def testDirectories(self):
self.assertTrue(os.path.exists(cifarDirectories.base()))
self.assertTrue(os.path.exists(cifarDirectories.code()))
self.assertTrue(os.path.exists(cifarDirectories.tests()))
self.assertTrue(os.path.exists(cifarDirectories.data()))
self.assertTrue(os.path.exists(cifarDirectories.cifarKaggle()))
self.assertTrue(os.path.exists(cifarDirectories.demos()))
self.assertTrue(os.path.exists(cifarDirectories.DeepLearningTutorials()))
self.assertTrue(os.path.exists(cifarDirectories.DeepLearningTutorialsCode()))
def testDirectories(self):
self.assertTrue(os.path.exists(cifarDirectories.base()))
self.assertTrue(os.path.exists(cifarDirectories.code()))
self.assertTrue(os.path.exists(cifarDirectories.tests()))
self.assertTrue(os.path.exists(cifarDirectories.data()))
self.assertTrue(os.path.exists(cifarDirectories.cifarKaggle()))
self.assertTrue(os.path.exists(cifarDirectories.demos()))
self.assertTrue(
os.path.exists(cifarDirectories.DeepLearningTutorials()))
self.assertTrue(
os.path.exists(cifarDirectories.DeepLearningTutorialsCode()))
def __init__(self):
class Subdata(object):
def __init__(self):
self.x = []
self.y = []
def append(self, x, y):
self.x.append(x)
self.y.append(y)
def array(self):
return numpy.array(self.x, dtype=numpy.float32), numpy.array(self.y)
def irisType(iris):
if iris == 'Iris-setosa': return 0
if iris == 'Iris-versicolor': return 1
if iris == 'Iris-virginica': return 2
return 0
maxSepalLength = 7.9
maxSepalWidth = 4.4
maxPetalLength = 6.9
maxPetalWidth = 2.5
train = Subdata()
test = Subdata()
valid = Subdata()
randomState = random.getstate()
random.seed(42)
irisPath = os.path.join(cifarDirectories.data(), 'iris.data')
f = open(irisPath, 'r')
reader = csv.reader(f, delimiter=',')
for row in reader:
if len(row) > 0:
sepalLength = float(row[0]) / maxSepalLength
sepalWidth = float(row[1]) / maxSepalWidth
petalLength = float(row[2]) / maxPetalLength
petalWidth = float(row[3]) / maxPetalWidth
x = [sepalLength, sepalWidth, petalLength, petalWidth]
y = irisType(row[4])
r = random.random()
if r < 0.8:
train.append(x, y)
elif r < 0.9:
test.append(x, y)
else:
valid.append(x, y)
f.close()
random.setstate(randomState)
Dataset.__init__(self, train.array(), valid.array(), test.array())
def getData(filename):
path = os.path.join(cifarDirectories.data(), 'cifar-10-python.tar.gz')
f = tarfile.open(path, 'r:gz')
data = cPickle.load(f.extractfile(filename))
f.close()
return data
def mnist():
path = os.path.join(cifarDirectories.data(), 'mnist.pkl.gz')
f = gzip.open(path, 'rb')
data = cPickle.load(f)
f.close()
return data
def mnist():
path = os.path.join(cifarDirectories.data(), 'mnist.pkl.gz')
f = gzip.open(path, 'rb')
data = cPickle.load(f)
f.close()
return data
