WithExtraAttributes" data-platform-hinted="data-platform-hinted" data-togglable="Parameters"><div class="content sourceset-depenent-content" data-active="" data-togglable=":kmath-tensors:dokkaHtmlPartial/commonMain"><div data-togglable="Parameters"><div class="table" data-togglable="Parameters"><div class="table-row" data-filterable-current=":kmath-tensors:dokkaHtmlPartial/commonMain" data-filterable-set=":kmath-tensors:dokkaHtmlPartial/commonMain"><div class="main-subrow keyValue WithExtraAttributes"><div class=""><span class="inline-flex">other</span></div><div><div class="title"><div data-togglable="Parameters"><p class="paragraph">tensor to be multiplied.</p></div></div></div></div></div></div></div></div></div></div></div>
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<h2 class="">Sources</h2>
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<div class="table" data-togglable="Sources"><a data-name="1622973833%2FSource%2F-1345790395" anchor-label="https://github.com/mipt-npm/kmath/tree/master/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt#L199" id="1622973833%2FSource%2F-1345790395" data-filterable-set=":kmath-tensors:dokkaHtmlPartial/commonMain"></a>
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<div class=""><span class="inline-flex"><a href="https://github.com/mipt-npm/kmath/tree/master/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt#L199">common source</a><span class="anchor-wrapper"><span class="anchor-icon" pointing-to="1622973833%2FSource%2F-1345790395"></span>
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