remove linewidth error message

1 files changed, 108 insertions, 42 deletions

diff --git a/ot/da.py b/ot/da.py
index 4f9bce5..1dd4011 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -17,14 +17,18 @@ from .optim import cg
 from .optim import gcg
 
 
-def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False):
+def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
+                     numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
+                     log=False):
     """
-    Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization
+    Solve the entropic regularization optimal transport problem with nonconvex
+    group lasso regularization
 
     The function solves the following optimization problem:
 
     .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma)
+        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)
+        + \eta \Omega_g(\gamma)
 
         s.t. \gamma 1 = a
 
@@ -34,11 +38,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
     where :
 
     - M is the (ns,nt) metric cost matrix
-    - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
-    - :math:`\Omega_g` is the group lasso  regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1`   where  :math:`\mathcal{I}_c` are the index of samples from class c in the source domain.
+    - :math:`\Omega_e` is the entropic regularization term
+        :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+    - :math:`\Omega_g` is the group lasso  regulaization term
+      :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1`
+      where  :math:`\mathcal{I}_c` are the index of samples from class c
+      in the source domain.
     - a and b are source and target weights (sum to 1)
 
-    The algorithm used for solving the problem is the generalised conditional gradient as proposed in  [5]_ [7]_
+    The algorithm used for solving the problem is the generalised conditional
+    gradient as proposed in  [5]_ [7]_
 
 
     Parameters
@@ -78,8 +87,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
     References
     ----------
 
-    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
-    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
+    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+       "Optimal Transport for Domain Adaptation," in IEEE
+       Transactions on Pattern Analysis and Machine Intelligence ,
+       vol.PP, no.99, pp.1-1
+    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015).
+       Generalized conditional gradient: analysis of convergence
+       and applications. arXiv preprint arXiv:1510.06567.
 
     See Also
     --------
@@ -114,14 +128,18 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
     return transp
 
 
-def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False):
+def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
+                     numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
+                     log=False):
     """
-    Solve the entropic regularization optimal transport problem with group lasso regularization
+    Solve the entropic regularization optimal transport problem with group
+    lasso regularization
 
     The function solves the following optimization problem:
 
     .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma)
+        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+
+        \eta \Omega_g(\gamma)
 
         s.t. \gamma 1 = a
 
@@ -131,11 +149,16 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
     where :
 
     - M is the (ns,nt) metric cost matrix
-    - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
-    - :math:`\Omega_g` is the group lasso  regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2`   where  :math:`\mathcal{I}_c` are the index of samples from class c in the source domain.
+    - :math:`\Omega_e` is the entropic regularization term
+      :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+    - :math:`\Omega_g` is the group lasso regulaization term
+      :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2`
+      where  :math:`\mathcal{I}_c` are the index of samples from class
+      c in the source domain.
     - a and b are source and target weights (sum to 1)
 
-    The algorithm used for solving the problem is the generalised conditional gradient as proposed in  [5]_ [7]_
+    The algorithm used for solving the problem is the generalised conditional
+    gradient as proposed in  [5]_ [7]_
 
 
     Parameters
@@ -175,8 +198,12 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
     References
     ----------
 
-    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
-    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
+    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+       "Optimal Transport for Domain Adaptation," in IEEE Transactions
+       on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015).
+       Generalized conditional gradient: analysis of convergence and
+       applications. arXiv preprint arXiv:1510.06567.
 
     See Also
     --------
@@ -203,16 +230,22 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte
                     W[labels_a == lab, i] = temp / n
         return W
 
-    return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, numInnerItermax=numInnerItermax, stopThr=stopInnerThr, verbose=verbose, log=log)
+    return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax,
+               numInnerItermax=numInnerItermax, stopThr=stopInnerThr,
+               verbose=verbose, log=log)
 
 
-def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs):
+def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
+                            verbose2=False, numItermax=100, numInnerItermax=10,
+                            stopInnerThr=1e-6, stopThr=1e-5, log=False,
+                            **kwargs):
     """Joint OT and linear mapping estimation as proposed in [8]
 
     The function solves the following optimization problem:
 
     .. math::
-        \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta  \|L -I\|^2_F
+        \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F +
+          \mu<\gamma,M>_F + \eta  \|L -I\|^2_F
 
         s.t. \gamma 1 = a
 
@@ -221,8 +254,10 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
              \gamma\geq 0
     where :
 
-    - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns)
-    - :math:`L` is a dxd linear operator that approximates the barycentric mapping
+    - M is the (ns,nt) squared euclidean cost matrix between samples in
+       Xs and Xt (scaled by ns)
+    - :math:`L` is a dxd linear operator that approximates the barycentric
+      mapping
     - :math:`I` is the identity matrix (neutral linear mapping)
     - a and b are uniform source and target weights
 
@@ -277,7 +312,9 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
     References
     ----------
 
-    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
+    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
+        "Mapping estimation for discrete optimal transport",
+        Neural Information Processing Systems (NIPS), 2016.
 
