Kaggle-Predict Calorie Expenditure-(回歸+xgb+cat+lgb+模型融合+預測結果)

Predict Calorie Expenditure

題意：

給出每個人的基本信息，預測運動后的卡路里消耗值。

數據處理：

1.構造出人體機能、運動相關的特征值。
2.所有特征值進行從新組合，注意唯獨爆炸
3.對連續信息分箱變成離散

建立模型：

1.xgb模型，lgb模型，cat模型
2.使用stack堆疊融合，使用3折交叉驗證
3.對xgb、lgb、cat進行K折交叉驗證，最終和stack進行結果融合。

代碼：
import os
import sys
import warnings
import numpy as np
import pandas as pd
import seaborn
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from matplotlib import pyplot as plt
import lightgbm
from mlxtend.regressor import StackingCVRegressor
from sklearn import clone
from sklearn.ensemble import VotingRegressor, StackingClassifier, StackingRegressor
from sklearn.linear_model import Lasso, LogisticRegression, RidgeCV
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, make_scorer, mean_squared_log_error
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridgedef init():os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # 僅輸出錯誤日志warnings.simplefilter('ignore')  # 忽略警告日志pd.set_option('display.width', 1000)pd.set_option('display.max_colwidth', 1000)pd.set_option("display.max_rows", 1000)pd.set_option("display.max_columns", 1000)def show_dataframe(df):print("查看特征值和特征值類型\n" + str(df.dtypes) + "\n" + "-" * 100)print("查看前10行信息\n" + str(df.head()) + "\n" + "-" * 100)print("查看每個特征值的各種數據統計信息\n" + str(df.describe()) + "\n" + "-" * 100)print("輸出重復行的個數\n" + str(df.duplicated().sum()) + "\n" + "-" * 100)print("查看每列的缺失值個數\n" + str(df.isnull().sum()) + "\n" + "-" * 100)print("查看缺失值的具體信息\n" + str(df.info()) + "\n" + "-" * 100)#print("輸出X所有值出現的是什么，還有對應出現的次數\n" + str(df['X'].value_counts()) + "\n" + "-" * 100)def show_relation(data, colx, coly):  # 輸出某一特征值與目標值的關系if data[colx].dtype == 'object' or data[colx].dtype == 'category' or len(data[colx].unique()) < 20:seaborn.boxplot(x=colx, y=coly, data=data)else:plt.scatter(data[colx], data[coly])plt.xlabel(colx)plt.ylabel(coly)plt.show()# 自定義RMSLE評分函數（GridSearchCV需要最大化評分，因此返回負RMSLE）
def rmsle_scorer(y_true, y_pred):y_pred = np.clip(y_pred, 1e-15, None)  # 防止對0取對數y_true = np.clip(y_true, 1e-15, None)log_error = np.log(y_pred + 1) - np.log(y_true + 1)rmsle = np.sqrt(np.mean(log_error ** 2))return -rmsle  # 返回負值，因為GridSearchCV默認最大化評分if __name__ == '__main__':init()df_train = pd.read_csv('/kaggle/input/playground-series-s5e5/train.csv')df_test = pd.read_csv('/kaggle/input/playground-series-s5e5/test.csv')#for col in df_train.columns:#   show_relation(df_train, col, 'Calories')#特征工程df_all = pd.concat([df_train.drop(['id', 'Calories'], axis=1), df_test.drop(['id'], axis=1)], axis=0)df_all['Sex'] = df_all['Sex'].map({'male': 0, 'female': 1})df_all = df_all.reset_index(drop=True)#構造BMIdf_all['BMI'] = df_all['Weight'] / (df_all['Height'] / 100) ** 2#Harris-Benedict公式df_all['BMR'] = 0df_all.loc[df_all['Sex'] == 0, 'BMR'] = 88.362 + (13.397 * df_all['Weight']) + (4.799 * df_all['Height']) - (5.677 * df_all['Age'])df_all.loc[df_all['Sex'] == 1, 'BMR'] = 447.593 + (9.247 * df_all['Weight']) + (3.098 * df_all['Height']) - (4.330 * df_all['Age'])# 數值特征標準化#numeric_features = ['Age', 'Height', 'Weight', 'Duration', 'Heart_Rate', 'Body_Temp']#scaler = StandardScaler()#df_all[numeric_features] = scaler.fit_transform(df_all[numeric_features])#運動強度特征df_all['Max_HR'] = 220 - df_all['Age']  # 最大心率df_all['HR_Reserve_Ratio'] = df_all['Heart_Rate'] / df_all['Max_HR']#交互特征df_all['Weight_Duration'] = df_all['Weight'] * df_all['Duration']df_all['Sex_Weight'] = df_all['Sex'] * df_all['Weight']# 構造運動功率特征df_all['workload'] = df_all['Weight'] * df_all['Duration'] * df_all['Heart_Rate'] / 1000# 構造生理特征交互項df_all['age_heart_ratio'] = df_all['Age'] / df_all['Heart_Rate']# 時間維度特征（如有時間戳）df_all['hour_of_day'] = df_all['Duration']/60/24# 組合特征numeric_cols = df_all.columnsfor i in range(len(numeric_cols)):feature_1 = numeric_cols[i]for j in range(i + 1, len(numeric_cols)):feature_2 = numeric_cols[j]df_all[f'{feature_1}_x_{feature_2}'] = df_all[feature_1] * df_all[feature_2]#數值歸一化#scaler = RobustScaler()#df_all = scaler.fit_transform(df_all)now_col = ['Age', 'Height', 'Weight', 'Duration', 'Heart_Rate', 'Body_Temp', 'BMI']for i in now_col:df_all[i + "_box"] = pd.cut(df_all[i], bins=10, labels=False, right=False)X_train = df_all[:df_train.shape[0]]Y_train = np.log1p(df_train['Calories'])x_test = df_all[df_train.shape[0]:]#xgbmodel_xgb =estimator=XGBRegressor(random_state=42,n_estimators=8000,objective='reg:squarederror',eval_metric='rmse',device='cuda',learning_rate=0.05,max_depth=8,colsample_bytree=0.75,subsample=0.9,#reg_lambda = 1,#reg_alpha = 0.5,early_stopping_rounds=500,)#lgbmodel_lgb = lightgbm.LGBMRegressor(n_estimators=3000,  # 增加迭代次數配合早停learning_rate=0.03,  # 減小學習率num_leaves=15,  # 限制模型復雜度min_child_samples=25,  # 增加最小葉子樣本數reg_alpha=0.1,  # L1正則化reg_lambda=0.1,  # L2正則化objective='regression_l1',  # 改用MAE損失early_stopping_rounds=500,)#catmodel_cat = CatBoostRegressor(iterations=3500,learning_rate=0.02,depth=12,loss_function='RMSE',l2_leaf_reg=3,random_seed=42,eval_metric='RMSE',early_stopping_rounds=200,verbose=1000,task_type='GPU',)#融合#創建基模型列表（需禁用早停以生成完整預測）base_models = [('xgb', XGBRegressor(early_stopping_rounds=None,  # 禁用早停**{k: v for k, v in model_xgb.get_params().items() if k != 'early_stopping_rounds'})),('lgb', LGBMRegressor(early_stopping_rounds=None,  # 禁用早停**{k: v for k, v in model_lgb.get_params().items() if k != 'early_stopping_rounds'})),('cat', CatBoostRegressor(early_stopping_rounds=None,  # 禁用早停**{k: v for k, v in model_cat.get_params().items() if k != 'early_stopping_rounds'}))]meta_model = RidgeCV()model_stack = StackingRegressor(estimators=base_models,final_estimator=meta_model,cv=3,  # 使用3折交叉驗證生成元特征passthrough=False,  # 不使用原始特征verbose=1)FOLDS = 20KF = KFold(n_splits=FOLDS, shuffle=True, random_state=42)cat_features = ['Sex']oof_cat = np.zeros(len(df_train))pred_cat = np.zeros(len(df_test))oof_xgb = np.zeros(len(df_train))pred_xgb = np.zeros(len(df_test))oof_lgb = np.zeros(len(df_train))pred_lgb = np.zeros(len(df_test))for i, (train_idx, valid_idx) in enumerate(KF.split(X_train, Y_train)):print('#' * 15, i + 1, '#' * 15)## SPLIT DSx_train, y_train = X_train.iloc[train_idx], Y_train.iloc[train_idx]x_valid, y_valid = X_train.iloc[valid_idx], Y_train.iloc[valid_idx]## CATBOOST fitmodel_cat.fit(x_train, y_train, eval_set=[(x_valid, y_valid)], cat_features=cat_features,use_best_model=True, verbose=0)## XGB FIRmodel_xgb.fit(x_train, y_train, eval_set=[(x_valid, y_valid)], verbose=0)## LGB MODELmodel_lgb.fit(x_train, y_train, eval_set=[(x_valid, y_valid)])## PREDICTION CATBOOSToof_cat[valid_idx] = model_cat.predict(x_valid)pred_cat += model_cat.predict(x_test)## PREDICTION XGBoof_xgb[valid_idx] = model_xgb.predict(x_valid)pred_xgb += model_xgb.predict(x_test)## PREDICTION LGBoof_lgb[valid_idx] = model_lgb.predict(x_valid)pred_lgb += model_lgb.predict(x_test)cat_rmse = mean_squared_error(y_valid, oof_cat[valid_idx]) ** 0.5xgb_rmse = mean_squared_error(y_valid, oof_xgb[valid_idx]) ** 0.5lgb_rmse = mean_squared_error(y_valid, oof_lgb[valid_idx]) ** 0.5print(f'FOLD {i + 1} CATBOOST_RMSE = {cat_rmse:.4f} <=> XGB_RMSE = {xgb_rmse:.4f} <=> LGB_RMSE = {lgb_rmse:.4f}')#預測pred_cat /= FOLDSpred_xgb /= FOLDSpred_lgb /= FOLDSpred_stack = model_stack.predict(df_test)pred_all = np.expm1(pred_xgb) * 0.1 + np.expm1(pred_stack) * 0.80 + np.expm1(pred_cat) * 0.1submission = pd.DataFrame({'id': df_test['id'],'Calories': pred_all})submission['Calories'] = np.clip(submission['Calories'], a_min=1, a_max=20*df_test['Duration'])submission.to_csv('/kaggle/working/submission.csv', index=False)
代碼

