Cause-and-effect structure learning method based on flow characteristics
A learning method and causal technology, applied in the field of causal structure learning based on flow features, which can solve the problems of ineffective processing of continuous data, multiple independence tests, and time-consuming
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[0076] In this embodiment, the flow feature-based causal structure learning method for linear arbitrary distribution data is carried out as follows:
[0077] Step 1. Define the time t; and initialize t=0; define the limit value of the number of features as max; for recording the maximum value of the final number of features;
[0078] Step 2. Define the feature set as EF, and initialize the feature set at the tth moment as Used to record the currently selected feature set;
[0079] Step 3, define variable j; and initialize j=1;
[0080] Step 4. Determine whether j≤max is true, if true, randomly generate the jth feature X j , representing the newly generated features, the jth feature X j Has m values; and initializes the jth feature X j The Markov blanket MB(X j ) is empty, initialize the jth feature X j The newly added feature set FA(X j ) is empty, initialize the jth feature X j The redundant feature set FD(X j ) is empty; and execute step 5; if not established, end ...
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