, compute_r_squared, print_model, get_highest_pair, forward_selection_r_squared, \ output_sel, select_best_k, test_foward_select # useful defined constants for the city data COMPLAINT_COLS = range(0, 7) CRIME_TOTAL_COL = 7 file = "city" variables, data = read_file("data/{}/training.csv".format(file)) if __name__ == "__main__": #Task 1a print(file, "Task", "1A") print(r_squared_table(variables, data, CRIME_TOTAL_COL) ) print() #\n #Task 1b print(file, "Task", "1B") model_1b = Model(variables, data, CRIME_TOTAL_COL, list(COMPLAINT_COLS) ) r2 = compute_r_squared(model_1b.beta, model_1b.pre_col, model_1b.dep_col) print(print_model (model_1b.pre_var, r2) ) print()#\n #Task 2 print(file, "Task", "2") print(get_highest_pair(variables, data, list(COMPLAINT_COLS), CRIME_TOTAL_COL) ) print() #\n #Task 3a
select_best_k, test_foward_select, ) # useful defined constants for the stock data STOCKS = range(0, 11) DJIA = 11 file = "stock" variables, data = read_file("data/{}/training.csv".format(file)) if __name__ == "__main__": # Task 1a print(file, "Task", "1A") print(r_squared_table(variables, data, STOCKS)) print() # \n # Task 1b print(file, "Task", "1B") model_1b = Model(variables, data, STOCKS, list(COMPLAINT_COLS)) r2 = compute_r_squared(model_1b.beta, model_1b.pre_col, model_1b.dep_col) print(print_model(model_1b.pre_var, r2)) print() # \n # Task 2 print(file, "Task", "2") print(get_highest_pair(variables, data, list(COMPLAINT_COLS), STOCKS)) print() # \n # Task 3a