def __init__(self, arg, attribute='C', values=[0.1, 1, 10, 100, 1000]):
"""
:Parameters:
- `arg` - another Param object, or the classifier to be used
- `attribute` - the attribute of the classifier that needs tuning
- `values` - a list of values to try
"""
if arg.__class__ == self.__class__:
other = arg
self.attribute = other.attribute
self.values = other.values[:]
self.classifiers = [
classifier.__class__(classifier)
for classifier in other.classifiers
]
for i in range(len(self)):
misc.mysetattr(self.classifiers[i], self.attribute,
self.values[i])
elif hasattr(arg, 'type') and arg.type == 'classifier':
self.attribute = attribute
self.values = values
self.classifiers = [
arg.__class__(arg) for i in range(len(self.values))
]
for i in range(len(self)):
misc.mysetattr(self.classifiers[i], self.attribute,
self.values[i])
elif type(arg) == type([]):
self.classifiers = [
arg[i].__class__(arg[i]) for i in range(len(arg))
]
def __init__(self, arg, attribute = 'C', values = [0.1, 1, 10, 100, 1000]) :
"""
:Parameters:
- `arg` - another Param object, or the classifier to be used
- `attribute` - the attribute of the classifier that needs tuning
- `values` - a list of values to try
"""
if arg.__class__ == self.__class__ :
other = arg
self.attribute = other.attribute
self.values = other.values[:]
self.classifiers = [classifier.__class__(classifier)
for classifier in other.classifiers]
for i in range(len(self)) :
misc.mysetattr(self.classifiers[i], self.attribute, self.values[i])
elif hasattr(arg, 'type') and arg.type == 'classifier' :
self.attribute = attribute
self.values = values
self.classifiers = [arg.__class__(arg)
for i in range(len(self.values))]
for i in range(len(self)) :
misc.mysetattr(self.classifiers[i], self.attribute, self.values[i])
elif type(arg) == type([]) :
self.classifiers = [arg[i].__class__(arg[i])
for i in range(len(arg))]
def __init__(self,
arg,
attribute1='C',
values1=[0.1, 1, 10, 100, 1000],
attribute2='kernel.gamma',
values2=[0.001, 0.01, 0.1, 1, 10]):
"""
:Parameters:
- `arg` - another Param object, or the classifier to be used
- `attribute1` - the first attribute of the classifier that needs tuning
- `values1` - a list of values to try for attribute1
- `attribute2` - the second attribute
- `values2` - a list of values to try for attribute2
"""
if arg.__class__ == self.__class__:
other = arg
self.attribute1 = other.attribute1
self.values1 = other.values1[:]
self.attribute2 = other.attribute2
self.values2 = other.values2[:]
self.classifiers = [
classifier.__class__(classifier)
for classifier in other.classifiers
]
elif hasattr(arg, 'type') and arg.type == 'classifier':
self.attribute1 = attribute1
self.values1 = values1
self.attribute2 = attribute2
self.values2 = values2
self.classifiers = [
arg.__class__(arg) for i in range(len(values1) * len(values2))
]
for i in range(len(self.values1)):
for j in range(len(self.values2)):
classifierID = i * len(self.values2) + j
misc.mysetattr(self.classifiers[classifierID], self.attribute1,
self.values1[i])
misc.mysetattr(self.classifiers[classifierID], self.attribute2,
self.values2[j])
def __init__(self, arg,
attribute1 = 'C', values1 = [0.1, 1, 10, 100, 1000],
attribute2 = 'kernel.gamma', values2 = [0.001, 0.01, 0.1, 1, 10]) :
"""
:Parameters:
- `arg` - another Param object, or the classifier to be used
- `attribute1` - the first attribute of the classifier that needs tuning
- `values1` - a list of values to try for attribute1
- `attribute2` - the second attribute
- `values2` - a list of values to try for attribute2
"""
if arg.__class__ == self.__class__ :
other = arg
self.attribute1 = other.attribute1
self.values1 = other.values1[:]
self.attribute2 = other.attribute2
self.values2 = other.values2[:]
self.classifiers = [classifier.__class__(classifier)
for classifier in other.classifiers]
elif hasattr(arg, 'type') and arg.type == 'classifier' :
self.attribute1 = attribute1
self.values1 = values1
self.attribute2 = attribute2
self.values2 = values2
self.classifiers = [arg.__class__(arg)
for i in range(len(values1) * len(values2))]
for i in range(len(self.values1)) :
for j in range(len(self.values2)) :
classifierID = i * len(self.values2) + j
misc.mysetattr(self.classifiers[classifierID],
self.attribute1,
self.values1[i])
misc.mysetattr(self.classifiers[classifierID],
self.attribute2,
self.values2[j])