使用k折交叉驗證，對預測結果再進行訓練預測。

import os
import sys
import warnings
import numpy as np
import pandas as pd
import seaborn
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from matplotlib import pyplot as plt
import lightgbm
from mlxtend.regressor import StackingCVRegressor
from sklearn import clone
from sklearn.ensemble import VotingRegressor, StackingClassifier, StackingRegressor
from sklearn.linear_model import Lasso, LogisticRegression, RidgeCV
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, make_scorer, mean_squared_log_error
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridgedef init():os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # 僅輸出錯誤日志warnings.simplefilter('ignore')  # 忽略警告日志pd.set_option('display.width', 1000)pd.set_option('display.max_colwidth', 1000)pd.set_option("display.max_rows", 1000)pd.set_option("display.max_columns", 1000)def show_dataframe(df):print("查看特征值和特征值類型\n" + str(df.dtypes) + "\n" + "-" * 100)print("查看前10行信息\n" + str(df.head()) + "\n" + "-" * 100)print("查看每個特征值的各種數據統計信息\n" + str(df.describe()) + "\n" + "-" * 100)print("輸出重復行的個數\n" + str(df.duplicated().sum()) + "\n" + "-" * 100)print("查看每列的缺失值個數\n" + str(df.isnull().sum()) + "\n" + "-" * 100)print("查看缺失值的具體信息\n" + str(df.info()) + "\n" + "-" * 100)#print("輸出X所有值出現的是什么，還有對應出現的次數\n" + str(df['X'].value_counts()) + "\n" + "-" * 100)def show_relation(data, colx, coly):  # 輸出某一特征值與目標值的關系if data[colx].dtype == 'object' or data[colx].dtype == 'category' or len(data[colx].unique()) < 20:seaborn.boxplot(x=colx, y=coly, data=data)else:plt.scatter(data[colx], data[coly])plt.xlabel(colx)plt.ylabel(coly)plt.show()def show_score(model_name, pred):mse = mean_squared_error(y_val, pred)mae = mean_absolute_error(y_val, pred)score = r2_score(y_val, pred)print(model_name)print(f"{'MSE':<10}{mse:<15.4f}")print(f"{'MAE':<10}{mae:<15.4f}")print(f"{'R2':<10}{score:<15.4f}")print("-" * 100)# Function to calculate RMSLE
def rmsle(y_true, y_pred):return np.sqrt(np.mean(np.power(np.log1p(y_true) - np.log1p(y_pred), 2)))if __name__ == '__main__':init()df_train = pd.read_csv('train.csv')df_test = pd.read_csv('test.csv')#for col in df_train.columns:#   show_relation(df_train, col, 'Calories')#特征工程df_all = pd.concat([df_train.drop(['id', 'Calories'], axis=1), df_test.drop(['id'], axis=1)], axis=0)df_all['Sex_encoded'] = df_all['Sex'].map({'male': 0, 'female': 1})df_all.drop(['Sex'], axis=1, inplace=True)df_all = df_all.reset_index(drop=True)#構造BMIdf_all['BMI'] = df_all['Weight'] / (df_all['Height'] / 100) ** 2#Harris-Benedict公式df_all['BMR'] = 0df_all.loc[df_all['Sex_encoded'] == 0, 'BMR'] = 88.362 + (13.397 * df_all['Weight']) + (4.799 * df_all['Height']) - (5.677 * df_all['Age'])df_all.loc[df_all['Sex_encoded'] == 1, 'BMR'] = 447.593 + (9.247 * df_all['Weight']) + (3.098 * df_all['Height']) - (4.330 * df_all['Age'])# 數值特征標準化#numeric_features = ['Age', 'Height', 'Weight', 'Duration', 'Heart_Rate', 'Body_Temp']#scaler = StandardScaler()#df_all[numeric_features] = scaler.fit_transform(df_all[numeric_features])#運動強度特征df_all['Max_HR'] = 220 - df_all['Age']  # 