def __main__():
matrix = [[0.7, 0.9, 0.4, 0.6, 1], [0.6, 0.9, 0.3, 0.7, 1],
[0.6, 0.8, 0.3, 0.5, 1], [0.3, 0.5, 0.7, 0.2, 1],
[0.3, 0.4, 0.8, 0.3, 1], [0.4, 0.5, 0.6, 0.3, 1],
[0.9, 0.4, 0.5, 0.9, 0], [0.8, 0.5, 0.4, 0.8, 0],
[0.2, 0.6, 0.7, 1.0, 0], [0.1, 0.7, 0.8, 0.8, 0]]
instance_selection_obj = InstanceSelection(matrix)
instance_selection_obj.apply()
feature_selection_obj = FeatureSelection(
instance_selection_obj.representative_instances_list[0])
feature_selection_obj.apply(instance_selection_obj)
print(instance_selection_obj.representative_instances_list)
print(feature_selection_obj.rep_feature_set)
for size in range(MAX_ITERATIONS-1,MAX_ITERATIONS):
np.random.shuffle(data)
test_data = data[:size]
#Representative Instance Selection
start_time1 = time.time()
InstanceSelector = InstanceSelection(test_data)
InstanceSelector.apply()
end_time1 = time.time()
algo1_time = end_time1-start_time1
#Feature Selection
start_time2 = time.time()
feature_selection_obj = FeatureSelection(InstanceSelector.representative_instances_list[0])
feature_selection_obj.apply(InstanceSelector)
end_time2 = time.time()
algo2_time = end_time2-start_time2
# print("Algo 1 time : ",algo1_time)
# print("Algo 2 time : ",algo2_time)
representative_instances = InstanceSelector.representative_instances_list
# print("Instance set : ",representative_instances)
feature_set = list(feature_selection_obj.rep_feature_set)
# print("Feature set : ",feature_set)
# time_taken[size] = end_time-start_time
# print(len(InstanceSelector.representative_instances_list[0]))
print("{:27s} |{:8s}|{:8s}|{:8s}|{:8s}".format("Model","Accuracy","Precision","Recall","F1-score"))
print("-"*65)
for model in models: