# coding=utf-8 import cv2 import numpy as np import random import os from argparse import ArgumentParser ALPHA = 5 def build_parser(): parser = ArgumentParser() parser.add_argument('--original', dest='ori', required=True) parser.add_argument('--image', dest='img', required=True) parser.add_argument('--result', dest='res', required=True) parser.add_argument('--alpha', dest='alpha', default=ALPHA) return parser def main(): parser = build_parser() options = parser.parse_args() ori = options.ori img = options.img res = options.res alpha = options.alpha if not os.path.isfile(ori): parser.error("original image %s does not exist." % ori) if not os.path.isfile(img): parser.error("image %s does not exist." % img) decode(ori,img,res,alpha) def decode(ori_path, img_path, res_path, alpha): ori = cv2.imread(ori_path) img = cv2.imread(img_path) ori_f = np.fft.fft2(ori) img_f = np.fft.fft2(img) height, width = ori.shape[0], ori.shape[1] watermark = (ori_f - img_f) / alpha watermark = np.real(watermark) res = np.zeros(watermark.shape) random.seed(height + width) x = range(height/2) y = range(width) random.shuffle(x) random.shuffle(y) for i in range(height/2): for j in range(width): res[x[i]][y[j]] = watermark[i][j] cv2.imwrite(res_path,res,[int(cv2.IMWRITE_JPEG_QUALITY), 100]) if __name__ == '__main__': main()

# coding=utf-8 import world from world import z from world import x as x2 x = 100 y = 20 print(y) print (wor1d.y) print(z) print(x2)

# coding=utf-8 import world from world import x as x2 x = 100 y = 20 print(y) print(world.y) print(world.z) print(x2) 其中world.py文件中应该包含以下内容： python x = 10 y = 30 z = 50 ...

帮我重写如下python代码，#coding=utf-8-sig import re import os import xlrd import xlwt import time import datetime import math #import pandas import sys import shutil #通用部分 def time_mc(number_t): localTime = time.strftime("%Y%m%d%H%M%S", time.localtime()) time_mc_n

#coding=utf-8-sig import re import os import xlrd import xlwt import time import datetime import math import sys import shutil # 通用部分 def time_mc(number_t): localTime = time.strftime("%Y%m%d%H%M...

# coding: utf-8 import sys, os from catdog.deep_convnet import DeepConvNet sys.path.append(os.pardir) # 为了导入父目录而进行的设定 import numpy as np import matplotlib.pyplot as plt from input_data import load_data

具体来说，该脚本导入了 sys、os、numpy、matplotlib.pyplot 和 load_data 函数。其中，sys 和 os 模块用于设置导入路径，numpy 用于处理数组数据，matplotlib.pyplot 用于绘制图表。另外，该脚本还导入了一个 ...

给出下列代码的注释：1/usr/ bin /python3 # coding = utf -8 import sys for line in sys . stdin : line = line . strip () letters = line . split (,') for letter in letters : print ('% s \t1'% letter [-1][-1]) #!/ usr / bin /python3# coding - utf -8 import sys cur _ letter = None cur _ count =0 for line in sys . stdin : line = line . stripO letter , count = line . split ) if letter == cur _ letter : cur _ count +=1 else : if cur _ Jetter != None : print ("% s \ t % d "%( cur _ letter , cur _ count )) cur _ letter = letter cur _ count =1 print ("% s \ t % d "%( cur _ letter , cur _ count ))

2. 设置编码格式为 UTF-8 3. 导入 sys 模块 4. 从标准输入读取每一行，去除行末的空格，并按逗号分隔每个字符串 5. 遍历每个字符串的最后一个字符，并输出该字符以及数字 1 第二部分是针对第二个代码块的注释，大致...

