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)
