A1054.The Dominant Color

本文介绍了一种算法,用于从给定分辨率的图像中找出占据面积超过一半的严格主导颜色。通过遍历像素并使用哈希映射来跟踪每种颜色出现的次数,最终确定主导颜色。

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Behind the scenes in the computer’s memory, color is always talked about as a series of 24 bits of information for each pixel. In an image, the color with the largest proportional area is called the dominant color. A strictly dominant color takes more than half of the total area. Now given an image of resolution M by N (for example, 800x600), you are supposed to point out the strictly dominant color.

Input Specification:

Each input file contains one test case. For each case, the first line contains 2 positive numbers: M (<=800) and N (<=600) which are the resolutions of the image. Then N lines follow, each contains M digital colors in the range [0, 224). It is guaranteed that the strictly dominant color exists for each input image. All the numbers in a line are separated by a space.

Output Specification:

For each test case, simply print the dominant color in a line.

Sample Input:
5 3
0 0 255 16777215 24
24 24 0 0 24
24 0 24 24 24
Sample Output:
24

Code:

#include "cstdio"
#include "map"

using namespace std;

int main()
{
    int n,m,col;
    scanf("%d%d",&n,&m);
    map<int,int> mp;
    for(int i = 0; i < n; i++)
    {
        for(int j = 0; j < m; j++)
        {
            scanf("%d",&col);
            if(mp.find(col) != mp.end())
            {
                mp[col]++;
            }
            else
            {
                mp[col] = 1;
            }
        }
    }

    int k = 0, MAX = 0;
    for(map<int,int>::iterator it = mp.begin(); it != mp.end(); it++)
    {
       if(it->second > MAX)
       {
           k = it->first;
           MAX = it->second;
       }
    }
    printf("%d\n",k);
    return 0;
}

