dir_loc = os.path.split(split[0])[1]

帮我给每一行代码添加注释 class DeepKalmanFilter(nn.Module): def init(self, config): super(DeepKalmanFilter, self).init() self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) self.transition = Transition(config.z_dim, config.trans_hidden_dim) self.posterior = Posterior( config.z_dim, config.post_hidden_dim, config.obs_dim ) self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) self.config = config @staticmethod def reparametrization(mu, sig): return mu + torch.randn_like(sig) * sig @staticmethod def kl_div(mu0, sig0, mu1, sig1): return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) def loss(self, obs): time_step = obs.size(1) batch_size = obs.size(0) overshoot_len = self.config.overshooting kl = torch.Tensor([0]).to(self.config.device) reconstruction = torch.Tensor([0]).to(self.config.device) emit_sig = self.emit_log_sigma.exp() for s in range(self.config.sampling_num): z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) for t in range(time_step): trans_loc, trans_sig = self.transition(z_q_t) post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) z_q_t = self.reparametrization(post_loc, post_sig) emit_loc = self.emitter(z_q_t) reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig + self.emit_log_sigma * batch_size / 2).sum() if t > 0: over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) else: over_loc = trans_loc.unsqueeze(0) over_sig = trans_sig.unsqueeze(0) overshooting = self.reparametrization(over_loc, over_sig) kl = kl + self.kl_div(post_loc.expand_as(over_loc), post_sig.expand_as(over_sig), over_loc, over_sig) / min(t + 1, self.config.overshooting) reconstruction = reconstruction / self.config.sampling_num kl = kl / self.config.sampling_num return reconstruction, kl

over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) # 转换器的位置 over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) # 转换器的协方差 else: # 如果是第一步 over_loc = ...

下面pytorch代码转化为paddlepaddle2.2.2代码： import torch import torch.distributions as tdist def random_noise_levels(): """Generates random noise levels from a log-log linear distribution.""" log_min_shot_noise = np.log(0.0001) log_max_shot_noise = np.log(0.012) log_shot_noise = torch.FloatTensor(1).uniform_(log_min_shot_noise, log_max_shot_noise) shot_noise = torch.exp(log_shot_noise) line = lambda x: 2.18 * x + 1.20 n = tdist.Normal(loc=torch.tensor([0.0]), scale=torch.tensor([0.26])) log_read_noise = line(log_shot_noise) + n.sample() read_noise = torch.exp(log_read_noise) return shot_noise, read_noise def add_noise(image, shot_noise=0.01, read_noise=0.0005): """Adds random shot (proportional to image) and read (independent) noise.""" image = image.permute(1, 2, 0) # Permute the image tensor to HxWxC format from CxHxW format variance = image * shot_noise + read_noise n = tdist.Normal(loc=torch.zeros_like(variance), scale=torch.sqrt(variance)) noise = n.sample() out = image + noise out = out.permute(2, 0, 1) # Re-Permute the tensor back to CxHxW format return out

n = pdist.Normal(loc=paddle.zeros([1]), scale=paddle.ones([1]) * 0.26) log_read_noise = line(log_shot_noise) + n.sample() read_noise = paddle.exp(log_read_noise) return shot_noise, read_noise def...

若X = data.loc[:, data.columns != 'day_28_flg'] X_imputed = imputer.fit_transform(X)，我该怎么改X_imputed = imputer.fit_transform(X) X_imputed = pd.get_dummies(X_imputed)

X = data.loc[:, data.columns != 'day_28_flg'] X_imputed = imputer.fit_transform(X) X_imputed = pd.DataFrame(X_imputed, columns=X.columns) # 将numpy数组转换为DataFrame，并设置列名 X_imputed = pd.get_...

