java criteria exist_Java Criteria.addExists方法代碼示例

原创 于 2021-02-25 21:31:59 发布 · 141 阅读 · 0 ·
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#java criteria exist

此代码实现了一种方法来获取没有对应任命资金锁定的职位锁定记录。通过查询数据库中与特定大学财政年度相关的职位锁定，并检查是否存在对应的任命资金锁定，从而找出孤儿职位锁定。

import org.apache.ojb.broker.query.Criteria; //導入方法依賴的package包/類

/**

* @see org.kuali.kfs.module.bc.document.dataaccess.BudgetConstructionLockDao#getOrphanedPositionLocks(java.lang.String)

*/

public List getOrphanedPositionLocks(String lockUnivId) {

Criteria criteria = new Criteria();

if (StringUtils.isNotBlank(lockUnivId)) {

criteria.addEqualTo(BCPropertyConstants.POSITION_LOCK_USER_IDENTIFIER, lockUnivId);

}

else {

criteria.addNotNull(BCPropertyConstants.POSITION_LOCK_USER_IDENTIFIER);

}

ReportQueryByCriteria query = QueryFactory.newReportQuery(BudgetConstructionPosition.class, criteria);

query.addOrderByAscending(KFSPropertyConstants.UNIVERSITY_FISCAL_YEAR);

query.addOrderByAscending(BCPropertyConstants.POSITION_NUMBER);

List allPositionLocks = (List) getPersistenceBrokerTemplate().getCollectionByQuery(query);

List orphanedPositionLocks = new ArrayList();

for (BudgetConstructionPosition position : allPositionLocks) {

Criteria criteria2 = new Criteria();

criteria2.addEqualTo(KFSPropertyConstants.UNIVERSITY_FISCAL_YEAR, position.getUniversityFiscalYear());

criteria2.addEqualTo(BCPropertyConstants.POSITION_NUMBER, position.getPositionNumber());

Criteria subCrit = new Criteria();

subCrit.addEqualToField(KFSPropertyConstants.UNIVERSITY_FISCAL_YEAR, Criteria.PARENT_QUERY_PREFIX + KFSPropertyConstants.UNIVERSITY_FISCAL_YEAR);

subCrit.addEqualToField(KFSPropertyConstants.CHART_OF_ACCOUNTS_CODE, Criteria.PARENT_QUERY_PREFIX + KFSPropertyConstants.CHART_OF_ACCOUNTS_CODE);

subCrit.addEqualToField(KFSPropertyConstants.ACCOUNT_NUMBER, Criteria.PARENT_QUERY_PREFIX + KFSPropertyConstants.ACCOUNT_NUMBER);

subCrit.addEqualToField(KFSPropertyConstants.SUB_ACCOUNT_NUMBER, Criteria.PARENT_QUERY_PREFIX + KFSPropertyConstants.SUB_ACCOUNT_NUMBER);

subCrit.addEqualTo(BCPropertyConstants.APPOINTMENT_FUNDING_LOCK_USER_ID, position.getPositionLockUserIdentifier());

ReportQueryByCriteria subQuery = QueryFactory.newReportQuery(BudgetConstructionFundingLock.class, subCrit);

subQuery.setAttributes(new String[] { "1" });

criteria2.addExists(subQuery);

List appointmentFundingLocks = (List) getPersistenceBrokerTemplate().getCollectionByQuery(QueryFactory.newQuery(PendingBudgetConstructionAppointmentFunding.class, criteria2));

if (appointmentFundingLocks == null || appointmentFundingLocks.isEmpty()) {

orphanedPositionLocks.add(position);

}

}

return orphanedPositionLocks;

