@RealSaitama

from itertools import count, islice, product as P def only(s, D=1): # numbers with >= D digits only from s yield from (int("".join(p)) for d in count(D) for p in P(s, repeat=d)) def agen(): # generator of terms aset, an, minan = {0}, 0, 1 while True: yield an an, s = minan, set(str(an))

import numpy as np import scipy.stats as st import matplotlib.pyplot as plt def compute_thresholds_normal(n, x_min=-6, x_max=6, dx=0.01): """ Compute optimal stopping thresholds t_i for selecting the maximum of n i.i.d. N(0,1) draws using dynamic programming. """ xs = np.arange(x_min, x_max+dx, dx)

l = 7 c = 2.4 r = (c**2+l**2)**0.5 x = c + r y=c*x print(x, y) d = l**2 -2*c**2-2*c*r print(d, r) x1 = (c+r +d**0.5) /2 x2 = (c+r -d**0.5) /2

import numpy as np import scipy.optimize as opt from scipy.optimize import differential_evolution, basinhopping import itertools from typing import List, Tuple, Dict, Any import time class FunctionOptimizer: """ Optimizer for the function:

ts = [ ('this will probably not be fun', 'tiks zjop twrggfrf uwz kl pcx'), ('hopefully you will find satisfaction at least', 'hprehxmoc ctx cmrs mqtl zjdqcqjnfsaa nh ztnhj'), ('really this is very tedious', 'rfclnb wlkw lx zkyd bklrvdc') ] 0,1,2, 0,2,3, 1,3,4, 2,4,5, 3,5,6, 4,6,7, 5,7,8, 6,8,9, 7,9,10 def gen(): i = 0 while 1: if i < 3:

from math import hypot, acos, cos, sin, pi, atan2 class Segment: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 def length(self): return hypot(self.p1.x - self.p2.x, self.p1.y - self.p2.y)

from math import dist import heapq as hq from scipy.spatial import Delaunay # Requires scipy def bridge(pts): n = len(pts) if n <= 1: return 0 # Compute Delaunay triangulation

import math from typing import List, Tuple, Optional, Dict class FibonacciHeapNode: """Node in a Fibonacci heap.""" def __init__(self, key: float, value: int): self.key = key self.value = value self.parent: Optional['FibonacciHeapNode'] = None

#include <iostream> #include <vector> #include <algorithm> std::vector<int> parent, rank; void make_set(int v) { parent[v] = v; rank[v] = 0; }

class Edge: def __init__(self, u, v, weight): self.u = u self.v = v self.weight = weight def __str__(self): return f"({self.u},{self.v},{self.weight})" def __lt__(self, other):

class Edge: def __init__(self, u, v, weight): self.u = u self.v = v self.weight = weight def __str__(self): return f"({self.u},{self.v},{self.weight})" def __lt__(self, other):

// Transform function definitions to support tail call optimization via trampolines function transformToTCO(functionDefs) { const functions = new Map(); // Parse function definitions functionDefs.forEach(([name, params, body]) => { functions.set(name, { params, body }); }); // Transform each function body to use trampolines

tests = [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ], 45 ], [ [

# Claude AI Hamiltonian path finder class HamiltonianPathSolver: def __init__(self, graph): self.graph = [list(graph[k]) for k in sorted(graph.keys())] # adjacency list representation self.n = len(graph) self.forced_start = None self.forced_end = None def find_hamiltonian_path(self): """Main function to find Hamiltonian path with preprocessing"""

from fractions import Fraction arr = [[1, 1, 1, 1, 0, 0, 1, 0, 0, 0] , [1, 1, 1, 0, 1, 0, 0, 1, 0, 1] , [1, 1, 1, 0, 0, 1, 0, 0, 1, 0] , [1, 0, 0, 1, 1, 1, 1, 0, 0, 1] , [0, 1, 0, 1, 1, 1, 0, 1, 0, 1] , [0, 0, 1, 1, 1, 1, 0, 0, 1, 1] , [1, 0, 0, 1, 0, 0, 1, 1, 1, 0] , [0, 1, 0, 0, 1, 0, 1, 1, 1, 1] ,

tests = [ [[ [1, 0, 1], [0, 0, 0], [1, 0, 1] ],[(1, 1)]], [[ [0, 1, 0], [1, 0, 0], [0, 0, 1]

op = lambda x, y: x + y tree = {} def build(node, L, R): if L == R: tree[node] = xs[L] else: mid = (L + R) // 2 build(2 * node, L, mid) build(2 * node + 1, mid + 1, R)

arr = [] a, b = 1234, 4321 # for x in range(1, a + 1): # for y in range(1, b + 1): # arr.append((x * y, x, y)) # arr.sort(key=lambda x: -x[0]) def makePalindrome(number): num_str = str(number) digits = len(num_str)

tests = [ (4748, 47), (123, 122), (5347, 3475), (121, 12), (213, 12), (321, 132), (7234723, 7234), (122, 21), (12, 1),

xs = [2, 1, 5, 3, 4] op = lambda x, y: x + y tree = {} def build(node, L, R): if L == R: tree[node] = xs[L] else: mid = (L + R) // 2 build(2 * node, L, mid)