From 37977810ab538fe461ef8f3ec16434c59f6f7c5a Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 9 Jun 2017 13:11:41 +0200
Subject: add doc ot.gpu.bregman

---
 ot/gpu/bregman.py | 74 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)

(limited to 'ot/gpu/bregman.py')

diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py
index cc610b7..2c3e317 100644
--- a/ot/gpu/bregman.py
+++ b/ot/gpu/bregman.py
@@ -9,6 +9,80 @@ import cudamat
 
 def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
                 log=False, returnAsGPU=False):
+    """
+    Solve the entropic regularization optimal transport problem on GPU
+
+    The function solves the following optimization problem:
+
+    .. math::
+        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
+
+        s.t. \gamma 1 = a
+
+             \gamma^T 1= b
+
+             \gamma\geq 0
+    where :
+
+    - M is the (ns,nt) metric cost matrix
+    - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+    - a and b are source and target weights (sum to 1)
+
+    The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_
+
+
+    Parameters
+    ----------
+    a : np.ndarray (ns,)
+        samples weights in the source domain
+    b : np.ndarray (nt,)
+        samples in the target domain
+    M_GPU : cudamat.CUDAMatrix (ns,nt)
+        loss matrix
+    reg : float
+        Regularization term >0
+    numItermax : int, optional
+        Max number of iterations
+    stopThr : float, optional
+        Stop threshol on error (>0)
+    verbose : bool, optional
+        Print information along iterations
+    log : bool, optional
+        record log if True
+    returnAsGPU : bool, optional
+        return the OT matrix as a cudamat.CUDAMatrix
+
+    Returns
+    -------
+    gamma : (ns x nt) ndarray
+        Optimal transportation matrix for the given parameters
+    log : dict
+        log dictionary return only if log==True in parameters
+
+    Examples
+    --------
+
+    >>> import ot
+    >>> a=[.5,.5]
+    >>> b=[.5,.5]
+    >>> M=[[0.,1.],[1.,0.]]
+    >>> ot.sinkhorn(a,b,M,1)
+    array([[ 0.36552929,  0.13447071],
+           [ 0.13447071,  0.36552929]])
+
+
+    References
+    ----------
+
+    .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013
+
+
+    See Also
+    --------
+    ot.lp.emd : Unregularized OT
+    ot.optim.cg : General regularized OT
+
+    """    
     # init data
     Nini = len(a)
     Nfin = len(b)
-- 
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