Python test_randomly 예제들

예제 #1

    bases += [LinearClassify, LogisticClassify]

    def test(trd, trc, ted, tec):
        print('bc', '\n')
        bc = BaggedClassify(bases[np.random.randint(len(bases))], 20, trd, trc)
        print(bc, '\n')
        #		print(bc.predict(ted), '\n')
        #		print(bc.predict_soft(ted), '\n')
        #		print(bc.confusion(ted, tec), '\n')
        print(bc.auc(ted, tec), '\n')
        print(bc.roc(ted, tec), '\n')
        err = bc.err(ted, tec)
        print(err, '\n')
        return err

    avg_err = test_randomly(bd1, bc1, 0.8, test)

    print('avg_err')
    print(avg_err)

## DETERMINISTIC TESTING #######################################################

#	np.set_printoptions(linewidth=200)
#
#	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/classifier-data.csv'))]
#	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
#	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
#	classes = [float(row[-1].lower()) for row in csv.reader(open('../data/classifier-data.csv'))]
#	trc = np.asarray(classes[0:40] + classes[50:90] + classes[100:140])
#	tec = np.asarray(classes[40:50] + classes[90:100] + classes[140:150])
#

예제 #2

    bd1, bc1 = np.array(bd1), np.array(bc1)

    def test(trd, trc, ted, tec):
        print('tc', '\n')
        tc = TreeClassify(trd, trc)
        print(tc, '\n')
        #	print(tc.predict(ted), '\n')
        #	print(tc.predict_soft(ted), '\n')
        #	print(tc.confusion(ted, tec), '\n')
        #	print(tc.auc(ted, tec), '\n')
        #	print(tc.roc(ted, tec), '\n')
        err = tc.err(ted, tec)
        print(err, '\n')
        return err

    avg_err = test_randomly(data, classes, 0.8, test)

    print('avg_err')
    print(avg_err)

## DETERMINISTIC TESTING #######################################################

#	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/classifier-data.csv'))]
#	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
#	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
#	classes = [float(row[-1].lower()) for row in csv.reader(open('../data/classifier-data.csv'))]
#	trc = np.asarray(classes[0:40] + classes[50:90] + classes[100:140])
#	tec = np.asarray(classes[40:50] + classes[90:100] + classes[140:150])
#
#	btrd = trd[0:80,:]
#	bted = ted[0:20,:]

예제 #3

파일: gradboost.py 프로젝트: austinsherron/Python-Machine-Learning

        err = gd.mse(ted, tec)
        print('gd.mse =', err, '\n')

        print('gd.predict(ted)')
        print(gd.predict(ted))

        print('gd.get_n()')
        print(gd.get_n())

        gd.train(base, 6, X2, Y2)

        print('gd.get_n()')
        print(gd.get_n())

        err = gd.mse(ted, tec)
        print('gd.mse =', err, '\n')

        print('gd.predict(ted)')
        print(gd.predict(ted))

        return err

    avg_err = test_randomly(data, predictions, 0.8, test=test, end=1)

    print('avg_err')
    print(avg_err)

################################################################################
################################################################################
###############################################################################

예제 #4

파일: gauss_bayes_classify.py 프로젝트: austinsherron/Python-Machine-Learning

	bd1,bc1 = np.array(bd1), np.array(bc1)

	def test(trd, trc, ted, tec):
		print('gbc', '\n')
		gbc = GaussBayesClassify(trd, trc)
		print(gbc, '\n')
#		print(gbc.predict(ted), '\n')
#		print(gbc.predict_soft(ted), '\n')
#		print(gbc.confusion(ted, tec), '\n')
#		print(gbc.auc(ted, tec), '\n')
#		print(gbc.roc(ted, tec), '\n')
		err = gbc.err(ted, tec)
		print(err, '\n')
		return err

	avg_err = test_randomly(data, classes, 0.8, test)

	print('avg_err')
	print(avg_err)

