|
|
@ -174,6 +174,7 @@ class Binary :
|
|
|
|
i = np.random.choice(col_count,size)
|
|
|
|
i = np.random.choice(col_count,size)
|
|
|
|
values = values[-i]
|
|
|
|
values = values[-i]
|
|
|
|
col_count = N
|
|
|
|
col_count = N
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
matrix = [ np.zeros(col_count,dtype=np.float32) for i in np.arange(row_count)]
|
|
|
|
matrix = [ np.zeros(col_count,dtype=np.float32) for i in np.arange(row_count)]
|
|
|
@ -203,10 +204,12 @@ class Binary :
|
|
|
|
# Let's treat the case of missing values i.e nulls
|
|
|
|
# Let's treat the case of missing values i.e nulls
|
|
|
|
#
|
|
|
|
#
|
|
|
|
row_count,col_count = column.size,values.size
|
|
|
|
row_count,col_count = column.size,values.size
|
|
|
|
if row_count * col_count > size and row_count < size:
|
|
|
|
if col_count > size :
|
|
|
|
N = np.divide(size,row_count).astype(int)
|
|
|
|
# N = np.divide(size,row_count).astype(int)
|
|
|
|
i = np.random.choice(col_count,N)
|
|
|
|
# N =
|
|
|
|
|
|
|
|
i = np.random.choice(col_count,size)
|
|
|
|
values = values[-i]
|
|
|
|
values = values[-i]
|
|
|
|
|
|
|
|
col_count = N
|
|
|
|
return values
|
|
|
|
return values
|
|
|
|
|
|
|
|
|
|
|
|
def _Export(self,df) :
|
|
|
|
def _Export(self,df) :
|
|
|
|