}

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import numpy as np import cv2 import glob import os # 标定板设置 - 根据实际使用的标定板修改 CHECKERBOARD = (7, 9) # 棋盘格内部角点数量 (宽度, 高度) SQUARE_SIZE = 25 # 棋盘格方块实际尺寸 (毫米) def stereo_calibrate(left_dir, right_dir): # 准备3D世界坐标点 (0,0,0), (1,0,0), (2,0,0) .... objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32) objp[:, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2) * SQUARE_SIZE # 存储3D点和2D图像点 obj_points = [] # 世界坐标系中的3D点 left_points = [] # 左相机图像中的2D点 right_points = [] # 右相机图像中的2D点 # 获取左右相机图像路径 left_images = sorted(glob.glob(os.path.join(left_dir, '*.png'))) right_images = sorted(glob.glob(os.path.join(right_dir, '*.png'))) # 检测棋盘格角点 for left_path, right_path in zip(left_images, right_images): # 读取左右图像 left_img = cv2.imread(left_path) right_img = cv2.imread(right_path) # 转换为灰度图 gray_left = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY) gray_right = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY) # 寻找棋盘格角点 ret_left, corners_left = cv2.findChessboardCorners( gray_left, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE) ret_right, corners_right = cv2.findChessboardCorners( gray_right, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE) # 如果两个相机都检测到角点 if ret_left and ret_right: # 亚像素级角点精确化 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners_left = cv2.cornerSubPix(gray_left, corners_left, (11,11), (-1,-1), criteria) corners_right = cv2.cornerSubPix(gray_right, corners_right, (11,11), (-1,-1), criteria) # 添加点对 obj_points.append(objp) left_points.append(corners_left) right_points.append(corners_right) # 可视化角点 (可选) cv2.drawChessboardCorners(left_img, CHECKERBOARD, corners_left, ret_left) cv2.drawChessboardCorners(right_img, CHECKERBOARD, corners_right, ret_right) cv2.imshow('Left', left_img) cv2.imshow('Right', right_img) cv2.waitKey(500) cv2.destroyAllWindows() # 单目标定获取初始参数 ret, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera( obj_points, left_points, gray_left.shape[::-1], None, None) ret, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera( obj_points, right_points, gray_right.shape[::-1], None, None) # 双目标定 flags = cv2.CALIB_FIX_INTRINSIC criteria_stereo = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) ret, mtxL, distL, mtxR, distR, R, T, E, F = cv2.stereoCalibrate( obj_points, left_points, right_points, mtxL, distL, mtxR, distR, gray_left.shape[::-1], criteria=criteria_stereo, flags=flags) print("双目标定完成!") print("旋转矩阵 R:\n", R) print("平移向量 T:\n", T) print("左相机内参:\n", mtxL) print("右相机内参:\n", mtxR) return mtxL, distL, mtxR, distR, R, T if __name__ == "__main__": # 设置左右相机图像文件夹路径 left_images_dir = "path/to/left_camera_images" right_images_dir = "path/to/right_camera_images" # 执行双目标定 results = stereo_calibrate(left_images_dir, right_images_dir) # 保存标定结果 (可选) np.savez("stereo_calibration.npz", mtxL=results[0], distL=results[1], mtxR=results[2], distR=results[3], R=results[4], T=results[5]) 在前面这个代码前，加上将多个raw格式图片转化为png格式图片的代码，其中，将这个代码读取图片的路径改为folder = 'stereo' # left_*.png / right_*.png；
最新发布
11-07
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04-09
