def create_param_tree():
root=ModelSelectionParameters()
c1=ModelSelectionParameters("C1")
root.append_child(c1)
c1.build_values(-1.0, 1.0, R_EXP)
c2=ModelSelectionParameters("C2")
root.append_child(c2)
c2.build_values(-1.0, 1.0, R_EXP)
gaussian_kernel=GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included, simply 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(-1.0, 1.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
power_kernel=PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included, simply write to the mailing list
power_kernel.print_modsel_params()
param_power_kernel=ModelSelectionParameters("kernel", power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree=ModelSelectionParameters("degree")
param_power_kernel_degree.build_values(1.0, 2.0, R_LINEAR)
param_power_kernel.append_child(param_power_kernel_degree)
metric=MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included, simply write to the mailing list
metric.print_modsel_params()
param_power_kernel_metric1=ModelSelectionParameters("distance", metric)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k=ModelSelectionParameters("k")
param_power_kernel_metric1_k.build_values(1.0, 2.0, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
return root
def minkowski_metric (): print 'MinkowskiMetric' from shogun.Features import RealFeatures from shogun.Distance import MinkowskiMetric feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) k=3 distance=MinkowskiMetric(feats_train, feats_train, k) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix()
def distance_minkowski_modular (fm_train_real=traindat,fm_test_real=testdat,k=3): from shogun.Features import RealFeatures from shogun.Distance import MinkowskiMetric feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=MinkowskiMetric(feats_train, feats_train, k) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def modelselection_parameter_tree_modular(dummy):
from shogun.ModelSelection import ParameterCombination
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.Kernel import PowerKernel
from shogun.Kernel import GaussianKernel
from shogun.Kernel import DistantSegmentsKernel
from shogun.Distance import MinkowskiMetric
root = ModelSelectionParameters()
combinations = root.get_combinations()
combinations.get_num_elements()
c = ModelSelectionParameters('C')
root.append_child(c)
c.build_values(1, 11, R_EXP)
power_kernel = PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
#power_kernel.print_modsel_params()
param_power_kernel = ModelSelectionParameters('kernel', power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree = ModelSelectionParameters('degree')
param_power_kernel_degree.build_values(1, 1, R_EXP)
param_power_kernel.append_child(param_power_kernel_degree)
metric1 = MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
#metric1.print_modsel_params()
param_power_kernel_metric1 = ModelSelectionParameters('distance', metric1)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k = ModelSelectionParameters('k')
param_power_kernel_metric1_k.build_values(1, 12, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
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)
root.append_child(param_gaussian_kernel)
param_gaussian_kernel_width = ModelSelectionParameters('width')
param_gaussian_kernel_width.build_values(1, 2, R_EXP)
param_gaussian_kernel.append_child(param_gaussian_kernel_width)
ds_kernel = DistantSegmentsKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
#ds_kernel.print_modsel_params()
param_ds_kernel = ModelSelectionParameters('kernel', ds_kernel)
root.append_child(param_ds_kernel)
param_ds_kernel_delta = ModelSelectionParameters('delta')
param_ds_kernel_delta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_delta)
param_ds_kernel_theta = ModelSelectionParameters('theta')
param_ds_kernel_theta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_theta)
# root.print_tree()
combinations = root.get_combinations()
# for i in range(combinations.get_num_elements()):
# combinations.get_element(i).print_tree()
return
def modelselection_parameter_tree_modular():
from shogun.ModelSelection import ParameterCombination
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import DynamicParameterCombinationArray
from shogun.Kernel import PowerKernel
from shogun.Kernel import GaussianKernel
from shogun.Kernel import DistantSegmentsKernel
from shogun.Distance import MinkowskiMetric
root = ModelSelectionParameters()
combinations = root.get_combinations()
combinations.get_num_elements()
c = ModelSelectionParameters('C')
root.append_child(c)
c.build_values(1, 11, R_EXP)
power_kernel = PowerKernel()
param_power_kernel = ModelSelectionParameters('kernel', power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree = ModelSelectionParameters('degree')
param_power_kernel_degree.build_values(1, 1, R_EXP)
param_power_kernel.append_child(param_power_kernel_degree)
metric1 = MinkowskiMetric(10)
param_power_kernel_metric1 = ModelSelectionParameters('distance', metric1)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k = ModelSelectionParameters('k')
param_power_kernel_metric1_k.build_values(1, 12, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
gaussian_kernel = GaussianKernel()
param_gaussian_kernel = ModelSelectionParameters('kernel', gaussian_kernel)
root.append_child(param_gaussian_kernel)
param_gaussian_kernel_width = ModelSelectionParameters('width')
param_gaussian_kernel_width.build_values(1, 2, R_EXP)
param_gaussian_kernel.append_child(param_gaussian_kernel_width)
ds_kernel = DistantSegmentsKernel()
param_ds_kernel = ModelSelectionParameters('kernel', ds_kernel)
root.append_child(param_ds_kernel)
param_ds_kernel_delta = ModelSelectionParameters('delta')
param_ds_kernel_delta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_delta)
param_ds_kernel_theta = ModelSelectionParameters('theta')
param_ds_kernel_theta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_theta)
root.print_tree()
combinations = root.get_combinations()
for i in range(combinations.get_num_elements()):
combinations.get_element(i).print_tree()
return
def modelselection_parameter_tree_modular(dummy):
from shogun.ModelSelection import ParameterCombination
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import DynamicParameterCombinationArray
from shogun.Kernel import PowerKernel
from shogun.Kernel import GaussianKernel
from shogun.Kernel import DistantSegmentsKernel
from shogun.Distance import MinkowskiMetric
root=ModelSelectionParameters()
combinations=root.get_combinations()
combinations.get_num_elements()
c=ModelSelectionParameters('C');
root.append_child(c)
c.build_values(1, 11, R_EXP)
power_kernel=PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
power_kernel.print_modsel_params()
param_power_kernel=ModelSelectionParameters('kernel', power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree=ModelSelectionParameters('degree')
param_power_kernel_degree.build_values(1, 1, R_EXP)
param_power_kernel.append_child(param_power_kernel_degree)
metric1=MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
metric1.print_modsel_params()
param_power_kernel_metric1=ModelSelectionParameters('distance', metric1)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k=ModelSelectionParameters('k')
param_power_kernel_metric1_k.build_values(1, 12, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
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)
root.append_child(param_gaussian_kernel)
param_gaussian_kernel_width=ModelSelectionParameters('width')
param_gaussian_kernel_width.build_values(1, 2, R_EXP)
param_gaussian_kernel.append_child(param_gaussian_kernel_width)
ds_kernel=DistantSegmentsKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
ds_kernel.print_modsel_params()
param_ds_kernel=ModelSelectionParameters('kernel', ds_kernel)
root.append_child(param_ds_kernel)
param_ds_kernel_delta=ModelSelectionParameters('delta')
param_ds_kernel_delta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_delta)
param_ds_kernel_theta=ModelSelectionParameters('theta')
param_ds_kernel_theta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_theta)
root.print_tree()
combinations=root.get_combinations()
for i in range(combinations.get_num_elements()):
combinations.get_element(i).print_tree()
return