| 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 | |