逐行解释这段代码 column = list(average.columns) data = average.loc[:, column[0]:column[-3]] # 自变量 target = average.loc[:, ['TIMEsurvival', 'EVENTdeath']] for i in range(1, 101): X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=i) # feature = Spearman(X_train, 0.85) #spearman第一行 # feature = list(feature['feature']) #spearman第二行 # X_train = X_train.loc[:, feature] #spearman第三行 train_index = X_train.index train_column = X_train.columns zscore_scaler = preprocessing.StandardScaler() X_train = zscore_scaler.fit_transform(X_train) X_train = pd.DataFrame(X_train, index=train_index, columns=train_column) # X_test = X_test.loc[:, feature] #spearman第四行 test_index = X_test.index test_column = X_test.columns X_test = zscore_scaler.transform(X_test) X_test = pd.DataFrame(X_test, index=test_index, columns=test_column) train = pd.concat([X_train, y_train], axis=1)

2. data = average.loc[:, column[0]:column[-3]]：从 average 数据中选取所有行和 column[0] 到 column[-3] 列的数据，赋值给 data。这里的 column[-3] 表示从最后一列开始往前数第三列。 3. target ...

res6 = pd.DataFrame() for col in data_t.columns: # 求取最大值和最小值 max_value = data_t[col].max() min_value = data_t[col].min() # 找到最大值和最小值所在的月份 max_month = data_t[col][data_t[col] == max_value].index.month min_month = data_t[col][data_t[col] == min_value].index.month # 统计每个月份出现的次数 max_count = np.bincount(max_month) min_count = np.bincount(min_month) # 找到出现次数最多的月份 max_idx = np.argmax(max_count) min_idx = np.argmax(min_count) # 将结果存入res6中 res6.loc[col, '最大值所在月份'] = max_idx res6.loc[col, '最小值所在月份'] = min_idx对代码调整实现正常运行

res6.loc[col, '最大值所在月份'] = max_idx res6.loc[col, '最小值所在月份'] = min_idx 主要的修改包括： - max_month 和 min_month 的计算方式从 data_t[col][data_t[col] == max_value].index....

for train_index, test_index in split.split(data, data["label"]): strat_train_set = data.loc[train_index] strat_test_set = data.loc中strat_test_set = data.loc是什么意思

？回答：strat_test_set = data.loc 是一行代码，它的作用是基于索引位置从数据集中选择测试数据集。通过.loc方法，我们可以根据布尔索引选择行和列。在这个例子中，它是选择测试数据集的行。

详细解释一下这段代码，每一句都要进行注解：for _, (tr_idx, te_idx) in enumerate(tqdm(groups, total=5, desc="Folds")): tr_idx = pd.Series(tr_idx).sample(n=2000000,random_state=42).values multioutput_regressor = LGBMMultiOutputRegressor(lgb.LGBMRegressor(**best_params_)) x_train = train.loc[tr_idx, cols].to_numpy() y_train = train.loc[tr_idx, pcols].to_numpy() x_test = train.loc[te_idx, cols].to_numpy() y_test = train.loc[te_idx, pcols].to_numpy() multioutput_regressor.fit( x_train, y_train, eval_set=(x_test, y_test), eval_metric=custom_average_precision, early_stopping_rounds=15, verbose = 0, ) regs.append(multioutput_regressor) cv = metrics.average_precision_score(y_test, multioutput_regressor.predict(x_test).clip(0.0,1.0)) cvs.append(cv) print(cvs) print(np.mean(cvs))

eval_metric指定了评估指标为自定义的平均精度（custom_average_precision），early_stopping_rounds表示在15轮内没有提升则停止训练，verbose=0表示不输出训练过程中的日志信息。 python regs.append...

