I want to extend my __init__ method by an extra parameter Tol (tolerance) which serves as a threshold for converting a given matrix to a sparse matrix. All elements of the original matrix which have an absolute value less than Tol should be considered as zeros. Give Tol a default value 0
this is my code
this is my code
import numpy as np class SparseMatrix: def __init__(self,matrix): self.matrix=np.asarray(matrix) self.intern_represent = 'CSR' self.intern_represent = 'CSC' @property def values (self): return self.matrix [np.nonzero(self.matrix)] def __repr__(self): return (' A= '+ str(self.values) + '\nIA= ' + str(self.IA) + '\nJA= '+ str(self.JA)) @property def IA (self): IA =[] IA.append (0) for i in range (1,np.shape(self.matrix)[0]+1): num=IA [i-1] + np.count_nonzero(self.matrix [i-1]) IA.append(num) IA=np.asarray (IA) return IA @property def JA (self): index=np.nonzero(self.matrix) return index [1] @property def number_of_nonzero(self): return len (self.values) def change (self,i,j,new): self.matrix [i][j]=new return new def ConvertCSC(self): return np.transpose(self.matrix) def CSReqaulCSC(self): if (self.matrix==np.transpose(self.matrix)).all(): return True else: return False all