itamped. All this functionality is modeled by the feature diagram in Figure For all the tasks in this Assignment ve will use this tool with the features given in this diagram. Legend: Abstract Feature Concrete Feature Mandatory Optional Alternative Group Figure 1: The Feature Diagram of "SECompress" Performance Data The performance data, given in a csv file, contains different configurations of SECompress with performance measurements, in the same format you saw in the lecture (Slide 26). 1234567Id,secompress,encryption,aes,blowfish,algorithm,rar,zip,signature,timestamp,segmentation,onehundredmb,onegb,performance0,1,0,0,0,1,1,0,0,0,0,0,0,7501,1,0,0,0,1,1,0,0,0,1,1,0,7732,1,0,0,0,1,1,0,0,0,1,0,1,7703,1,0,0,0,1,1,0,0,1,0,0,0,7504,1,0,0,0,1,1,0,0,1,1,1,0,773 CART Data Structure For this assignment sheet we use the following internal datastructure for a CART. We represent a CART as a python dict with exactly the entries as shown in the example below. This example CART has three nodes. The root node "X", and the two child nodes "XL" and "XR". As you can see the child nodes only have a name and a mean but all other fields are set to None. A parent node also has a name and a mean but additionally a feature by which the split is performed, the error of the split and two sucessors. It is important to use exaclty these names for the features: features = ["secompress", "encryption", "aes", "blowfish", "algorithm", "rar", "zip", "signature", "timestamp", "segmentation", "onehundredmb", "onegb"] c) Given a CART and a partial configuration, compute a complete configuration which is in line with the partial configuration and has the least predicted costs. The partial configuration and the configuration are python sets. [4 Points ] 1 def get_optimal_configuration(cart, partial_configuration): 2 \# TODO: Write your code here. And change the return. 3 return \{"rar", "timestamp"\}