第47题 Integer to Roman

本文介绍了一种将整数转换为罗马数字的Java算法,适用于1到3999之间的整数。

Given an integer, convert it to a roman numeral.

Input is guaranteed to be within the range from 1 to 3999.

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Solution in Java:
public class Solution {
    public String intToRoman(int num) {
        int val[] = {1000, 500, 100, 50, 10, 5, 1};
        String chrs[] = {"M", "D", "C", "L", "X", "V", "I"};
        
        String result = "";
        
        int digi = num/val[0];
        for(int j=0; j<digi; j++) result+=chrs[0];
        num = num%val[0];
        
        for(int i=2; i<7; i+=2){
            digi = num/val[i];
            num = num%val[i];
            if(digi>8) result+=chrs[i]+chrs[i-2];
            else if(digi<4){
                for(int j=0; j<digi; j++)   result+=chrs[i];
            }
            else if(digi==4){   
                result+=chrs[i]+chrs[i-1];
            }
            else{//5<=digi<=8
                result+=chrs[i-1];
                digi = digi-5;  //wrong statement: digi = digi%val[i-1]
                for(int j=0; j<digi; j++)   result+=chrs[i];
            }
        }
        return result;
        
    }
}

Note:
数组用一组值初始化:用大括号。。。。。。。
基于# =============================== # 第二部分:按月评估预测精度 # =============================== best_model_name = comparison_df.index[0] best_model_predictions = predictions[best_model_name] # 使用最终集成模型的测试集预测结果 pred_test = best_model_predictions # 统一变量名用于后续分析 y_test = y_test_original # 创建DataFrame df_test = pd.DataFrame({ 'dates_test': dates_test, 'y_test': y_test, 'pred_test': pred_test }) # 提取月份和年份 df_test['month'] = df_test['dates_test'].dt.month df_test['year'] = df_test['dates_test'].dt.year # 初始化评估列表 evaluation = [] # 遍历每个月份 months = sorted(df_test['month'].unique()) for month in months: month_data = df_test[df_test['month'] == month] for year in sorted(month_data['year'].unique()): specific_month_data = month_data[month_data['year'] == year] if specific_month_data.empty: continue total_days = len(specific_month_data) accurate_count = 0 overestimate_count = 0 underestimate_count = 0 # 逐日评估 for _, row in specific_month_data.iterrows(): lower_bound = row['pred_test'] * 0.75 upper_bound = row['pred_test'] * 1.25 actual_value = row['y_test'] if lower_bound <= actual_value <= upper_bound: accurate_count += 1 elif actual_value < lower_bound: overestimate_count += 1 elif actual_value > upper_bound: underestimate_count += 1 # 计算比率 accuracy_rate = accurate_count / total_days overestimate_rate = overestimate_count / total_days underestimate_rate = underestimate_count / total_days # 存储结果 evaluation.append({ 'Year': year, 'Month': month, 'Accuracy Rate': accuracy_rate, 'Overestimate Rate': overestimate_rate, 'Underestimate Rate': underestimate_rate }) # 转换为 DataFrame 并输出 evaluation_df = pd.DataFrame(evaluation) print("\n📊 Monthly Evaluation Results:") print(evaluation_df) import pandas as pd import numpy as np import matplotlib.pyplot as plt # 设置全局字体为 Times New Roman plt.rcParams['font.family'] = 'Times New Roman' # 假定 evaluation_df 已存在 evaluation_df['Year'] = evaluation_df['Year'].astype(int) evaluation_df['Month'] = evaluation_df['Month'].astype(int) # 创建 YearMonth 列用于排序 evaluation_df['YearMonth'] = evaluation_df['Year'].astype(str) + '-' + evaluation_df['Month'].astype(str).str.zfill(2) evaluation_df = evaluation_df.sort_values(by='YearMonth').reset_index(drop=True) # 2. 绘图设置 # ----------------------------- plt.figure(figsize=(18, 7)) # 稍微加宽,让柱子之间显得更宽松 ind = ind = np.arange(len(evaluation_df)) width = 0.3 # 减小柱子宽度,制造更多空白 # 绘制柱状图:从左到右为 Underestimate, Accuracy, Overestimate accuracy_bar = plt.bar(ind- width, evaluation_df['Accuracy Rate'], width, color='green', label='Accuracy Rate') underestimate_bar = plt.bar(ind , evaluation_df['Underestimate Rate'], width, color='red', label='Underestimate Rate') overestimate_bar = plt.bar(ind + width, evaluation_df['Overestimate Rate'], width, color='blue', label='Overestimate Rate') # 添加数值标签 for bars in [ accuracy_bar, underestimate_bar,overestimate_bar]: for bar in bars: height = bar.get_height() plt.text(bar.get_x() + bar.get_width() / 2.0, height, f'{height:.1%}', ha='center', va='bottom', fontsize=9) # 折线图(只画准确率) plt.plot(ind- width, evaluation_df['Accuracy Rate'], marker='o', linestyle='-', color='green', label='Accuracy Line', linewidth=2, markersize=5) # 设置标和坐标轴 plt.title('AQI Range Forecast Accuracy Evaluation', fontsize=16) plt.xlabel('Month', fontsize=12) plt.ylabel('Rate', fontsize=12) # x轴标签旋转 plt.xticks(ind, evaluation_df['YearMonth'], rotation=45) # ❗关键修改:将图例放在图表外部右侧,防止遮挡 plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) # 自动调整布局,防止裁剪 plt.tight_layout() # 显示图形 plt.show()下面的代码出现# 定义AQI分类函数(返回英文标签,与labels一致) def classify_aqi(aqi): if aqi <= 50: return 'Excellent' elif aqi <= 100: return 'Good' elif aqi <= 150: return 'Light pollution' elif aqi <= 200: return 'Moderate pollution' elif aqi <= 300: return 'Heavy pollution' else: return 'Severe pollution' # 