给出以下代码的注释：#!/ usr / bin /python3# coding - utf -8 import sys for line in sys . stdin : line . strip ) features = line . split ) for feature in features : feature _ list = feature . split (":") if len ( feature _ list )==3: print ("% s \t1"% feature _ list [0]) #!/ usr / bin /python3# coding = utf -8 import sys cur _ feature = None cur _ count =0 for line in sys . stdin : line = line . strip ) feature , count = line . splitO if cur _ feature == feature : cur _ count +=1 else : if cur _ feature != None : print (% s \ t % d "%( cur _ feature , cur _ count )) cur _ feature = feature cur _ count =1 print ("% s \ t % d "%( cur _ feature , cur _ count ))

- # coding - utf -8：指定脚本的编码格式为 UTF-8。 - import sys：导入 sys 模块，用于访问标准输入和输出。 - for line in sys.stdin: 逐行读取标准输入的数据。 - line.strip()：去除行首和行尾的空白字符。 - ...

#coding=utf-8 import unittest from ddt import ddt from ddt import data # 导入data # 类之前定义装饰器，表示在类中要使用ddt了 @ddt class MyTestCase(unittest.TestCase): def setUp(self) -> None: print('{:=^20}'.format("测试开始")) def tearDown(self) -> None: print("{:=^20}".format('测试结束')) # data用于设定参数 @data('战狼', '哪吒', '流浪地球', '复仇者联盟') def test_1(self, txt): print(txt) if name == 'main': unittest.main(verbosity=2)

这段代码是一个使用了ddt库进行参数化测试的Python单元测试代码，其中： - 使用@ddt装饰器表示在类中要使用ddt库； - 使用@data注解来设定参数； - test_1方法接收参数txt，并进行打印输出； - 在if __name__ == '_...

# coding=utf-8 from rdkit import Chem import pandas as pd # 读取CSV文件 data = pd.read_csv('dataSetA.csv') # 提取SMILES列 smiles = data['rxn_Smiles'] # 遍历每个SMILES字符串并打印 rxn_SMILES for smi in smiles: print(smi) mol = Chem.MolFromSmiles(smi) if mol is not None: Chem.Draw.MolToMPL(mol) # 在Matplotlib中绘制结构式

你的代码看起来是用于读取 CSV 文件中的 rxn_Smiles 列，并使用 RDKit 库将其转换为 RDKit 分子对象，并在 Matplotlib 中绘制结构式。这是一个基本的流程，但请确保你已经正确导入了所需的模块和库，并且已经正确...

# coding :utf-8 import pandas as pd import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split import matplotlib as mpl import matplotlib.pyplot as plt from sklearn import svm import matplotlib.patches as mpatches from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.pipeline import Pipeline def iris_type(s): it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2} return it[s] if name=="main": data=np.loadtxt('.\\iris.data',delimiter=',',converters={4:iris_type}) x=data[:,0:2] y=data[:,4] x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1) #Ir = Pipeline([('sc', StandardScaler()), 'poly', PolynomialFeatures(degree=2)),('clf', LogisticRegression()) ]) # Ir=LogisticRegression(C=4.6) Ir=svm.SVC(C=1,kernel='rbf',decision_function_shape='ovo') Ir.fit(x_train,y_train) y_hat=Ir.predict(x_test) print('训练集准确度：' ,Ir.score(x_train,y_train)) print('测试集准确度：' ,Ir.score(x_test,y_test)) err_y = y_test[y_test != y_hat] print(y_test) print(err_y) #画图

好的，我现在需要帮助用户解决如何使用Python的sklearn库通过支持向量机（SVM）对鸢尾花数据集进行分类并绘制决策边界的问题。...grid = GridSearchCV(SVC(), params, cv=5) grid.fit(X_train, y_train) ---

# -- coding: UTF-8 -- import numpy as np import queue import prettytable as pt def find_zero(num): tmp_x, tmp_y = np.where(num == 0) return tmp_x[0], tmp_y[0] def swap(num_data, direction): x, y = find_zero(num_data) num = np.copy(num

num[x][y-1], num[x][y] elif direction == 'right': num[x][y], num[x][y+1] = num[x][y+1], num[x][y] return num def is_valid(num): if np.array_equal(num, [[1,2,3],[4,5,6],[7,8,0]]): return True else: ...

