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use pyo3::prelude::*;
/// Perform brute force substring search.
///
/// # Arguments
///
/// * `text` - The text to search within.
/// * `pattern` - The pattern to search for.
///
/// # Returns
///
/// Returns the index of the first occurrence of `pattern` in `text`, or `-1` if not found.
#[pyfunction]
pub fn BrutalForceSearch(text: &str, pattern: &str) -> isize {
let n = text.len();
let m = pattern.len();
for i in 0..=(n - m) {
let mut j = 0;
while j < m && pattern.as_bytes()[j] == text.as_bytes()[i + j] {
j += 1;
}
if j == m {
return i as isize;
}
}
-1
}
/// Returns a vector of indices where the pattern matches in the text using KMP algorithm.
///
/// # Arguments
///
/// * `text` - The text to search for the pattern.
/// * `pattern` - The pattern to search for in the text.
///
/// # Returns
///
/// A Python list containing the indices where the pattern matches in the text.
#[pyfunction]
pub fn KMPSearch(text: &str, pattern: &str) -> PyResult<Vec<usize>> {
// Build the KMP table (prefix function)
let mut kmp_table = vec![0; pattern.len()];
let mut j = 0;
for (i, c) in pattern.chars().enumerate().skip(1) {
while j > 0 && c != pattern.chars().nth(j).unwrap() {
j = kmp_table[j - 1];
}
if c == pattern.chars().nth(j).unwrap() {
j += 1;
kmp_table[i] = j;
}
}
// Perform the search
let mut matches = vec![];
let mut i = 0;
let mut j = 0;
while i < text.len() {
if pattern.chars().nth(j).unwrap() == text.chars().nth(i).unwrap() {
i += 1;
j += 1;
}
if j == pattern.len() {
matches.push(i - j);
j = kmp_table[j - 1];
} else if i < text.len() && pattern.chars().nth(j).unwrap() != text.chars().nth(i).unwrap() {
if j != 0 {
j = kmp_table[j - 1];
} else {
i += 1;
}
}
}
Ok(matches)
}
/// Implements the Boyer-Moore string search algorithm.
#[pyfunction]
pub fn BoyerMooreSearch(text: &str, pattern: &str) -> Option<usize> {
let n = text.len();
let m = pattern.len();
if m == 0 {
return Some(0); // Empty pattern matches at the start
}
let mut bad_char_skip = vec![m; 256]; // Assuming ASCII characters
for (i, &ch) in pattern[..m-1].as_bytes().iter().enumerate() {
bad_char_skip[ch as usize] = (m - 1 - i) as usize;
}
let mut i = m - 1;
while i < n {
let mut j = m - 1;
while text.as_bytes()[i] == pattern.as_bytes()[j] {
if j == 0 {
return Some(i);
}
i -= 1;
j -= 1;
}
i += bad_char_skip[text.as_bytes()[i] as usize].max(1) as usize;
}
None
}
use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
/// Rabin-Karp algorithm for substring search.
///
/// # Arguments
///
/// * `text` - The text in which to search for the pattern.
/// * `pattern` - The pattern to search for.
/// * `prime` - A prime number to use for hashing.
///
/// # Returns
///
/// A list of starting indices where the pattern is found in the text.
#[pyfunction]
pub fn RabinKarpSearch(text: &str, pattern: &str, prime: u64) -> Vec<usize> {
let n = text.len();
let m = pattern.len();
let mut result = Vec::new();
let base: u64 = 256;
if m > n {
return result;
}
// Compute the hash value of the pattern and the first window of the text
let mut pattern_hash: u64 = 0;
let mut text_hash: u64 = 0;
let mut h: u64 = 1;
for _ in 0..m-1 {
h = (h * base) % prime;
}
for i in 0..m {
pattern_hash = (base * pattern_hash + pattern.as_bytes()[i] as u64) % prime;
text_hash = (base * text_hash + text.as_bytes()[i] as u64) % prime;
}
// Slide the pattern over the text one by one
for i in 0..=n-m {
// Check the hash values of current window of text and pattern
if pattern_hash == text_hash {
// Check for characters one by one
let mut j = 0;
while j < m {
if text.as_bytes()[i+j] != pattern.as_bytes()[j] {
break;
}
j += 1;
}
if j == m {
result.push(i);
}
}
// Calculate hash value for next window of text
if i < n-m {
text_hash = (base * (text_hash - (text.as_bytes()[i] as u64 * h) % prime) + text.as_bytes()[i+m] as u64) % prime;
if text_hash < 0 {
text_hash = (text_hash + prime) % prime;
}
}
}
result
}