最大心率df_all['HR_Reserve_Ratio'] = df_all['Heart_Rate'] / df_all['Max_HR']#交互特征df_all['Weight_Duration'] = df_all['Weight'] * df_all['Duration']df_all['Sex_Weight'] = df_all['Sex_encoded'] * df_all['Weight']# 構造運動功率特征df_all['workload'] = df_all['Weight'] * df_all['Duration'] * df_all['Heart_Rate'] / 1000# 構造生理特征交互項df_all['age_heart_ratio'] = df_all['Age'] / df_all['Heart_Rate']# 時間維度特征（如有時間戳）df_all['hour_of_day'] = df_all['Duration']/60/24# 組合特征numeric_cols = df_all.columnsfor i in range(len(numeric_cols)):feature_1 = numeric_cols[i]for j in range(i + 1, len(numeric_cols)):feature_2 = numeric_cols[j]df_all[f'{feature_1}_x_{feature_2}'] = df_all[feature_1] * df_all[feature_2]#數值歸一化#scaler = RobustScaler()#df_all = scaler.fit_transform(df_all)# 分箱，把連續變成離散的，看你在哪一類now_col = ['Age', 'Height', 'Weight', 'Duration', 'Heart_Rate', 'Body_Temp', 'BMI']for i in now_col:df_all[i + "_box"] = pd.cut(df_all[i], bins=10, labels=False, right=False)baseline_temp = 37.0# Calculate 'Temp_Change' for the training datadf_all['Temp_Change'] = df_all['Body_Temp'] - baseline_temp# Calculate 'Intensity' for the training datadf_all['Intensity'] = df_all['Heart_Rate'] / df_all['Duration']# Calculate 'Heart_Rate_Ratio' for the training datadf_all['Heart_Rate_Ratio'] = df_all['Heart_Rate'] / df_all['Age']# Calculate 'Duration_x_HeartRate' for the training datadf_all['Duration_x_HeartRate'] = df_all['Duration'] * df_all['Heart_Rate']# Calculate 'Weight_x_Duration' for the training datadf_all['Weight_x_Duration'] = df_all['Weight'] * df_all['Duration']# Calculate 'Height_x_Duration' for the training datadf_all['Height_x_Duration'] = df_all['Height'] * df_all['Duration']# Calculate 'Weight_x_Height' for the training datadf_all['Weight_x_Height'] = df_all['Weight'] * df_all['Height']# Calculate 'Weight_x_Intensity' for the training datadf_all['Weight_x_Intensity'] = df_all['Weight'] * df_all['Intensity']# Calculate 'Height_x_Intensity' for the training datadf_all['Height_x_Intensity'] = df_all['Height'] * df_all['Intensity']X_train = df_all[:df_train.shape[0]]Y_train = np.log1p(df_train['Calories'])x_test = df_all[df_train.shape[0]:]#xgbmodel_xgb =estimator=XGBRegressor(random_state=42,n_estimators=8000,objective='reg:squarederror',eval_metric='rmse',device='cuda',learning_rate=0.05,max_depth=8,colsample_bytree=0.75,subsample=0.9,#reg_lambda = 1,#reg_alpha = 0.5,early_stopping_rounds=200,)#lgbmodel_lgb = lightgbm.LGBMRegressor(n_estimators=3000,  # 增加迭代次數配合早停learning_rate=0.03,  # 減小學習率num_leaves=15,  # 限制模型復雜度min_child_samples=25,  # 增加最小葉子樣本數reg_alpha=0.1,  # L1正則化reg_lambda=0.1,  # L2正則化objective='regression_l1',  # 改用MAE損失early_stopping_rounds=200,eval_metric='RMSE',)#catmodel_cat = CatBoostRegressor(iterations=3500,learning_rate=0.02,depth=12,loss_function='RMSE',l2_leaf_reg=3,random_seed=42,eval_metric='RMSE',verbose=1000,task_type='GPU',early_stopping_rounds=200,)#k折交叉print("🔄Generating Out-of-Fold (OOF) predictions and Test predictions for Base Models...