Find a nearest station

本文介绍了一种方法,通过已知的城市地图找到每个网格点到最近车站的最短距离,利用垂直和水平道路定义的距离计算规则。

Description

Since dandelion has left the hometown so long,she finds it’s difficult to find the station in the city.So she needs you ,a clear programmer, to help her.
Now you know the map of the city, which has showed every station in the city.You are asked to find the shortest distance between every grid and the stations.You should notice that the road in dandelion’s hometown is vertical or horizontal,so the distance of two girds is defined as |x1-x2|+|y1-y2|.

Input

The input consists of several test cases. Each test case start with a line containing two number, n, m(1 <= n, m ≤ 182), the rows and the columns of city. Then n lines follow, each contain exact m characters, representing the type of block in it. (0 for empty place ,1 for station).The data will contains at least one station.

Output

For every case ,print a matrix with n rows and m columns, the number in the i row and j column stands for the distance from this grid to the shortest station.

Sample Input

3 4
0001
0011
0110

Sample Output

3 2 1 0
2 1 0 0
1 0 0 1

#include <iostream>
#include <cstring>
#include <stdlib.h>
#include <cstdio>
#include <queue>
#define a 999

using namespace std;

char map[a][a];
int dis[a][a], vis[a][a];
int dir[4][2] = {1,0, -1,0, 0,1, 0,-1};
int m,n;

struct node
{
    int x,y;
};

void solve()
{
    queue<node> q;
    node next, now;
    int i,j;

    memset(dis, 0, sizeof(dis));
    memset(vis, 0, sizeof(vis));

    for (i = 0; i<n; i++)
    {
        scanf("%s", map[i]);
        for (j = 0; j < m; j++)
        {
            if (map[i][j] == '1')
            {

                now.x = i; now.y = j;
                q.push(now); //push the node now into the queue
                vis[i][j] = 1;
            }
        }
    }

    while (!q.empty())
    {
        now = q.front();
        q.pop();

        for(i = 0; i<4; i++)
        {
            next.x = now.x + dir[i][0];
            next.y = now.y + dir[i][1];

            if (next.x > n || next.x < 0 || next.y > m || next.y < 0)
                continue;
            if (vis[next.x][next.y])
                continue;

            q.push(next);
            dis[next.x][next.y] = dis[now.x][now.y] + 1;//retrodict
            vis[next.x][next.y] = 1;

        }
    }
}

int main(){

    int i,j;

    while (scanf ("%d%d", &n, &m) != -1)
    {
        solve();
        for(i = 0; i < n; i++)
        {
            printf("%d", dis[i][0]);
            for(j = 1; j < m; j++)
            {
                printf(" %d", dis[i][j]);
            }
            printf("\n");
        }
    }



