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