omitakahiro

from __future__ import annotations

class TrieNode:
    
    def __init__(self, char: str):
        self.char = char
        self.is_end = False
        self.children = {}
        
    def add_child(self, char: str) -> None:

omitakahiro

import time

def stopwatch(info):
    def _decorator(func):
        def wrapper(*args, **kwargs):
            t1 = time.time()
            output = func(*args, **kwargs)
            t2 = time.time()
 print('%s: %.3fms' % (info,(t2-t1)*1000) )

omitakahiro

Scipy does not currently provide a routine for cholesky decomposition of a sparse matrix, and one have to rely on another external package such as scikit.sparse for the purpose. Here I implement cholesky decomposition of a sparse matrix only using scipy functions. Our implementation relies on sparse LU deconposition.

The following function receives a sparse symmetric positive-definite matrix A and returns a spase lower triangular matrix L such that A = LL^T.

from scipy.sparse import linalg as splinalg
import scipy.sparse as sparse
import sys

def sparse_cholesky(A): # The input matrix A must be a sparse symmetric positive-definite.