# -- coding=utf-8 -- import time import random import copy import cv2 import os import math import numpy as np from skimage.util import random_noise from lxml import etree, objectify import xml.etree.ElementTree as ET import argparse # 显示图片 def show_pic(img, bboxes=None): ''' 输入: img:图像array bboxes:图像的所有boudning box list, 格式为[[x_min, y_min, x_max, y_max]....] names:每个box对应的名称 ''' for i in range(len(bboxes)): bbox = bboxes[i] x_min = bbox[0] y_min = bbox[1] x_max = bbox[2] y_max = bbox[3] cv2.rectangle(img, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 3) cv2.namedWindow('pic', 0) # 1表示原图 cv2.moveWindow('pic', 0, 0) cv2.resizeWindow('pic', 1200, 800) # 可视化的图片大小 cv2.imshow('pic', img) cv2.waitKey(0) cv2.destroyAllWindows() # 图像均为cv2读取 class DataAugmentForObjectDetection(): def init(self, rotation_rate=0.5, max_rotation_angle=5, crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5, add_noise_rate=0.5, flip_rate=0.5, cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5, is_addNoise=True, is_changeLight=True, is_cutout=True, is_rotate_img_bbox=True, is_crop_img_bboxes=True, is_shift_pic_bboxes=True, is_filp_pic_bboxes=True): # 配置各个操作的属性 self.rotation_rate = rotation_rate self.max_rotation_angle = max_rotation_angle self.crop_rate = crop_rate self.shift_rate = shift_rate self.change_light_rate = change_light_rate self.add_noise_rate = add_noise_rate self.flip_rate = flip_rate self.cutout_rate = cutout_rate self.cut_out_length = cut_out_length self.cut_out_holes = cut_out_holes self.cut_out_threshold = cut_out_threshold # 是否使用某种增强方式 self.is_addNoise = is_addNoise self.is_changeLight = is_changeLight

import numpy as np def rotate_image(image, angle): height, width = image.shape[:2] center = (width / 2, height / 2) rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0) rotated_image = ...

# -- coding: utf-8 -- """ Created on Mon Jan 15 16:02:48 2018 @author: fangp Cross validation for NeoDTI. """ %cd /my_file/NeoDTI-master/src import numpy as np # from sets import Set import pickle import tensorflow as tf from sklearn.metrics import roc_auc_score from sklearn.metrics import average_precision_score # from sklearn.cross_validation import KFold from sklearn.model_selection import KFold from utils import * from tflearn.activations import relu # from sklearn.cross_validation import train_test_split,StratifiedKFold from sklearn.model_selection import train_test_split, StratifiedKFold import sys # from optparse import OptionParser # parser = OptionParser() # parser.add_option("-d", "--d", default=1024, help="The embedding dimension d") # parser.add_option("-n","--n",default=1, help="global norm to be clipped") # parser.add_option("-k","--k",default=512,help="The dimension of project matrices k") # parser.add_option("-t","--t",default = "o",help="Test scenario") # parser.add_option("-r","--r",default = "ten",help="positive negative ratio") # (opts, args),_ = parser.parse_args() import argparse # 创建解析器 parser = argparse.ArgumentParser(description="Process some options.") # 添加选项 parser.add_argument("-d", "--d", type=int, default=1024, help="The embedding dimension d") parser.add_argument("-n", "--n", type=int, default=1, help="Global norm to be clipped") parser.add_argument("-k", "--k", type=int, default=512, help="The dimension of project matrices k") parser.add_argument("-t", "--t", type=str, default="o", help="Test scenario") parser.add_argument("-r", "--r", type=str, default="ten", help="Positive negative ratio") # 解析参数（忽略未知参数） opts, _ = parser.parse_known_args() 为上面的tensorflow配置GPU

import tensorflow as tf # 检查GPU是否可用 if tf.config.list_physical_devices('GPU'): print("GPU detected") # 设置GPU内存动态增长（避免占满显存） physical_devices = tf.config.list_physical_...

