def create_dataset(filename): dataset = ClassificationDataSet(13, 1, class_labels=['0', '1', '2']) football_data = FootballDataCsv(filename) total_min = football_data.total_min() total_max = football_data.total_max() for data in football_data: normalized_features = [normalize(x, min_value=total_min, max_value=total_max) for x in data.to_list()] dataset.addSample(normalized_features, [data.binarized_output]) dataset.assignClasses() dataset._convertToOneOfMany() return dataset
def create_dataset(files_path): dataset = ClassificationDataSet(40 * 30, 1, class_labels=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']) for filename in os.listdir(files_path): name, extension = filename.split('.') number = name.split('-')[0].replace('img', '') img_path = os.path.join(files_path, filename) cv2_img = cv2.imread(img_path, cv2.CV_LOAD_IMAGE_GRAYSCALE) flattened_img = flatten_img(cv2_img) dataset.addSample(flattened_img, [NUMBERS_TO_CLASS[number]]) dataset.assignClasses() dataset._convertToOneOfMany() return dataset
def generateClassificationData(size, nClasses=3): """ generate a set of points in 2D belonging to two or three different classes """ if nClasses==3: means = [(-1,0),(2,4),(3,1)] else: means = [(-2,0),(2,1),(6,0)] cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])] dataset = ClassificationDataSet(2, 1, nb_classes=nClasses) for _ in xrange(size): for c in range(3): input = multivariate_normal(means[c],cov[c]) dataset.addSample(input, [c%nClasses]) dataset.assignClasses() return dataset
def generateClassificationData(size, nClasses=3): """ generate a set of points in 2D belonging to two or three different classes """ if nClasses == 3: means = [(-1, 0), (2, 4), (3, 1)] else: means = [(-2, 0), (2, 1), (6, 0)] cov = [diag([1, 1]), diag([0.5, 1.2]), diag([1.5, 0.7])] dataset = ClassificationDataSet(2, 1, nb_classes=nClasses) for _ in xrange(size): for c in range(3): input = multivariate_normal(means[c], cov[c]) dataset.addSample(input, [c % nClasses]) dataset.assignClasses() return dataset
def generate_data( hour_to_use_app = 10): """ Generate sample data to verify a classification learning NN. """ dataset = ClassificationDataSet(4, 1, nb_classes=2) for month in xrange(1,12): # month 12 reserved for tests for day in xrange(1,8): for hour in xrange(0,24): for minute in xrange(1,7): if hour == hour_to_use_app : c = 1 else : c = 0 input = [month,day,hour,minute] dataset.addSample(input, [c]) dataset.assignClasses() return dataset
def generate_data(hour_to_use_app=10, test=False): """ Generate sample data to verify a classification learning NN. """ dataset = ClassificationDataSet(4, 1, nb_classes=2) if test: Dmonth, Emonth = (12, 13) else: Dmonth, Emonth = (1, 12) # month 12 reserved for tests for month in xrange(Dmonth, Emonth): for day in xrange(1, 8): for hour in xrange(0, 24): for minute in xrange(1, 7): if hour == hour_to_use_app: c = 1 else: c = 0 input = [month, day, hour, minute] dataset.addSample(input, [c]) dataset.assignClasses() return dataset
color = [[214, 0, 0], [210, 62, 90], [147, 10, 36], [212, 52, 52], [229, 76, 98], [204, 0, 67], [188, 62, 109], [235, 0, 51], [16, 39, 213], [18, 157, 159], [53, 198, 153], [23, 119, 150], [0, 95, 255], [37, 74, 136], [4, 45, 196], [10, 143, 255]] # 0 is red, 1 is blue color_class = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1] # add samples to dataset for i in range(len(color)): indata = color[i] outdata = color_class[i] trndata.addSample(indata, [outdata]) print('[%d, %d, %d], [%d]' % (indata[0], indata[1], indata[2], outdata)) trndata.assignClasses() net = buildNetwork(trndata.indim, 6, trndata.outdim) # net = buildNetwork( # trndata.indim, 6, trndata.outdim, recurrent=False, bias=False # ) trainer = BackpropTrainer(net, dataset=trndata, learningrate=0.01, momentum=0.5, verbose=True) # # train on dataset with X epochs # t.trainOnDataset(ds, 50) # t.testOnData(verbose=True)
# 0 is red, 1 is blue color_class = [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1 ] # add samples to dataset for i in range(len(color)): indata = color[i] outdata = color_class[i] trndata.addSample(indata, [outdata]) print('[%d, %d, %d], [%d]' % (indata[0], indata[1], indata[2], outdata)) trndata.assignClasses() net = buildNetwork(trndata.indim, 6, trndata.outdim) # net = buildNetwork( # trndata.indim, 6, trndata.outdim, recurrent=False, bias=False # ) trainer = BackpropTrainer( net, dataset=trndata, learningrate=0.01, momentum=0.5, verbose=True ) # # train on dataset with X epochs # t.trainOnDataset(ds, 50) # t.testOnData(verbose=True) print('')