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<h1>Source code for dscript.models.interaction</h1><div class="highlight"><pre> |
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<span></span><span class="sd">"""</span> |
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<span class="sd">Interaction model classes.</span> |
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<span class="sd">"""</span> |
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
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<span class="kn">import</span> <span class="nn">torch</span> |
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<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span> |
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<span class="kn">import</span> <span class="nn">torch.functional</span> <span class="k">as</span> <span class="nn">F</span> |
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<div class="viewcode-block" id="LogisticActivation"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.LogisticActivation">[docs]</a><span class="k">class</span> <span class="nc">LogisticActivation</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Implementation of Generalized Sigmoid</span> |
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<span class="sd"> Applies the element-wise function:</span> |
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<span class="sd"> :math:`\\sigma(x) = \\frac{1}{1 + \\exp(-k(x-x_0))}`</span> |
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<span class="sd"> :param x0: The value of the sigmoid midpoint</span> |
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<span class="sd"> :type x0: float</span> |
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<span class="sd"> :param k: The slope of the sigmoid - trainable - :math:`k \\geq 0`</span> |
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<span class="sd"> :type k: float</span> |
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<span class="sd"> :param train: Whether :math:`k` is a trainable parameter</span> |
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<span class="sd"> :type train: bool</span> |
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<span class="sd"> """</span> |
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x0</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
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<span class="nb">super</span><span class="p">(</span><span class="n">LogisticActivation</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">x0</span> <span class="o">=</span> <span class="n">x0</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">k</span><span class="p">)]))</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="o">.</span><span class="n">requiresGrad</span> <span class="o">=</span> <span class="n">train</span> |
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<div class="viewcode-block" id="LogisticActivation.forward"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.LogisticActivation.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Applies the function to the input elementwise</span> |
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<span class="sd"> :param x: :math:`(N \\times *)` where :math:`*` means, any number of additional dimensions</span> |
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<span class="sd"> :type x: torch.Tensor</span> |
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<span class="sd"> :return: :math:`(N \\times *)`, same shape as the input</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x0</span><span class="p">))),</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span> |
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<span class="k">return</span> <span class="n">out</span></div> |
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<span class="k">def</span> <span class="nf">clip</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Restricts sigmoid slope :math:`k` to be greater than or equal to 0, if :math:`k` is trained.</span> |
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<span class="sd"> :meta private:</span> |
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<span class="sd"> """</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div> |
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<div class="viewcode-block" id="ModelInteraction"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.ModelInteraction">[docs]</a><span class="k">class</span> <span class="nc">ModelInteraction</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Main D-SCRIPT model. Contains an embedding and contact model and offers access to those models. Computes pooling operations on contact map to generate interaction probability.</span> |
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<span class="sd"> :param embedding: Embedding model</span> |
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<span class="sd"> :type embedding: dscript.models.embedding.FullyConnectedEmbed</span> |
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<span class="sd"> :param contact: Contact model</span> |
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<span class="sd"> :type contact: dscript.models.contact.ContactCNN</span> |
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<span class="sd"> :param use_cuda: Whether the model should be run on GPU</span> |
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<span class="sd"> :type use_cuda: bool</span> |
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<span class="sd"> :param pool_size: width of max-pool [default 9]</span> |
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<span class="sd"> :type pool_size: bool</span> |
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<span class="sd"> :param theta_init: initialization value of :math:`\\theta` for weight matrix [default: 1]</span> |
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<span class="sd"> :type theta_init: float</span> |
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<span class="sd"> :param lambda_init: initialization value of :math:`\\lambda` for weight matrix [default: 0]</span> |
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<span class="sd"> :type lambda_init: float</span> |
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<span class="sd"> :param gamma_init: initialization value of :math:`\\gamma` for global pooling [default: 0]</span> |
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<span class="sd"> :type gamma_init: float</span> |
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<span class="sd"> :param use_W: whether to use the weighting matrix [default: True]</span> |
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<span class="sd"> :type use_W: bool</span> |
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<span class="sd"> """</span> |
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> |
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<span class="bp">self</span><span class="p">,</span> |
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<span class="n">embedding</span><span class="p">,</span> |
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<span class="n">contact</span><span class="p">,</span> |
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<span class="n">pool_size</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> |
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<span class="n">theta_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
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<span class="n">lambda_init</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
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<span class="n">gamma_init</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
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<span class="n">use_W</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
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<span class="p">):</span> |
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<span class="nb">super</span><span class="p">(</span><span class="n">ModelInteraction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">use_W</span> <span class="o">=</span> <span class="n">use_W</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">LogisticActivation</span><span class="p">(</span><span class="n">x0</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="o">=</span> <span class="n">embedding</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">contact</span> <span class="o">=</span> <span class="n">contact</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_W</span><span class="p">:</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="n">theta_init</span><span class="p">]))</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">lambda_</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="n">lambda_init</span><span class="p">]))</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">maxPool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">pool_size</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="n">gamma_init</span><span class="p">]))</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">clip</span><span class="p">()</span> |
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<span class="k">def</span> <span class="nf">clip</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Clamp model values</span> |
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<span class="sd"> :meta private:</span> |
