def RunKPCAShogun(q):
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
data = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(data.T)
with totalTimer:
# Get the new dimensionality, if it is necessary.
dimension = re.search('-d (\d+)', options)
if not dimension:
d = data.shape[1]
else:
d = int(dimension.group(1))
if (d > data.shape[1]):
Log.Fatal("New dimensionality (" + str(d) + ") cannot be greater "
+ "than existing dimensionality (" + str(data.shape[1]) + ")!")
q.put(-1)
return -1
# Get the kernel type and make sure it is valid.
kernel = re.search("-k ([^\s]+)", options)
if not kernel:
Log.Fatal("Choose kernel type, valid choices are 'linear'," +
" 'hyptan', 'polynomial' and 'gaussian'.")
q.put(-1)
return -1
elif kernel.group(1) == "polynomial":
degree = re.search('-D (\d+)', options)
degree = 1 if not degree else int(degree.group(1))
kernel = PolyKernel(dataFeat, dataFeat, degree, True)
elif kernel.group(1) == "gaussian":
kernel = GaussianKernel(dataFeat, dataFeat, 2.0)
elif kernel.group(1) == "linear":
kernel = LinearKernel(dataFeat, dataFeat)
elif kernel.group(1) == "hyptan":
kernel = SigmoidKernel(dataFeat, dataFeat, 2, 1.0, 1.0)
else:
Log.Fatal("Invalid kernel type (" + kernel.group(1) + "); valid "
+ "choices are 'linear', 'hyptan', 'polynomial' and 'gaussian'.")
q.put(-1)
return -1
# Perform Kernel Principal Components Analysis.
model = KernelPCA(kernel)
model.set_target_dim(d)
model.init(dataFeat)
model.apply_to_feature_matrix(dataFeat)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunKPCAShogun(q):
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
data = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(data.T)
with totalTimer:
# Get the new dimensionality, if it is necessary.
dimension = re.search('-d (\d+)', options)
if not dimension:
d = data.shape[1]
else:
d = int(dimension.group(1))
if (d > data.shape[1]):
Log.Fatal("New dimensionality (" + str(d) +
") cannot be greater " +
"than existing dimensionality (" +
str(data.shape[1]) + ")!")
q.put(-1)
return -1
# Get the kernel type and make sure it is valid.
kernel = re.search("-k ([^\s]+)", options)
if not kernel:
Log.Fatal(
"Choose kernel type, valid choices are 'linear'," +
" 'hyptan', 'polynomial' and 'gaussian'.")
q.put(-1)
return -1
elif kernel.group(1) == "polynomial":
degree = re.search('-D (\d+)', options)
degree = 1 if not degree else int(degree.group(1))
kernel = PolyKernel(dataFeat, dataFeat, degree, True)
elif kernel.group(1) == "gaussian":
kernel = GaussianKernel(dataFeat, dataFeat, 2.0)
elif kernel.group(1) == "linear":
kernel = LinearKernel(dataFeat, dataFeat)
elif kernel.group(1) == "hyptan":
kernel = SigmoidKernel(dataFeat, dataFeat, 2, 1.0, 1.0)
else:
Log.Fatal(
"Invalid kernel type (" + kernel.group(1) +
"); valid " +
"choices are 'linear', 'hyptan', 'polynomial' and 'gaussian'."
)
q.put(-1)
return -1
# Perform Kernel Principal Components Analysis.
model = KernelPCA(kernel)
model.set_target_dim(d)
model.init(dataFeat)
model.apply_to_feature_matrix(dataFeat)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
