passing tests

1 files changed, 72 insertions, 75 deletions

diff --git a/ot/da.py b/ot/da.py
index ab5f860..f789396 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -357,7 +357,8 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
 
     def loss(L, G):
         """Compute full loss"""
-        return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * np.sum(G * M) + eta * np.sum(sel(L - I0)**2)
+        return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * \
+            np.sum(G * M) + eta * np.sum(sel(L - I0)**2)
 
     def solve_L(G):
         """ solve L problem with fixed G (least square)"""
@@ -557,7 +558,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
 
     def loss(L, G):
         """Compute full loss"""
-        return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L))
+        return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * \
+            np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L))
 
     def solve_L_nobias(G):
         """ solve L problem with fixed G (least square)"""
@@ -634,25 +636,26 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
         return G, L
 
 
-def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False):
+def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
+                      wt=None, bias=True, log=False):
     """ return OT linear operator between samples
 
     The function estimate the optimal linear operator that align the two
-    empirical distributions. This is equivalent to estimating the closed 
-    form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` 
+    empirical distributions. This is equivalent to estimating the closed
+    form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)`
     and :math:`N(\mu_t,\Sigma_t)` as proposed in [14].
-    
+
     The linear operator from source to target :math:`M`
 
     .. math::
         M(x)=Ax+b
-        
+
     where :
-    
+
     .. math::
         A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2}
         \Sigma_s^{-1/2}
-    .. math::  
+    .. math::
         b=\mu_t-A\mu_s
 
     Parameters
@@ -666,7 +669,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False):
     ws : np.ndarray (ns,1), optional
         weights for the source samples
     wt : np.ndarray (ns,1), optional
-        weights for the target samples  
+        weights for the target samples
     bias: boolean, optional
         estimate bias b else b=0 (default:True)
     log : bool, optional
@@ -686,55 +689,52 @@ def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False):
     References
     ----------
 
-    .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of 
+    .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of
         distributions", Journal of Optimization Theory and Applications
         Vol 43, 1984
 
 
     """
 
-    d=xs.shape[1]    
-    
+    d = xs.shape[1]
+
     if bias:
-        mxs=xs.mean(0,keepdims=True)
-        mxt=xt.mean(0,keepdims=True)
-        
-        xs=xs-mxs
-        xt=xt-mxt
+        mxs = xs.mean(0, keepdims=True)
+        mxt = xt.mean(0, keepdims=True)
+
+        xs = xs - mxs
+        xt = xt - mxt
     else:
-        mxs=np.zeros((1,d))
-        mxt=np.zeros((1,d))
+        mxs = np.zeros((1, d))
+        mxt = np.zeros((1, d))
 
-    
     if ws is None:
-        ws=np.ones((xs.shape[0],1))/xs.shape[0]
-    
+        ws = np.ones((xs.shape[0], 1)) / xs.shape[0]
+
     if wt is None:
-        wt=np.ones((xt.shape[0],1))/xt.shape[0]    
-
-    Cs=(xs*ws).T.dot(xs)/ws.sum()+reg*np.eye(d)
-    Ct=(xt*wt).T.dot(xt)/wt.sum()+reg*np.eye(d)
-    
-    
-    Cs12=linalg.sqrtm(Cs)
-    Cs_12=linalg.inv(Cs12)
-        
-    M0=linalg.sqrtm(Cs12.dot(Ct.dot(Cs12)))
-        
-    A=Cs_12.dot(M0.dot(Cs_12))
-    
-    b=mxt-mxs.dot(A)
-    
+        wt = np.ones((xt.shape[0], 1)) / xt.shape[0]
+
+    Cs = (xs * ws).T.dot(xs) / ws.sum() + reg * np.eye(d)
+    Ct = (xt * wt).T.dot(xt) / wt.sum() + reg * np.eye(d)
+
+    Cs12 = linalg.sqrtm(Cs)
+    Cs_12 = linalg.inv(Cs12)
+
+    M0 = linalg.sqrtm(Cs12.dot(Ct.dot(Cs12)))
+
+    A = Cs_12.dot(M0.dot(Cs_12))
+
+    b = mxt - mxs.dot(A)
+
     if log:
-        log={}
-        log['Cs']=Cs
-        log['Ct']=Ct
-        log['Cs12']=Cs12
-        log['Cs_12']=Cs_12
-        return A,b,log
+        log = {}
+        log['Cs'] = Cs
+        log['Ct'] = Ct
+        log['Cs12'] = Cs12
+        log['Cs_12'] = Cs_12
+        return A, b, log
     else:
-        return A,b
-
+        return A, b
 
