label_count[label] = 0
时间: 2024-01-03 09:02:51 浏览: 18
这段代码应该是在将一个类别`label`添加到`label_count`字典中,并将该类别对应的值初始化为0。`label_count`字典用于统计数据集中不同类别的样本数量,每个类别对应一个键,键对应的值为该类别在数据集中出现的次数。`label`是一个字符串类型的变量,代表一个样本的类别。
如果`label`不在`label_count`字典的键中,说明该类别在数据集中还没有出现过,需要将该类别添加到`label_count`字典中并初始化为0。`label_count[label] = 0`可以将键为`label`的键值对添加到`label_count`字典中,并将该键对应的值初始化为0。如果`label`已经在`label_count`字典的键中出现过,就不需要做任何操作。
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bins = np.array([0, 0.5, 1, 2, 10]) # 股价上涨的公司 shares_up = shares_price[shares_price['increase'] > 0] # 按涨幅进行分组 shares_up['label'] = pd.cut(shares_up['increase'], bins) # 分组统计 up_label_count = shares_up[['label', 'code']].groupby('label').count() up_label_count['占比'] = up_label_count['code'] / up_label_count.sum().values sns.barplot(x=up_label_count['占比'], y=up_label_count.index)修改代码,使配色好看
bins = np.array([0, 0.5, 1, 2, 10])
shares_up = shares_price[shares_price['increase'] > 0]
shares_up['label'] = pd.cut(shares_up['increase'], bins)
up_label_count = shares_up[['label', 'code']].groupby('label').count()
up_label_count['占比'] = up_label_count['code'] / up_label_count.sum().values
plt.figure(figsize=(8, 6))
sns.set(style="whitegrid")
sns.barplot(x=up_label_count['占比'], y=up_label_count.index, palette="Blues_d")
plt.xlabel("占比", size=12)
plt.ylabel("涨幅", size=12)
plt.title("股价上涨的公司涨幅分布", size=14)
torch.argmax(label_count, dim=1)
This code will return the index of the maximum value in each row of the tensor "label_count".
For example, if "label_count" is a tensor of shape (3, 5), where each row represents the count of a label in a dataset of 5 classes, the code will return a tensor of shape (3,) containing the index of the most frequent label in each row.
Note that "dim=1" specifies that the operation is performed along the second axis (columns) of the tensor.