feat: add Knuth-Morris-Pratt (KMP) string matching algorithm in C++

KMP.cpp

@@ -0,0 +1,84 @@
+#include <iostream>
+#include <vector>
+#include <string>
+
+using namespace std;
+
+// Build the prefix (LPS) array for the pattern
+vector<int> buildLPS(const string &pattern) {
+    int m = pattern.size();
+    vector<int> lps(m, 0);
+
+    int len = 0; // length of the previous longest prefix suffix
+    int i = 1;
+
+    while (i < m) {
+        if (pattern[i] == pattern[len]) {
+            len++;
+            lps[i] = len;
+            i++;
+        } else {
+            if (len != 0) {
+                len = lps[len - 1]; // fallback
+            } else {
+                lps[i] = 0;
+                i++;
+            }
+        }
+    }
+
+    return lps;
+}
+
+// KMP algorithm to search for pattern in text
+vector<int> KMPSearch(const string &text, const string &pattern) {
+    int n = text.size();
+    int m = pattern.size();
+    vector<int> result;
+
+    if (m == 0 || n < m) return result;
+
+    vector<int> lps = buildLPS(pattern);
+
+    int i = 0; // index for text
+    int j = 0; // index for pattern
+
+    while (i < n) {
+        if (text[i] == pattern[j]) {
+            i++;
+            j++;
+        }
+
+        if (j == m) {
+            result.push_back(i - j); // match found
+            j = lps[j - 1]; // continue searching
+        } else if (i < n && text[i] != pattern[j]) {
+            if (j != 0) {
+                j = lps[j - 1];
+            } else {
+                i++;
+            }
+        }
+    }
+
+    return result;
+}
+
+int main() {
+    string text = "abxabcabcaby";
+    string pattern = "abcaby";
+
+    vector<int> matches = KMPSearch(text, pattern);
+
+    if (!matches.empty()) {
+        cout << "Pattern found at indices: ";
+        for (int idx : matches) {
+            cout << idx << " ";
+        }
+        cout << "\n";
+    } else {
+        cout << "Pattern not found.\n";
+    }
+
+    return 0;
+}

README.md

@@ -1,2 +1,33 @@
 # Hacktoberfest2025
 Hacktoberfest2025
+
+---
+
+## 📝 Pull Request Description (you can use this when submitting)
+
+**Title:**  
+`feat: add Dijkstra’s shortest path algorithm + tests`
+
+**Description:**  
+This pull request introduces:
+
+- A Python implementation of **Dijkstra’s shortest path algorithm** in `algorithms/python/dijkstra.py`  
+- Unit tests in `algorithms/python/test_dijkstra.py` to validate simple graphs  
+- Documentation in `README.md` for usage and how to run tests
+
+**Why this is helpful:**  
+- Adds a commonly used graph algorithm to the repository  
+- Helps users understand and test the algorithm  
+- Improves the breadth of algorithms covered in the repo
+
+**Testing checklist:**  
+- Ran `python -m unittest` successfully  
+- Verified correct shortest path outputs on simple graph examples  
+- README links and instructions are correctly formatted
+
+Please let me know if you’d like me to include JavaScript or other language versions of this algorithm too, or help with integrating it into the repository’s structure!  
+
+---
+
+If you like, I can fetch the actual file tree of **Gdrive097/Algo** to tailor the patch exactly, so you just paste and commit. Do you want me to do that and share the diff?
+::contentReference[oaicite:0]{index=0}

RabinKarp.cpp

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+#include <iostream>
+#include <string>
+#include <vector>
+
+using namespace std;
+
+// Rabin-Karp Algorithm for pattern searching
+// Returns indices of all occurrences of pattern in text
+vector<int> rabinKarp(const string &text, const string &pattern) {
+    const int prime = 101; // A prime number for hashing
+    int m = pattern.size();
+    int n = text.size();
+    vector<int> result;
+
+    if (m > n) return result;
+
+    long long patternHash = 0, textHash = 0, power = 1;
+
+    // Compute initial hash values for pattern and first window of text
+    for (int i = 0; i < m; i++) {
+        patternHash = (patternHash * prime + pattern[i]);
+        textHash = (textHash * prime + text[i]);
+        if (i > 0) power *= prime;
+    }
+
+    // Slide over the text
+    for (int i = 0; i <= n - m; i++) {
+        if (patternHash == textHash) {
+            // Hashes match, verify characters one by one
+            bool match = true;
+            for (int j = 0; j < m; j++) {
+                if (text[i + j] != pattern[j]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) result.push_back(i);
+        }
+
+        // Calculate hash for next window
+        if (i < n - m) {
+            textHash = (textHash - text[i] * power) * prime + text[i + m];
+        }
+    }
+
+    return result;
+}
+
+int main() {
+    string text = "ababcabcabababd";
+    string pattern = "ababd";
+
+    vector<int> occurrences = rabinKarp(text, pattern);
+
+    if (!occurrences.empty()) {
+        cout << "Pattern found at indices: ";
+        for (int idx : occurrences) {
+            cout << idx << " ";
+        }
+        cout << "\n";
+    } else {
+        cout << "Pattern not found in the text.\n";
+    }
+
+    return 0;
+}

algorithms/python/dijkstra.py

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+import heapq
+from typing import List, Tuple, Dict
+
+def dijkstra(graph: Dict[int, List[Tuple[int, int]]], source: int) -> Dict[int, int]:
+    """
+    graph: adjacency list representation
+      graph[u] = list of (v, weight) edges
+    Returns distances from source to all reachable nodes.
+    """
+    dist = {node: float('inf') for node in graph}
+    dist[source] = 0
+    pq = [(0, source)]  # (distance, node)
+    visited = set()
+
+    while pq:
+        d, u = heapq.heappop(pq)
+        if u in visited:
+            continue
+        visited.add(u)
+
+        for (v, w) in graph.get(u, []):
+            nd = d + w
+            if nd < dist[v]:
+                dist[v] = nd
+                heapq.heappush(pq, (nd, v))
+
+    return dist

algorithms/python/test_dijkstra.py

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+import unittest
+from dijkstra import dijkstra
+
+class TestDijkstra(unittest.TestCase):
+    def test_simple_graph(self):
+        graph = {
+            0: [(1, 4), (2, 1)],
+            1: [(3, 1)],
+            2: [(1, 2), (3, 5)],
+            3: []
+        }
+        dist = dijkstra(graph, 0)
+        self.assertEqual(dist[0], 0)
+        self.assertEqual(dist[1], 3)  # 0 → 2 → 1 cost 1 + 2
+        self.assertEqual(dist[2], 1)
+        self.assertEqual(dist[3], 4)  # via 0 → 2 → 1 → 3 or 0→1→3 whichever minimal
+
+if __name__ == "__main__":
+    unittest.main()