    return 0;
}
我要参加中国移动梧桐杯数据赛道的比赛,我将会提供我目前在它的系统中得分最高的代码,请你在这个代码的基础上,帮我进行调整来帮助我在这个比赛中获取更高的分数,请你直接在我下面的代码上进行修改改好之后发给我,注意!要确保你给我的代码可以顺利运行,并告知我大概运行完的耗时是多久。保证用了你的代码提交之后,我在它的比赛系统中所得的分数比我最好的分数还要高! 我的代码如下: import pandas as pd from pandas import Series import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix,classification_report from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier import lightgbm as lgb # import catboost as cb import xgboost as xgb from sklearn.linear_model import BayesianRidge from scipy.stats import randint, uniform import warnings pd.set_option('display.max_columns', None) # None表示显示所有列 ''' 在「Configuration」标签页的「Environment variables」下方,找到「VM options」(若没有则手动添加), 输入-Xmx2g(表示限制最大使用内存为 2GB,可根据电脑内存调整,如 4GB 内存的电脑建议设为 1-2GB)。 ''' warnings.filterwarnings('ignore') df_train= pd.read_csv( '/home/jovyan/output/dataA/train.csv') append_list=[16936, 24227, 10469, 3867, 32621, 25670, 704, 8715, 36938, 9599, 26901, 5466, 30038, 27722, 20097, 44076, 44510, 25927, 43500, 25292, 20980, 36553, 31715, 48212, 13993, 45508, 45573, 13915, 17362, 17902, 3870, 18499, 38359, 21417, 14355, 28542, 10915, 11047, 6392, 29410, 23132, 32978, 5528, 4123, 43106, 28447, 46005, 6201, 8425, 26277, 43955, 47491, 6683, 32659, 15943, 8895, 19256, 38659, 195, 34322, 37899, 40851, 12141, 46259, 23288, 42114, 35746, 34509, 18905, 9410, 38030, 28636, 37029, 47061, 31024, 29217, 18273, 31329, 26872, 26247, 26156, 40498, 40571, 20331, 34265, 34441, 33867, 28519, 43126, 28026, 13541, 10904, 11650, 31719, 44779, 20918, 34806, 2329, 11595, 30801] df_train=pd.concat([df_train,df_train.iloc[append_list]]) #删除异常值 df_dbscan=df_train.drop(['user_id','registration_date','residence_base_station_id' ,'residence_cell_id','tariff_id','registration_channel_id'],axis=1) from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler # 对数据进行标准化 scaler = StandardScaler() df_scaled = scaler.fit_transform(df_dbscan) # 使用 DBSCAN 进行离群值检测,调整参数 dbscan = DBSCAN(eps=4.1, min_samples=3)#final_accuracy: 0.9604713689148218 final_f1: 0.9604110347555872 final_score: 0.9604532686670514 # dbscan = DBSCAN(eps=4, min_samples=3)#final_accuracy: 0.9602194787379973 final_f1: 0.9601591212171151 final_score: 0.9602013714817326 # dbscan = DBSCAN(eps=4.1, min_samples=4)#final_accuracy: 0.9588813908269472 final_f1: 0.9588206772705757 final_score: 0.9588631767600357 df_dbscan['labels'] = dbscan.fit_predict(df_scaled) # 计算离群值的数量(labels 为 -1 的是离群值) outliers_count = (df_dbscan['labels'] == -1).sum() print('离群值样本个数:', outliers_count) # 删除离群值样本 df_train_improtant = df_train[df_dbscan['labels'] == -1] df_train=pd.concat([df_train,df_train_improtant]).reset_index(drop=True) # exit() df_train['data']='train' df_testA = pd.read_csv('/home/jovyan/output/dataA/testA.csv') # df_testA.to_csv('testA1.csv') # exit() df_testA['data']='test' # df_train_1=df_train.drop(["is_positive"],axis=0,inplace=True) df = pd.concat([df_train,df_testA],join="inner") # df.drop(["Unnamed: 0"],axis=1,inplace=True)#删除索引列 #1.