max_sim_every_row = np.max(sim_matrix, axis=1)什么意思
时间: 2023-11-25 09:06:16 浏览: 29
这段代码的作用是计算相似度矩阵中每一行的最大值。具体来说,`sim_matrix`是一个相似度矩阵,每一行代表一个样本或者数据点与其他样本之间的相似度。通过使用 `np.max(sim_matrix, axis=1)`,我们可以找到每一行中的最大值,即该样本与其他样本中最相似的那个值。这将返回一个包含每行最大值的一维数组 `max_sim_every_row`。
相关问题
np.argmax(y_pre, axis=1是做什么
这是在一个预测结果的概率分布中,返回最大概率对应的类别标签。其中,y_pre是一个形状为(batch_size, num_classes)的矩阵,表示每个样本属于每个类别的概率分布。axis=1表示在每一行中找到最大值的索引,也就是最可能的类别标签。函数返回一个形状为(batch_size,)的一维数组,其中每个元素都是对应样本的最可能的类别标签。
np.argmax(bp_c, axis=1).squeeze()
As an AI language model, I do not have access to the context or the variables used in your code. However, I can explain the general functionality of "np.argmax(bp_c, axis=1).squeeze()" based on NumPy library.
"np.argmax" returns the indices of the maximum values along an axis. Here, it is applied to the "bp_c" array with "axis=1" which means it will return the index of the maximum value for each row.
".squeeze()" is used to remove single-dimensional entries from the shape of an array. In this case, it is used to remove any single-dimensional entries from the output of "np.argmax".
Therefore, "np.argmax(bp_c, axis=1).squeeze()" returns a 1D NumPy array containing the index of the maximum value for each row of "bp_c" array with any single-dimensional entries removed.