Falko
2014-10-10 00:02:23
你要比较两个信号，例如A和B在下面的示例中：
import numpy as np
A = np.random.rand(5)
B = np.random.rand(5)
print "A:", A
print "B:", B
输出：
A: [ 0.66926369 0.63547359 0.5294013 0.65333154 0.63912645]
B: [ 0.17207719 0.26638423 0.55176735 0.05251388 0.90012135]
分析单个信号
每个信号的标准偏差都不是你所需要的：
print "standard deviation of A:", np.std(A)
print "standard deviation of B:", np.std(B)
输出：
standard deviation of A: 0.0494162021651
standard deviation of B: 0.304319034639
分析差异
取而代之的是，你可以计算差值并应用一些通用的度量，例如绝对差之和（SAD），平方差之和（SSD）或相关系数：
print "difference:", A - B
print "SAD:", np.sum(np.abs(A - B))
print "SSD:", np.sum(np.square(A - B))
print "correlation:", np.corrcoef(np.array((A, B)))[0, 1]
输出：
difference: [ 0.4971865 0.36908937 -0.02236605 0.60081766 -0.2609949 ]
SAD: 1.75045448355
SSD: 0.813021824351
correlation: -0.38247081
热门帖子
热门github
1
2
3
5
6
7
8
11
13
14
15
Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)