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Without inheriting from numpy.ndarray. ndarray functionality missing

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Max Zwiessele 2013-10-02 19:15:24 +01:00
parent 3b40a47cbc
commit 36e60362da
1 changed files with 90 additions and 25 deletions
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115
GPy/core/parameter.py
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@ -6,8 +6,9 @@ Created on 4 Sep 2013
import re import re
import itertools import itertools
import numpy import numpy
from GPy.core.transformations import Logexp from GPy.core.transformations import Logexp, NegativeLogexp
from GPy.core.index_operations import ParameterIndexOperations from GPy.core.index_operations import ParameterIndexOperations
from types import FunctionType
_index_re = re.compile('(?:_(\d+))+') # pattern match for indices _index_re = re.compile('(?:_(\d+))+') # pattern match for indices
def translate_param_names_to_parameters(param_names): def translate_param_names_to_parameters(param_names):
@ -34,13 +35,13 @@ class Parameters(object):
pass pass
def _get_params(self): def _get_params(self):
return numpy.hstack([x._get_params() for x in self._params]) return numpy.fromiter(itertools.chain(*itertools.imap(lambda x: x._get_params(), self._params)), dtype=numpy.float64, count=sum(self.sizes))
def _set_params(self, params): def _set_params(self, params):
[p._set_params(params[s]) for s in self._param_slices] [p._set_params(params[s]) for s in self._param_slices]
def _get_params_transformed(self): def _get_params_transformed(self):
return numpy.hstack([x._get_params_transformed() for x in self._params]) return numpy.fromiter(itertools.chain(*itertools.imap(lambda x: x._get_params_transformed(), self._params)), dtype=numpy.float64, count=sum(self.sizes))
@property @property
def names(self): def names(self):
@ -81,13 +82,28 @@ class Parameter(object):
def __init__(self, name, value, constraint=None, *args, **kwargs): def __init__(self, name, value, constraint=None, *args, **kwargs):
self.name = name self.name = name
self.constraints = ParameterIndexOperations(self)
self._value = value self._value = value
self._current_slice = slice(None) self._current_slice = slice(None)
# def attribute_func(value, name):
for name in dir(value): # value_func = self.value.__getattribute__(name)
if not hasattr(self, name): # if isinstance(value_func, FunctionType):
self.__setattr__(name, value.__getattribute__(name)) # def f(*args, **kwargs):
# with self.slicing():
# return self.value.__getattribute__(name)(*args, **kwargs)
# else:
# with self.slicing():
# return self.value.__getattribute__(name)(*args, **kwargs)
# try:
# f.__doc__ = value_func.__doc__
# except AttributeError:
# # no docstring present
# pass
# return f
#
# for name in dir(value):
# if not hasattr(self, name):
# self.__setattr__(name, attribute_func(value, name))#value.__getattribute__(name))
self.constraints = ParameterIndexOperations(self)
@property @property
def value(self): def value(self):
@ -102,6 +118,9 @@ class Parameter(object):
def shape(self): def shape(self):
return self.value.shape return self.value.shape
@property @property
def realshape(self):
return self._value.shape
@property
def _desc(self): def _desc(self):
if self.size <= 1: if self.size <= 1:
return "%f"%self.value return "%f"%self.value
@ -109,56 +128,102 @@ class Parameter(object):
return self.shape return self.shape
@property @property
def _constr(self): def _constr(self):
return ' '.join([str(c) if c else '' for c in self.constraints.properties.keys()]) return ' '.join([str(c) if c else '' for c in self.constraints.keys()])
def _set_params(self, param): def _set_params(self, param):
self.value.flat = param with self.slicing():
self.value.flat = param
def _get_params(self): def _get_params(self):
return self.value.flat with self.slicing():
return self.value.flat
