在matlab中用wasser

在MATLAB中使用Wasserstein距离，你需要使用`wasserstein`函数。这个函数可以计算两个概率分布之间的Wasserstein距离，也称为Earth Mover's Distance。它衡量了将一个分布转化为另一个分布所需的最小成本。

下面是一个使用`wasserstein`函数计算Wasserstein距离的例子：

% 定义两个概率分布
P = [0.2, 0.3, 0.5];
Q = [0.1, 0.4, 0.5];

% 计算Wasserstein距离
distance = wasserstein(P, Q);
disp(distance);

在这个例子中，我们定义了两个概率分布P和Q，并使用`wasserstein`函数计算它们之间的Wasserstein距离。计算结果将会被打印出来。

请注意，使用`wasserstein`函数需要先安装Optimization Toolbox。如果你没有安装这个工具箱，可以在MATLAB命令窗口中运行以下命令来安装：

install_toolbox optimization

希望这个例子能够帮助你在MATLAB中使用Wasserstein距离！如果有任何其他问题，请随时询问。

相关问题

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

这段文字讨论了异质网络方法相对于同质网络方法更接近现实的原因，并介绍了知识图谱和知识图谱嵌入模型在链接预测任务中的应用。作者提出了一些方法来解决多信息融合和归纳能力限制等问题，包括使用药物的局部信息如化学结构，以及利用多种信息来提高药物相互作用识别的性能。这段文字主要是关于药物相互作用预测领域的研究。

latex数学公式分段

在LaTeX中，可以使用不同的环境来实现数学公式的分段。其中常用的包括align、align*和split等。引用中提到的gather和gather*环境也可以用于分段公式。

使用align环境可以在多个公式之间对齐，并且每个公式都会单独编号。例如：
\begin{align}
\cos 2x &= \cos^2x-\sin^2x \\
= 2\cos^2x-1
\end{align}

使用align*环境则可以实现多行公式的对齐，但不会为每个公式自动编号。例如：
\begin{align*}
\cos 2x &= \cos^2x-\sin^2x \\
&= 2\cos^2x-1
\end{align*}

另外，还可以使用split环境在一个公式中分段，这样可以避免公式编号。例如：
\begin{equation}
\begin{split}
\cos 2x &= \cos^2x-\sin^2x \\
&= 2\cos^2x-1
\end{split}
\end{equation}

需要注意的是，以上环境需要在导言区引入相应的宏包，例如amsmath或amssymb。可以参考引用中提到的amsmath与amssymb宏包来使用这些环<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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