Added docstrings

1 files changed, 92 insertions, 22 deletions

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index e9635a1..a350d60 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -321,8 +321,8 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False):
     .. math::
         \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j])
 
-        s.t. \gamma 1 = a
-             \gamma^T 1= b
+        s.t. \gamma 1 = a,
+             \gamma^T 1= b,
              \gamma\geq 0
     where :
 
@@ -330,28 +330,27 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False):
     - x_a and x_b are the samples
     - a and b are the sample weights
 
-    Uses the algorithm proposed in [1]_
+    Uses the algorithm detailed in [1]_
 
     Parameters
     ----------
     x_a : (ns,) or (ns, 1) ndarray, float64
-        Source histogram (uniform weight if empty list)
+        Source dirac locations (on the real line)
     x_b : (nt,) or (ns, 1) ndarray, float64
-        Target histogram (uniform weight if empty list)
+        Target dirac locations (on the real line)
     a : (ns,) ndarray, float64
         Source histogram (uniform weight if empty list)
     b : (nt,) ndarray, float64
         Target histogram (uniform weight if empty list)
+    metric: str, optional (default='sqeuclidean')
+        Metric to be used. Only strings listed in ... are accepted.
+        Due to implementation details, this function runs faster when
+        `'sqeuclidean'`, `'cityblock'`,  or `'euclidean'` metrics are used.
     dense: boolean, optional (default=True)
         If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt).
         Otherwise returns a sparse representation using scipy's `coo_matrix`
-        format.
-        Due to implementation details, this function runs faster when
+        format. Due to implementation details, this function runs faster when
         dense is set to False.
-    metric: str, optional (default='sqeuclidean')
-        Metric to be used. Has to be a string.
-        Due to implementation details, this function runs faster when
-        `'sqeuclidean'` or `'euclidean'` metrics are used.
     log: boolean, optional (default=False)
         If True, returns a dictionary containing the cost.
         Otherwise returns only the optimal transportation matrix.
@@ -368,28 +367,28 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False):
     --------
 
     Simple example with obvious solution. The function emd_1d accepts lists and
-    perform automatic conversion to numpy arrays
+    performs automatic conversion to numpy arrays
 
     >>> import ot
     >>> a=[.5, .5]
     >>> b=[.5, .5]
-    >>> x_a = [0., 2.]
+    >>> x_a = [2., 0.]
     >>> x_b = [0., 3.]
-    >>> ot.emd_1d(a, b, x_a, x_b)
-    array([[ 0.5,  0. ],
-           [ 0. ,  0.5]])
+    >>> ot.emd_1d(x_a, x_b, a, b)
+    array([[0. ,  0.5],
+           [0.5,  0. ]])
 
     References
     ----------
 
-    .. [1] TODO
+    .. [1]  Peyré, G., & Cuturi, M. (2017). "Computational Optimal
+        Transport", 2018.
 
     See Also
     --------
     ot.lp.emd : EMD for multidimensional distributions
     ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the
         transportation matrix)
-
     """
     a = np.asarray(a, dtype=np.float64)
     b = np.asarray(b, dtype=np.float64)
@@ -418,10 +417,9 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False):
     G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])),
                    shape=(a.shape[0], b.shape[0]))
     if dense:
-        G = G.todense()
+        G = G.toarray()
     if log:
-        log = {}
-        log['cost'] = cost
+        log = {'cost': cost}
         return G, log
     return G
 
@@ -430,10 +428,82 @@ def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False):
     """Solves the Earth Movers distance problem between 1d measures and returns
     the loss
 
+
+    .. math::
+        \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j])
+
+        s.t. \gamma 1 = a,
+             \gamma^T 1= b,
+             \gamma\geq 0
+    where :
+
+    - d is the metric
+    - x_a and x_b are the samples
+    - a and b are the sample weights
+
+    Uses the algorithm detailed in [1]_
+
+    Parameters
+    ----------
+    x_a : (ns,) or (ns, 1) ndarray, float64
+        Source dirac locations (on the real line)
+    x_b : (nt,) or (ns, 1) ndarray, float64
+        Target dirac locations (on the real line)
+    a : (ns,) ndarray, float64
+        Source histogram (uniform weight if empty list)
+    b : (nt,) ndarray, float64
+        Target histogram (uniform weight if empty list)
+    metric: str, optional (default='sqeuclidean')
+        Metric to be used. Only strings listed in ... are accepted.
+        Due to implementation details, this function runs faster when
+        `'sqeuclidean'`, `'cityblock'`,  or `'euclidean'` metrics are used.
+    dense: boolean, optional (default=True)
+        If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt).
+        Otherwise returns a sparse representation using scipy's `coo_matrix`
+        format. Only used if log is set to True. Due to implementation details,
+        this function runs faster when dense is set to False.
+    log: boolean, optional (default=False)
+        If True, returns a dictionary containing the transportation matrix.
+        Otherwise returns only the loss.
+
+    Returns
+    -------
+    loss: float
+        Cost associated to the optimal transportation
+    log: dict
+        If input log is True, a dictionary containing the Optimal transportation
+        matrix for the given parameters
+
+
+    Examples
+    --------
+
+    Simple example with obvious solution. The function emd2_1d accepts lists and
+    performs automatic conversion to numpy arrays
+
+    >>> import ot
+    >>> a=[.5, .5]
+    >>> b=[.5, .5]
+    >>> x_a = [2., 0.]
+    >>> x_b = [0., 3.]
+    >>> ot.emd2_1d(x_a, x_b, a, b)
+    0.5
+
+    References
+    ----------
+
+    .. [1]  Peyré, G., & Cuturi, M. (2017). "Computational Optimal
+        Transport", 2018.
+
+    See Also
+    --------
+    ot.lp.emd2 : EMD for multidimensional distributions
+    ot.lp.emd_1d : EMD for 1d distributions (returns the transportation matrix
+        instead of the cost)
     """
     # If we do not return G (log==False), then we should not to cast it to dense
     # (useless overhead)
-    G, log_emd = emd_1d(a=a, b=b, x_a=x_a, x_b=x_b, metric=metric,
+    G, log_emd = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric=metric,
                         dense=dense and log, log=True)
     cost = log_emd['cost']
     if log: