83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from typing import List, Optional, Dict, Any
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from problems.min_path_sum.problem import MinPathSumProblem
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router = APIRouter(
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prefix="/min_path_sum",
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tags=["min_path_sum"],
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responses={404: {"description": "Not found"}},
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)
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problem_solver = MinPathSumProblem()
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class GenerateRequest(BaseModel):
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rows: int = 5
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cols: int = 5
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min_val: int = 1
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max_val: int = 20
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class SolveRequest(BaseModel):
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grid: List[List[int]]
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algorithms: List[str] = ["dp", "recursion"]
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class BenchmarkRequest(BaseModel):
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sizes: List[int] = [4, 6, 8, 10, 12] # Assuming square grids for simplicity in benchmark input, or handle appropriately
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algorithms: List[str] = ["dp", "recursion"]
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@router.post("/generate")
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async def generate_case(req: GenerateRequest):
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return problem_solver.generate_case(rows=req.rows, cols=req.cols, min_val=req.min_val, max_val=req.max_val)
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@router.post("/solve")
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async def solve_case(req: SolveRequest):
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input_data = {"grid": req.grid}
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results = []
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# Check dimensions for safety check if needed
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rows = len(req.grid)
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cols = len(req.grid[0]) if rows > 0 else 0
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for algo in req.algorithms:
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try:
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# Recursion safety limit
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if algo == "recursion" and (rows + cols > 18):
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results.append({
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"algorithm": algo,
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"error": "Input too large for Recursion (limit rows+cols <= 18)",
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"skipped": True
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})
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continue
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res = problem_solver.solve(input_data, algo)
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results.append(res)
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except ValueError as e:
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results.append({"algorithm": algo, "error": str(e)})
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return results
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@router.post("/benchmark")
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async def benchmark(req: BenchmarkRequest):
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benchmark_results = []
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for size in req.sizes:
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# Use size as both rows and cols for benchmarking
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input_data = problem_solver.generate_case(rows=size, cols=size)
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size_result = {"size": size, "algorithms": []}
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for algo in req.algorithms:
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try:
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if algo == "recursion" and (size + size > 18):
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size_result["algorithms"].append({
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"algorithm": algo,
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"time_seconds": None,
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"skipped": True
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})
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continue
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res = problem_solver.solve(input_data, algo)
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size_result["algorithms"].append(res)
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except Exception as e:
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size_result["algorithms"].append({"algorithm": algo, "error": str(e)})
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benchmark_results.append(size_result)
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return benchmark_results
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