CircleCI update of dev docs (3042).

Author: Circle CI

master/_downloads/0228827bdcc2a6735b1faf26c864145d/plot_OT_1D_smooth.zip

@@ -176,14 +176,14 @@ <h1>Source code for ot.gromov._lowrank</h1><div class="highlight"><pre>
 
 <span class="sd">    where :</span>
 
-<span class="sd">    - :math: `A` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the source domain.</span>
-<span class="sd">    - :math: `B` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the target domain.</span>
-<span class="sd">    - :math: `\mathcal{Q}_{A,B}` is quadratic objective function of the Gromov Wasserstein plan.</span>
-<span class="sd">    - :math: `Q` and `R` are the low-rank matrix decomposition of the Gromov-Wasserstein plan.</span>
-<span class="sd">    - :math: `g` is the weight vector for the low-rank decomposition of the Gromov-Wasserstein plan.</span>
+<span class="sd">    - :math:`A` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the source domain.</span>
+<span class="sd">    - :math:`B` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the target domain.</span>
+<span class="sd">    - :math:`\mathcal{Q}_{A,B}` is quadratic objective function of the Gromov Wasserstein plan.</span>
+<span class="sd">    - :math:`Q` and `R` are the low-rank matrix decomposition of the Gromov-Wasserstein plan.</span>
+<span class="sd">    - :math:`g` is the weight vector for the low-rank decomposition of the Gromov-Wasserstein plan.</span>
 <span class="sd">    - :math:`\mathbf{a}` and :math:`\mathbf{b}` are source and target weights (histograms, both sum to 1).</span>
-<span class="sd">    - :math: `r` is the rank of the Gromov-Wasserstein plan.</span>
-<span class="sd">    - :math: `\mathcal{C(a,b,r)}` are the low-rank couplings of the OT problem.</span>
+<span class="sd">    - :math:`r` is the rank of the Gromov-Wasserstein plan.</span>
+<span class="sd">    - :math:`\mathcal{C(a,b,r)}` are the low-rank couplings of the OT problem.</span>
 <span class="sd">    - :math:`H((Q,R,g))` is the values of the three respective entropies evaluated for each term.</span>
 
 

master/_downloads/06f3dd7c3258690ab730be0a658b5e36/plot_ssw_unif_torch.zip

@@ -1098,18 +1098,18 @@ <h1>Source code for ot.gromov._partial</h1><div class="highlight"><pre>
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 
-<span class="sd">    G : array-like, shape(ns,nt)</span>
+<span class="sd">    G : array-like, shape(ns, nt)</span>
 <span class="sd">        The transport map at a given iteration of the FW</span>
-<span class="sd">    deltaG : array-like (ns,nt)</span>
+<span class="sd">    deltaG : array-like, shape (ns, nt)</span>
 <span class="sd">        Difference between the optimal map `Gc` found by linearization in the</span>
 <span class="sd">        FW algorithm and the value at a given iteration</span>
 <span class="sd">    cost_G : float</span>
 <span class="sd">        Value of the cost at `G`</span>
-<span class="sd">    df_G : array-like (ns,nt)</span>
+<span class="sd">    df_G : array-like, shape (ns, nt)</span>
 <span class="sd">        Gradient of the GW cost at `G`</span>
-<span class="sd">    df_Gc : array-like (ns,nt)</span>
+<span class="sd">    df_Gc : array-like, shape (ns, nt)</span>
 <span class="sd">        Gradient of the GW cost at `Gc`</span>
-<span class="sd">    M : array-like (ns,nt)</span>
+<span class="sd">    M : array-like, shape (ns, nt)</span>
 <span class="sd">        Cost matrix between the features.</span>
 <span class="sd">    reg : float</span>
 <span class="sd">        Regularization parameter.</span>
@@ -1128,7 +1128,7 @@ <h1>Source code for ot.gromov._partial</h1><div class="highlight"><pre>
 <span class="sd">        nb of function call. Useless here</span>
 <span class="sd">    cost_G : float</span>
 <span class="sd">        The value of the cost for the next iteration</span>
-<span class="sd">    df_G : array-like (ns,nt)</span>
+<span class="sd">    df_G : array-like, shape (ns, nt)</span>
 <span class="sd">        Updated gradient of the GW cost</span>
 