数据缺失 发现没有空缺值 # print(df.info()) #2.查看用户id是否有重复 数据重复 结果显示没有重复用户id has_duplicates = df['user_id'].nunique() == len(df) print(has_duplicates) # 分别取出数值类别列名称 和类别列名称 和需要特殊处理的列 # for column in df.columns: # if df[column].dtype=='object': # category_cols.append(column) # elif df[column].dtype=='float64' or 'int64': # numeric_cols.append(column) numeric_cols = ['age', 'over_limit_data(MB)', 'call_duration(minutes)', 'monthly_call_count', 'monthly_weekend_call_count', 'avg_call_duration(minutes)', 'avg_weekday_call_duration(minutes)', 'avg_weekend_call_duration(minutes)', 'residence_duration_9to11', 'residence_duration_11to14', 'residence_duration_14to17', 'residence_duration_17to21', 'residence_duration_21to23', 'residence_duration_24to6', 'total_residence_duration'] category_cols = ['user_id','registration_date', 'gender', 'uses_education_app', 'uses_entertainment_app', 'uses_shopping_app'] special_cols=['registration_channel_id''3-2-3', 'residence_base_station_id 2-剩下的', 'residence_cell_id 3-3-剩下的' ,'tariff_id''4-4-4', 'tariff_price(RMB)数值需要分几个小组', 'total_data(MB)数值需要分几个小组', 'total_voice(minutes)数值需要分几个小组'] print(len(numeric_cols)+len(category_cols)+len(special_cols)) #3.异常值处理 #5.特征工程处理 def preprocess(df): ###label字段处理 tariff_price(RMB) total_data(MB) df['year'] #1.分隔日期字段处理 df['registration_date']=df['registration_date'].str.replace('/','-') df['year'] = pd.to_datetime(df['registration_date']).dt.year # 日期 df['month'] = pd.to_datetime(df['registration_date']).dt.month # 时间 df['day'] = pd.to_datetime(df['registration_date']).dt.day # 周 #2. 第一到四季节 春夏秋冬 上学期间 和放假期间 df['season1']=df['month'].apply(lambda x: 1 if 1<=int(x)<=3 else 0) df['season2']=df['month'].apply(lambda x: 1 if 3 df['avg_call_duration(minutes)'].quantile(0.9)).astype( int) # 长通话用户标记 # 流量使用特征:超量比例、流量/通话行为关联 df['over_limit_ratio'] = df['over_limit_data(MB)'] / (df['total_data(MB)'] + 1e-6) # 超量流量占比 df['over_limit_samll']=df['over_limit_data(MB)'].apply(lambda x: 1 if 0100 else 0) df['data_per_call'] = df['total_data(MB)'] / (df['monthly_call_count'] + 1e-6) # 每次通话伴随流量使用 # APP使用组合特征:多APP使用行为(教育+购物=高价值用户?) df['multi_app_flag'] = ( (df['uses_education_app'] + df['uses_entertainment_app'] + df['uses_shopping_app']) >= 2).astype(int) df['edu_shopping_flag'] = (df['uses_education_app'] & df['uses_shopping_app']).astype(int) # 教育+购物用户 # ---------------------- 3. 