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#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function import os os.environ['QT_X11_NO_MITSHM'] = '1' os.environ['DISPLAY'] = ':0' import rospy import cv2 import numpy as np import time import yaml import logging import threading import signal import sys from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError from robot_package.msg import TR_Arm_Msg # 配置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger('robust_hand_eye_calibration') class RobustCalibrator: def __init__(self): rospy.init_node('robust_hand_eye_calibration', anonymous=True) # 参数配置 self.pattern_size = rospy.get_param('~pattern_size', (6, 8)) self.square_size = rospy.get_param('~square_size', 0.02) self.min_poses = rospy.get_param('~min_poses', 15) self.max_poses = rospy.get_param('~max_poses', 20) # 数据存储 self.gripper_poses = [] self.target_poses = [] self.images = [] self.camera_info = None self.T_cam_end = None # ROS工具 self.bridge = CvBridge() self.current_image = None self.current_arm_pose = None self.last_corners = None # 生成世界坐标系点 self.objp = np.zeros((self.pattern_size[0]*self.pattern_size[1], 3), np.float32) self.objp[:, :2] = np.mgrid[0:self.pattern_size[0], 0:self.pattern_size[1]].T.reshape(-1, 2) * self.square_size logger.info("鲁棒手眼标定系统已启动") # 订阅者 rospy.Subscriber("/ascamera/rgb0/image", Image, self.image_callback) rospy.Subscriber("/ascamera/rgb0/camera_info", CameraInfo, self.camera_info_callback) rospy.Subscriber("/TR_Arm_topic", TR_Arm_Msg, self.arm_pose_callback) # 创建调试图像发布者 self.debug_pub = rospy.Publisher("/calibration/debug_image", Image, queue_size=1) # 设置信号处理 signal.signal(signal.SIGINT, self.signal_handler) signal.signal(signal.SIGTERM, self.signal_handler) # 状态监控线程 self.monitor_thread = threading.Thread(target=self.monitor_system) self.monitor_thread.daemon = True self.monitor_thread.start() def signal_handler(self, signum, frame): """处理中断信号""" logger.warning("收到中断信号 %d，正在安全关闭...", signum) self.cleanup_resources() rospy.signal_shutdown("外部中断") sys.exit(0) def monitor_system(self): """系统监控线程""" while not rospy.is_shutdown(): # 检查系统资源 try: # 监控GPU内存（适用于NVIDIA Jetson） if os.path.exists('/sys/devices/gpu.0'): with open('/sys/devices/gpu.0/load', 'r') as f: gpu_load = int(f.read().strip()) if gpu_load > 90: logger.warning("GPU负载过高: %d%%，考虑降低图像分辨率", gpu_load) # 监控CPU温度 if os.path.exists('/sys/class/thermal/thermal_zone0/temp'): with open('/sys/class/thermal/thermal_zone0/temp', 'r') as f: temp = int(f.read().strip()) / 1000.0 if temp > 75: logger.warning("CPU温度过高: %.1f°C，暂停处理10秒", temp) rospy.sleep(10.0) rospy.sleep(5.0) # 每5秒检查一次 except Exception as e: logger.error("监控错误: %s", e) rospy.sleep(10.0) def cleanup_resources(self): """清理资源""" logger.info("清理系统资源...") try: # 关闭所有OpenCV窗口 cv2.destroyAllWindows() # 保存当前进度 if len(self.gripper_poses) > 0: self.save_progress() logger.info("资源清理完成") except Exception as e: logger.error("清理资源时出错: %s", e) def save_progress(self, filename="calibration_progress.yaml"): """保存当前进度""" try: data = { 'gripper_poses': [pose.tolist() for pose in self.gripper_poses], 'target_poses': [pose.tolist() for pose in self.target_poses], 'num_poses': len(self.gripper_poses), 'last_update': time.strftime("%Y-%m-%d %H:%M:%S") } with open(filename, 'w') as f: yaml.dump(data, f, default_flow_style=False) logger.info("标定进度已保存至: %s", filename) return True except Exception as e: logger.error("保存进度失败: %s", e) return False def load_progress(self, filename="calibration_progress.yaml"): """加载之前保存的进度""" try: if not os.path.exists(filename): logger.warning("进度文件不存在: %s", filename) return False with open(filename, 'r') as f: data = yaml.safe_load(f) self.gripper_poses = [np.array(pose) for pose in data['gripper_poses']] self.target_poses = [np.array(pose) for pose in data['target_poses']] logger.info("已从 %s 加载进度: %d 个位姿", filename, len(self.gripper_poses)) return True except Exception as e: logger.error("加载进度失败: %s", e) return False def image_callback(self, msg): try: # 使用独立线程处理图像，避免阻塞主线程 threading.Thread(target=self.process_image, args=(msg,)).start() except Exception as e: logger.error("图像处理线程错误: %s", e) def