File size: 1,861 Bytes
8304f29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import numpy as np
class SparseMatrix():
def __init__(self,triple):
self.matrix_user = {}
self.matrix_item = {}
for item in triple:
if item[0] not in self.matrix_user:
self.matrix_user[item[0]] = {}
if item[1] not in self.matrix_item:
self.matrix_item[item[1]] = {}
self.matrix_user[item[0]][item[1]] = item[2]
self.matrix_item[item[1]][item[0]] = item[2]
self.elemNum = len(triple)
self.size = len(self.matrix_user), len(self.matrix_item)
def row(self,r):
if r not in self.matrix_user:
return {}
else:
return self.matrix_user[r]
def col(self,c):
if c not in self.matrix_item:
return {}
else:
return self.matrix_item[c]
def dense_row(self,r):
if r not in self.matrix_user:
return np.zeros((1,self.size[1]))
else:
array = np.zeros((1,self.size[1]))
ind = list(self.matrix_user[r].keys())
val = list(self.matrix_user[r].values())
array[0][ind] = val
return array
def dense_col(self,c):
if c not in self.matrix_item:
return np.zeros((1,self.size[0]))
else:
array = np.zeros((1,self.size[0]))
ind = list(self.matrix_item[c].keys())
val = list(self.matrix_item[c].values())
array[0][ind] = val
return array
def elem(self,r,c):
if not self.contain(r,c):
return 0
return self.matrix_user[r][c]
def contain(self,r,c):
if r in self.matrix_user and c in self.matrix_user[r]:
return True
return False
def elem_count(self):
return self.elemNum
def size(self):
return self.size
|