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<span class="sd"> """</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">contact</span><span class="o">.</span><span class="n">clip</span><span class="p">()</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_W</span><span class="p">:</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">lambda_</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
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<div class="viewcode-block" id="ModelInteraction.embed"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.ModelInteraction.embed">[docs]</a> <span class="k">def</span> <span class="nf">embed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Project down input language model embeddings into low dimension using projection module</span> |
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<span class="sd"> :param z: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z: torch.Tensor</span> |
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<span class="sd"> :return: D-SCRIPT projection :math:`(b \\times N \\times d)`</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
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<span class="k">return</span> <span class="n">z</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="n">z</span><span class="p">)</span></div> |
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<div class="viewcode-block" id="ModelInteraction.cpred"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.ModelInteraction.cpred">[docs]</a> <span class="k">def</span> <span class="nf">cpred</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Project down input language model embeddings into low dimension using projection module</span> |
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<span class="sd"> :param z0: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z0: torch.Tensor</span> |
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<span class="sd"> :param z1: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z1: torch.Tensor</span> |
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<span class="sd"> :return: Predicted contact map :math:`(b \\times N \\times M)`</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">e0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed</span><span class="p">(</span><span class="n">z0</span><span class="p">)</span> |
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<span class="n">e1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed</span><span class="p">(</span><span class="n">z1</span><span class="p">)</span> |
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<span class="n">B</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">contact</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">e0</span><span class="p">,</span> <span class="n">e1</span><span class="p">)</span> |
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<span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">contact</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">B</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">C</span></div> |
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<div class="viewcode-block" id="ModelInteraction.map_predict"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.ModelInteraction.map_predict">[docs]</a> <span class="k">def</span> <span class="nf">map_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Project down input language model embeddings into low dimension using projection module</span> |
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<span class="sd"> :param z0: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z0: torch.Tensor</span> |
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<span class="sd"> :param z1: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z1: torch.Tensor</span> |
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<span class="sd"> :return: Predicted contact map, predicted probability of interaction :math:`(b \\times N \\times d_0), (1)`</span> |
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<span class="sd"> :rtype: torch.Tensor, torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cpred</span><span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">)</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_W</span><span class="p">:</span> |
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<span class="c1"># Create contact weighting matrix</span> |
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<span class="n">N</span><span class="p">,</span> <span class="n">M</span> <span class="o">=</span> <span class="n">C</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> |
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<span class="n">x1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">((</span><span class="n">N</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">((</span><span class="n">N</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'cuda'</span><span class="p">:</span> |
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<span class="n">x1</span> <span class="o">=</span> <span class="n">x1</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> |
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<span class="n">x1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lambda_</span> <span class="o">*</span> <span class="n">x1</span><span class="p">)</span> |
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<span class="n">x2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">M</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">((</span><span class="n">M</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="p">((</span><span class="n">M</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> |
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'cuda'</span><span class="p">:</span> |
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<span class="n">x2</span> <span class="o">=</span> <span class="n">x2</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> |
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<span class="n">x2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lambda_</span> <span class="o">*</span> <span class="n">x2</span><span class="p">)</span> |
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<span class="n">W</span> <span class="o">=</span> <span class="n">x1</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">x2</span> |
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<span class="n">W</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">W</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> |
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<span class="n">yhat</span> <span class="o">=</span> <span class="n">C</span> <span class="o">*</span> <span class="n">W</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="n">yhat</span> <span class="o">=</span> <span class="n">C</span> |
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<span class="n">yhat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxPool</span><span class="p">(</span><span class="n">yhat</span><span class="p">)</span> |
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<span class="c1"># Mean of contact predictions where p_ij > mu + gamma*sigma</span> |
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<span class="n">mu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">yhat</span><span class="p">)</span> |
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<span class="n">sigma</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">yhat</span><span class="p">)</span> |
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<span class="n">Q</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">yhat</span> <span class="o">-</span> <span class="n">mu</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">))</span> |
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<span class="n">phat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">Q</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">Q</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
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<span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">phat</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">C</span><span class="p">,</span> <span class="n">phat</span></div> |
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<div class="viewcode-block" id="ModelInteraction.predict"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.interaction.ModelInteraction.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Project down input language model embeddings into low dimension using projection module</span> |
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<span class="sd"> :param z0: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z0: torch.Tensor</span> |
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<span class="sd"> :param z1: Language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type z1: torch.Tensor</span> |
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<span class="sd"> :return: Predicted probability of interaction</span> |
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<span class="sd"> :rtype: torch.Tensor, torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">_</span><span class="p">,</span> <span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_predict</span><span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">phat</span></div> |
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<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> :meta private:</span> |
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<span class="sd"> """</span> |
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">)</span></div> |
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</pre></div> |
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