对真实值和预测值进行AQI类别分类 df_test['y_test_category'] = df_test['y_test'].apply(classify_aqi) df_test['pred_test_category'] = df_test['pred_test'].apply(classify_aqi) # 创建AQI类别级别映射(对应英文标签) category_levels = { 'Excellent': 1, 'Good': 2, 'Light pollution': 3, 'Moderate pollution': 4, 'Heavy pollution': 5, 'Severe pollution': 6 } # 根据类别级别判断高估还是低估(返回英文状态) def assess_prediction(row): actual_level = category_levels[row['y_test_category']] predicted_level = category_levels[row['pred_test_category']] if actual_level == predicted_level: return 'Accurate level' elif predicted_level == actual_level + 1: return 'Overestimate level' elif predicted_level == actual_level - 1: return 'Underestimate level' else: return 'Significant Deviation' # 相差一个以上级别 # 应用函数判断每天的预测 df['assessment'] = df.apply(assess_prediction, axis=1) # 按月汇总 monthly_stats = df.groupby(df['date'].dt.to_period('M')).assessment.value_counts().unstack().fillna(0) # 计算准确率、高估率和低估率 monthly_stats['total'] = monthly_stats.sum(axis=1) monthly_stats['Level Accuracy Rate'] = monthly_stats['Accurate level'] / monthly_stats['total'] monthly_stats['Level Overestimate Rate'] = monthly_stats['Overestimate level'] / monthly_stats['total'] monthly_stats['Level Underestimate Rate'] = monthly_stats['Underestimate level'] / monthly_stats['total']出现yError Traceback (most recent call last) File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3805, in Index.get_loc(self, key) 3804 try: -> 3805 return self._engine.get_loc(casted_key) 3806 except KeyError as err: File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc() File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc() File pandas\\_libs\\hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item() File pandas\\_libs\\hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'y_test_category' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[11], line 44 41 return 'Significant Deviation' # 相差一个以上级别 43 # 应用函数判断每天的预测 ---> 44 df['assessment'] = df.apply(assess_prediction, axis=1) 46 # 按月汇总 47 monthly_stats = df.groupby(df['date'].dt.to_period('M')).assessment.value_counts().unstack().fillna(0) File D:\anaconda3\Lib\site-packages\pandas\core\frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10360 from pandas.core.apply import frame_apply 10362 op = frame_apply( 10363 self, 10364 func=func, (...) 10372 kwargs=kwargs, 10373 ) > 10374 return op.apply().__finalize__(self, method="apply") File D:\anaconda3\Lib\site-packages\pandas\core\apply.py:916, in FrameApply.apply(self) 913 elif self.raw: 914 return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs) --> 916 return self.apply_standard() File D:\anaconda3\Lib\site-packages\pandas\core\apply.py:1063, in FrameApply.apply_standard(self) 1061 def apply_standard(self): 1062 if self.engine == "python": -> 1063 results, res_index = self.apply_series_generator() 1064 else: 1065 results, res_index = self.apply_series_numba() File D:\anaconda3\Lib\site-packages\pandas\core\apply.py:1081, in FrameApply.apply_series_generator(self) 1078 with option_context("mode.chained_assignment", None): 1079 for i, v in enumerate(series_gen): 1080 # ignore SettingWithCopy here in case the user mutates -> 1081 results[i] = self.func(v, *self.args, **self.kwargs) 1082 if isinstance(results[i], ABCSeries): 1083 # If we have a view on v, we need to make a copy because 1084 # series_generator will swap out the underlying data 1085 results[i] = results[i].copy(deep=False) Cell In[11], line 32, in assess_prediction(row) 31 def assess_prediction(row): ---> 32 actual_level = category_levels[row['y_test_category']] 33 predicted_level = category_levels[row['pred_test_category']] 34 if actual_level == predicted_level: File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1121, in Series.__getitem__(self, key) 1118 return self._values[key] 1120 elif key_is_scalar: -> 1121 return self._get_value(key) 1123 # Convert generator to list before going through hashable part 1124 # (We will iterate through the generator there to check for slices) 1125 if is_iterator(key): File D:\anaconda3\Lib\site-packages\pandas\core\series.py:1237, in Series._get_value(self, label, takeable) 1234 return self._values[label] 1236 # Similar to Index.get_value, but we do not fall back to positional -> 1237 loc = self.index.get_loc(label) 1239 if is_integer(loc): 1240 return self._values[loc] File D:\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3812, in Index.get_loc(self, key) 3807 if isinstance(casted_key, slice) or ( 3808 isinstance(casted_key, abc.Iterable) 3809 and any(isinstance(x, slice) for x in casted_key) 3810 ): 3811 raise InvalidIndexError(key) -> 3812 raise KeyError(key) from err 3813 except TypeError: 3814 # If we have a listlike key, _check_indexing_error will raise 3815 # InvalidIndexError. Otherwise we fall through and re-raise 3816 # the TypeError. 3817 self._check_indexing_error(key) KeyError: 'y_test_category'问
最新发布
11-21
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