以下代码(1)报错:C:\Users\zh\AppData\Roaming\JetBrains\PyCharm2024.1\scratches\scratch_1.py:165: DeprecationWarning: Call to deprecated function create_named_range (Assign scoped named ranges directly to worksheets or global ones to the workbook. Deprecated in 3.1). wb.create_named_range( 尝试修复(这个脚本会读取原始Excel文件,添加用于计算ESG得分的各列,并设置公式结构。最终结果将保存为3.xlsx,所有计算将在Excel中执行。) (1): ```python import pandas as pd import openpyxl from openpyxl.utils import get_column_letter from openpyxl.styles import PatternFill def add_esg_formulas(input_path, output_path): # 加载工作簿和工作表 wb = openpyxl.load_workbook(input_path) ws = wb.active # 添加新列标题 new_columns = [ "基础ESG得分", "行业系数", "动态进步分", "供应链分", "垄断矫正分", "数据异常扣分", "总分", "MSCI转换分", "晨星转换分", "标普转换分", "华证转换分", "中证转换分", "Wind转换分", "Wind评级提升分", "减排技术研发投入率★", "Tier1供应商合规率★", "碳强度年降幅★", "赫芬达尔指数★", "平台佣金率★", "社会议题投入占比★" ] start_col = ws.max_column + 1 for i, col_name in enumerate(new_columns): col_letter = get_column_letter(start_col + i) ws[f"{col_letter}1"] = col_name # 为需要补充数据的列添加黄色背景 if "★" in col_name: for row in range(2, ws.max_row + 1): ws[f"{col_letter}{row}"].fill = PatternFill( start_color="FFFF00", end_color="FFFF00", fill_type="solid" ) # 设置公式 for row in range(3, ws.max_row + 1): # 从第3行开始(数据行) # 字母评级转换公式 letter_rating_formula = ( f'IF(ISBLANK(B{row}), "", ' f'IF(B{row}="AAA",9,' f'IF(B{row}="AA",8,' f'IF(B{row}="A",7,' f'IF(B{row}="BBB",6,' f'IF(B{row}="BB",5,' f'IF(B{row}="B",4,' f'IF(B{row}="CCC",3,3)))))))' ) # 晨星评分转换公式 morningstar_formula = ( f'IF(ISBLANK(E{row}), "", ' f'IF(E{row}>=40,9,' f'IF(E{row}>=30,7,' f'IF(E{row}>=20,6,' f'IF(E{row}>=10,5,3))))' ) # 基础ESG得分公式 base_esg_formula = ( f'=((IFERROR({get_column_letter(start_col+6)}{row},0)+' f'IFERROR({get_column_letter(start_col+7)}{row},0)+' f'IFERROR({get_column_letter(start_col+8)}{row},0)+' f'IFERROR({get_column_letter(start_col+9)}{row},0)+' f'IFERROR({get_column_letter(start_col+10)}{row},0)+' f'IFERROR({get_column_letter(start_col+11)}{row},0))/' f'MAX(1,COUNT({get_column_letter(start_col+6)}{row},' f'{get_column_letter(start_col+7)}{row},' f'{get_column_letter(start_col+8)}{row},' f'{get_column_letter(start_col+9)}{row},' f'{get_column_letter(start_col+10)}{row},' f'{get_column_letter(start_col+11)}{row})))*0.4' ) # 行业系数公式 industry_formula = ( f'=IF(OR(T{row}="能源类",T{row}="工业类"),' f'IF({get_column_letter(start_col+15)}{row}>=' f'IF(T{row}="能源类",0.08,0.05),1.2,0.9),' f'IF(OR(T{row}="科技类",T{row}="消费类"),' f'IF({get_column_letter(start_col+18)}{row}>=' f'IF(T{row}="科技类",0.025,0.018),1.1,1.0),1.0))' ) # Wind评级提升分公式 wind_improve_formula = ( f'=((IF(AND(NOT(ISBLANK(S{row})),NOT(ISBLANK(R{row}))),' f'MAX(VLOOKUP(R{row},RatingTable,2,0)-VLOOKUP(S{row},RatingTable,2,0),0),0)+' f'IF(AND(NOT(ISBLANK(R{row})),NOT(ISBLANK(Q{row}))),' f'MAX(VLOOKUP(Q{row},RatingTable,2,0)-VLOOKUP(R{row},RatingTable,2,0),0),0))/3)*10' ) # 动态进步分公式 progress_formula = ( f'={get_column_letter(start_col+12)}{row}+' f'MIN(10,({get_column_letter(start_col+13)}{row}/1.8)*10)+' f'MIN(10,({get_column_letter(start_col+14)}{row}/0.7)*10)' ) # 垄断矫正分公式 monopoly_formula = ( f'=IF(AND(OR(T{row}="金融类",T{row}="能源类"),U{row}="国企"),' f'-5*{get_column_letter(start_col+16)}{row},' f'IF(AND(OR(T{row}="科技类",T{row}="消费类"),' f'IF({get_column_letter(start_col+17)}{row}>0.1,-3*{get_column_letter(start_col+17)}{row},0),0))' ) # 数据异常扣分公式 penalty_formula = ( f'=IF(ABS(' f'AVERAGE({get_column_letter(start_col+6)}{row},{get_column_letter(start_col+8)}{row})' f'-AVERAGE({get_column_letter(start_col+9)}{row},{get_column_letter(start_col+10)}{row},{get_column_letter(start_col+11)}{row})' f')>=2,-3,0)' ) # 总分公式 total_formula = ( f'=({get_column_letter(start_col)}{row}*{get_column_letter(start_col+1)}{row})' f'+{get_column_letter(start_col+2)}{row}' f'+{get_column_letter(start_col+3)}{row}' f'+{get_column_letter(start_col+4)}{row}' f'+{get_column_letter(start_col+5)}{row}' ) # 写入公式 ws[f"{get_column_letter(start_col+6)}{row}"] = letter_rating_formula # MSCI转换分 ws[f"{get_column_letter(start_col+7)}{row}"] = morningstar_formula # 晨星转换分 for col_offset in [8, 9, 10, 11]: # 标普/华证/中证/Wind转换分 ws[f"{get_column_letter(start_col+col_offset)}{row}"] = letter_rating_formula.replace("B{row}", get_column_letter(2+col_offset-8)+str(row)) ws[f"{get_column_letter(start_col)}{row}"] = base_esg_formula # 基础ESG得分 ws[f"{get_column_letter(start_col+1)}{row}"] = industry_formula # 行业系数 ws[f"{get_column_letter(start_col+12)}{row}"] = wind_improve_formula # Wind评级提升分 ws[f"{get_column_letter(start_col+2)}{row}"] = progress_formula # 动态进步分 ws[f"{get_column_letter(start_col+4)}{row}"] = monopoly_formula # 垄断矫正分 ws[f"{get_column_letter(start_col+5)}{row}"] = penalty_formula # 数据异常扣分 ws[f"{get_column_letter(start_col+6)}{row}"] = total_formula # 总分 # 创建评级转换表 ws["A1000"] = "评级转换表" ratings = ["AAA", "AA", "A", "BBB", "BB", "B", "CCC"] scores = [9, 8, 7, 6, 5, 4, 3] for i, (rating, score) in enumerate(zip(ratings, scores), start=1001): ws[f"A{i}"] = rating ws[f"B{i}"] = score # 定义名称"RatingTable"引用这个区域 if "RatingTable" not in wb.defined_names: wb.create_named_range( "RatingTable", ws, f"$A$1001:$B${1000+len(ratings)}" ) # 添加说明文本 ws["A1050"] = "★需要手动补充的数据项:" ws["A1051"] = "1. 减排技术研发投入率 = (自主研发减碳技术投入/总营收)" ws["A1052"] = "2. Tier1供应商ESG合规率 = 接入区块链碳管理平台的供应商比例" ws["A1053"] = "3. 碳强度年降幅 = (上年碳排放强度 - 本年碳排放强度)/上年碳排放强度" ws["A1054"] = "4. 赫芬达尔指数(HHI) = Σ(企业市场份额)^2 (金融/能源类国企填写)" ws["A1055"] = "5. 平台商户佣金率 (科技/消费类填写)" ws["A1056"] = "6. 社会议题投入占比 = 数据隐私/安全投入/总营收" # 保存工作簿 wb.save(output_path) # 执行函数 input_file = "D:/2.xlsx" output_file = "D:/3.xlsx" add_esg_formulas(input_file, output_file) ``` ### 功能说明: 1. **添加的列**: - 基础ESG得分、行业系数、动态进步分等核心计算列 - 各评级机构的转换分列 - 带★号的外部数据补充列(标记为黄色背景) - 总分列 2. **核心公式实现**: - **基础ESG得分**:自动转换各机构评级为分数,计算平均值后乘以40% - **行业系数**:根据行业类型和补充数据动态调整 - **动态进步分**:包含Wind评级提升、减排技术投入和供应链进步 - **垄断矫正**:针对金融/能源国企和科技/消费平台企业 - **数据异常扣分**:检测国内外评级差异 3. **特殊处理**: - 创建评级转换表(AAA→9分,AA→8分,...,CCC→3分) - 添加详细的数据补充说明(A1050-A1056) - 黄色背景标记需要手动补充的数据单元格 4. **使用说明**: - 在黄色标记的★列补充相应数据 - 总分列会自动计算最终ESG得分 - ≥75分表示高概率上榜福布斯ESG 50 此脚本保留了原始设计的所有核心逻辑,同时确保所有计算都在Excel中执行。用户只需在黄色单元格补充外部数据,即可自动生成最终ESG评分。
最新发布
07-05
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