process_image(self, msg): """在独立线程中处理图像""" try: self.current_image = self.bridge.imgmsg_to_cv2(msg, "bgr8") self.detect_corners() except CvBridgeError as e: logger.error("图像转换错误: %s", e) def detect_corners(self): """检测角点并发布调试图像""" if self.current_image is None or self.camera_info is None: return try: gray = cv2.cvtColor(self.current_image, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, self.pattern_size, None) if ret: criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners_refined = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria) self.last_corners = corners_refined # 创建调试图像 debug_img = self.current_image.copy() cv2.drawChessboardCorners(debug_img, self.pattern_size, corners_refined, ret) # 添加状态信息 status_text = f"Corners Detected [{len(self.gripper_poses)}/{self.max_poses}]" cv2.putText(debug_img, status_text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) # 发布调试图像 try: debug_msg = self.bridge.cv2_to_imgmsg(debug_img, "bgr8") self.debug_pub.publish(debug_msg) except CvBridgeError as e: logger.error("发布调试图像错误: %s", e) except Exception as e: logger.error("角点检测错误: %s", e) def camera_info_callback(self, msg): if self.camera_info is None: self.camera_info = { 'K': np.array(msg.K).reshape(3,3), 'D': np.array(msg.D), 'width': msg.width, 'height': msg.height } logger.info("相机内参已获取") def arm_pose_callback(self, msg): if len(msg.homogeneousMatrix) == 16: self.current_arm_pose = np.array(msg.homogeneousMatrix).reshape(4,4).astype(np.float64) def capture_data(self): if not all([self.current_image, self.current_arm_pose, self.camera_info]): logger.error("数据不完整，无法采集") return False try: gray = cv2.cvtColor(self.current_image, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, self.pattern_size, None) if not ret: logger.warning("未检测到棋盘格") return False criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners_refined = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria) ret, rvec, tvec = cv2.solvePnP(self.objp, corners_refined, self.camera_info['K'], self.camera_info['D']) if not ret: logger.error("solvePnP失败") return False R_board_cam, _ = cv2.Rodrigues(rvec) T_board_cam = np.eye(4) T_board_cam[:3, :3] = R_board_cam T_board_cam[:3, 3] = tvec.flatten() self.gripper_poses.append(self.current_arm_pose.copy()) self.target_poses.append(T_board_cam.copy()) self.images.append(self.current_image.copy()) logger.info("成功采集位姿数据: %d/%d", len(self.gripper_poses), self.max_poses) # 定期保存进度 if len(self.gripper_poses) % 5 == 0: self.save_progress() return True except Exception as e: logger.exception("数据采集失败: %s", e) return False def calibrate(self): if len(self.gripper_poses) < self.min_poses: logger.error("需要至少%d个位姿数据, 当前: %d", self.min_poses, len(self.gripper_poses)) return None try: R_gripper2base, t_gripper2base = [], [] R_target2cam, t_target2cam = [], [] for i in range(len(self.gripper_poses)-1): inv_pose = np.linalg.inv(self.gripper_poses[i]) A = np.dot(inv_pose, self.gripper_poses[i+1]) R_gripper2base.append(A[:3, :3]) t_gripper2base.append(A[:3, 3]) inv_target = np.linalg.inv(self.target_poses[i]) B = np.dot(inv_target, self.target_poses[i+1]) R_target2cam.append(B[:3, :3]) t_target2cam.append(B[:3, 3]) R_cam2gripper = np.eye(3) t_cam2gripper = np.zeros(3) # 尝试不同的标定方法 methods = [ cv2.CALIB_HAND_EYE_TSAI, cv2.CALIB_HAND_EYE_PARK, cv2.CALIB_HAND_EYE_HORAUD ] best_error = float('inf') best_result = None for method in methods: try: R, t = cv2.calibrateHandEye( R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper, t_cam2gripper, method=method ) # 计算误差 error = self.calculate_error(R, t) logger.info("方法 %d 标定误差: %.6f", method, error) if error < best_error: best_error = error best_result = (R, t) except Exception as e: logger.warning("标定方法 %d 失败: %s", method, str(e)) if best_result is None: logger.error("所有标定方法均失败") return None R_cam2gripper, t_cam2gripper = best_result self.T_cam_end = np.eye(4) self.T_cam_end[:3, :3] = R_cam2gripper self.T_cam_end[:3, 3] = t_cam2gripper logger.info("最佳标定误差: %.6f", best_error) logger.info("相机到机械臂末端的变换矩阵 T_cam_end:\n%s", self.T_cam_end) return self.T_cam_end except Exception as e: logger.exception("标定失败: %s", e) return None def calculate_error(self, R, t): """计算标定误差""" errors = [] for i in range(len(self.gripper_poses)): # 计算预测的标定板位姿 T_cam_end = np.eye(4) T_cam_end[:3, :3] = R T_cam_end[:3, 3] = t.flatten() predicted_target = T_cam_end.dot(self.gripper_poses[i]).dot(np.linalg.inv(T_cam_end)) # 计算与实测位姿的差异 error = np.linalg.norm(predicted_target[:3, 3] - self.target_poses[i][:3, 3]) errors.append(error) return np.mean(errors) def save_calibration(self, filename="hand_eye_calibration.yaml"): if self.T_cam_end is None: logger.error("尚未标定，无法保存结果") return False try: # 计算最终误差 final_error = self.calculate_error(self.T_cam_end[:3, :3], self.T_cam_end[:3, 3]) data = { 'T_cam_end': self.T_cam_end.tolist(), 'camera_matrix': self.camera_info['K'].tolist(), 'distortion_coefficients': self.camera_info['D'].tolist(), 'calibration_date': time.strftime("%Y-%m-%d %H:%M:%S"), 'num_poses': len(self.gripper_poses), 'pattern_size': list(self.pattern_size), 'square_size': self.square_size, 'calibration_error': float(final_error) } with open(filename, 'w') as f: yaml.dump(data, f, default_flow_style=False) logger.info("标定结果已保存至: %s (误差: %.6f)", filename, final_error) # 保存采集的图像 self.save_calibration_images() # 清理进度文件 if os.path.exists("calibration_progress.yaml"): os.remove("calibration_progress.yaml") return True except Exception as e: logger.exception("保存失败: %s", e) return False def save_calibration_images(self): """保存采集的图像用于验证""" try: save_dir = "calibration_images" os.makedirs(save_dir, exist_ok=True) for i, img in enumerate(self.images): filename = os.path.join(save_dir, f"pose_{i:02d}.png") cv2.imwrite(filename, img) logger.info("保存了%d张标定图像到目录: %s", len(self.images), save_dir) return True except Exception as e: logger.error("保存图像失败: %s", e) return False def main(): # 修复X11环境 os.system('xhost +local:') # 允许本地连接 os.system('export DISPLAY=:0') # 确保显示设置 # 初始化校准器 calibrator = RobustCalibrator() # 尝试加载之前的进度 calibrator.load_progress() rospy.sleep(2.0) # 等待初始数据 logger.info("\n===== 鲁棒标定系统操作指南 =====") logger.info("1. 移动机械臂使棋盘格在相机视野中央") logger.info("2. 按回车键采集当前位姿 (需要至少%d个不同位姿)", calibrator.min_poses) logger.info("3. 采集完成后输入 'c' 开始标定") logger.info("4. 标定完成后输入 's' 保存结果") logger.info("5. 输入 'q' 退出程序") logger.info("6. 系统会自动保存进度，意外中断后可恢复") try: while not rospy.is_shutdown() and len(calibrator.gripper_poses) < calibrator.max_poses: cmd = raw_input("等待命令 (回车采集/'c'标定/'s'保存/'q'退出): ").strip().lower() if cmd == '': if calibrator.capture_data(): logger.info("数据采集成功") elif cmd == 'c': if len(calibrator.gripper_poses) < calibrator.min_poses: logger.warning("需要至少%d个位姿，当前只有%d个", calibrator.min_poses, len(calibrator.gripper_poses)) else: result = calibrator.calibrate() if result is not None: logger.info("标定成功") elif cmd == 's': if calibrator.T_cam_end is None: logger.warning("请先执行标定 ('c')") else: calibrator.save_calibration() elif cmd == 'q': logger.info("程序退出") calibrator.cleanup_resources() break except rospy.ROSInterruptException: calibrator.cleanup_resources() except Exception as e: logger.exception("主循环错误: %s", e) calibrator.cleanup_resources() if __name__ == "__main__": main() 修改
07-17
