修改1
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@ -1,29 +1,51 @@
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import numpy as np
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from typing import List, Tuple
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from typing import List, Dict, Tuple
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import numpy as np
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# 动态生成收益函数
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def revenue_functions(num_projects):
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functions = []
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for i in range(num_projects):
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# 这里可以根据实际需求调整系数
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a = -0.1 - 0.05 * i # 二次项系数，每次递减 0.05
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b = 2 + 0.5 * i # 一次项系数，每次递增 0.5
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def r(x, a=a, b=b):
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return a * x**2 + b * x
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functions.append(r)
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return functions
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def dpsa(num_projects, total_resource, num_iterations, step_size):
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# 初始化每个项目的资源分配为 0
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resource_allocation = [0] * num_projects
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revenue_funs = revenue_functions(num_projects)
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for _ in range(num_iterations):
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# 保存上一次的资源分配结果
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prev_allocation = resource_allocation.copy()
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for i in range(num_projects):
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# 计算当前项目的梯度（导数）
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gradient = (revenue_funs[i](resource_allocation[i] + step_size) - revenue_funs[i](resource_allocation[i])) / step_size
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# 更新资源分配
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resource_allocation[i] += gradient * step_size
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# 确保资源分配不超过总资源
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if sum(resource_allocation) > total_resource:
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# 调整资源分配以满足约束条件
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excess = sum(resource_allocation) - total_resource
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# 按比例减少每个项目的资源分配
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factor = (total_resource - sum(resource_allocation[:i])) / (sum(resource_allocation[i:]))
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for j in range(i, num_projects):
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resource_allocation[j] *= factor
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# 判断是否收敛
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if np.linalg.norm(np.array(resource_allocation) - np.array(prev_allocation)) < 1e-6:
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break
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# 计算总收益
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total_revenue = sum(revenue_funs[i](resource_allocation[i]) for i in range(num_projects))
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return resource_allocation, total_revenue
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def bilinear_interpolation(
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X_NUM: int,
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Y_NUM: int,
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X: float,
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Y: float,
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X0: List[float],
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Y0: List[float],
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Z0: np.ndarray
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) -> Tuple[float, float, float, List[float]]:
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"""
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双线性插值计算
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:param X_NUM: X方向点数 (整数)
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:param Y_NUM: Y方向点数 (整数)
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:param X: 目标X坐标 (浮点数)
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:param Y: 目标Y坐标 (浮点数)
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:param X0: X坐标数组 (浮点数列表)
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:param Y0: Y坐标数组 (浮点数列表)
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:param Z0: 二维数据数组 (numpy.ndarray, 形状为 Y_NUM x X_NUM)
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:return: 元组 (Z, ZX, ZY, Z_OUT)
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"""
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X0_arr = np.array(X0) # 转换为NumPy数组
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Z = np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))])
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ZX = (np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X + 0.1))]) - Z) / 0.1
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ZY = (np.interp(Y + 0.1, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))]) - Z) / 0.1
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Z_OUT = Z0.flatten().tolist()
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return Z, ZX, ZY, Z_OUT
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35
app/main.py
35
app/main.py
@ -1,8 +1,7 @@
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import numpy as np
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from flask import Flask, request, jsonify
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from flasgger import Swagger
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import json
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from algorithms import bilinear_interpolation
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from algorithms import *
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app = Flask(__name__)
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@ -48,35 +47,21 @@ def calculate():
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input_data = json.loads(request_data["text"])
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# 提取输入参数
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X_NUM = input_data["Par"][0]["Data"]
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Y_NUM = input_data["Par"][1]["Data"]
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X = input_data["Par"][2]["Data"]
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Y = input_data["Par"][3]["Data"]
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X0 = input_data["Par"][4]["Data"]
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Y0 = input_data["Par"][5]["Data"]
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Z0 = input_data["Par"][6]["Data"]
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num_projects = input_data["Par"][0]["Data"]
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total_resource = input_data["Par"][1]["Data"]
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num_iterations = input_data["Par"][2]["Data"]
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resource_allocation, total_revenue = dpsa(num_projects, total_resource, num_iterations,0.1)
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# 调用算法函数
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Z, ZX, ZY, Z_OUT = bilinear_interpolation(
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X_NUM, Y_NUM, X, Y, X0, Y0, np.array(Z0).reshape(Y_NUM, X_NUM)
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)
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# 构建输出JSON
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output_data = {
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"funcName": "calculate_main",
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"className": "",
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"par": [
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{"name": "X_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": X_NUM},
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{"name": "Y_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": Y_NUM},
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{"name": "X", "dataType": 1, "arrayType": 0, "isOut": 2, "data": X},
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{"name": "Y", "dataType": 2, "arrayType": 0, "isOut": 2, "data": Y},
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{"name": "X0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": X0},
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{"name": "Y0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Y0},
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{"name": "Z0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Z0},
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{"name": "Z", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(Z)},
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{"name": "ZX", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZX)},
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{"name": "ZY", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZY)},
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{"name": "Z_OUT", "dataType": 1, "arrayType": 1, "isOut": 1, "data": Z_OUT}
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{"name": "num_projects", "dataType": 0, "arrayType": 0, "isOut": 2, "data": num_projects},
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{"name": "total_resource", "dataType": 1, "arrayType": 0, "isOut": 2, "data": total_resource},
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{"name": "num_iterations", "dataType": 0, "arrayType": 0, "isOut": 2, "data": num_iterations},
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{"name": "resource_allocation", "dataType": 1, "arrayType": 1, "isOut": 1, "data": resource_allocation},
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{"name": "total_revenue", "dataType": 1, "arrayType": 0, "isOut": 1, "data": total_revenue}
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]
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}
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@ -1,5 +1,5 @@
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flask==3.0.3
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numpy==2.2.4
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numpy==1.26.4
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flasgger==0.9.7.1
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werkzeug==3.0.3
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jinja2==3.1.4
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