# brute force string matching algorithm in python with code examples

The Brute Force String Matching algorithm is a method for finding a specific pattern of characters within a larger text. This algorithm compares each character of the pattern with the corresponding characters of the text, moving from left to right, until a mismatch is found or the entire pattern has been matched.

In Python, we can implement the Brute Force String Matching algorithm using a simple for loop. The following code demonstrates an example of the algorithm in action:

```def brute_force_string_match(text, pattern):
m = len(pattern)
n = len(text)
for i in range(n-m+1):
j = 0
while j < m and text[i+j] == pattern[j]:
j += 1
if j == m:
return i
return -1

text = "hello, world!"
pattern = "world"
print(brute_force_string_match(text, pattern))
```

In this example, the function takes two parameters: the text and the pattern. The length of the pattern (m) and the text (n) are determined using the built-in len() function.

The outer for loop iterates through the text, starting at the first character and ending at the (n-m+1)th character. For each iteration of the outer loop, the inner while loop compares the characters of the pattern with the corresponding characters of the text. If a mismatch is found, the inner loop breaks and the outer loop continues with the next iteration. If the entire pattern is matched, the function returns the index of the starting character of the match in the text.

If no match is found, the function returns -1.

This is a basic example of Brute Force String Matching algorithm in Python, it's a simple and easy to understand but it has a time complexity of O(m*n) which makes it inefficient for large inputs. There are other more efficient algorithms like Knuth-Morris-Pratt (KMP) and Boyer-Moore that can be used for pattern matching in large texts.

One of the most efficient algorithms for string matching is the Knuth-Morris-Pratt (KMP) algorithm. Unlike the Brute Force algorithm, KMP uses information from previously matched characters to avoid unnecessary comparisons. The algorithm preprocesses the pattern to create a partial match table, which is used to determine the next character to be compared in the event of a mismatch.

Here is an example of the KMP algorithm implemented in Python:

```def KMPSearch(pat, txt):
M = len(pat)
N = len(txt)
lps = *M
j = 0
computeLPSArray(pat, M, lps)
i = 0
while i < N:
if pat[j] == txt[i]:
i += 1
j += 1
if j == M:
print("Found pattern at index " + str(i-j))
j = lps[j-1]
elif i < N and pat[j] != txt[i]:
if j != 0:
j = lps[j-1]
else:
i += 1
return -1

def computeLPSArray(pat, M, lps):
len = 0
lps = 0
i = 1
while i < M:
if pat[i] == pat[len]:
len += 1
lps[i] = len
i += 1
else:
if len != 0:
len = lps[len-1]
else:
lps[i] = 0
i += 1
```

In this example, the KMPSearch function takes two parameters: the pattern (pat) and the text (txt). The function first calls the computeLPSArray function, which preprocesses the pattern to create the partial match table (lps). The KMPSearch function then uses the lps array to determine the next character to compare in the event of a mismatch.

Another efficient algorithm for string matching is the Boyer-Moore algorithm. This algorithm uses two heuristics to skip characters that do not match the pattern, the Bad Character Heuristic and the Good Suffix Heuristic.

The Bad Character Heuristic skips characters in the text that do not match the last character of the pattern. The Good Suffix Heuristic skips characters that match a suffix of the pattern.

Here is an example of the Boyer-Moore algorithm implemented in Python:

```def boyer_moore(text, pattern):
n = len(text)
m = len(pattern)
last = {}
for i in range(m):
last[pattern[i]] = i
i = m - 1
j = m - 1
while i < n:
if text[i] == pattern[j]:
if j == 0:
return i
else:
i -= 1
j -= 1
else:
lo = last.get(text[i], -1)
i += m - min(j, lo + 1)
j = m - 1
return -1
```

In this example, the boyer_moore function takes two parameters: the text and the pattern. The function uses a dictionary (last) to store the last occurrence

## Popular questions

1. What is the time complexity of the Brute Force String Matching algorithm in Python?

The time complexity of the Brute Force String Matching algorithm in Python is O(m*n), where m is the length of the pattern and n is the length of the text.

1. How does the Brute Force String Matching algorithm in Python compare characters in the pattern and text?

The Brute Force String Matching algorithm in Python compares each character of the pattern with the corresponding characters of the text, moving from left to right, until a mismatch is found or the entire pattern has been matched.

1. What is the purpose of the outer for loop in the Brute Force String Matching algorithm in Python?

The outer for loop in the Brute Force String Matching algorithm in Python iterates through the text, starting at the first character and ending at the (n-m+1)th character. For each iteration of the outer loop, the inner while loop compares the characters of the pattern with the corresponding characters of the text.

1. How does the Brute Force String Matching algorithm in Python determine if a match has been found?

The Brute Force String Matching algorithm in Python determines if a match has been found by comparing the entire pattern with the corresponding characters of the text. If the entire pattern is matched, the function returns the index of the starting character of the match in the text.

1. What are some alternative algorithms to the Brute Force String Matching algorithm in Python that are more efficient?

Some alternative algorithms to the Brute Force String Matching algorithm in Python that are more efficient include the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms. These algorithms use information from previously matched characters or heuristics to avoid unnecessary comparisons and improve the time complexity.

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