def create_param_tree():
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import ParameterCombination
from shogun.Kernel import GaussianKernel, PolyKernel
root=ModelSelectionParameters()
tau=ModelSelectionParameters("tau")
root.append_child(tau)
# also R_LINEAR/R_LOG is available as type
min=-1
max=1
type=R_EXP
step=1.5
base=2
tau.build_values(min, max, type, step, base)
# gaussian kernel with width
gaussian_kernel=GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel=ModelSelectionParameters("kernel", gaussian_kernel)
gaussian_kernel_width=ModelSelectionParameters("width");
gaussian_kernel_width.build_values(5.0, 8.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
# polynomial kernel with degree
poly_kernel=PolyKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
poly_kernel.print_modsel_params()
param_poly_kernel=ModelSelectionParameters("kernel", poly_kernel)
root.append_child(param_poly_kernel)
# note that integers are used here
param_poly_kernel_degree=ModelSelectionParameters("degree")
param_poly_kernel_degree.build_values(1, 2, R_LINEAR)
param_poly_kernel.append_child(param_poly_kernel_degree)
return root
def create_param_tree():
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import ParameterCombination
from shogun.Kernel import GaussianKernel, PolyKernel
root = ModelSelectionParameters()
tau = ModelSelectionParameters("tau")
root.append_child(tau)
# also R_LINEAR/R_LOG is available as type
min = -1
max = 1
type = R_EXP
step = 1.5
base = 2
tau.build_values(min, max, type, step, base)
# gaussian kernel with width
gaussian_kernel = GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel = ModelSelectionParameters("kernel", gaussian_kernel)
gaussian_kernel_width = ModelSelectionParameters("width")
gaussian_kernel_width.build_values(5.0, 8.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
# polynomial kernel with degree
poly_kernel = PolyKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
poly_kernel.print_modsel_params()
param_poly_kernel = ModelSelectionParameters("kernel", poly_kernel)
root.append_child(param_poly_kernel)
# note that integers are used here
param_poly_kernel_degree = ModelSelectionParameters("degree")
param_poly_kernel_degree.build_values(1, 2, R_LINEAR)
param_poly_kernel.append_child(param_poly_kernel_degree)
return root
