def metric(self):
totalTimer = Timer()
with totalTimer:
if self.method_param["kernel"] == "polynomial":
if "degree" in self.method_param:
degree = int(self.method_param["degree"])
else:
degree = 1
kernel = mlpy.kernel_polynomial(self.data[0],
self.data[0],
d=degree)
elif self.method_param["kernel"] == "gaussian":
kernel = mlpy.kernel_gaussian(self.data[0],
self.data[0],
sigma=2)
elif self.method_param["kernel"] == "linear":
kernel = mlpy.kernel_linear(self.data[0], self.data[0])
elif self.method_param["kernel"] == "hyptan":
kernel = mlpy.kernel_sigmoid(self.data[0], self.data[0])
model = mlpy.KPCA()
model.learn(kernel)
out = model.transform(kernel, **self.build_opts)
metric = {}
metric["runtime"] = totalTimer.ElapsedTime()
return metric
def RunKPCAMlpy():
totalTimer = Timer()
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
data = np.genfromtxt(self.dataset, delimiter=',')
try:
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[0]
# Get the kernel type and make sure it is valid.
if not "kernel" in options:
Log.Fatal("Choose kernel type, valid choices are 'polynomial', " +
"'gaussian', 'linear' and 'hyptan'.")
return -1
if options["kernel"] == "polynomial":
if "degree" in options:
degree = int(options.pop("degree"))
else:
degree = 1
kernel = mlpy.kernel_polynomial(data, data, d=degree)
elif options["kernel"] == "gaussian":
kernel = mlpy.kernel_gaussian(data, data, sigma=2)
elif options["kernel"] == "linear":
kernel = mlpy.kernel_linear(data, data)
elif options["kernel"] == "hyptan":
kernel = mlpy.kernel_sigmoid(data, data)
else:
Log.Fatal("Invalid kernel type (" + kernel.group(1) + "); valid " +
"choices are 'polynomial', 'gaussian', 'linear' and 'hyptan'.")
return -1
options.pop("kernel")
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Perform Kernel Principal Components Analysis.
model = mlpy.KPCA()
model.learn(kernel)
out = model.transform(kernel, k=d)
except Exception as e:
Log.Fatal("Exception: " + str(e))
return -1
return totalTimer.ElapsedTime()
def RunKPCAMlpy(q):
totalTimer = Timer()
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
data = np.genfromtxt(self.dataset, delimiter=',')
try:
with totalTimer:
# Get the new dimensionality, if it is necessary.
dimension = re.search('-d (\d+)', options)
if not dimension:
d = data.shape[0]
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 'polynomial', " +
"'gaussian', 'linear' and 'hyptan'.")
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 = mlpy.kernel_polynomial(data, data, d=degree)
elif kernel.group(1) == "gaussian":
kernel = mlpy.kernel_gaussian(data, data, sigma=2)
elif kernel.group(1) == "linear":
kernel = mlpy.kernel_linear(data, data)
elif kernel.group(1) == "hyptan":
kernel = mlpy.kernel_sigmoid(data, data)
else:
Log.Fatal("Invalid kernel type (" + kernel.group(1) + "); valid " +
"choices are 'polynomial', 'gaussian', 'linear' and 'hyptan'.")
q.put(-1)
return -1
# Perform Kernel Principal Components Analysis.
model = mlpy.KPCA()
model.learn(kernel)
out = model.transform(kernel, k=d)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
Kl = mlpy.kernel_linear(X, X) # compute the kernel matrix linear_kfda = mlpy.KFDA(lmb=0.001) linear_kfda.learn(Kl, y) # compute the tranformation vector zl = linear_kfda.transform(Kl) # embedded x into the kernel fisher space # Using Gaussian kernel sig = 1 Kg = mlpy.kernel_gaussian(X, X, sigma=sig) # compute the kernel matrix gaussian_kfda = mlpy.KFDA(lmb=0.001) gaussian_kfda.learn(Kg, y) # compute the tranformation vector zg = gaussian_kfda.transform(Kg) # embedded x into the kernel fisher space gaussian_kfda._coeff # alpha # Using sigmoid kernel gam=0.1 Ks = mlpy.kernel_sigmoid(X, X, gamma=gam, b=1.0) # compute the kernel matrix sigmoid_kfda = mlpy.KFDA(lmb=0.001) sigmoid_kfda.learn(Ks, y) # compute the tranformation vector zs = sigmoid_kfda.transform(Ks) # embedded x into the kernel fisher space sigmoid_kfda._coeff # Using polynomial kernel gam = 1.0 Kp = mlpy.kernel_polynomial(X, X, gamma=gam, b=1.0, d=2.0) # compute the kernel matrix polynomial_kfda = mlpy.KFDA(lmb=0.001) polynomial_kfda.learn(Kp, y) # compute the tranformation vector zp = polynomial_kfda.transform(Kp) # embedded x into the kernel fisher space polynomial_kfda._coeff # Step 3 of LDA: Solving the generalized eigenvalue problem for the matrix SW−1SB # Next, we will solve the generalized eigenvalue problem
def RunKPCAMlpy(q):
totalTimer = Timer()
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
data = np.genfromtxt(self.dataset, delimiter=',')
try:
with totalTimer:
# Get the new dimensionality, if it is necessary.
dimension = re.search('-d (\d+)', options)
if not dimension:
d = data.shape[0]
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 'polynomial', "
+ "'gaussian', 'linear' and 'hyptan'.")
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 = mlpy.kernel_polynomial(data, data, d=degree)
elif kernel.group(1) == "gaussian":
kernel = mlpy.kernel_gaussian(data, data, sigma=2)
elif kernel.group(1) == "linear":
kernel = mlpy.kernel_linear(data, data)
elif kernel.group(1) == "hyptan":
kernel = mlpy.kernel_sigmoid(data, data)
else:
Log.Fatal(
"Invalid kernel type (" + kernel.group(1) +
"); valid " +
"choices are 'polynomial', 'gaussian', 'linear' and 'hyptan'."
)
q.put(-1)
return -1
# Perform Kernel Principal Components Analysis.
model = mlpy.KPCA()
model.learn(kernel)
out = model.transform(kernel, k=d)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time