gmm_ridge.py 文件源码

def split_data(self,n,v=5):
        ''' The function split the data into v folds. Whatever the number of sample per class
        Input:
            n : the number of samples
            v : the number of folds
        Output: None        
        '''
        step = n //v  # Compute the number of samples in each fold
        sp.random.seed(1)   # Set the random generator to the same initial state
        t = sp.random.permutation(n)    # Generate random sampling of the indices

        indices=[]
        for i in range(v-1):            # group in v fold
            indices.append(t[i*step:(i+1)*step])
        indices.append(t[(v-1)*step:n])

        for i in range(v):
            self.iT.append(sp.asarray(indices[i]))
            l = range(v)
            l.remove(i)
            temp = sp.empty(0,dtype=sp.int64)
            for j in l:            
                temp = sp.concatenate((temp,sp.asarray(indices[j])))
            self.it.append(temp)