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('')