统计特征(增强泛化) ---------------------- # 基站停留稳定性:各时段停留时长标准差(越小越稳定) residence_cols = [col for col in df.columns if 'residence_duration' in col] df['residence_std'] = df[residence_cols].std(axis=1) df['residence_max_ratio'] = df[residence_cols].max(axis=1) / df['total_residence_duration'] # 最长停留时段占比 # 套餐性价比特征:单价流量、单价通话 df['data_per_price'] = df['total_data(MB)'] / (df['tariff_price(RMB)'] + 1e-6) df['voice_per_price'] = df['total_voice(minutes)'] / (df['tariff_price(RMB)'] + 1e-6) # ---------------------- 4. 特殊字段处理(优化编码逻辑) ---------------------- # 基站/渠道ID:提取层级特征(避免原代码字符切割的冗余) df['registration_channel_level1'] = df['registration_channel_id'].astype(str).str[:2] # 一级渠道 df['registration_channel_level2'] = df['registration_channel_id'].astype(str).str[2:4] # 二级渠道 df['residence_cell_level1'] = df['residence_cell_id'].astype(str).str[:3] # 一级基站 df['tariff_type'] = df['tariff_id'].astype(str).str[:4] # 套餐类型前缀 # 数值分箱:使用分位数分箱(更适应数据分布,避免原代码固定区间的偏差) category_cols = [ 'tariff_price(RMB)', 'total_data(MB)', 'year', 'registration_channel_id_f3', 'registration_channel_id_m2' , 'residence_base_station_id_f2', 'residence_base_station_id_remain', 'residence_cell_id_f3', 'residence_cell_id_m3', 'residence_cell_id_remain', 'tariff_id_f4', 'tariff_id_m4', 'tariff_id_remain', 'multi_app_flag', 'edu_shopping_flag', 'long_call_flag', 'tariff_type','residence_cell_level1'] #5.对应标签进行编码处理 # 使用get_dummies进行One-Hot编码 df = pd.get_dummies(df, columns=category_cols) #6.删除不需要的列 df.drop(['registration_date','registration_channel_id','residence_base_station_id','residence_cell_id','tariff_id'],axis=1,inplace=True) #is_positive return df #生成训练集合测试集 df=preprocess(df) #4.正负样本数据均衡 from sklearn.neighbors import NearestNeighbors def smote_synthetic_samples(minority_samples, n_samples, k_neighbors=5, random_state=42): """ 实现SMOTE算法的核心部分,生成合成样本 参数: - minority_samples: 少数类样本 - n_samples: 需要生成的合成样本数量 - k_neighbors: 近邻数量 - random_state: 随机种子 """ np.random.seed(random_state) synthetic_samples = [] # 拟合KNN模型 knn = NearestNeighbors(n_neighbors=k_neighbors) knn.fit(minority_samples) for _ in range(n_samples): # 随机选择一个少数类样本 idx = np.random.randint(0, len(minority_samples)) sample = minority_samples[idx] # 找到K个最近邻 neighbors = knn.kneighbors(sample.reshape(1, -1), return_distance=False)[0] # 随机选择一个邻居 neighbor_idx = np.random.choice(neighbors) neighbor = minority_samples[neighbor_idx] # 生成合成样本 alpha = np.random.random() synthetic = sample + alpha * (neighbor - sample) synthetic_samples.append(synthetic) return np.array(synthetic_samples) def balance_by_smote(df, target_col='标签', k_neighbors=5, random_state=42): """使用自定义SMOTE算法实现样本均衡""" # 分离多数类和少数类 class_counts = df[target_col].value_counts() majority_class_idx = class_counts.idxmax() minority_class_idx = class_counts.idxmin() majority_class = df[df[target_col] == majority_class_idx] minority_class = df[df[target_col] == minority_class_idx] # 计算需要生成的样本数量 n_samples_needed = len(majority_class) - len(minority_class) # 提取少数类特征 minority_features = minority_class.drop(target_col, axis=1).values # 