## 4 Results and Analysis
Table 3 is the definition of the randomness and a completely independent confusion matrix, where the initialisms correspond to the following:  
True positive (_TP_) refers to the correct number of positive samples for classification, which are predicted to be positive samples and are actually positive samples. False positive (_FP_) refers to the number of negative samples mistakenly marked as positive samples; that is, the actual number of negative samples predicted to be positive samples, so they are false. True negative (_TN_) refers to the correct number of negative samples for classification, which are predicted to be negative samples and are actually negative samples. False negative (_FN_) refers to the number of positive samples mistakenly marked as negative samples; that is, the actual number of positive samples predicted to be negative samples, so they are false. _TP + FP + TN + FN_: total number of samples. _TP + FN = P_: the actual number of positive samples. _FP + TN = N_: actual negative sample count. _TP + FP_: the total number of positive samples predicted, including correct and incorrect predictions. _TN + FN_: the total number of negative samples predicted, including correct and incorrect predictions. The indicators below are often used to evaluate the classification performance of classifiers:  
* \(\hat{\mathrm{Y}}\) Precision: the ratio of the number of samples correctly identified as \(P\) to the total number of samples identified as \(P\). In terms of the determined results, this parameter can serve as a basis for determining classification accuracy, reflecting the ability of the neural network to \"find the right\" positive samples. \[P=\frac{TP}{TP+FP} \tag{16}\]
* \(\hat{\mathrm{Y}}\) Recall: the ratio of the number of samples correctly classified as \(P\) to the total number of real _P_-class samples. In terms of real samples, this parameter can determine the comprehensiveness of neural networks in sample classification. \[R=\frac{TP}{TP+FN} \tag{17}\]
* \(\hat{\mathrm{Y}}\) Specificity: the proportion of samples classified to be correct among all negative samples, which measures the neural network's ability to recognize negative samples. \[S=\frac{TN}{FP+TN} \tag{18}\]  
The following Table 4 is the performance of three indicators of each category on the test dataset. It can be seen that the trained neural network also performs well on the new data set. The classification accuracy can reach 87.2%, which verifies the feasibility of our idea. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Result**} & \multicolumn{2}{c}{**Predicted Result**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline Positive & _TP_ & _FN_ \\ Negative & _FP_ & _TN_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of classification results. Following the above experimental process, the RWP and RPGM models were subjected to the same experiment, and their performance on their respective test datasets was as follows:  
From Figure 5, it can be seen that the overall network performance of RPGM is relatively good. Therefore, in the aforementioned motion scenario, the network performance labels are set to 3 categories: 0, 1, and 2. According to Figure 9, the trained neural network for RWP has the lowest accuracy, approximately 84%, with greater randomness and a completely independent node motion pattern. From Figure 10, it can be seen that even if the number of network classifications (label types) is changed, good classification performance can still be maintained. The classification accuracy of RPGM for the test dataset is 87.407%, further verifying the reliability of network performance classification based on our proposed five indicators for describing motion performance.