def testIrisReplacement(trainfile, testfile, attrfile): iris_train = pd.read_csv(trainfile, header=None) iris_test = pd.read_csv(testfile, header=None) iris_attr = pd.read_csv(attrfile, header=None) input, output, input_test, output_test = digitalize(iris_train, iris_test, iris_attr,"iris") input = input.astype(np.float) input = np.concatenate((input, output), axis=1) test_input = np.concatenate((input_test, output_test), axis=1) replaceRate = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] strategies = ['fitness-proportional', 'tournament', 'rank'] cor_list = [] strategy_list = [] for j in range (len(strategies)): print ('Strategy: ' + strategies[j]) for i10 in range(1, 10, 1): i = i10 / 10 print('Replacement rate: ' + str(i)) ga = GA(100, 6, 48, 3, i, 0.1, 1, 1, 30, strategies[j], 'iris') cor, bestHypo = ga.fit(input) testAcc = ga.predict(bestHypo, test_input) print("Accuracy: " + str(testAcc) +"\n" ) cor_list.append(testAcc) cor_array = np.array(cor_list) strategy_list.append(cor_array) cor_list = [] print ('correct rate for fitness-proportional, tournament, rank strategy at vary replacement rate are') print (strategy_list)
def testTennis(trainFile, testFile, attrFile): tennis_train = pd.read_csv(trainFile, sep=" ", header=None) tennis_test = pd.read_csv(testFile, sep=" ", header=None) tennis_attr = pd.read_csv(attrFile, sep=" ", header=None) input, output, input_test, output_test = digitalize(tennis_train, tennis_test, tennis_attr,"tennis") input = np.concatenate((input, output), axis=1) test_input = np.concatenate((input_test, output_test), axis=1) # p, numOfRulesPerHypo, numofattr, numofoutput, r, m, fit_threshold, stopGeneration, strategy, dataType): ga = GA(100, 4, 10, 2, 0.3, 0.1, 1, 1, 30, 'fitness-proportional', 'tennis') trainAcc, bestHypo = ga.fit(input) print ('Training data accuracy is: ' + str(trainAcc[0])) testAcc = ga.predict(bestHypo, test_input) print ('Test data accuracy is: ' + str(testAcc[0])) ga.printRules(bestHypo)
def testIris(trainfile, testfile, attrfile): iris_train = pd.read_csv(trainfile, header=None) iris_test = pd.read_csv(testfile, header=None) iris_attr = pd.read_csv(attrfile, header=None) input, output, input_test, output_test = digitalize(iris_train, iris_test, iris_attr,"iris") input = input.astype(np.float) input = np.concatenate((input, output), axis=1) test_input = np.concatenate((input_test, output_test), axis=1) # p, numOfRulesPerHypo, numofattr, numofoutput, r, m, numOfChangeBitsPerRule, fit_threshold, stopGeneration, strategy, dataType): tournament fitness-proportional rank # ga = GA(100, 6, 28, 3, 0.4, 0.2, 1, 0.8, 100, 'rank', 'iris') # (6*2*4+3)*4 *4 ga = GA(400, 6, 48, 3, 0.3, 0.1, 1, 1, 50, 'tournament', 'iris') correct, bestHypo = ga.fit(input) print ('correct rate on traning data' + str(correct)) ga.printRules(bestHypo) cor = ga.predict(bestHypo, test_input)
def testIrisSelection(trainfile, testfile, attrfile): iris_train = pd.read_csv(trainfile, header=None) iris_test = pd.read_csv(testfile, header=None) iris_attr = pd.read_csv(attrfile, header=None) input, output, input_test, output_test = digitalize(iris_train, iris_test, iris_attr,"iris") input = input.astype(np.float) input = np.concatenate((input, output), axis=1) test_input = np.concatenate((input_test, output_test), axis=1) # stopGen = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] strategies = ['fitness-proportional', 'tournament', 'rank'] cor_list = [] strategy_list = [] for j in range (len(strategies)): ga = GA(100, 6, 48, 3, 0.3, 0.1, 1, 1, 50, strategies[j], 'iris') print ('Strategy: ' + strategies[j]) cor, bestHypo = ga.fit(input, outputGen=True) for i in range (len(cor)): cor2 = ga.predict(bestHypo[i], test_input) cor_list.append(cor2) cor_array = np.array(cor_list) strategy_list.append(cor_array) cor_list = [] print ('correct rate for fitness-proportional, tournament, rank strategy at vary generations are') print (strategy_list)
def demo_func(x): x1, x2, x3 = x return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2 from ga import GA ga = GA(func=demo_func, lb=[-1, -10, -5], ub=[2, 10, 2], max_iter=500) best_x, best_y = ga.fit() import pandas as pd import matplotlib.pyplot as plt FitV_history = pd.DataFrame(ga.FitV_history) fig, ax = plt.subplots(2, 1) ax[0].plot(FitV_history.index, FitV_history.values, '.', color='red') plt_max = FitV_history.max(axis=1) ax[1].plot(plt_max.index, plt_max, label='max') ax[1].plot(plt_max.index, plt_max.cummax()) plt.show()