✨ feat: create celebrity-problem.py and top-sort.py

python/Recursion/celebrity-problem.py

@@ -0,0 +1,44 @@
+##!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+celebrity problem
+"""
+__author__ = "prakashsellathurai"
+__copyright__ = "Copyright 2021"
+__version__ = "1.0.1"
+__email__ = "prakashsellathurai@gmail.com"
+
+
+from random import randrange
+
+def celeb(G):
+    n = len(G)
+
+    u,v = 0,1
+    for c in range(2,n+1):
+        if G[u][v]: u=c
+        else: v=c
+
+    if u==n: c=v
+    else:   c=u
+
+    for v in range(n):
+        if c==v: continue
+        if G[c][v]: break
+        if not G[v][c]: break
+    else:
+        return c
+    return None
+
+if __name__ == "__main__":
+    n = 100
+    G  = [[randrange(2) for i in range(n)] for i in range(n)]
+
+    c = randrange(n)
+
+    for i in range(n):
+        G[i][c] = True
+        G[c][i] = False
+
+    print(celeb(G))

python/Recursion/top-sort.py

@@ -0,0 +1,87 @@
+##!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Topological sorting
+"""
+__author__ = "prakashsellathurai"
+__copyright__ = "Copyright 2021"
+__version__ = "1.0.1"
+__email__ = "prakashsellathurai@gmail.com"
+
+
+from random import randrange
+from collections import defaultdict
+
+def naive_topsort(G,S=None):
+    if S is None: S = set(G)
+
+    if len(S) == 1: return list(S)
+
+    v = S.pop()
+
+    seq = naive_topsort(G,S)
+
+    min_i = 0
+    for i,u in enumerate(seq):
+        if v in G[u]: min_i = i+1
+    seq.insert(min_i,v)
+    return seq
+
+
+def topsort(G):
+    count = dict((u, 0) for u in G) # The in-degree for each node
+    for u in G:
+        for v in G[u]:
+            count[v] += 1 # Count every in-edge
+    Q = [u for u in G if count[u] == 0] # Valid initial nodes
+    S = [] # The result
+    while Q: # While we have start nodes...
+        u = Q.pop() # Pick one
+        S.append(u) # Use it as first of the rest
+        for v in G[u]:
+            count[v] -= 1 # "Uncount" its out-edges
+            if count[v] == 0: # New valid start nodes?
+                Q.append(v) # Deal with them next
+    return S
+
+
+class randRangesparse(object):
+    """docstring for randRangesparse."""
+    def __init__(self, n):
+        super(randRangesparse, self).__init__()
+        self.n = n
+        self.weights = self.regenerate_weights()
+
+    def regenerate_weights(self):
+        return {i:self.n-i for i in range(self.n)}
+
+    def get_random(self):
+        coin = randrange(self.n)
+
+        while self.weights[coin] != 0:
+            if self.weights[coin] != 0:
+                self.weights[coin]-=1
+                return coin
+            else:
+                coin = randrange(self.n-1)
+
+        self.weights = self.regenerate_weights()
+        return self.get_random()
+
+
+
+
+
+
+if __name__ == "__main__":
+
+    rng = randRangesparse(10)
+    G = [[rng.get_random() for _ in range(10)]  for _ in range(10)]
+
+    print(naive_topsort(G))
+
+
+
+
+