# -- coding: utf-8 -- import os import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score from sklearn.model_selection import TimeSeriesSplit from skopt import BayesSearchCV from skopt.space import Integer, Real from scipy.stats import probplot import joblib # ================== 全局配置 ================== OUTPUT_DIR = r"D:\Undergraduate thesis\paper\results" # 结果统一保存目录 os.makedirs(OUTPUT_DIR, exist_ok=True) # 自动创建目录 # ================== 数据加载与预处理 ================== def load_data(file_path): df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date', encoding='gbk') df.sort_index(inplace=True) # 异常值处理（IQR方法） q1 = df['Close'].quantile(0.25) q3 = df['Close'].quantile(0.75) iqr = q3 - q1 df = df[(df['Close'] > q1 - 1.5iqr) & (df['Close'] < q3 + 1.5iqr)] return df.ffill() # 前向填充缺失值 # ================== 高级特征工程 ================== def create_features(df): # 滞后特征 for lag in [1, 3, 5, 10, 20]: df[f'Close_lag_{lag}'] = df['Close'].shift(lag) # 技术指标 df['MA5'] = df['Close'].rolling(5).mean() df['RSI'] = 100 - (100 / (1 + (df['Close'].diff(1).clip(lower=0).rolling(14).mean() / df['Close'].diff(1).clip(upper=0).abs().rolling(14).mean()))) df['MACD'] = df['Close'].ewm(span=12).mean() - df['Close'].ewm(span=26).mean() # 日期特征 df['DayOfWeek'] = df.index.dayofweek df['Is_Month_End'] = df.index.is_month_end.astype(int) return df.dropna() # ================== 贝叶斯优化模型 ================== def bayesian_optimization(X_train, y_train): search_spaces = { 'n_estimators': Integer(100, 500), 'max_depth': Integer(5, 30), 'min_samples_split': Integer(2, 20), 'min_samples_leaf': Integer(1, 10), 'max_features': Real(0.1, 1.0, prior='uniform') } opt = BayesSearchCV(

import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.metrics import ...

# -- coding: utf-8 -- import numpy as np import torch import torch.nn as nn import torch.optim as optim from pypuf.simulation import ArbiterPUF from pypuf.io import random_inputs from sklearn.model_selection import train_test_split # ========== 1. 定义神经网络模型 ========== class APUFModel(nn.Module): def init(self, input_size): super(APUFModel, self).init() self.fc = nn.Sequential( nn.Linear(input_size, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 1), nn.Sigmoid() ) def forward(self, x): return self.fc(x) # ========== 2. 权重保存函数 ========== def save_weights_to_csv(weights, filename): """保存模型权重到CSV文件""" if isinstance(weights, torch.Tensor): weights = weights.detach().numpy() np.savetxt(filename, weights, delimiter=',') # ========== 3. 创建APUF实例 ========== n = 64 apuf = ArbiterPUF(n=n, noisiness=0, seed=1) # ========== 4. 生成CRP数据集 ========== num_crps = 100000 challenges = random_inputs(n=n, N=num_crps, seed=2) responses = apuf.eval(challenges) # 转换为PyTorch Tensor X = challenges.reshape(num_crps, n).astype(np.float32) y = ((responses + 1) // 2).astype(np.float32).reshape(-1, 1) # ========== 5. 划分数据集 ========== X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # 转换为PyTorch Dataset train_dataset = torch.utils.data.TensorDataset( torch.from_numpy(X_train), torch.from_numpy(y_train) ) test_dataset = torch.utils.data.TensorDataset( torch.from_numpy(X_test), torch.from_numpy(y_test) ) # ========== 6. 初始化模型和优化器 ========== model = APUFModel(input_size=n) criterion = nn.BCELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # ========== 7. 训练前保存初始权重 ========== initial_weights = model.fc[0].weight.data.clone() save_weights_to_csv(initial_weights, "initial_weights.csv") # ========== 8. 训练循环 ========== batch_size = 512 train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, shuffle=True ) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=batch_size ) best_accuracy = 0.0 early_stop = False for epoch in range(100): # 最大100个epoch if early_stop: break # 训练阶段 model.train() for inputs, labels in train_loader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 验证阶段 model.eval() correct = 0 total = 0 with torch.no_grad(): for inputs, labels in test_loader: outputs = model(inputs) predicted = (outputs > 0.5).float() total += labels.size(0) correct += (predicted == labels).sum().item() accuracy = correct / total print(f"Epoch {epoch+1}: 准确率 {accuracy:.2%}") # 准确率达标则停止 if accuracy > 0.90: print(f"达到90%准确率，提前终止训练！") early_stop = True # ========== 9. 训练后保存最终权重 ========== final_weights = model.fc[0].weight.data.clone() save_weights_to_csv(final_weights, "final_weights.csv") # ========== 10. 验证最终结果 ========== model.eval() with torch.no_grad(): test_outputs = model(torch.from_numpy(X_test)) test_predicted = (test_outputs > 0.5).float() final_accuracy = (test_predicted.numpy() == y_test).mean() print(f"\n最终测试准确率: {final_accuracy:.2%}") print("初始权重已保存至 initial_weights.csv") print("最终权重已保存至 final_weights.csv") 这些是使用pytorch来实现的，现在将pytorch替换为tensorflow来实现相同的功能