生成合成样本 synthetic_features = smote_synthetic_samples( minority_features, n_samples_needed, k_neighbors, random_state ) # 创建合成样本的DataFrame synthetic_df = pd.DataFrame( synthetic_features, columns=minority_class.drop(target_col, axis=1).columns ) synthetic_df[target_col] = minority_class_idx # 合并所有样本 balanced_df = pd.concat([majority_class, minority_class, synthetic_df], ignore_index=True) print(f"SMOTE 后的数据类别分布:") print(balanced_df[target_col].value_counts(normalize=True)) return balanced_df # 特征标准化 scaler = StandardScaler() X_train = scaler.fit_transform(df[df['data']=='train'].drop(['data','user_id'],axis=1)) X_testA= scaler.transform(df[df['data']=='test'].drop(['data','user_id'],axis=1)) X_testA=pd.DataFrame(X_testA) #样本均衡 data_smoth_before=pd.DataFrame(X_train) data_smoth_before['is_positive']=df_train['is_positive'] #新的smoth import pandas as pd from sklearn.neighbors import NearestNeighbors from sklearn.utils import shuffle data_somth_after=balance_by_smote(data_smoth_before,target_col='is_positive') #打散生成的数据 from sklearn.utils import shuffle data_somth_after = shuffle(data_somth_after) y=data_somth_after['is_positive'] X=data_somth_after.drop(['is_positive'],axis=1) # 特征筛选代码(保留前 500 个重要特征) # 训练基础LGB模型筛选特征 temp_lgb = lgb.LGBMClassifier(random_state=42, n_estimators=100) temp_lgb.fit(X, y) # 计算特征重要性并筛选前500个 feat_importance = pd.Series(temp_lgb.feature_importances_, index=X.columns) top_feat = feat_importance.sort_values(ascending=False).head(600).index X = X[top_feat] # 训练集仅保留top550特征 X_testA = X_testA[top_feat] # 测试集同步筛选 # y=y_train_tomek # X=X_train_tomek X.columns =X.columns.astype(str) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2 ) #建模预测 import catboost as cb Logistic_Regression=LogisticRegression(max_iter=1000, random_state=42) # 类别权重,) Random_Forest= RandomForestClassifier(random_state=42) # XGB用scale_pos_weight表示权重比 Gradient_Boosting_model=GradientBoostingClassifier(random_state=42) XGBoost_model=xgb.XGBClassifier(random_state=42, use_label_encoder=False, eval_metric='logloss') LightGBM_model=lgb.LGBMClassifier(random_state=42)# 类别权重 CatBoost_model=cb.CatBoostClassifier(random_state=42, verbose=0) # CatBoost_model=cb.CatBoostClassifier(random_state=42, verbose=0) # model_list=[Logistic_Regression,KNearest_Neighbors,Random_Forest,Gradient_Boosting_model,XGBoost_model,LightGBM_model,CatBoost_model] # model_list=[Logistic_Regression,KNearest_Neighbors,Random_Forest,Gradient_Boosting_model,XGBoost_model,LightGBM_model] # ensemble_train_results =[] # ensemble_test_results =[] # # for model in model_list: # #模型训练 # model_train=model.fit(X_train, y_train) # # 预测 # y_pred = model_train.predict(X_test) # y_pred_proba = model_train.predict_proba(X_test)[:, 1] if len(np.unique(y)) == 2 else None # #将测试集的预测结果保存起来,一会集成模型要用 # # ensemble_test_results.append(Series(y_pred_proba)) # #将训练集的预测结果保存起来,一会集成模型要用 # # y_train_proba=model_train.predict_proba(X_train)[:, 1] if len(np.unique(y)) == 2 else None # # ensemble_train_results.append(Series(y_train_proba)) # # 评估 # accuracy = accuracy_score(y_test, y_pred) # precision = precision_score(y_test, y_pred, average='weighted') # recall = recall_score(y_test, y_pred, average='weighted') # f1 = f1_score(y_test, y_pred, average='weighted') # print('accuracy:',accuracy,'f1:',f1,'score:',0.3*f1+0.7*accuracy) #6.参数调优 # 使用随机搜索优化超参数 # random_search = RandomizedSearchCV( # best_model, # param_distributions=param_dist, # n_iter=20, # 搜索20组参数组合 # cv=5, # 5折交叉验证 # scoring='f1_weighted', # n_jobs=-1, # 使用所有可用的CPU # random_state=42, # verbose=1 # ) #模型融合 # 根据以上AUC的结果,选择: LR 和 SVC 和 XGB 当做基模型 #%% # 把以上3个模型的预测结果集成起来 # ensemble_test_concat = pd.concat(ensemble_test_results, axis=1) # ensemble_train_concat = pd.concat(ensemble_train_results, axis=1) #%% #%% from sklearn.ensemble import VotingClassifier voting_clf = VotingClassifier(estimators=[('RF',Random_Forest),('GB',Gradient_Boosting_model), ('XGB',XGBoost_model),('LGBM',LightGBM_model), ('cb', CatBoost_model)],voting = 'soft') # # 采用贝叶斯回归作为结果融合的模型(final model) # # clf = BayesianRidge() # clf =LogisticRegression(max_iter=1000, random_state=42) # # 在训练数据上进行训练 # clf.fit(ensemble_train_concat, y_train) # #%% #训练softvote模型 voting_clf.fit(X_train, y_train) # 预测test样本 y_final_pred = voting_clf.predict(X_test) # 用训练集的OOF预测找最优阈值 # def find_best_threshold(y_true, y_proba, step=0.005): # """遍历阈值,找到最大化0.3*F1 + 0.7*准确率的阈值""" # best_score = 0.0 # best_threshold = 0.5 # thresholds = np.arange(0.005, 1.0, step) # # for threshold in thresholds: # y_pred = (y_proba >= threshold).astype(int) # acc = accuracy_score(y_true, y_pred) # f1 = f1_score(y_true, y_pred) # score = 0.3 * f1 + 0.7 * acc # # if score > best_score: # best_score = score # best_threshold = threshold # # print(f"最优阈值:{best_threshold:.2f},对应得分:{best_score:.4f}") # return best_threshold, best_score # accuracy = accuracy_score(y_test, y_final_pred ) precision = precision_score(y_test,y_final_pred , average='weighted') recall = recall_score(y_test,y_final_pred , average='weighted') f1 = f1_score(y_test,y_final_pred , average='weighted') print('final_accuracy:', accuracy, 'final_f1:', f1, 'final_score:', 0.3 * f1 + 0.7 * accuracy) # 预测test样本 y_testA_pred = voting_clf.predict(X_testA) # y_testA_final_pred= Series((y_testA_pred >= threshold).astype(int)) print(y_testA_pred) X_testA['is_positive']=Series(y_testA_pred ) X_testA['user_id']=df[df['data']=='test']['user_id'].values X_testA[['user_id','is_positive']].to_csv('submitA.csv',index=False,encoding='utf-8') 注意只能使用他们比赛环境中自带的库,不允许安装其他第三方库: 它的库只有以下: Package Version ------------------------------ ------------ absl-py 2.0.0 alembic 1.12.0 altair 5.1.2 anyio 4.0.0 argon2-cffi 23.1.0 argon2-cffi-bindings 21.2.0 arrow 1.3.0 asttokens 2.4.0 astunparse 1.6.3 async-generator 1.10 async-lru 2.0.4 attrs 23.1.0 Babel 2.13.0 backcall 0.2.0 backports.functools-lru-cache 1.6.5 beautifulsoup4 4.12.2 bleach 6.1.0 blinker 1.6.3 bokeh 3.3.0 boltons 23.0.0 Bottleneck 1.3.7 Brotli 1.1.0 cached-property 1.5.2 cachetools 5.3.1 catboost 1.2.8 certifi 2023.7.22 certipy 0.1.3 cffi 1.16.0 charset-normalizer 3.3.0 click 8.1.7 cloudpickle 3.0.0 colorama 0.4.6 comm 0.1.4 conda 23.9.0 conda-package-handling 2.2.0 conda_package_streaming 0.9.0 contourpy 1.1.1 cryptography 41.0.4 cycler 0.12.1 Cython 3.0.4 cytoolz 0.12.2 dask 2023.10.0 debugpy 1.8.0 decorator 5.1.1 defusedxml 0.7.1 dill 0.3.7 distributed 2023.10.0 entrypoints 0.4 et-xmlfile 1.1.0 exceptiongroup 1.1.3 executing 1.2.0 fastjsonschema 2.18.1 filelock 3.13.1 flatbuffers 23.5.26 fonttools 4.43.1 fqdn 1.5.1 fsspec 2023.9.2 gast 0.5.4 gitdb 4.0.10 GitPython 3.1.40 gmpy2 2.1.2 google-auth 2.23.3 google-auth-oauthlib 1.0.0 google-pasta 0.2.0 graphviz 0.21 greenlet 3.0.0 grpcio 1.59.0 h5py 3.10.0 idna 3.4 imagecodecs 2023.9.18 imageio 2.31.5 importlib-metadata 6.8.0 importlib-resources 6.1.0 ipykernel 6.25.2 ipympl 0.9.3 ipython 8.16.1 ipython-genutils 0.2.0 ipywidgets 8.1.1 isoduration 20.11.0 jedi 0.19.1 Jinja2 3.1.2 joblib 1.3.2 json5 0.9.14 jsonpatch 1.33 jsonpointer 2.4 jsonschema 4.19.1 jsonschema-specifications 2023.7.1 jupyter_client 8.4.0 jupyter_core 5.4.0 jupyter-events 0.8.0 jupyter-lsp 2.2.0 jupyter_server 2.8.0 jupyter-server-mathjax 0.2.6 jupyter_server_terminals 0.4.4 jupyter-telemetry 0.1.0 jupyterhub 4.0.2 jupyterlab 4.0.7 jupyterlab-language-pack-zh-CN 4.4.post0 jupyterlab-pygments 0.2.2 jupyterlab_server 2.25.0 jupyterlab-widgets 3.0.9 keras 2.14.0 kiwisolver 1.4.5 lazy_loader 0.3 libclang 16.0.6 libmambapy 1.5.2 lightgbm 4.6.0 llvmlite 0.40.1 locket 1.0.0 lz4 4.3.2 Mako 1.2.4 mamba 1.5.2 Markdown 3.5 MarkupSafe 2.1.3 matplotlib 3.8.0 matplotlib-inline 0.1.6 mistune 3.0.1 ml-dtypes 0.2.0 mpmath 1.3.0 msgpack 1.0.6 munkres 1.1.4 narwhals 2.5.0 nbclient 0.8.0 nbconvert 7.9.2 nbdime 3.2.1 nbformat 5.9.2 nest-asyncio 1.5.8 networkx 3.2 notebook 7.0.6 notebook_shim 0.2.3 numba 0.57.1 numexpr 2.8.7 numpy 1.24.4 nvidia-nccl-cu12 2.28.3 oauthlib 3.2.2 openpyxl 3.1.2 opt-einsum 3.3.0 overrides 7.4.0 packaging 23.2 pamela 1.1.0 pandas 2.1.1 pandocfilters 1.5.0 parso 0.8.3 partd 1.4.1 patsy 0.5.3 pexpect 4.8.0 pickleshare 0.7.5 Pillow 10.1.0 pip 23.3 pkgutil_resolve_name 1.3.10 platformdirs 3.11.0 plotly 6.3.0 pluggy 1.3.0 prometheus-client 0.17.1 prompt-toolkit 3.0.39 protobuf 4.24.3 psutil 5.9.5 ptyprocess 0.7.0 pure-eval 0.2.2 py-cpuinfo 9.0.0 pyarrow 13.0.0 pyasn1 0.5.0 pyasn1-modules 0.3.0 pycosat 