\n" + "-"*70 + "\n")# --- Prediction Storage ---# Arrays to store out-of-fold (OOF)add_pred_val_cat = np.zeros(len(X_train))add_pred_val_xgb = np.zeros(len(X_train))add_pred_val_lgb = np.zeros(len(X_train))# Arrays to store test predictions (accumulated across folds for averaging)add_pred_test_cat = np.zeros(len(x_test))add_pred_test_xgb = np.zeros(len(x_test))add_pred_test_lgb = np.zeros(len(x_test))kf = KFold(n_splits=5, shuffle=True, random_state=42)for fold, (train_index, val_index) in enumerate(kf.split(X_train, Y_train)):print(f"\n---Fold {fold + 1}/{kf.n_splits} ---")x_train, x_val = X_train.iloc[train_index], X_train.iloc[val_index]y_train, y_val = Y_train.iloc[train_index], Y_train.iloc[val_index]#Apply log1p transformation to target for trainingy_train_log1p = np.log1p(y_train)y_val_log1p = np.log1p(y_val)# --- CatBoost Training and Prediction ---print("  ? Training CatBoost...")model_cat.fit(x_train, y_train_log1p,eval_set=[(x_val, y_val_log1p)],verbose=0  # Set to 100 if you want to see progress)pred_val_cat = model_cat.predict(x_val)pred_test_cat = model_cat.predict(x_test)# --- XGBoost Training and Prediction ---print("  ? Training XGBoost...")model_xgb.fit(x_train, y_train_log1p,eval_set=[(x_val, y_val_log1p)],verbose=0  # Set to 100 if you want to see progress)pred_val_xgb = model_xgb.predict(x_val)pred_test_xgb = model_xgb.predict(x_test)# --- LGBM Training and Prediction ---print("  ? Training LGBM...")model_lgb.fit(x_train, y_train_log1p,eval_set=[(x_val, y_val_log1p)],)pred_val_lgb = model_lgb.predict(x_val)pred_test_lgb = model_lgb.predict(x_test)# --- Store OOF and Test Predictions (transformed back to original scale) ---add_pred_val_cat[val_index] = np.expm1(pred_val_cat)add_pred_val_xgb[val_index] = np.expm1(pred_val_xgb)add_pred_val_lgb[val_index] = np.expm1(pred_val_lgb)add_pred_test_cat += np.expm1(pred_test_cat) / kf.n_splitsadd_pred_test_xgb += np.expm1(pred_test_xgb) / kf.n_splitsadd_pred_test_lgb += np.expm1(pred_test_lgb) / kf.n_splits# Ensure all predictions are non-negativeadd_pred_val_cat[add_pred_val_cat < 0] = 0add_pred_val_xgb[add_pred_val_xgb < 0] = 0add_pred_val_lgb[add_pred_val_lgb < 0] = 0# Note: test predictions will also be non-negative after final prediction step# Calculate and print RMSLE for individual models on this foldprint(f"  CatBoost RMSLE (Fold {fold + 1}): {rmsle(y_val, add_pred_val_cat[val_index]):.4f}")print(f"  XGBoost RMSLE (Fold {fold + 1}): {rmsle(y_val, add_pred_val_xgb[val_index]):.4f}")print(f"  LGBM RMSLE (Fold {fold + 1}): {rmsle(y_val, add_pred_val_lgb[val_index]):.4f}")x_meta_train = pd.DataFrame({'cat_pred': add_pred_val_cat,'xgb_pred': add_pred_val_xgb,'lgbm_pred': add_pred_val_lgb,})y_meta_train = Y_trainx_meta_test = pd.DataFrame({'cat_pred': add_pred_test_cat,'xgb_pred': add_pred_test_xgb,'lgbm_pred': add_pred_test_lgb,})model_meta = Ridge(random_state=42)model_meta.fit(x_meta_train, y_meta_train)print(f" meta RMSLE :{rmsle(y_meta_train, model_meta.predict(x_meta_train)):.4f}")pred_all = np.expm1(model_meta.predict(x_meta_test))submission = pd.DataFrame({'id': df_test['id'],'Calories': pred_all})submission['Calories'] = np.clip(submission['Calories'], a_min=df_test['Duration'], a_max=20*df_test['Duration'])submission.to_csv('submission.csv', index=False)

