`max` 函数的参数是一个可迭代对象,其中的元素是元组,通过比较元组中的第一个元素(`val`,即隶属度值)来找到最大值。 5. 整体表达式 `max((val, idx) for (idx, val) in enumerate(membership_mat[i]))` 返回的是具有最大隶属度值的元组 `(max_val, idx)`,其中 `max_val` 是最大的隶属度值,`i...
The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The ...
( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
#find all instances of "baz" and also the first two elements after "baz" for idx, item in enumerate(seq): if item == "baz": print(item) print(seq[idx+1]) print(seq[idx+2]) 请注意,与 不同readline,索引不会推进迭代器,因此for idx, item in enumerate(seq):仍会迭代“qux”和“tro...
""" ] result = text_analytics_client.analyze_sentiment(documents, show_opinion_mining=True) docs = [doc for doc in result if not doc.is_error] print("Let's visualize the sentiment of each of these documents") for idx, doc in enumerate(docs): print(f"Document text: {documents[id...
程序停在了 for step,data in enumerate(loader),下面是部分bug信息 Traceback (most recent call last): ... File ".../torch/utils/data/dataloader.py", line 206, in __next__ idx, batch = self.data_queue.get() File "/usr/lib/python2.7/multiprocessing/queues.py", line 378, in get ret...
我创建了一个网格,然后使用countourf方法绘制了网格,现在我用以下代码在网格中加载数据: for idx, cl in enumerate (np.unique(y)): plt.scatter (x=X[y == cl, 0], y= X[y == cl, 1], alpha=0.8, c=colors[idx], marker= markers [idx], label = cl, edgecolor = 'black') alpha, ...
foridx, speciesinenumerate(dataset.target_names): X, y = dataset.data, dataset.target clf.fit(X, y == idx) rules = clf.rules_[0:3] print("Rules for iris", species) forruleinrules: print(rule) print print(20*'=') print
best_values = [] for fold, (trn_idx, val_idx) in enumerate(folds): if only_folds and fold not in only_folds: # not empty continue # skip if not empty and not in logger.info(f'training fold {fold} with only_fold setting: {only_folds}') logger.info('current split is: len= {...
RV的数据聚合主要体现在MOC当中,即Map Of Content,是通过dataview插件来实现的,其基本原理,就是通过对特定目标的markdown文件集进行扫描,设置相应的过滤条件,最终以列表的形式展现数据。这里提到的markdown文件,就是OB最为基本的文件,实际上是带着一定标签的纯文本文件,因此,文件会有自己的一些属性,比如大小,创建时间...