<s> import crosscat . utils . general_utils as gu <EOL> class EngineTemplate ( object ) : <EOL> def __init__ ( self , seed = None ) : <EOL> pass <EOL> def initialize ( self , M_c , M_r , T , seed , initialization = '<STR_LIT>' ) : <EOL> M_c , M_r , X_L , X_D = dict ( ) , dict ( ) , dict ( ) , [ ] <EOL> return X_L , X_D <EOL> def analyze ( self , M_c , T , X_L , X_D , seed , kernel_list = ( ) , n_steps = <NUM_LIT:1> , c = ( ) , <EOL> r = ( ) , <EOL> max_iterations = - <NUM_LIT:1> , max_time = - <NUM_LIT:1> , do_diagnostics = False , <EOL> diagnostics_every_N = <NUM_LIT:1> , <EOL> ROW_CRP_ALPHA_GRID = ( ) , COLUMN_CRP_ALPHA_GRID = ( ) , <EOL> S_GRID = ( ) , MU_GRID = ( ) , <EOL> N_GRID = <NUM_LIT> , <EOL> ) : <EOL> X_L_prime , X_D_prime = dict ( ) , [ ] <EOL> return X_L_prime , X_D_prime <EOL> def simple_predictive_sample ( self , M_c , X_L , X_D , Y , Q , seed , n = <NUM_LIT:1> ) : <EOL> samples = [ ] <EOL> return samples <EOL> def simple_predictive_probability ( self , M_c , X_L , X_D , Y , Q , n ) : <EOL> p = None <EOL> return p <EOL> def simple_predictive_probability_multistate ( self , M_c , X_L_list , X_D_list , Y , Q , n ) : <EOL> p = None <EOL> return p <EOL> def mutual_information ( self , M_c , X_L_list , X_D_list , Q , seed , <EOL> n_samples = <NUM_LIT:1000> ) : <EOL> return None <EOL> def row_structural_typicality ( self , X_L_list , X_D_list , row_id ) : <EOL> return None <EOL> def column_structural_typicality ( self , X_L_list , col_id ) : <EOL> return None <EOL> def predictive_probability ( self , M_c , X_L , X_D , T , Q , n = <NUM_LIT:1> ) : <EOL> return None <EOL> def predictive_probability_multistate ( self , M_c , X_L_list , X_D_list , T , Q , n = <NUM_LIT:1> ) : <EOL> return None <EOL> def similarity ( self , M_c , X_L_list , X_D_list , given_row_id , target_row_id , target_columns = None ) : <EOL> return None <EOL> def impute ( self , M_c , X_L , X_D , Y , Q , seed , n ) : <EOL> e = None <EOL> return e <EOL> def impute_and_confidence ( self , M_c , X_L , X_D , Y , Q , seed , n ) : <EOL> e , confidence = None , None <EOL> return e , confidence <EOL> def conditional_entropy ( M_c , X_L , X_D , d_given , d_target , <EOL> n = None , max_time = None ) : <EOL> e = None <EOL> return e <EOL> def predictively_related ( self , M_c , X_L , X_D , d , <EOL> n = None , max_time = None ) : <EOL> m = [ ] <EOL> return m <EOL> def contextual_structural_similarity ( self , X_D , r , d ) : <EOL> s = [ ] <EOL> return s <EOL> def structural_similarity ( self , X_D , r ) : <EOL> s = [ ] <EOL> return s <EOL> def structural_anomalousness_columns ( self , X_D ) : <EOL> a = [ ] <EOL> return a <EOL> def structural_anomalousness_rows ( self , X_D ) : <EOL> a = [ ] <EOL> return a <EOL> def predictive_anomalousness ( self , M_c , X_L , X_D , T , q , n ) : <EOL> a = [ ] <EOL> return a </s>