本文來自互聯網用戶投稿，該文觀點僅代表作者本人，不代表本站立場。本站僅提供信息存儲空間服務，不擁有所有權，不承擔相關法律責任。
如若轉載，請注明出處：http://www.pswp.cn/web/81854.shtml
繁體地址，請注明出處：http://hk.pswp.cn/web/81854.shtml
英文地址，請注明出處：http://en.pswp.cn/web/81854.shtml

如若內容造成侵權/違法違規/事實不符，請聯系多彩編程網進行投訴反饋email:809451989@qq.com，一經查實，立即刪除！

相關文章

第十二篇：MySQL 分布式架構演進與云原生數據庫探索

第十二篇：MySQL 分布式架構演進與云原生數據庫探索

本篇聚焦 MySQL 在互聯網架構演進過程中的角色變化&#xff0c;探討其從單體向分布式、再向云原生架構轉型的關鍵技術路徑與實踐建議。 一、傳統單體架構下的 MySQL 應用模式 在早期項目中&#xff0c;MySQL 多用于中小型應用&#xff1a; 單節點部署&#xff1b; 水平擴展難…
閱讀更多...
JVM——回顧：JVM的起源、特性與系統構成

JVM——回顧：JVM的起源、特性與系統構成

引入 在當今數字化時代&#xff0c;Java語言及其運行環境Java虛擬機&#xff08;JVM&#xff09;在軟件開發領域占據著舉足輕重的地位。從大型企業級應用到各類移動應用&#xff0c;JVM憑借其獨特的特性和強大的功能&#xff0c;為開發者提供了高效且穩定的運行環境。 JVM的起…
閱讀更多...
大疆上云API+流媒體服務器部署實現直播功能

大疆上云API+流媒體服務器部署實現直播功能

根據官網文檔上云API&#xff0c;先將官方提供的Demo部署起來&#xff0c;后端和前端服務環境搭建請參考官方文檔。因為官方文檔沒有對直播這塊的環境搭建進行說明&#xff0c;所以下面主要對直播功能環境搭建做一個記錄&#xff0c;僅供參考&#xff0c;如有不足之處&#xff…
閱讀更多...
計算機網絡 HTTP篇常見面試題總結

