enabled some more getting/setting parameters, such as regular expressions and params

GPy/core/parameterization/observable_array.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-__updated__ = '2014-03-31'
+__updated__ = '2014-04-15'
 
 import numpy as np
 from parameter_core import Observable, Pickleable

GPy/core/parameterization/param.py

@@ -3,6 +3,7 @@
 
 import itertools
 import numpy
+np = numpy
 from parameter_core import OptimizationHandlable, adjust_name_for_printing
 from observable_array import ObsAr
 
@@ -118,10 +119,6 @@ class Param(OptimizationHandlable, ObsAr):
         except AttributeError: pass  # returning 0d array or float, double etc
         return new_arr
 
-    def __setitem__(self, s, val):
-        super(Param, self).__setitem__(s, val)
-
-
     def _raveled_index(self, slice_index=None):
         # return an index array on the raveled array, which is formed by the current_slice
         # of this object
@@ -311,15 +308,15 @@ class ParamConcatenation(object):
     #===========================================================================
     def __getitem__(self, s):
         ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
-        params = [p.param_array[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p.param_array[ind[ps]])]
+        params = [p.param_array.flat[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p.param_array.flat[ind[ps]])]
         if len(params)==1: return params[0]
         return ParamConcatenation(params)
     def __setitem__(self, s, val, update=True):
         if isinstance(val, ParamConcatenation):
             val = val.values()
         ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
-        vals = self.values(); vals[s] = val; del val
-        [numpy.place(p, ind[ps], vals[ps])
+        vals = self.values(); vals[s] = val
+        [numpy.copyto(p, vals[ps], where=ind[ps])
          for p, ps in zip(self.params, self._param_slices_)]
         if update:
             self.update_all_params()
@@ -411,3 +408,42 @@ class ParamConcatenation(object):
         return "\n".join(strings)
     def __repr__(self):
         return "\n".join(map(repr,self.params))
+
+    def __ilshift__(self, *args, **kwargs):
+        self[:] = np.ndarray.__ilshift__(self.values(), *args, **kwargs)
+
+    def __irshift__(self, *args, **kwargs):
+        self[:] = np.ndarray.__irshift__(self.values(), *args, **kwargs)
+
+    def __ixor__(self, *args, **kwargs):
+        self[:] = np.ndarray.__ixor__(self.values(), *args, **kwargs)
+
+    def __ipow__(self, *args, **kwargs):
+        self[:] = np.ndarray.__ipow__(self.values(), *args, **kwargs)
+
+    def __ifloordiv__(self, *args, **kwargs):
+        self[:] = np.ndarray.__ifloordiv__(self.values(), *args, **kwargs)
+
+    def __isub__(self, *args, **kwargs):
+        self[:] = np.ndarray.__isub__(self.values(), *args, **kwargs)
+
+    def __ior__(self, *args, **kwargs):
+        self[:] = np.ndarray.__ior__(self.values(), *args, **kwargs)
+
+    def __itruediv__(self, *args, **kwargs):
+        self[:] = np.ndarray.__itruediv__(self.values(), *args, **kwargs)
+
+    def __idiv__(self, *args, **kwargs):
+        self[:] = np.ndarray.__idiv__(self.values(), *args, **kwargs)
+
+    def __iand__(self, *args, **kwargs):
+        self[:] = np.ndarray.__iand__(self.values(), *args, **kwargs)
+
+    def __imod__(self, *args, **kwargs):
+        self[:] = np.ndarray.__imod__(self.values(), *args, **kwargs)
+
+    def __iadd__(self, *args, **kwargs):
+        self[:] = np.ndarray.__iadd__(self.values(), *args, **kwargs)
+
+    def __imul__(self, *args, **kwargs):
+        self[:] = np.ndarray.__imul__(self.values(), *args, **kwargs)

GPy/core/parameterization/parameter_core.py

@@ -15,8 +15,9 @@ Observable Pattern for patameterization
 
 from transformations import Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
 import numpy as np
+import re
 
-__updated__ = '2014-03-31'
+__updated__ = '2014-04-15'
 
 class HierarchyError(Exception):
     """
@@ -28,7 +29,15 @@ def adjust_name_for_printing(name):
     Make sure a name can be printed, alongside used as a variable name.
     """
     if name is not None:
-        return name.replace(" ", "_").replace(".", "_").replace("-", "_m_").replace("+", "_p_").replace("!", "_I_").replace("**", "_xx_").replace("*", "_x_").replace("/", "_l_").replace("@", '_at_')
+        name2 = name
+        name = name.replace(" ", "_").replace(".", "_").replace("-", "_m_")
+        name = name.replace("+", "_p_").replace("!", "_I_")
+        name = name.replace("**", "_xx_").replace("*", "_x_")
+        name = name.replace("/", "_l_").replace("@", '_at_')
+        name = name.replace("(", "_of_").replace(")", "")
+        if re.match(r'^[a-zA-Z_][a-zA-Z0-9-_]*$', name) is None:
+            raise NameError, "name {} converted to {} cannot be further converted to valid python variable name!".format(name2, name)
+        return name
     return ''
 
 

GPy/core/parameterization/parameterized.py

@@ -185,6 +185,8 @@ class Parameterized(Parameterizable):
             return ParamConcatenation(paramlist)
 
     def __setitem__(self, name, value, paramlist=None):
+        if value is None:
+            return # nothing to do here
         if isinstance(name, (slice, tuple, np.ndarray)):
             try:
                 self.param_array[name] = value
@@ -198,7 +200,7 @@ class Parameterized(Parameterizable):
 
     def __setattr__(self, name, val):
         # override the default behaviour, if setting a param, so broadcasting can by used
-        if hasattr(self, '_parameters_'):
+        if hasattr(self, "_parameters_"):
             pnames = self.parameter_names(False, adjust_for_printing=True, recursive=False)
             if name in pnames: self._parameters_[pnames.index(name)][:] = val; return
         object.__setattr__(self, name, val);

GPy/testing/model_tests.py

@@ -130,6 +130,17 @@ class MiscTests(unittest.TestCase):
         m2.kern[:] = m.kern[''].values()
         np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
 
+    def test_model_set_params(self):
+        m = GPy.models.GPRegression(self.X, self.Y)
+        lengthscale = np.random.uniform()
+        m.kern.lengthscale = lengthscale
+        np.testing.assert_equal(m.kern.lengthscale, lengthscale)
+        m.kern.lengthscale *= 1
+        m['.*var'] -= .1
+        np.testing.assert_equal(m.kern.lengthscale, lengthscale)
+        m.optimize()
+        print m
+
     def test_model_optimize(self):
         X = np.random.uniform(-3., 3., (20, 1))
         Y = np.sin(X) + np.random.randn(20, 1) * 0.05