然而，在用户提供的代码中，我们仍然发现了一个问题：在`detect_corners`方法中，使用了f-string（`status_text = f"Corners Detected [{len(self.gripper_poses)}/{self.max_poses}]"`），这可能导致在低于Python...
from math import * import numpy as np import pandas as pd import os import json import sys import csv #权重文件位置 WEIGHTS_JSON="../result/weight.json" #采样次数 SAMPLE_TIMES=2 #Fbwm输入目录 FBWM_INPUT_DIR="../input/fuzzy_bwm_input/" #Fbwm输入文件 FBWM_INPUT_PATH=os.path.join(FBWM_INPUT_DIR,"c4_c5_fuzzy_bwm.xlsx") #权重方法 WEIGHTMETHOD="bwm" #排序方法 RANKMETHOD="mabac" #结果目录 RESULT_DIR="../result/analysis/mc/tri_dis/" #攻击数量 E_NUMBER=20 #初始化结果目录 os.makedirs(RESULT_DIR,exist_ok=True)#递归创建，已存在则跳过 #清空目录文件 for file in os.listdir(RESULT_DIR): file_path=os.path.join(RESULT_DIR,file) try: os.remove(file_path) except OSError as e: print(f"无法删除旧文件{file_path},原因{e}") #逆变换采样函数 def c_tfn(tfn): l,m,u=tfn[0],tfn[1],tfn[2] if not (l<=m<=u): raise ValueError("tfn格式错误") r=np.random.rand()#生成[0,1]之间的随机数 if not(l==m and m==u): p=(m-l)/(u-l) if r<p: return l+sqrt(r*(u-l)*(m-l)) else: return u-sqrt((1-r)*(u-m)*(u-l)) elif l==m and u==m: return l#恒定值 elif l==m and u!=m: return u-sqrt((1-r)*(u-m)*(u-l)) elif u==m and l!=m: return l+sqrt(r*(u-l)*(m-l)) #读取权重评估数据 address=FBWM_INPUT_PATH df=pd.read_excel(address, sheet_name = "Sheet1", skiprows = 2, nrows= 9, usecols=[1,2,3],header=None) df.columns=["Criteria","Best","Worst"] Best = df["Criteria"][df[df['Best'] == "Equally importance"].index.tolist()[0]] Worst = df["Criteria"][df[df['Worst'] == "Equally importance"].index.tolist()[0]] df.columns = ["Criteria", "Best", "Worst"] Cnum = df.shape[0] df.set_index(df["Criteria"], inplace=True) #print(df) # Fuzzification Fuzzy = {"Equally importance": [1, 1, 1], "Weakly important": [2 / 3, 1, 3 / 2], "Fairly important": [3 / 2, 2, 5 / 2], "Very important": [5 / 2, 3, 7 / 2], "Absolutely important": [7 / 2, 4, 9 / 2]} #输出作为后续输入 mc_bwminput_output_path=os.path.join(RESULT_DIR,"mc_bwm_input.xlsx") result={} #蒙特卡洛循环 for sample_idx in range(SAMPLE_TIMES): df_sampled = df.copy() #print("原始列名：",df.columns) if "Number" not in df.columns: df.insert(loc=0,column="Number",value=range(1,10)) df.columns=["Number","Criteria","Best","Worst"] #print("改后列名：",df.columns) #print(df) for i in ["Best", "Worst"]: for j in range(df.shape[0]): #df_sampled[i][j] =c_tfn(Fuzzy[df[i][j]])#随机采样 # 获取列位置 col_idx = df.columns.get_loc(i) # 获取原始值 original_value = df.iloc[j, col_idx] # 采样并赋值到 df_sampled df_sampled.iloc[j, df_sampled.columns.get_loc(i)] = c_tfn(Fuzzy[original_value]) df_sampled.to_excel(mc_bwminput_output_path,index=False)#随机采样后的bwm输入 weightmethod=WEIGHTMETHOD rankmethod=RANKMETHOD #权重计算 os.system('/usr/bin/time -f"%e %M" -a -o ../result/tmp/time.log python3 ../lib/Weight/{}/{}.py {} {}'.format(weightmethod , weightmethod , mc_bwminput_output_path, '../result/weight.json')) #指标计算 #os.system('/usr/bin/time -f"%e %M" -a -o ../result/tmp/time.log python3 ../lib/cal_metrics.py {} {}'.format('../result/tmp/topo.json', '../result/path_metrics.csv')) #排名计算 os.system('/usr/bin/time -f"%e %M" -a -o ../result/tmp/time.log python3 ../lib/Rank/{}/{}.py {} {} {} {} {}'.format(rankmethod , rankmethod , '../result/tmp/topo.json',WEIGHTS_JSON, '../result/tmp/data.csv', '../result/rank.json', '../result/path_metrics.csv')) try: with open('../result/rank.json',"r") as f: rank=json.load(f)#读取排名文件 except Exception as e: print(f"排名读取出错：{e}") try: with open(WEIGHTS_JSON,"r") as m: weight=json.load(m)#读取权重文件 except Exception as e: print(f"权重读取出错：{e}") #整合输出 result[sample_idx]={ "weight":weight, "ranking":rank["ranking"] } #输出排序结果 output_path=os.path.join(RESULT_DIR,"monte_carlo_tfn.json") with open(output_path,"w") as f: json.dump(result,f,indent=2) #计算排名的平均值和标准差 #提取排名 edge_rank={} for i in range(SAMPLE_TIMES): edge_rank[i]=[result[i]["ranking"][j][0] for j in range(E_NUMBER)]#排序 #print(edge_rank) #获取扰动前的前十名 p=os.system('/usr/bin/time -f"%e %M" -a -o ../result/tmp/time.log python3 ../lib/Rank/{}/{}.py {} {} {} {} {}'.format(RANKMETHOD , RANKMETHOD , '../result/tmp/topo.json',WEIGHTS_JSON, '../result/tmp/data.csv', '../result/rank.json', '../result/path_metrics.csv')) try: with open('../result/rank.json',"r") as f: rank_before=json.load(f)#读取排名文件 except Exception as e: print(f"排名读取出错：{e}") edge_Ten_rank=[rank_before["ranking"][i][0] for i in range(10)]#取前十名 #print(edge_Ten_rank) #统计 rank_rank={} for edge in edge_Ten_rank: rank_rank[edge]=[]#边的排名统计 for i in range(SAMPLE_TIMES): for edge in edge_Ten_rank: for j in range(E_NUMBER): if edge_rank[i][j]==edge: rank_rank[edge].append(j+1) #得到排名的结果rank_rank #print(rank_rank) #计算均值与标准差 mean_rank=[] std_rank=[] for edge in edge_Ten_rank: mean_rank.append(np.mean(rank_rank[edge])) std_rank.append(np.std(rank_rank[edge],ddof=0)) mean_std_output_path=os.path.join(RESULT_DIR,"mean_std_rank_tfn.csv") with open(mean_std_output_path,"w",newline="") as f: writer=csv.writer(f) writer.writerow(edge_Ten_rank)#排名 writer.writerow(mean_rank)#均值 writer.writerow(std_rank)#标准差 #--------补充权重均值和标准差的统计----------# criteria_weight = {} for j in range(1,10): criterion = 'C' + str(j) criteria_weight[criterion] = [] res_path = RESULT_DIR + 'monte_carlo_tfn.json' with open(res_path, 'r') as tmp_res_file: res_data = json.load(tmp_res_file) for i in range(0, SAMPLE_TIMES): weight_values = res_data[str(i)]['weight']['weight'] for k,weight_value in enumerate(weight_values): cri_key = 'C' + str(k+1) criteria_weight[cri_key].append(weight_value) criteria_list = [] mean_weights = [] std_weights = [] for key,w_list in criteria_weight.items(): criteria_list.append(key) mean_weights.append(np.mean(w_list)) std_weights.append(np.std(w_list)) criteria_weight_mean_std_output_path=os.path.join(RESULT_DIR,"criteria_weight_mean_std_rank_tfn.csv") with open(criteria_weight_mean_std_output_path,"w",newline="") as f: writer=csv.writer(f) writer.writerow(criteria_list) writer.writerow(mean_weights) writer.writerow(std_weights) 我已经加了number列了啊
08-31
## ✅ 示例完整修复代码段（蒙特卡洛循环部分） ```python # 蒙特卡洛循环 for sample_idx in range(SAMPLE_TIMES): df_sampled = df.copy() # ✅ 确保 df_sampled 中也有 Number 列 if "Number" not in df_...
jpa CriteriaQuery exists 查询
bewithme的专栏
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      原文    https://stackoverflow.com/questions/13859780/building-a-query-using-not-exists-in-jpa-criteria-api   SELECT table1 FROM table1 table1 WHERE NOT EXISTS (SELECT table2 ...
hibernate criteria where exists查询
sutonline的博客
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hibernate criteria where exists查询使用的方法是sqlRestrictioncriteria.add(Restrictions.sqlRestriction(" exists (select 1 from xxx as r where r.xxx = {alias}.xxx)"){alias}会替换成entity创建criteria的alias.
java的exists_java – 具有exists子句的hibernate标准
weixin_32503491的博客
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我认为这个链接可以是有用的：http://mikedesjardins.net/2008/09/22/hibernate-criteria-subqueries-exists/它包含以下关于create n如何存在条件的示例：“您真正想要做的是获得所有披萨订单,其中存在相关的小比萨饼,换句话说,您尝试仿效的SQL查询是SELECT *FROM PIZZA_ORDERWHERE EXISTS (SE...
criteria
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概述 Criteria是一种比hql更面向对象的查询方式。Criteria 可使用 Criterion 和 Projection 设置查询条件。可以设置 FetchMode( 联合查询抓取的模式 ) ，设置排序方式，Criteria 还可以设置 FlushModel （冲刷 Session 的方式）和 LockMode （数据库锁模式）。 Criterion 是 Criteria
使用Hibernate Criteria实现in子查询和exists子查询
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