请详细解释一下这段代码，每一句需要注解：for , (tr_idx, te_idx) in enumerate(tqdm(groups, total=5, desc="Folds")): tr_idx = pd.Series(tr_idx).sample(n=2000000,random_state=42).values multioutput_regressor = LGBMMultiOutputRegressor(lgb.LGBMRegressor(**best_params)) x_train = train.loc[tr_idx, cols].to_numpy() y_train = train.loc[tr_idx, pcols].to_numpy() x_test = train.loc[te_idx, cols].to_numpy() y_test = train.loc[te_idx, pcols].to_numpy() multioutput_regressor.fit( x_train, y_train, eval_set=(x_test, y_test), eval_metric=custom_average_precision, early_stopping_rounds=15, verbose = 0, ) regs.append(multioutput_regressor) cv = metrics.average_precision_score(y_test, multioutput_regressor.predict(x_test).clip(0.0,1.0)) cvs.append(cv) print(cvs) print(np.mean(cvs))

这段代码用于进行交叉验证，其中每一句的作用...clip 方法用于将预测结果限制在 0 和 1 之间，避免出现无效结果。 python print(cvs) print(np.mean(cvs)) 输出所有折叠的平均精度，并输出平均精度的平均值。

import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import SVDRecommender triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) K=50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] recommender = SVDRecommender(K) U, S, Vt = recommender.fit(urm) Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = recommender.recommend(uTest, urm, 10) Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)这段代码报错了，为什么？给出修改后的代码

song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] recommendations = recommendations.append({'user': user, 'song': song['song'], 'score': song['fractional_play_count'], 'rank':...

代码：import os import pandas as pd print("开始计算一口价") folder_path = "D:/一口价生成" file_name = "活动.xlsx" new_file_name = "一口价及升价.xlsx" activity_file_path = os.path.join(folder_path, file_name) activity_df = pd.read_excel(activity_file_path) discounts = {"6.9折": 0.69, "5折": 0.5, "3.9折": 0.39} new_price_df = pd.DataFrame(columns=["商品编码", "价格"]) for index, row in activity_df.iterrows(): if row["活动"] in discounts: standard_price = row["标准售价"] discount = discounts.get(row["活动"], 1) new_price = round(standard_price * discount, 2) product_code = str(row["商品编码"]).zfill(7) new_price_df.loc[index] = [product_code, new_price] else: product_code = str(row["商品编码"]).zfill(7) try: new_price = int(row["活动"]) except ValueError: # 处理折扣率类型 if "折" in row["活动"]: discount = float(row["活动"].strip("折")) / 10 new_price = round(row["标准售价"] * discount, 2) else: new_price = float(row["活动"]) new_price_df.loc[index] = [product_code, new_price] new_file_path = os.path.join(folder_path, new_file_name) new_price_df.to_excel(new_file_path, index=False) print("已生成") 无法将 "活动" 列中的int类型和float类型的数值输出到“一口价”的表格上，请修改代码，需要“活动”列中的int和float类型的值输出。

可以尝试将代码中的以下部分： try: new_price = int(row["活动"]) except ValueError: if "折" in row["活动"]: discount = float(row["活动"].strip("折")) / 10 new_price = round(row["标准售价"] * ...

优化代码 def module_split(self, save_on=True): """ split module data :param save_on: :return: """ for ms in range(self.mod_num): m_sn = self.module_list[ms] module_path = os.path.join(self.result_path_down, m_sn) cols_obj = ChuNengPackMustCols(ms, self.mod_cell_num, self.mod_cell_num) # 传入当前的module序号（如0,1,2,3,4），电芯电压个数，温度NTC个数。 aim_cols = [i for i in cols_obj.total_cols if i in self.df.columns] print(m_sn, aim_cols) self.modules[m_sn] = rename_cols_normal(self.df.loc[:, aim_cols], ms, self.mod_cell_num) print("after change cols name:", ms, m_sn, self.modules[m_sn].columns.tolist()) self.modules[m_sn].dropna(axis=0, how='any', subset=['soc'], inplace=True) volt_col = [f'volt{i}' for i in range(self.mod_cell_num)] temp_col = [f'temp{i}' for i in range(self.mod_cell_num)] self.modules[m_sn].dropna(axis=0, how='any', subset=volt_col, inplace=True) self.modules[m_sn] = stat(self.modules[m_sn], volt_col, temp_col) self.modules[m_sn].reset_index(drop=True, inplace=True) print(self.modules[m_sn]['discharge_ah'].iloc[-1]) self.module_cap[m_sn] = [self.modules[m_sn]['discharge_ah'].iloc[-1], self.modules[m_sn]['charge_ah'].iloc[-1], self.modules[m_sn]['soh'].iloc[-1]] self.module_peaks[m_sn] = list(quick_report(self.modules[m_sn], module_path, f'quick_report_{m_sn[:8]}')) # check soc status mod_soc = self.modules[m_sn]['soc'] self.module_soc_sig[m_sn] = [np.nanmedian(mod_soc), np.max(mod_soc), np.min(mod_soc)] if save_on: single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_box.png', 'box', 'SOC') single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_violin.png', 'violin', 'SOC')

module_path = os.path.join(self.result_path_down, m_sn) cols_obj = ChuNengPackMustCols(ms, self.mod_cell_num, self.mod_cell_num) aim_cols = [i for i in cols_obj.total_cols if i in self.df.columns] ...

etg, netd = NetG(opt), NetD(opt) map_location = lambda storage, loc: storage if opt.netd_path: print(opt.netd_path) netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) if opt.netg_path: netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) netd.to(device) netg.to(device) # 定义优化器和损失 optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999)) criterion = t.nn.BCELoss().to(device) # 真图片label为1，假图片label为0 # noises为生成网络的输入 true_labels = t.ones(opt.batch_size).to(device) fake_labels = t.zeros(opt.batch_size).to(device) fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() epochs = range(opt.max_epoch)的含义

2. 如果opt.netd_path和opt.netg_path不为空，则加载之前训练好的网络参数。 3. 定义Adam优化器和二元交叉熵损失函数，并将它们移动到GPU或CPU上。 4. 定义真实和虚假图片的标签，以及用于生成网络的输入噪声。 5...

import pandas as pd from sklearn.model_selection import GroupShuffleSplit df = pd.read_csv('horse_race_data.csv') gss = GroupShuffleSplit(test_size=.40, n_splits=1, \ random_state=7).split(df, groups=df['id']) # 生成训练集和验证集的索引 X_train_inds, X_test_inds = next(gss) train_data= df.iloc[X_train_inds] X_train = train_data.loc[:, ~train_data.columns.isin(['id','rank'])] y_train = train_data.loc[:, train_data.columns.isin(['rank'])] test_data= df.iloc[X_test_inds] X_test = test_data.loc[:, ~test_data.columns.isin(['rank'])] y_test = test_data.loc[:, test_data.columns.isin(['rank'])]

然后，使用GroupShuffleSplit函数将数据集按照指定的组进行划分，其中test_size参数设置为0.40，表示将40%的数据划分为验证集，n_splits参数设置为1，表示只进行一次划分，random_state参数设置为7，用于生成随机数...

strat_shuff_split = StratifiedShuffleSplit(n_splits=1, test_size=2504, random_state=42) # Get the index values from the generator train_idx, test_idx = next(strat_shuff_split.split(df[feature_cols], df['Attrition_Flag'])) # Create the data sets x_train = df.loc[train_idx, feature_cols] y_train = df.loc[train_idx, 'Attrition_Flag'] x_test = df.loc[test_idx, feature_cols] y_test = df.loc[test_idx, 'Attrition_Flag'] x_train 这段代码是要做什么

这段代码是为了将数据集进行分割和划分成训练集和测试集，其中： - StratifiedShuffleSplit 是一个用于分层随机分割数据集的类，它可以将数据集随机地分成训练集和测试集，保证在分割后各个类别的样本比例与原始...

dir_loc = os.path.split(split[0])[1]

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若X = data.loc[:, data.columns != 'day_28_flg'] X_imputed = imputer.fit_transform(X)，我该怎么改X_imputed = imputer.fit_transform(X) X_imputed = pd.get_dummies(X_imputed)

for train_index, test_index in split.split(data, data["label"]): strat_train_set = data.loc[train_index] strat_test_set = data.loc中strat_test_set = data.loc是什么意思

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