修改1

app/algorithms.py

@@ -1,29 +1,51 @@
 import numpy as np
-from typing import List, Tuple
+from typing import List, Dict, Tuple
+
+import numpy as np
+
+# 动态生成收益函数
+def revenue_functions(num_projects):
+    functions = []
+    for i in range(num_projects):
+        # 这里可以根据实际需求调整系数
+        a = -0.1 - 0.05 * i  # 二次项系数，每次递减 0.05
+        b = 2 + 0.5 * i  # 一次项系数，每次递增 0.5
+        def r(x, a=a, b=b):
+            return a * x**2 + b * x
+        functions.append(r)
+    return functions
+
+def dpsa(num_projects, total_resource, num_iterations, step_size):
+    # 初始化每个项目的资源分配为 0
+    resource_allocation = [0] * num_projects
+    revenue_funs = revenue_functions(num_projects)
+
+    for _ in range(num_iterations):
+        # 保存上一次的资源分配结果
+        prev_allocation = resource_allocation.copy()
+
+        for i in range(num_projects):
+            # 计算当前项目的梯度（导数）
+            gradient = (revenue_funs[i](resource_allocation[i] + step_size) - revenue_funs[i](resource_allocation[i])) / step_size
+
+            # 更新资源分配
+            resource_allocation[i] += gradient * step_size
+
+            # 确保资源分配不超过总资源
+            if sum(resource_allocation) > total_resource:
+                # 调整资源分配以满足约束条件
+                excess = sum(resource_allocation) - total_resource
+                # 按比例减少每个项目的资源分配
+                factor = (total_resource - sum(resource_allocation[:i])) / (sum(resource_allocation[i:]))
+                for j in range(i, num_projects):
+                    resource_allocation[j] *= factor
+
+        # 判断是否收敛
+        if np.linalg.norm(np.array(resource_allocation) - np.array(prev_allocation)) < 1e-6:
+            break
+
+    # 计算总收益
+    total_revenue = sum(revenue_funs[i](resource_allocation[i]) for i in range(num_projects))
+
+    return resource_allocation, total_revenue
 
-def bilinear_interpolation(
-    X_NUM: int,
-    Y_NUM: int,
-    X: float,
-    Y: float,
-    X0: List[float],
-    Y0: List[float],
-    Z0: np.ndarray
-) -> Tuple[float, float, float, List[float]]:
-    """
-    双线性插值计算
-    :param X_NUM: X方向点数 (整数)
-    :param Y_NUM: Y方向点数 (整数)
-    :param X: 目标X坐标 (浮点数)
-    :param Y: 目标Y坐标 (浮点数)
-    :param X0: X坐标数组 (浮点数列表)
-    :param Y0: Y坐标数组 (浮点数列表)
-    :param Z0: 二维数据数组 (numpy.ndarray, 形状为 Y_NUM x X_NUM)
-    :return: 元组 (Z, ZX, ZY, Z_OUT)
-    """
-    X0_arr = np.array(X0)  # 转换为NumPy数组
-    Z = np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))])
-    ZX = (np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X + 0.1))]) - Z) / 0.1
-    ZY = (np.interp(Y + 0.1, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))]) - Z) / 0.1
-    Z_OUT = Z0.flatten().tolist()
-    return Z, ZX, ZY, Z_OUT

app/main.py

@@ -1,8 +1,7 @@
-import numpy as np
 from flask import Flask, request, jsonify
 from flasgger import Swagger
 import json
-from algorithms import bilinear_interpolation
+from algorithms import *
 
 app = Flask(__name__)
 
@@ -48,35 +47,21 @@ def calculate():
     input_data = json.loads(request_data["text"])
     
     # 提取输入参数
-    X_NUM = input_data["Par"][0]["Data"]
+    num_projects = input_data["Par"][0]["Data"]
-    Y_NUM = input_data["Par"][1]["Data"]
+    total_resource = input_data["Par"][1]["Data"]
-    X = input_data["Par"][2]["Data"]
+    num_iterations = input_data["Par"][2]["Data"]
-    Y = input_data["Par"][3]["Data"]
+    resource_allocation, total_revenue = dpsa(num_projects, total_resource, num_iterations,0.1)
-    X0 = input_data["Par"][4]["Data"]
-    Y0 = input_data["Par"][5]["Data"]
-    Z0 = input_data["Par"][6]["Data"]
-
     # 调用算法函数
-    Z, ZX, ZY, Z_OUT = bilinear_interpolation(
-        X_NUM, Y_NUM, X, Y, X0, Y0, np.array(Z0).reshape(Y_NUM, X_NUM)
-    )
-    
     # 构建输出JSON
     output_data = {
         "funcName": "calculate_main",
         "className": "",
         "par": [
-            {"name": "X_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": X_NUM},
+            {"name": "num_projects", "dataType": 0, "arrayType": 0, "isOut": 2, "data": num_projects},
-            {"name": "Y_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": Y_NUM},
+            {"name": "total_resource", "dataType": 1, "arrayType": 0, "isOut": 2, "data": total_resource},
-            {"name": "X", "dataType": 1, "arrayType": 0, "isOut": 2, "data": X},
+            {"name": "num_iterations", "dataType": 0, "arrayType": 0, "isOut": 2, "data": num_iterations},
-            {"name": "Y", "dataType": 2, "arrayType": 0, "isOut": 2, "data": Y},
+            {"name": "resource_allocation", "dataType": 1, "arrayType": 1, "isOut": 1, "data": resource_allocation},
-            {"name": "X0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": X0},
+            {"name": "total_revenue", "dataType": 1, "arrayType": 0, "isOut": 1, "data": total_revenue}
-            {"name": "Y0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Y0},
-            {"name": "Z0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Z0},
-            {"name": "Z", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(Z)},
-            {"name": "ZX", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZX)},
-            {"name": "ZY", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZY)},
-            {"name": "Z_OUT", "dataType": 1, "arrayType": 1, "isOut": 1, "data": Z_OUT}
         ]
     }
     

requirements.txt

@@ -1,5 +1,5 @@
 flask==3.0.3
-numpy==2.2.4
+numpy==1.26.4
 flasgger==0.9.7.1
 werkzeug==3.0.3
 jinja2==3.1.4