Merge pull request #5 from dimitra-maoutsa/testing

Testing

Author: dimitra-maoutsa

DeterministicParticleFlowControl/reweighting/optimal_transport_reweighting.py

@@ -12,6 +12,7 @@
 from pyemd import emd_with_flow
 from scipy.spatial.distance import pdist, squareform
 
+__all__ = ["reweight_optimal_transport_multidim"]
 
 def reweight_optimal_transport_multidim(samples, weights):
 

DeterministicParticleFlowControl/score_estimators/score_function_estimators.py

@@ -84,7 +84,12 @@ def score_function_multid_seperate(X,Z,func_out=False, C=0.001,kern ='RBF',l=1,w
                                  employed for interpolation/estimation of
                                  the logarithmic gradient in the vicinity of the samples.
                                
-    (For estimation across all dimensions simultaneously see score_function_multid_seperate_all_dims )
+    For estimation across all dimensions simultaneously see also
+    
+    See also
+    ----------
+    score_function_multid_seperate_all_dims 
+    
     
     Parameters
     ----------
@@ -112,7 +117,7 @@ def score_function_multid_seperate(X,Z,func_out=False, C=0.001,kern ='RBF',l=1,w
     """
 
     if kern=='RBF':
-        
+        """
         #@numba.njit(parallel=True,fastmath=True)
         def Knumba(x,y,l,res,multil=False): #version of kernel in the numba form when the call already includes the output matrix
             if multil:                                        
@@ -127,8 +132,8 @@ def Knumba(x,y,l,res,multil=False): #version of kernel in the numba form when th
                 tempi = np.zeros((x.shape[0], y.shape[0] ), dtype=np.float64)                
                 my_cdist(x, y,tempi,'sqeuclidean') #this sets into the array tempi the cdist result
                 res = np.exp(-tempi/(2*l*l))
-            return 0
-        
+            #return 0
+        """
         def K(x,y,l,multil=False):
             if multil:                         
                 res = np.ones((x.shape[0],y.shape[0]))                
@@ -165,7 +170,7 @@ def grdx_K(x,y,l,which_dim=1,multil=False): #gradient with respect to the 1st ar
                 redifs = np.multiply(diffs[:,:,ii],K(x,y,l))/(l*l)            
             return redifs
 
-     
+        """
         def grdy_K(x,y): # gradient with respect to the second argument
             _,dim = x.shape
             diffs = x[:,None]-y            
@@ -184,6 +189,7 @@ def ggrdxy_K(x,y):
                 for jj in range(which_dim-1,which_dim):
                     redifs[ii, jj ] = np.multiply(np.multiply(diffs[:,:,ii],diffs[:,:,jj])+(l*l)*(ii==jj),K(x,y))/(l**4) 
             return -redifs
+        """
 
             #############################################################################
     elif kern=='periodic': ###############################################################################################
@@ -200,7 +206,7 @@ def K(x,y,l,multil=False):
 
           res = np.ones((x.shape[0],y.shape[0]))                
           for ii in range(len(l)): 
-              tempi = np.zeros((x[:,ii].size, y[:,ii].size ))
+              #tempi = np.zeros((x[:,ii].size, y[:,ii].size ))
               ##puts into tempi the cdist result
               #my_cdist(x[:,ii].reshape(-1,1), y[:,ii].reshape(-1,1),tempi, 'l1')              
               #res = np.multiply(res, np.exp(- 2* (np.sin(tempi/ 2 )**2) /(l[ii]*l[ii])) )
@@ -215,7 +221,7 @@ def K(x,y,l,multil=False):
             return res
 
       def grdx_K(x,y,l,which_dim=1,multil=False): #gradient with respect to the 1st argument - only which_dim
-          N,dim = x.shape            
+          #N,dim = x.shape            
           diffs = x[:,None]-y   
           #print('diffs:',diffs)
           #redifs = np.zeros((1*N,N))
@@ -324,7 +330,7 @@ def score_function_multid_seperate_all_dims(X,Z,func_out=False, C=0.001,kern ='R
     """
 
     if kern=='RBF':
-        
+        """
         #@numba.njit(parallel=True,fastmath=True)
         def Knumba(x,y,l,res,multil=False): #version of kernel in the numba form when the call already includes the output matrix
             if multil:                                        
@@ -340,6 +346,7 @@ def Knumba(x,y,l,res,multil=False): #version of kernel in the numba form when th
                 my_cdist(x, y,tempi,'sqeuclidean') #this sets into the array tempi the cdist result
                 res = np.exp(-tempi/(2*l*l))
             return 0
+        """
 
         def K(x,y,l,multil=False):
             if multil:   
@@ -558,7 +565,7 @@ def grdx_K(x,y,l,which_dim=1,multil=False): #gradient with respect to the 1st ar
             else:
                 redifs = np.multiply(diffs[:,:,ii],K(x,y,l))/(l*l)            
             return redifs            
-     
+        """
         def grdy_K(x,y): # gradient with respect to the second argument
             #N,dim = x.shape
             diffs = x[:,None]-y            
@@ -575,7 +582,7 @@ def ggrdxy_K(x,y):
                 for jj in range(which_dim-1,which_dim):
                     redifs[ii, jj ] = np.multiply(np.multiply(diffs[:,:,ii],diffs[:,:,jj])+(l*l)*(ii==jj),K(x,y))/(l**4) 
             return -redifs            
-     
+        """
     if isinstance(l, (list, tuple, np.ndarray)):
        ### for different lengthscales for each dimension 
        K_xz = K(X,Z,l,multil=True)