 <span class="sd">    References</span>
@@ -1272,7 +1272,7 @@ <h1>Source code for ot.gromov._partial</h1><div class="highlight"><pre>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    :math: `gamma` : (dim_a, dim_b) ndarray</span>
+<span class="sd">    :math:`gamma` : ndarray, shape (dim_a, dim_b)</span>
 <span class="sd">        Optimal transportation matrix for the given parameters</span>
 <span class="sd">    log : dict</span>
 <span class="sd">        log dictionary returned only if `log` is `True`</span>

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -247,7 +247,7 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    where :</span>
 
 <span class="sd">    - d is the metric</span>
-<span class="sd">    - x_a and x_b are the samples</span>
+<span class="sd">    - :math:`x_a` and :math:`x_b` are the samples</span>
 <span class="sd">    - a and b are the sample weights</span>
 
 <span class="sd">    This implementation only supports metrics</span>
@@ -257,21 +257,21 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    x_a : (ns,) or (ns, 1) ndarray, float64</span>
+<span class="sd">    x_a : ndarray of float64, shape (ns,) or (ns, 1)</span>
 <span class="sd">        Source dirac locations (on the real line)</span>
-<span class="sd">    x_b : (nt,) or (ns, 1) ndarray, float64</span>
+<span class="sd">    x_b : ndarray of float64, shape (nt,) or (ns, 1)</span>
 <span class="sd">        Target dirac locations (on the real line)</span>
-<span class="sd">    a : (ns,) ndarray, float64, optional</span>
+<span class="sd">    a : ndarray of float64, shape (ns,), optional</span>
 <span class="sd">        Source histogram (default is uniform weight)</span>
-<span class="sd">    b : (nt,) ndarray, float64, optional</span>
+<span class="sd">    b : ndarray of float64, shape (nt,), optional</span>
 <span class="sd">        Target histogram (default is uniform weight)</span>
 <span class="sd">    metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
 <span class="sd">        Metric to be used. Only works with either of the strings</span>
 <span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;`.</span>
 <span class="sd">    p: float, optional (default=1.0)</span>
 <span class="sd">         The p-norm to apply for if metric=&#39;minkowski&#39;</span>
 <span class="sd">    dense: boolean, optional (default=True)</span>
-<span class="sd">        If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt).</span>
+<span class="sd">        If True, returns :math:`\gamma` as a dense ndarray of shape (ns, nt).</span>
 <span class="sd">        Otherwise returns a sparse representation using scipy&#39;s `coo_matrix`</span>
 <span class="sd">        format. Due to implementation details, this function runs faster when</span>
 <span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;` metrics</span>
@@ -285,7 +285,7 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    gamma: (ns, nt) ndarray</span>
+<span class="sd">    gamma: ndarray, shape (ns, nt)</span>
 <span class="sd">        Optimal transportation matrix for the given parameters</span>
 <span class="sd">    log: dict</span>
 <span class="sd">        If input log is True, a dictionary containing the cost</span>
@@ -408,7 +408,7 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    where :</span>
 
 <span class="sd">    - d is the metric</span>
-<span class="sd">    - x_a and x_b are the samples</span>
+<span class="sd">    - :math:`x_a` and :math:`x_b` are the samples</span>
 <span class="sd">    - a and b are the sample weights</span>
 