計算機網絡 HTTP篇常見面試題總結

HTTP各版本區別 HTTP 1.0 無狀態、無連接&#xff1a;每次請求都需要建立新的 TCP&#xff0c;處理完后立即關閉&#xff0c;導致開銷較大。隊頭阻塞&#xff1a;每個請求必須按照順序依次處理&#xff0c;前面的請求未完成&#xff0c;后面的請求只能等待&#xff0c;減低了…
閱讀更多...
目標檢測：YOLO 模型詳解

目標檢測：YOLO 模型詳解

目錄 一、YOLO&#xff08;You Only Look Once&#xff09;模型講解 YOLOv1 YOLOv2 (YOLO9000) YOLOv3 YOLOv4 YOLOv5 YOLOv6 YOLOv7 YOLOv8 YOLOv9 YOLOv10 YOLOv11 YOLOv12 其他變體&#xff1a;PP-YOLO 二、YOLO 模型的 Backbone&#xff1a;Focus 結構 三、…
閱讀更多...
開源 FcDesigner 表單設計器組件事件詳解

開源 FcDesigner 表單設計器組件事件詳解

FcDesigner 是一款基于Vue的開源低代碼可視化表單設計器工具&#xff0c;通過數據驅動表單渲染。可以通過拖拽的方式快速創建表單&#xff0c;提高開發者對表單的開發效率&#xff0c;節省開發者的時間。并廣泛應用于在政務系統、OA系統、ERP系統、電商系統、流程管理等領域。 …
閱讀更多...
關于 smali：2. 從 Java 到 Smali 的映射

關于 smali：2. 從 Java 到 Smali 的映射

一、對照 Java 代碼與 Smali 代碼差異 1.1 方法調用差異&#xff1a;Java vs Smali Java 方法分類&#xff1a; 方法類型Java 示例Smali 指令特點說明靜態方法Utils.print("hi")invoke-static沒有 this 指針實例方法obj.show()invoke-virtual有 this&#xff0c;虛…
閱讀更多...
2025年05月29日Github流行趨勢

2025年05月29日Github流行趨勢

項目名稱&#xff1a;agenticSeek 項目地址url&#xff1a;https://github.com/Fosowl/agenticSeek項目語言&#xff1a;Python歷史star數&#xff1a;11898今日star數&#xff1a;2379項目維護者&#xff1a;Fosowl, steveh8758, klimentij, ganeshnikhil, apps/copilot-pull-…
閱讀更多...
Dubbo高頻面試題

Dubbo高頻面試題

引言 作為分布式服務框架的標桿&#xff0c;Dubbo憑借其高性能RPC通信、靈活的服務治理能力和豐富的容錯機制&#xff0c;成為Java技術棧中微服務領域的核心考點。本文系統梳理Dubbo高頻面試核心知識點&#xff0c;涵蓋容錯策略、負載均衡、注冊中心原理、服務上下線感知等關鍵…
閱讀更多...
氮氣吹掃電磁閥

氮氣吹掃電磁閥

一、氮氣吹掃電磁閥的概述 氮氣吹掃電磁閥是一種重要的工業自動控制設備&#xff0c;用于對工業設備中出現的雜質和沉淀物進行清理&#xff0c;以保證生產線的暢通和生產效率的穩定。其作用是在需要吹掃清洗的工業設備中&#xff0c;通過控制氣源的氣壓&#xff0c;打開電磁閥…
閱讀更多...
網絡安全的守護者：iVX 如何構建全方位防護體系

網絡安全的守護者：iVX 如何構建全方位防護體系

一、安全技術的三大趨勢 在數字化時代&#xff0c;網絡安全面臨著前所未有的挑戰。隨著企業級應用的普及&#xff0c;安全技術也在不斷演進。目前&#xff0c;安全技術架構的發展呈現出三大趨勢&#xff1a; 零信任架構的崛起&#xff1a;傳統的網絡安全依賴于邊界防護&#…
閱讀更多...
微軟云如何申請使用

