def crab_main(vectorized_train_data, vectorized_test_data):
crab_mat = parsers.crab_costum_matrix(vectorized_train_data)
model = MatrixPreferenceDataModel(crab_mat)
similarity = UserSimilarity(model, pearson_correlation)
recommender = UserBasedRecommender(model, similarity, with_preference=True)
result = []
for item in vectorized_test_data:
result.append(recommender.estimate_preference(item[0], item[1]))
return np.array(result)