 <span class="sd">    This implementation only supports metrics</span>
@@ -418,21 +418,21 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    x_a : (ns,) or (ns, 1) ndarray, float64</span>
+<span class="sd">    x_a : ndarray of float64, shape (ns,) or (ns, 1)</span>
 <span class="sd">        Source dirac locations (on the real line)</span>
-<span class="sd">    x_b : (nt,) or (ns, 1) ndarray, float64</span>
+<span class="sd">    x_b : ndarray of float64, shape (nt,) or (ns, 1)</span>
 <span class="sd">        Target dirac locations (on the real line)</span>
-<span class="sd">    a : (ns,) ndarray, float64, optional</span>
+<span class="sd">    a : ndarray of float64, shape (ns,), optional</span>
 <span class="sd">        Source histogram (default is uniform weight)</span>
-<span class="sd">    b : (nt,) ndarray, float64, optional</span>
+<span class="sd">    b : ndarray of float64, shape (nt,), optional</span>
 <span class="sd">        Target histogram (default is uniform weight)</span>
 <span class="sd">    metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
 <span class="sd">        Metric to be used. Only works with either of the strings</span>
 <span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;`.</span>
 <span class="sd">    p: float, optional (default=1.0)</span>
 <span class="sd">         The p-norm to apply for if metric=&#39;minkowski&#39;</span>
 <span class="sd">    dense: boolean, optional (default=True)</span>
-<span class="sd">        If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt).</span>
+<span class="sd">        If True, returns :math:`\gamma` as a dense ndarray of shape (ns, nt).</span>
 <span class="sd">        Otherwise returns a sparse representation using scipy&#39;s `coo_matrix`</span>
 <span class="sd">        format. Only used if log is set to True. Due to implementation details,</span>
 <span class="sd">        this function runs faster when dense is set to False.</span>
@@ -496,9 +496,9 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    M : (nr, nc) ndarray</span>
+<span class="sd">    M : ndarray, shape (nr, nc)</span>
 <span class="sd">        Matrix to shift</span>
-<span class="sd">    shifts: int or (nr,) ndarray</span>
+<span class="sd">    shifts: int or ndarray, shape (nr,)</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -1145,7 +1145,7 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    u_values: ndarray, shape (n, ...)</span>
+<span class="sd">    u_values : ndarray, shape (n, ...)</span>
 <span class="sd">        Samples</span>
 <span class="sd">    u_weights : ndarray, shape (n, ...), optional</span>
 <span class="sd">        samples weights in the source domain</span>

master/_downloads/135d9df766cd2fdce616d2b7c6016c04/plot_otda_jcpot.zip

@@ -116,7 +116,7 @@ <h1>Source code for ot.plot</h1><div class="highlight"><pre>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Plot matrix :math:`\mathbf{M}` with the source and target 1D distributions.</span>
 
 <span class="sd">    Creates a subplot with the source distribution :math:`\mathbf{a}` and target</span>
-<span class="sd">    distribution :math:`\mathbf{b}`t.</span>
+<span class="sd">    distribution :math:`\mathbf{b}`.</span>
 <span class="sd">    In &#39;yx&#39; mode (default), the source is on the left and</span>
 <span class="sd">    the target on the top, and in &#39;xy&#39; mode, source on the bottom (upside</span>
 <span class="sd">    down) and the target on the left.</span>
@@ -153,8 +153,9 @@ <h1>Source code for ot.plot</h1><div class="highlight"><pre>
 <span class="sd">    ax2 : target plot ax</span>
 <span class="sd">    ax3 : coupling plot ax</span>
 
-<span class="sd">    .. seealso::</span>
-<span class="sd">        :func:`rescale_for_imshow_plot`</span>
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :func:`rescale_for_imshow_plot`</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">assert</span> <span class="n">plot_style</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;yx&quot;</span><span class="p">,</span> <span class="s2">&quot;xy&quot;</span><span class="p">],</span> <span class="s2">&quot;plot_style should be &#39;yx&#39; or &#39;xy&#39;&quot;</span>
     <span class="n">na</span><span class="p">,</span> <span class="n">nb</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">shape</span>
@@ -275,8 +276,9 @@ <h1>Source code for ot.plot</h1><div class="highlight"><pre>
 <span class="sd">    yr : ndarray, shape (nx,)</span>
 <span class="sd">        Rescaled y values (due to slicing, may have less elements than y)</span>
 
-<span class="sd">    .. seealso::</span>
-<span class="sd">        :func:`plot1D_mat`</span>
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :func:`plot1D_mat`</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="c1"># slice over the y values that are in the y range</span>