# ===--------------------------------------------------------------------------------------===# # # This file implements the evaluator for the heilbronn problem for triangles, with # 11 points. # # ===--------------------------------------------------------------------------------------===# # # Some of the code in this file is adapted from: # # google-deepmind/alphaevolve_results: # Licensed under the Apache License v2.0. # # ===--------------------------------------------------------------------------------------===# import time import numpy as np import sys import os from importlib import __import__ import itertools BENCHMARK = 0.036529889880030156 TOL = 1e-6 NUM_POINTS = 11 def check_inside_triangle_wtol(points: np.ndarray, tol: float = 1e-6): """Checks that all points are inside the triangle with vertices (0,0), (1,0), (0.5, sqrt(3)/2). Args: points: Array of 2D points to check tol: Tolerance for numerical errors """ for x, y in points: cond1 = y >= -tol cond2 = np.sqrt(3) * x <= np.sqrt(3) - y + tol cond3 = y <= np.sqrt(3) * x + tol if not (cond1 and cond2 and cond3): raise ValueError( f"Point ({x}, {y}) is outside the equilateral triangle (tolerance: {tol})." ) def triangle_area(a: np.array, b: np.array, c: np.array) -> float: return np.abs(a[0] * (b[1] - c[1]) + b[0] * (c[1] - a[1]) + c[0] * (a[1] - b[1])) / 2 def evaluate(program_path: str): try: abs_program_path = os.path.abspath(program_path) program_dir = os.path.dirname(abs_program_path) module_name = os.path.splitext(os.path.basename(program_path))[0] points = None try: sys.path.insert(0, program_dir) program = __import__(module_name) start_time = time.time() points = program.heilbronn_triangle11() end_time = time.time() eval_time = end_time - start_time except Exception as err: raise err finally: if program_dir in sys.path: sys.path.remove(program_dir) if not isinstance(points, np.ndarray): points = np.array(points) if points.shape != (NUM_POINTS, 2): raise ValueError(f"Invalid shapes: points = {points.shape}, expected {(NUM_POINTS,2)}") check_inside_triangle_wtol(points, TOL) a = np.array([0, 0]) b = np.array([1, 0]) c = np.array([0.5, np.sqrt(3) / 2]) min_triangle_area = min( [triangle_area(p1, p2, p3) for p1, p2, p3 in itertools.combinations(points, 3)] ) min_area_normalized = min_triangle_area / triangle_area(a, b, c) return { "min_area_normalized": float(min_area_normalized), "combined_score": float(min_area_normalized / BENCHMARK), "eval_time": float(eval_time), } except Exception as e: return {"combined_score": 0.0, "error": str(e)}