     See Also
     --------
@@ -384,13 +421,18 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
         return G, L
 
 
-def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigma=1, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs):
+def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
+                            sigma=1, bias=False, verbose=False, verbose2=False,
+                            numItermax=100, numInnerItermax=10,
+                            stopInnerThr=1e-6, stopThr=1e-5, log=False,
+                            **kwargs):
     """Joint OT and nonlinear mapping estimation with kernels as proposed in [8]
 
     The function solves the following optimization problem:
 
     .. math::
-        \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta  \|L\|^2_\mathcal{H}
+        \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -
+        n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta  \|L\|^2_\mathcal{H}
 
         s.t. \gamma 1 = a
 
@@ -399,8 +441,10 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm
              \gamma\geq 0
     where :
 
-    - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns)
-    - :math:`L` is a ns x d linear operator on a kernel matrix that approximates the barycentric mapping
+    - M is the (ns,nt) squared euclidean cost matrix between samples in
+      Xs and Xt (scaled by ns)
+    - :math:`L` is a ns x d linear operator on a kernel matrix that
+      approximates the barycentric mapping
     - a and b are uniform source and target weights
 
     The problem consist in solving jointly an optimal transport matrix
@@ -458,7 +502,9 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm
     References
     ----------
 
-    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
+    .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
+       "Mapping estimation for discrete optimal transport",
+       Neural Information Processing Systems (NIPS), 2016.
 
     See Also
     --------
@@ -593,7 +639,9 @@ class OTDA(object):
     References
     ----------
 
-    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+       "Optimal Transport for Domain Adaptation," in IEEE Transactions on
+       Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
 
     """
 
@@ -606,7 +654,8 @@ class OTDA(object):
         self.computed = False
 
     def fit(self, xs, xt, ws=None, wt=None, norm=None):
-        """ Fit domain adaptation between samples is xs and xt (with optional weights)"""
+        """Fit domain adaptation between samples is xs and xt
+        (with optional weights)"""
         self.xs = xs
         self.xt = xt
 
@@ -669,7 +718,9 @@ class OTDA(object):
         References
         ----------
 
-        .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+        .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+          Regularized discrete optimal transport. SIAM Journal on Imaging
+          Sciences, 7(3), 1853-1882.
 
         """
         if direction > 0:  # >0 then source to target
@@ -708,10 +759,12 @@ class OTDA(object):
 
 class OTDA_sinkhorn(OTDA):
 
-    """Class for domain adaptation with optimal transport with entropic regularization"""
+    """Class for domain adaptation with optimal transport with entropic
+    regularization"""
 
     def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
-        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)"""
+        """Fit regularized domain adaptation between samples is xs and xt
+        (with optional weights)"""
         self.xs = xs
         self.xt = xt
 
@@ -731,10 +784,14 @@ class OTDA_sinkhorn(OTDA):
 
 class OTDA_lpl1(OTDA):
 
-    """Class for domain adaptation with optimal transport with entropic and group regularization"""
+    """Class for domain adaptation with optimal transport with entropic and
+    group regularization"""
 
-    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs):
-        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),  See ot.da.sinkhorn_lpl1_mm for fit parameters"""
+    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
+            **kwargs):
+        """Fit regularized domain adaptation between samples is xs and xt
+        (with optional weights),  See ot.da.sinkhorn_lpl1_mm for fit
+        parameters"""
         self.xs = xs
         self.xt = xt
 
@@ -754,10 +811,14 @@ class OTDA_lpl1(OTDA):
 
 class OTDA_l1l2(OTDA):
 
-    """Class for domain adaptation with optimal transport with entropic and group lasso regularization"""
+    """Class for domain adaptation with optimal transport with entropic
+    and group lasso regularization"""
 
-    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs):
-        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),  See ot.da.sinkhorn_lpl1_gl for fit parameters"""
+    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
+            **kwargs):
+        """Fit regularized domain adaptation between samples is xs and xt
+           (with optional weights),  See ot.da.sinkhorn_lpl1_gl for fit
+           parameters"""
         self.xs = xs
         self.xt = xt
 
@@ -777,7 +838,9 @@ class OTDA_l1l2(OTDA):
 
 class OTDA_mapping_linear(OTDA):
 
-    """Class for optimal transport with joint linear mapping estimation as in [8]"""
+    """Class for optimal transport with joint linear mapping estimation as in
+    [8]
+    """
 
     def __init__(self):
         """ Class initialization"""
@@ -820,9 +883,11 @@ class OTDA_mapping_linear(OTDA):
 
 class OTDA_mapping_kernel(OTDA_mapping_linear):
 
-    """Class for optimal transport with joint nonlinear mapping estimation as in [8]"""
+    """Class for optimal transport with joint nonlinear mapping
+    estimation as in [8]"""
 
-    def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', sigma=1, **kwargs):
+    def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian',
+            sigma=1, **kwargs):
         """ Fit domain adaptation between samples is xs and xt """
         self.xs = xs
         self.xt = xt
@@ -843,7 +908,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear):
 
         if self.computed:
             K = kernel(
-                x, self.xs, method=self.kernel, sigma=self.sigma, **self.kwargs)
+                x, self.xs, method=self.kernel, sigma=self.sigma,
+                **self.kwargs)
             if self.bias:
                 K = np.hstack((K, np.ones((x.shape[0], 1))))
             return K.dot(self.L)