在做2維度樣本分類的過程中,若我們能事先畫出訓練樣本在空間中的分散情形,這將有助於我們在設定SVM分類器的參數C的取值範圍. 例如:若畫出的訓練樣本的散佈較分散,我們可以得知此時採用的參數值可以取在較大的範圍. 所以本程式也是讓想要畫出資料樣本在平面的散佈情形者之一各可行工具.-2 dimensions in a sample to do the classification process, if we were able to pre-draw training samples dispersed in space situation, this will help us in setting the parameters of SVM classifier C range. For example: If the draw training samples to spread more decentralized, we can know at this time using the parameter values can be chosen in a larger scope. So let this program is also want to draw information on the spread of samples in the planar case, one of the possible tools.