def _get_params_transformed(self): def _get_params_transformed(self):
params = self.value.copy() with self.slicing():
params = self.value.copy()
def constrain(self, constraint):
with self.slicing():
self.constraints.add(constraint, self._current_slice)
def constrain_positive(self): def constrain_positive(self):
self.constraints.add(Logexp(), self._current_slice) self.constrain(Logexp())
self._current_slice = slice(None)
def constrain_negative(self):
self.constrain(NegativeLogexp())
def unconstrain(self, constraints=None):
with self.slicing():
if constraints is None:
constraints = self.constraints.keys()
elif not isinstance(constraints, (tuple, list, numpy.ndarray)):
constraints = [constraints]
for constr in constraints:
self.constraints.remove(constr, self._current_slice)
def unconstrain_positive(self):
self.unconstrain(Logexp())
def __getitem__(self, s): def __getitem__(self, s):
try: try:
self.value[s] self.value[s]
self._current_slice = s#[s if s else slice(s2) for s,s2 in itertools.izip_longest([s], self.shape, fillvalue=None)] self._current_slice = s#[s if s else slice(s2) for s,s2 in itertools.izip_longest([s], self.shape, fillvalue=None)]
return self return self
except IndexError as i: except IndexError as i:
self._current_slice = slice(None)
raise i raise i
def __setitem__(self, s, value): def __setitem__(self, s, value):
try: try:
self.value[s] = value self.value[s] = value
self._current_slice = slice(None)
return self return self
except IndexError as i: except IndexError as i:
raise i raise i
def __repr__(self, *args, **kwargs): # def __repr__(self, *args, **kwargs):
view = repr(self.value) # view = repr(self.value)
self._current_slice = slice(None) # self._current_slice = slice(None)
return view # return view
def __str__(self, format_spec=None): def __str__(self, format_spec=None):
if format_spec is None: with self.slicing():
return str(self.value) if format_spec is None:
return format_spec.format(self=self) constr_matrix = numpy.empty(self.realshape, dtype=object)
constr_matrix[:] = ''
for constr, indices in self.constraints.iteritems():
constr_matrix[indices] = numpy.vectorize(lambda x: " ".join([x,str(constr)]) if x else str(constr))(constr_matrix[indices])
constr_matrix = constr_matrix.astype(numpy.string_)[self._current_slice]
p = numpy.get_printoptions()['precision']
constr = constr_matrix.flat
ind = numpy.array(list(itertools.product(*itertools.imap(range, self.realshape))))[self.constraints.create_raveled_indices(self._current_slice),...]
c_name, x_name, i_name = "Constraint", "Value", "Index"
lc = max(reduce(lambda a,b: max(a, len(b)), constr_matrix.flat, 0), len(c_name))
lx = max(reduce(lambda a,b: max(a, len("{x:=.{0}G}".format(p,x=b))), self.value.flat, 0), len(x_name))
li = max(reduce(lambda a,b: max(a, len(str(b))), ind, 0), len(i_name))
header = " {i:^{3}s} | {x:^{1}s} | {c:^{0}s}".format(lc,lx,p,li, x=x_name, c=c_name, i=i_name)
return "\n".join([header]+[" {i:^{3}s} | {x: >{1}.{2}G} | {c:^{0}s}".format(lc,lx,p,li, x=x, c=constr.next(), i=i) for i,x in itertools.izip(ind,self.value.flat)])
return format_spec.format(self=self)
import contextlib
@contextlib.contextmanager
def slicing(self, *args, **kwargs):
try:
yield
finally:
self._current_slice = slice(None)
del contextlib
if __name__ == '__main__': if __name__ == '__main__':
X = numpy.random.randn(3,2) X = numpy.random.randn(2,4)
p = Parameter("X", X) p = Parameter("X", X)
p2 = Parameter("Y", numpy.random.randn(3,1)) p2 = Parameter("Y", numpy.random.randn(3,1))
p3 = Parameter("rbf_variance", numpy.random.rand(1)) p3 = Parameter("rbf_variance", numpy.random.rand(1))
p4 = Parameter("rbf_lengthscale", numpy.random.rand(2)) p4 = Parameter("rbf_lengthscale", numpy.random.rand(2))
params = Parameters([p,p2,p3,p4]) params = Parameters([p,p2,p3,p4])
# params.X[5].constrain_positive() params.X[1].constrain_positive()
print params.X
#params.X[1,1].constrain_positive() #params.X[1,1].constrain_positive()