Python+Excel脚本实现一键生成动态规划表

from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
import openpyxl
import pandas as pd

# 1️⃣ 精准读取：从 Excel 剥离业务参数
df_items = pd.read_excel("input_data.xlsx", usecols=[0, 1, 2]).dropna()
df_config = pd.read_excel("input_data.xlsx", usecols=[5, 6]).dropna()

item_names = df_items["物品名称"].tolist()
weights = df_items["物品大小 w[i]"].astype(int).tolist()
values = df_items["物品价值 v[i]"].astype(int).tolist()
max_capacity = int(df_config.loc[df_config["配置项"] == "最大容量 max_capacity", "参数值"].values[0])

# 2️⃣ 二维 DP 推演：完整记录状态转移轨迹
n = len(weights)
dp_2d = [[0] * (max_capacity + 1) for _ in range(n + 1)]
for i in range(1, n + 1):
    w, v = weights[i - 1], values[i - 1]
    for j in range(max_capacity + 1):
        if j < w:
            dp_2d[i][j] = dp_2d[i - 1][j]
        else:
            dp_2d[i][j] = max(dp_2d[i - 1][j], dp_2d[i - 1][j - w] + v)

# 3️⃣ 一维滚动数组 + 历史快照：空间优化 O(C)，过程全记录
dp_1d = [0] * (max_capacity + 1)
dp_1d_history = [list(dp_1d)]
for i in range(n):
    w, v = weights[i], values[i]
    for j in range(max_capacity, w - 1, -1):
        dp_1d[j] = max(dp_1d[j], dp_1d[j - w] + v)
    dp_1d_history.append(list(dp_1d))

# 4️⃣ Pandas 格式化输出：告别裸 print，表格即报表
columns = [str(j) for j in range(max_capacity + 1)]
index_labels = ["无物品"] + item_names

df_2d = pd.DataFrame(dp_2d, index=index_labels, columns=columns)
df_1d = pd.DataFrame(dp_1d_history, index=index_labels, columns=columns)
print(df_2d.to_string())
print(df_1d.to_string())

df_config.loc[df_config["配置项"] == "最大容量 max_capacity", "参数值"].values[0]

from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
import openpyxl
import pandas as pd

# ==============================================================================
# 1.输入参数的 Excel 文件 (input_data.xlsx)
# ==============================================================================
# 在这里原始数据表，
input_file = "input_data.xlsx"



# ==============================================================================
# 2. 读取与计算阶段：完全从 Excel 中加载数据，并计算生成最终的 DP 状态表
# ==============================================================================
# ---- A. 从 Excel 中读取数据 ----
df_read_items = pd.read_excel(input_file, usecols=[0, 1, 2]).dropna()
df_read_config = pd.read_excel(input_file, usecols=[5, 6]).dropna()

# 从读取到的 DataFrame 中还原出变量列表
item_names = df_read_items["物品名称"].tolist()
weights = df_read_items["物品大小 w[i]"].astype(int).tolist()
values = df_read_items["物品价值 v[i]"].astype(int).tolist()

# 从配置区精准提取最大容量数字
max_capacity = int(df_read_config.loc[df_read_config["配置项"] == "最大容量 max_capacity", "参数值"].values[0])


# ---- B. 核心动态规划算法核心逻辑 ----
n = len(weights)
# 1. 计算二维 DP 状态表
dp_2d = [[0] * (max_capacity + 1) for _ in range(n + 1)]
for i in range(1, n + 1):
    w, v = weights[i - 1], values[i - 1]
    for j in range(max_capacity + 1):
        if j < w:
            dp_2d[i][j] = dp_2d[i - 1][j]
        else:
            dp_2d[i][j] = max(dp_2d[i - 1][j], dp_2d[i - 1][j - w] + v)

# 2. 计算一维滚动数组并截取每轮的历史快照
dp_1d = [0] * (max_capacity + 1)
dp_1d_history = [list(dp_1d)]
for i in range(n):
    w, v = weights[i], values[i]
    for j in range(max_capacity, w - 1, -1):
        dp_1d[j] = max(dp_1d[j], dp_1d[j - w] + v)
    dp_1d_history.append(list(dp_1d))


# ---- C. 使用 Pandas 格式化输出最终内容 ----
columns = [str(j) for j in range(max_capacity + 1)]
index_labels = ["无物品"] + item_names

print("="*43 + " 最终输出 1：二维 DP 状态表 " + "="*43)
df_output_2d = pd.DataFrame(dp_2d, index=index_labels, columns=columns)
df_output_2d.index.name = "物品名称"
print(df_output_2d.to_string())

print("\n" + "="*41 + " 最终输出 2：一维滚动数组快照表 " + "="*41)
df_output_1d = pd.DataFrame(dp_1d_history, index=index_labels, columns=columns)
df_output_1d.index.name = "物品名称"
print(df_output_1d.to_string())