import numpy as np import tensorflow as tf from pypuf.simulation import ArbiterPUF from pypuf.io import random_inputs from sklearn.model_selection import train_test_split # ========== 1. 定义神经网络...

# coding: utf-8 import sys, os sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定 import numpy as np from dataset.mnist import load_mnist from PIL import Image def img_show(img): pil_img = Image.fromarray(np.uint8(img)) pil_img.show() (x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False) img = x_train[0] label = t_train[0] print(label) # 5 print(img.shape) # (784,) img = img.reshape(28, 28) # 把图像的形状变为原来的尺寸 print(img.shape) # (28, 28) 报错：urllib.error.HTTPError: HTTP Error 404: Not Found img_show(img)

这让我有点困惑，因为之前用类似的代码加载MNIST应该是没问题的。首先，我需要弄清楚为什么会出现这个错误。我记得有时候数据集可能需要从特定的URL下载...| 5 | 调整依赖版本 | pip install numpy==1.19.5 | ---

#coding=utf-8-sig import re import os import xlrd import xlwt import time import datetime import math #import pandas import sys import shutil #通用部分 def time_mc(number_t): localTime = time.strftime("%Y%m%d%H%M%S", time.localtime()) time_mc_name= localTime time.sleep(0.5) i_a=time_mc_name[0:4] i_b=time_mc_name[4:6] i_c=time_mc_name[6:8] i_d=time_mc_name[8:] i_d1=time_mc_name[8:10] i_d2=time_mc_name[10:12] i_d3=time_mc_name[12:] if number_t==0: return time_mc_name if number_t==1: i=i_a+"年"+i_b+"月"+i_c+"日"+i_d return i if number_t==2: i=i_a+"年"+i_b+"月"+i_c+"日"+i_d1+"时"+i_d2+"分"+i_d3+"秒" return i def mkdir1(path_mk): path=path_mk isExists=os.path.exists(path) if not isExists: os.makedirs(path) print(path+'----------创建成功') return True else: print(path+'----------目录已存在')

#coding=utf-8-sig import re import os import xlrd import xlwt import time import datetime import math import sys import shutil # 通用部分 def time_mc(number_t): localTime = time.strftime("%Y%m%d%H%M...

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帮我重写如下python代码，#coding=utf-8-sig import re import os import xlrd import xlwt import time import datetime import math #import pandas import sys import shutil #通用部分 def time_mc(number_t): localTime = time.strftime("%Y%m%d%H%M%S", time.localtime()) time_mc_n

# coding: utf-8 import sys, os from catdog.deep_convnet import DeepConvNet sys.path.append(os.pardir) # 为了导入父目录而进行的设定 import numpy as np import matplotlib.pyplot as plt from input_data import load_data

# -*- coding: UTF-8 -*- import numpy as np import queue import prettytable as pt def find_zero(num): tmp_x, tmp_y = np.where(num == 0) return tmp_x[0], tmp_y[0] def swap(num_data, direction): x, y = find_zero(num_data) num = np.copy(num

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