微軟云如何申請使用

微軟云&#xff08;Azure&#xff09;新手“開荒”指南&#xff1a;5步搞定賬戶&#xff0c;直達云端&#xff01; 還在為云計算的復雜門檻發愁嗎&#xff1f;別擔心&#xff01;當全球83%的企業都在加速“上云”&#xff0c;微軟智能云Azure憑借其在全球34個區域、200服務的龐…
閱讀更多...
magic-api配置Git插件教程

magic-api配置Git插件教程

一、配置gitee.com 1&#xff0c;生成rsa密鑰&#xff0c;在你的電腦右鍵使用管理員身份運行&#xff08;命令提示符&#xff09;&#xff0c;執行下面命令 ssh-keygen -t rsa -b 2048 -m PEM一直按回車鍵&#xff0c;不需要輸入內容 找到 你電腦中的~/.ssh/id_rsa.pub 文件…
閱讀更多...
ojs導入顯示空白頁錯誤信息

ojs導入顯示空白頁錯誤信息

ojs技術支持：ojs.net.cn error: Uncaught Error: Call to a member function getData() on null in /var/www/html/ojs3/classes/search/ArticleSearchIndex.inc.php:38 Stack trace: #0 /var/www/html/ojs3/plugins/importexport/esci/filter/esciXmlArticleFilter.inc.php(…
閱讀更多...
【ConvLSTM第一期】ConvLSTM原理

【ConvLSTM第一期】ConvLSTM原理

目錄 &#x1f9e0; 一、ConvLSTM 原理詳解1.1 背景1.2 ConvLSTM 的結構 參考 ConvLSTM&#xff08;Convolutional Long Short-Term Memory&#xff09;是一種結合了卷積神經網絡&#xff08;CNN&#xff09;與循環神經網絡&#xff08;RNN&#xff09;中 LSTM&#xff08;長短…
閱讀更多...
4.8.1 利用Spark SQL實現詞頻統計

4.8.1 利用Spark SQL實現詞頻統計

在利用Spark SQL實現詞頻統計的實戰中&#xff0c;首先需要準備單詞文件并上傳至HDFS。接著&#xff0c;可以通過交互式方法或創建Spark項目來實現詞頻統計。交互式方法包括讀取文本文件生成數據集&#xff0c;扁平化映射得到新數據集&#xff0c;然后將數據集轉成數據幀&#…
閱讀更多...
Linux相關概念和易錯知識點（41）（UDP、TCP報頭結構）

Linux相關概念和易錯知識點（41）（UDP、TCP報頭結構）

目錄 1.UDP&#xff08;1&#xff09;傳輸層&#xff08;2&#xff09;UDP報頭&#xff08;3&#xff09;緩沖區和sk_buff①緩沖區②sk_buff 2.TCP&#xff08;1&#xff09;發送和接受緩沖區&#xff08;2&#xff09;報頭結構①按序到達②可靠傳輸③流量控制④緊急指針 1.UDP…
閱讀更多...
光譜相機在生態修復監測中的應用

光譜相機在生態修復監測中的應用

光譜相機通過多維光譜數據采集與智能分析技術&#xff0c;在生態修復監測中構建起?“感知-評估-驗證”?的全周期管理體系&#xff0c;其核心應用方向如下&#xff1a; 一、土壤修復效能量化評估 ?重金屬污染動態監測? 通過短波紅外&#xff08;1000-2500nm&#xff09;波…
閱讀更多...
[網頁五子棋]項目介紹以及websocket的消息推送(輪詢操作)、報文格式和握手過程(建立連接過程)

[網頁五子棋]項目介紹以及websocket的消息推送(輪詢操作)、報文格式和握手過程(建立連接過程)

文章目錄 項目背景核心技術創建項目WebSocket消息推送輪詢操作 報文格式握手過程(建立連接過程) 項目背景 用戶模塊 用戶的注冊和登錄管理用戶的天梯分數&#xff0c;比賽場數&#xff0c;獲勝場數等信息 匹配模塊 依據用戶的天梯積分&#xff0c;來實現匹配機制 對戰模塊 把兩…
閱讀更多...
時序模型介紹

時序模型介紹

一.整體介紹 1.單變量 vs 多變量時序數據 單變量就是只根據時間預測&#xff0c;多變量還要考慮用戶 2.為什么不能用機器學習預測&#xff1a; a.時間不是影響標簽的關鍵因素 b.時間與標簽之間的聯系過于弱/過于復雜&#xff0c;因此時序模型依賴于時間與時間的相關性來進行預…
閱讀更多...
最新文章

Copyright @ 2022~2023