Python method for keyword matching using BF algorithm

In this paper, we describe the method of Python matching by BF algorithm. Share to everyone for your reference. The implementation method is as follows:

The code is as follows:

#!/usr/bin/python
#-*-Coding:utf-8
# filename BF
Import time
"""
T= "A big apple,this is a big apple,this are a big apple,this is a Big Apple."
p= "Apple"
"""
T= "Why is it called a vector space model?" In fact, we can think of each word as a dimension, and the frequency of the word as its value (direction), that is, the vector, so that each article of the word and its frequency constitutes an i-dimensional space map, the similarity of two documents is the proximity of two spatial graphs. Assuming that the article has only two dimensions, then the space map can be drawn in a plane Cartesian coordinate system, the reader can imagine two only two words of the article drawing to understand. "
p= "Readers"
I=0
Count=0
Start=time.time ()
while (I <=len (t)-len (p)):
J=0
while (T[i]==p[j]):
I=i+1
J=j+1
If J==len (p):
Break
Elif (J==len (p)-1):
Count=count+1
Else
I=i+1
J=0
Print Count
Print Time.time ()-start

Algorithm idea: The target string T and the pattern string P-word comparison, if the corresponding bit matching, then the next comparison; if not the same, p moves 1 bits to the right, starting from the 1th bit of p to start the comparison again.

Algorithm features: The overall direction of movement: can be considered in a fixed case, p sliding from left to right; when matching comparison, from the leftmost bit of P start to the right bit and the corresponding bit in the T string comparison. The sliding distance of P is 1, which causes the BF algorithm to match less efficiently (compared to other algorithms such as: BM,KMP, sliding without jumping).

The algorithm has a time complexity of O (len (t) *len (p)) and a spatial complexity of O (len (t) +len (p))

Hopefully this article will help you with Python programming.

