def RunKPCAShogun():
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.
if "new_dimensionality" in options:
d = int(options.pop("new_dimensionality"))
if (d > data.shape[1]):
Log.Fatal("New dimensionality (" + str(d) + ") cannot be greater "
+ "than existing dimensionality (" + str(data.shape[1]) + ")!")
return -1
else:
d = data.shape[1]
# Get the kernel type and make sure it is valid.
if "kernel" in options:
kernel = str(options.pop("kernel"))
else:
Log.Fatal("Choose kernel type, valid choices are 'linear'," +
" 'hyptan', 'polynomial' and 'gaussian'.")
return -1
if "degree" in options:
degree = int(options.pop("degree"))
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
if kernel == "polynomial":
kernel = PolyKernel(dataFeat, dataFeat, degree, True)
elif kernel == "gaussian":
kernel = GaussianKernel(dataFeat, dataFeat, 2.0)
elif kernel == "linear":
kernel = LinearKernel(dataFeat, dataFeat)
elif kernel == "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'.")
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:
return -1
return totalTimer.ElapsedTime()
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 preprocessor_kernelpca_modular(data, threshold, width):
from modshogun import RealFeatures
from modshogun import KernelPCA
from modshogun import GaussianKernel
features = RealFeatures(data)
kernel = GaussianKernel(features, features, width)
preprocessor = KernelPCA(kernel)
preprocessor.init(features)
preprocessor.set_target_dim(2)
# X=preprocessor.get_transformation_matrix()
X2 = preprocessor.apply_to_feature_matrix(features)
lx0 = len(X2)
modified_d1 = zeros((lx0, number_of_points_for_circle1))
modified_d2 = zeros((lx0, number_of_points_for_circle2))
modified_d1 = [X2[i][0:number_of_points_for_circle1] for i in range(lx0)]
modified_d2 = [
X2[i][number_of_points_for_circle1 : (number_of_points_for_circle1 + number_of_points_for_circle2)]
for i in range(lx0)
]
p.plot(modified_d1[0][:], modified_d1[1][:], "o", modified_d2[0][:], modified_d2[1][:], "x")
p.title("final data")
p.show()
return features
def preprocessor_kernelpca_modular (data, threshold, width): from modshogun import RealFeatures from modshogun import KernelPCA from modshogun import GaussianKernel features = RealFeatures(data) kernel = GaussianKernel(features,features,width) preprocessor = KernelPCA(kernel) preprocessor.init(features) preprocessor.set_target_dim(2) preprocessor.apply_to_feature_matrix(features) return features
def preprocessor_kernelpca_modular (data, threshold, width):
from modshogun import RealFeatures
from modshogun import KernelPCA
from modshogun import GaussianKernel
features = RealFeatures(data)
kernel=GaussianKernel(features,features,width)
preprocessor=KernelPCA(kernel)
preprocessor.init(features)
preprocessor.set_target_dim(2)
#X=preprocessor.get_transformation_matrix()
X2=preprocessor.apply_to_feature_matrix(features)
lx0=len(X2)
modified_d1=zeros((lx0,number_of_points_for_circle1))
modified_d2=zeros((lx0,number_of_points_for_circle2))
modified_d1=[X2[i][0:number_of_points_for_circle1] for i in range(lx0)]
modified_d2=[X2[i][number_of_points_for_circle1:(number_of_points_for_circle1+number_of_points_for_circle2)] for i in range(lx0)]
p.plot(modified_d1[0][:],modified_d1[1][:],'o',modified_d2[0][:],modified_d2[1][:],'x')
p.title('final data')
p.show()
return features
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