0.6.6 pycparser 2.21 pycurl 7.45.1 Pygments 2.16.1 PyJWT 2.8.0 pyOpenSSL 23.2.0 pyparsing 3.1.1 PySocks 1.7.1 python-dateutil 2.8.2 python-json-logger 2.0.7 pytz 2023.3.post1 PyWavelets 1.4.1 PyYAML 6.0.1 pyzmq 25.1.1 referencing 0.30.2 requests 2.31.0 requests-oauthlib 1.3.1 rfc3339-validator 0.1.4 rfc3986-validator 0.1.1 rpds-py 0.10.6 rsa 4.9 ruamel.yaml 0.17.39 ruamel.yaml.clib 0.2.7 scikit-image 0.22.0 scikit-learn 1.3.1 scipy 1.11.3 seaborn 0.13.0 Send2Trash 1.8.2 setuptools 68.2.2 six 1.16.0 smmap 3.0.5 sniffio 1.3.0 sortedcontainers 2.4.0 soupsieve 2.5 SQLAlchemy 2.0.22 stack-data 0.6.2 statsmodels 0.14.0 sympy 1.13.3 tables 3.9.1 tblib 2.0.0 tensorboard 2.14.1 tensorboard-data-server 0.7.1 tensorflow 2.14.0 tensorflow-estimator 2.14.0 tensorflow-io-gcs-filesystem 0.34.0 termcolor 2.3.0 terminado 0.17.1 threadpoolctl 3.2.0 tifffile 2023.9.26 tinycss2 1.2.1 tomli 2.0.1 toolz 0.12.0 torch 2.7.1+cpu tornado 6.3.3 tqdm 4.66.1 traitlets 5.11.2 truststore 0.8.0 types-python-dateutil 2.8.19.14 typing_extensions 4.12.2 typing-utils 0.1.0 tzdata 2023.3 uri-template 1.3.0 urllib3 2.0.7 wcwidth 0.2.8 webcolors 1.13 webencodings 0.5.1 websocket-client 1.6.4 Werkzeug 3.0.0 wheel 0.41.2 widgetsnbextension 4.0.9 wrapt 1.14.1 xgboost 3.0.5 xlrd 2.0.1 xyzservices 2023.10.0 zict 3.0.0 zipp 3.17.0 zstandard 0.21.0 注意!一定要保证运行完你给的代码比我原版的代码跑出来的分数要高!谢谢你!
10-27
源码来自:https://pan.quark.cn/s/a3a3fbe70177 AppBrowser(Application属性查看器,不需要越狱! ! ! ) 不需要越狱,调用私有方法 --- 获取完整的已安装应用列表、打开和删除应用操作、应用运行时相关信息的查看。 支持iOS10.X 注意 目前AppBrowser不支持iOS11应用查看, 由于iOS11目前还处在Beta版, 系统API还没有稳定下来。 等到Private Header更新了iOS11版本,我也会进行更新。 功能 [x] 已安装的应用列表 [x] 应用的详情界面 (打开应用,删除应用,应用的相关信息展示) [x] 应用运行时信息展示(LSApplicationProxy) [ ] 定制喜欢的字段,展示在应用详情界面 介绍 所有已安装应用列表(应用icon+应用名) 为了提供思路,这里只用伪代码,具体的私有代码调用请查看: 获取应用实例: 获取应用名和应用的icon: 应用列表界面展示: 应用列表 应用运行时详情 打开应用: 卸载应用: 获取info.plist文件: 应用运行时详情界面展示: 应用运行时详情 右上角,从左往右第一个按钮用来打开应用;第二个按钮用来卸载这个应用 INFO按钮用来解析并显示出对应的LSApplicationProxy类 树形展示LSApplicationProxy类 通过算法,将LSApplicationProxy类,转换成了字典。 转换规则是:属性名为key,属性值为value,如果value是一个可解析的类(除了NSString,NSNumber...等等)或者是个数组或字典,则继续递归解析。 并且会找到superClass的属性并解析,superClass如...
基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO)的LSTM分类预测研究(Matlab代码实现)内容概要:本文研究了一种基于遗传算法辅助异构改进的动态多群粒子群优化算法(GA-HIDMSPSO),并将其应用于LSTM神经网络的分类预测中,通过Matlab代码实现。该方法结合遗传算法的全局搜索能力与改进的多群粒子群算法的局部优化特性,提升LSTM模型在分类任务中的性能表现,尤其适用于复杂非线性系统的预测问题。文中详细阐述了算法的设计思路、优化机制及在LSTM参数优化中的具体应用,并提供了可复现的Matlab代码,属于SCI级别研究成果的复现与拓展。; 适合人群:具备一定机器学习和优化算法基础,熟悉Matlab编程,从事智能算法、时间序列预测或分类模型研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①提升LSTM在分类任务中的准确性与收敛速度;②研究混合智能优化算法(如GA与PSO结合)在神经网络超参数优化中的应用;③实现高精度分类预测模型,适用于电力系统故障诊断、电池健康状态识别等领域; 阅读建议:建议读者结合Matlab代码逐步调试运行,理解GA-HIDMSPSO算法的实现细节,重点关注种群划分、异构策略设计及与LSTM的集成方式,同时可扩展至其他深度学习模型的参数优化任务中进行对比实验。
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