The tree is binary, each internal node represent a segmentation choice. For exemple a DistanceNode? would discriminate data according to a distance threshold. At the moment the internal node type used are |
The tree is binary, each internal node represent a segmentation choice. For exemple a DistanceNode? would discriminate data according to a distance threshold. At the moment the internal node types used are |
*keeping approximately the same amount of data in each branch. It picks the median value for each segmentation axis, and corrects it to the closest standardised value. The standardised value allow the tree to be saved while maintaining a small file size (less than 600ko for a 35 rounds game even with long rounds). *the information gain of all possible internal node type. |
*keeping approximately the same amount of data in each branch. It picks the median value for each segmentation axis, and corrects it to the closest standardised value. The standardised value allow the tree to be saved while maintaining a small file size (less than 0.6ko for a 35 rounds game even with long rounds). *the information gain of all possible internal node type. See Segmentation/Prioritizing |
Two threads are used for the tree, one for inserting new observations and splitting leaves on the fly, another one is used for rebuilding the tree. |