## DETERMINISTIC TESTING #######################################################
#
#	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/classifier-data.csv'))]
#	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
#	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
#	classes = [float(row[-1].lower()) for row in csv.reader(open('../data/classifier-data.csv'))]
#	trc = np.asarray(classes[0:40] + classes[50:90] + classes[100:140])
#	tec = np.asarray(classes[40:50] + classes[90:100] + classes[140:150])
#
#	btrd = trd[0:80,:]
#	bted = ted[0:20,:]

예제 #5

파일: knn_regress.py 프로젝트: austinsherron/Python-Machine-Learning

if __name__ == "__main__":

    ## RANDOM TESTING ##############################################################

    data, predictions = load_data_from_csv("../data/regressor-data.csv", -1, float)
    data, predictions = arr(data), arr(predictions)

    def test(trd, trc, ted, tec):
        print("knnr", "\n")
        knnr = KNNRegress(trd, trc)
        print(knnr, "\n")
        err = knnr.mae(ted, tec)
        print(err, "\n")
        return err

    avg_err = test_randomly(data, predictions, 0.8, test)

    print("avg_err")
    print(avg_err)

## DETERMINISTIC TESTING #######################################################

# 	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/regressor-data.csv'))]
# 	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
# 	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
# 	trd2 = np.asarray(data[150:180] + data[200:230] + data[250:280])
# 	ted2 = np.asarray(data[180:200] + data[230:250] + data[280:300])
# 	trd3 = np.asarray(data[300:320] + data[350:370] + data[400:420])
# 	ted3 = np.asarray(data[320:350] + data[370:400] + data[420:450])
# 	predictions = [float(row[-1].lower()) for row in csv.reader(open('../data/regressor-data.csv'))]
# 	trp = np.asarray(predictions[0:40] + predictions[50:90] + predictions[100:140])

예제 #6

        print('lr.predict(ted)')
        print(lr.predict(ted))

        err = lr.mse(ted, tec)
        print('lr.mse(ted, tec) =', err)

        err = lr.mae(ted, tec)
        print('lr.mae(ted, tec) =', err)

        err = lr.rmse(ted, tec)
        print('lr.rmse(ted, tec) =', err)

        return err

    avg_err = test_randomly(X, Y, 0.8, test=test, end=100)

    print('avg_err')
    print(avg_err)

## DETERMINISTIC TESTING #######################################################

#	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/regressor-data.csv'))]
#	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
#	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
#	trd2 = np.asarray(data[150:180] + data[200:230] + data[250:280])
#	ted2 = np.asarray(data[180:200] + data[230:250] + data[280:300])
#	trd3 = np.asarray(data[300:320] + data[350:370] + data[400:420])
#	ted3 = np.asarray(data[320:350] + data[370:400] + data[420:450])
#
#	predictions = [float(row[-1].lower()) for row in csv.reader(open('../data/regressor-data.csv'))]

예제 #7

파일: logistic_regress.py 프로젝트: austinsherron/Python-Machine-Learning

		print('lr.predict(ted)')
		print(lr.predict(ted))

		err = lr.mse(ted, tec)
		print('lr.mse(ted, tec) =', err)

		err = lr.mae(ted, tec)
		print('lr.mae(ted, tec) =', err)

		err = lr.rmse(ted, tec)
		print('lr.rmse(ted, tec) =', err)

		return err

	avg_err = test_randomly(X, Y, 0.8, test=test, end=100)

	print('avg_err')
	print(avg_err)

## DETERMINISTIC TESTING #######################################################

#	data = [[float(val) for val in row[:-1]] for row in csv.reader(open('../data/regressor-data.csv'))]
#	trd = np.asarray(data[0:40] + data[50:90] + data[100:140])
#	ted = np.asarray(data[40:50] + data[90:100] + data[140:150])
#	trd2 = np.asarray(data[150:180] + data[200:230] + data[250:280])
#	ted2 = np.asarray(data[180:200] + data[230:250] + data[280:300])
#	trd3 = np.asarray(data[300:320] + data[350:370] + data[400:420])
#	ted3 = np.asarray(data[320:350] + data[370:400] + data[420:450])
#
#	predictions = [float(row[-1].lower()) for row in csv.reader(open('../data/regressor-data.csv'))]