def test_TransferFailure(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Tanh", units=8)], n_iter=1)
ae.fit(X)
nn = mlp.MultiLayerPerceptron(layers=[mlp.Layer("Tanh", units=4)])
assert_raises(AssertionError, ae.transfer, nn)
def test_TransferSuccess(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Tanh", units=4)], n_iter=1)
ae.fit(X)
nn = mlp.MultiLayerPerceptron(layers=[mlp.Layer("Tanh", units=4)])
ae.transfer(nn)
def test_LayerTypes(self):
X = numpy.zeros((8,12))
for l in ['autoencoder', 'denoising']:
ae = AE(layers=[L("Sigmoid", type=l, units=4)])
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def test_CostFunctions(self):
X = numpy.zeros((8,12))
for t in ['msre', 'mbce']:
ae = AE(layers=[L("Sigmoid", units=4, cost=t)], n_iter=1)
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def test_LayerTypes(self):
X = numpy.zeros((8, 12))
for l in ['autoencoder', 'denoising']:
ae = AE(layers=[L("Sigmoid", type=l, units=4)])
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def test_CostFunctions(self):
X = numpy.zeros((8, 12))
for t in ['msre', 'mbce']:
ae = AE(layers=[L("Sigmoid", units=4, cost=t)], n_iter=1)
y = ae.fit_transform(X)
assert_equals(type(y), numpy.ndarray)
assert_equals(y.shape, (8, 4))
def main():
print '[INFO, time: %s] Getting Data....' % (time.strftime('%H:%M:%S'))
preprocesser = Preprocess()
data= preprocesser.read()
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'Auto Encoder')
ae = AutoEncoder(
layers=[
Layer("Sigmoid",units=100)
],
learning_rate=0.01,
n_iter=40,
verbose=True,
)
ae.fit(data[:,:-1])
print '[INFO, time: %s] Transforming Data with %s ...' % (time.strftime('%H:%M:%S'), 'Auto Encoder')
splitRatio = 0.67
train, test = splitDataset(data, splitRatio)
train = np.asarray(train)
test = np.asarray(test)
trainX = train[:,:-1]
trainy = train[:,-1]
testX = test[:,:-1]
testy = test[:,-1]
transformed_trainX = ae.transform(trainX)
transformed_testX = ae.transform(testX)
print '[INFO, time: %s] Fitting %s ...' % (time.strftime('%H:%M:%S'), 'SVM - rbf kernel (i.e. gaussian) with default paramenters')
clf = SVC()
clf.fit(transformed_trainX, trainy)
print '[INFO, time: %s] Making Predictions...' % (time.strftime('%H:%M:%S'))
prediction = clf.predict(transformed_testX)
print '[RESULT, time: %s] accuracy = %f' % (time.strftime('%H:%M:%S'),accuracy_score(testy, prediction))
def test_FitVerbose(self):
X = numpy.zeros((8,4))
ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1, verbose=1)
ae.fit(X)
def test_FitVerbose(self):
X = numpy.zeros((8, 4))
ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1, verbose=1)
ae.fit(X)