 
 @deprecated("The class OTDA is deprecated in 0.3.1 and will be "
@@ -1288,42 +1288,42 @@ class LinearTransport(BaseTransport):
     """ OT linear operator between empirical distributions
 
     The function estimate the optimal linear operator that align the two
-    empirical distributions. This is equivalent to estimating the closed 
-    form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` 
+    empirical distributions. This is equivalent to estimating the closed
+    form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)`
     and :math:`N(\mu_t,\Sigma_t)` as proposed in [14].
-    
+
     The linear operator from source to target :math:`M`
 
     .. math::
         M(x)=Ax+b
-        
+
     where :
-    
+
     .. math::
         A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2}
         \Sigma_s^{-1/2}
-    .. math::  
+    .. math::
         b=\mu_t-A\mu_s
 
     Parameters
     ----------
     reg : float,optional
-        regularization added to the daigonals of convariances (>0) 
+        regularization added to the daigonals of convariances (>0)
     bias: boolean, optional
         estimate bias b else b=0 (default:True)
     log : bool, optional
         record log if True
-        
-        
+
+
     """
-    
-    def __init__(self, reg=1e-8,bias=True,log=False,
+
+    def __init__(self, reg=1e-8, bias=True, log=False,
                  distribution_estimation=distribution_estimation_uniform):
-        
-        self.bias=bias
-        self.log=log
-        self.reg=reg
-        self.distribution_estimation=distribution_estimation
+
+        self.bias = bias
+        self.log = log
+        self.reg = reg
+        self.distribution_estimation = distribution_estimation
 
     def fit(self, Xs=None, ys=None, Xt=None, yt=None):
         """Build a coupling matrix from source and target sets of samples
@@ -1349,17 +1349,15 @@ class LinearTransport(BaseTransport):
         self : object
             Returns self.
         """
-        
+
         self.mu_s = self.distribution_estimation(Xs)
         self.mu_t = self.distribution_estimation(Xt)
 
-        
-
         # coupling estimation
-        returned_ = OT_mapping_linear(Xs,Xt,reg=self.reg,
-                                      ws=self.mu_s.reshape((-1,1)),
-                                      wt=self.mu_t.reshape((-1,1)),
-                                      bias=self.bias,log=self.log)
+        returned_ = OT_mapping_linear(Xs, Xt, reg=self.reg,
+                                      ws=self.mu_s.reshape((-1, 1)),
+                                      wt=self.mu_t.reshape((-1, 1)),
+                                      bias=self.bias, log=self.log)
 
         # deal with the value of log
         if self.log:
@@ -1367,10 +1365,10 @@ class LinearTransport(BaseTransport):
         else:
             self.A_, self.B_, = returned_
             self.log_ = dict()
-        
+
         # re compute inverse mapping
-        self.A1_=linalg.inv(self.A_)
-        self.B1_=-self.B_.dot(self.A1_)
+        self.A1_ = linalg.inv(self.A_)
+        self.B1_ = -self.B_.dot(self.A1_)
 
         return self
 
@@ -1403,7 +1401,7 @@ class LinearTransport(BaseTransport):
         # check the necessary inputs parameters are here
         if check_params(Xs=Xs):
 
-            transp_Xs= Xs.dot(self.A_)+self.B_
+            transp_Xs = Xs.dot(self.A_) + self.B_
 
             return transp_Xs
 
@@ -1437,12 +1435,11 @@ class LinearTransport(BaseTransport):
         # check the necessary inputs parameters are here
         if check_params(Xt=Xt):
 
-            transp_Xt= Xt.dot(self.A1_)+self.B1_
+            transp_Xt = Xt.dot(self.A1_) + self.B1_
 
             return transp_Xt
 
 
-
 class SinkhornTransport(BaseTransport):
 
     """Domain Adapatation OT method based on Sinkhorn Algorithm