single_component_data_path = test_data_dir + "/data/test/single_component_complete_track.txt" double_component_data_path = test_data_dir + "/data/test/double_component_complete_track.txt" single_component_incom_data_path = test_data_dir + "/data/test/single_component_incomplete_track.txt" double_component_incom_data_path = test_data_dir + "/data/test/double_component_incomplete_track.txt" from solver.vanilla_MLC import RunVanillaMLC from solver.predict_performance import get_predict_performance import numpy as np test_instance = RunVanillaMLC() """ single component test """ # initialize test_instance.init(1) test_instance.load_data(single_component_data_path) # solve res = test_instance.solve() # print result print([0.2, 0.4, 0.6, 0.8, 0.9]) # truth print(res["q"]) # estimated # compare the performance print(get_predict_performance(test_instance.response_data, res["q"], res["p"])) """ double component test """ # initialize
single_component_data_path = test_data_dir + '/data/test/single_component_complete_track.txt' double_component_data_path = test_data_dir + '/data/test/double_component_complete_track.txt' from solver.vanilla_MLC import RunVanillaMLC test_instance = RunVanillaMLC() ''' single component test ''' # initialize test_instance.load_param(1) test_instance.load_data(single_component_data_path) # initialize test_instance.init() # solve test_instance.solve_EM() # print result print([0.2,0.4,0.6,0.8,0.9]) # truth print(test_instance.learning_curve_matrix[0]) # estimated ''' double component test ''' # initialize test_instance.load_param(2) test_instance.load_data(double_component_data_path) # initialize test_instance.init() # solve
