doc datasets.py

Author: rflamary

ot/bregman.py

@@ -276,14 +276,6 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal
     
     The optimization problem is solved suing the algorithm described in [4]
   
-  
-          distrib : distribution to unmix
-        D : Dictionnary 
-        M : Metric matrix in the space of the distributions to unmix
-        M0 : Metric matrix in the space of the 'abundance values' to solve for
-        h0 : prior on solution (generally uniform distribution)
-        reg,reg0 : transport regularizations
-        alpha : how much should we trust the prior ? ([0,1])
         
     Parameters
     ----------
@@ -300,7 +292,9 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal
     reg: float
         Regularization term >0 (Wasserstein data fitting)
     reg0: float
-        Regularization term >0 (Wasserstein reg with h0)        
+        Regularization term >0 (Wasserstein reg with h0)   
+    alpha: float
+        How much should we trust the prior ([0,1])
     numItermax: int, optional
         Max number of iterations
     stopThr: float, optional
@@ -318,7 +312,7 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal
     log: dict
         log dictionary return only if log==True in parameters     
   
-     References
+    References
     ----------
     
     .. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.

ot/datasets.py

@@ -8,14 +8,50 @@
 
 
 def get_1D_gauss(n,m,s):
-    "return a 1D histogram for a gaussian distribution (n bins, mean m and std s) "
+    """return a 1D histogram for a gaussian distribution (n bins, mean m and std s) 
+    
+    Parameters
+    ----------
+
+    n : int
+        number of bins in the histogram
+    m : float
+        mean value of the gaussian distribution
+    s : float
+        standard deviaton of the gaussian distribution
+
+  
+    Returns
+    -------
+    h : np.array (n,)
+          1D histogram for a gaussian distribution      
+    
+    """
     x=np.arange(n,dtype=np.float64)
     h=np.exp(-(x-m)**2/(2*s^2))
     return h/h.sum()
 
 
 def get_2D_samples_gauss(n,m,sigma):
-    "return samples from 2D gaussian (n samples, mean m and cov sigma) "
+    """return n samples drawn from 2D gaussian N(m,sigma) 
+    
+    Parameters
+    ----------
+
+    n : int
+        number of bins in the histogram
+    m : np.array (2,)
+        mean value of the gaussian distribution
+    sigma : np.array (2,2)
+        covariance matrix of the gaussian distribution
+
+  
+    Returns
+    -------
+    X : np.array (n,2)
+          n samples drawn from  N(m,sigma)       
+    
+    """
     if  np.isscalar(sigma):
         sigma=np.array([sigma,])
     if len(sigma)>1:
@@ -26,8 +62,26 @@ def get_2D_samples_gauss(n,m,sigma):
     return res
 
 def get_data_classif(dataset,n,nz=.5,**kwargs):
-    """
-    dataset generation
+    """ dataset generation for classification problems
+    
+    Parameters
+    ----------
+
+    dataset : str
+        type of classification problem (see code)
+    n : int
+        number of training samples
+    nz : float
+        noise level (>0)
+
+  
+    Returns
+    -------
+    X : np.array (n,d)
+          n observation of size d      
+    y : np.array (n,)
+          labels of the samples         
+
     """
     if dataset.lower()=='3gauss':
         y=np.floor((np.arange(n)*1.0/n*3))+1
@@ -50,15 +104,10 @@ def get_data_classif(dataset,n,nz=.5,**kwargs):
         x[y==3,0]=2. ; x[y==3,1]=0
 
         x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2)
-        x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2)   
-#    elif dataset.lower()=='sinreg':
-#        
-#        x=np.random.rand(n,1)
-#        y=4*x+np.sin(2*np.pi*x)+nz*np.random.randn(n,1) 
-         
+        x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2)          
     else:
         x=0
         y=0
         print("unknown dataset")
 
-    return x,y
+    return x,y.astype(int)

ot/utils.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 """
 Various function that can be usefull
 """
@@ -6,7 +7,21 @@
 
 
 def unif(n):
-    """ return a uniform histogram of length n (simplex) """
+    """ return a uniform histogram of length n (simplex) 
+    
+    Parameters
+    ----------
+
+    n : int
+        number of bins in the histogram
+  
+    Returns
+    -------
+    h : np.array (n,)
+        histogram of length n such that h_i=1/n for all i    
+    
+    
+    """
     return np.ones((n,))/n
 
 
@@ -22,9 +37,9 @@ def dist(x1,x2=None,metric='sqeuclidean'):
         matrix with n2 samples of size d (if None then x2=x1)
     metric : str, fun, optional
         name of the metric to be computed (full list in the doc of scipy),  If a string, 
-        the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, 
+        the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’,
         ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
-        ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, 
+        ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’,
         ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.
 
   
@@ -68,5 +83,5 @@ def dist0(n,method='lin_square'):
 
 
 def dots(*args):
-    """ Stupid but nice dots function for multiple matrix multiply """
+    """ dots function for multiple matrix multiply """
     return reduce(np.dot,args)