Added parameter files - Alan

GPy/core/parameter.py

@@ -0,0 +1,177 @@
+'''
+Created on 4 Sep 2013
+
+@author: maxz
+'''
+import re
+import itertools
+import numpy
+from GPy.core.transformations import Logexp
+_index_re = re.compile('(?:_(\d+))+')  # pattern match for indices
+
+def translate_param_names_to_parameters(param_names):
+    """
+    Naive translation from _get_param_names return to Parameters object.
+    Assumptions:
+     - array indices are at the and matching _\d+_\d+...
+     - names are in order and names match field names
+    """
+
+
+class Parameters(object):
+    def __init__(self, parameterlist, prefix=None, *args, **kwargs):
+        self._params = parameterlist
+        sizes = numpy.cumsum([0] + self.sizes)
+        self._param_slices = itertools.starmap(lambda start,stop: slice(start, stop), zip(sizes, sizes[1:]))
+        for p in parameterlist:
+            self.__setattr__(p.name, p)
+        
+    def grep_param_names(self, regexp):
+        """
+        Wrapper for parameterized.grep_param_names
+        """
+        pass
+    
+    def _get_params(self):
+        return numpy.hstack([x._get_params() for x in self._params])
+    
+    def _set_params(self, params):
+        [p._set_params(params[s]) for s in self._param_slices]
+    
+    def _get_params_transformed(self):
+        return numpy.hstack([x._get_params_transformed() for x in self._params])
+    
+    @property
+    def names(self):
+        return [x.name for x in self._params]
+
+    @property
+    def sizes(self):
+        return [x.size for x in self._params]
+    @property
+    def constraints(self):
+        return [x.constraints for x in self._params]
+    @property
+    def shapes(self):
+        return [x.shape for x in self._params]
+    @property
+    def _constrs(self):
+        return [x._constr for x in self._params]
+    @property
+    def _descs(self):
+        return [x._desc for x in self._params]
+    
+    def __str__(self, header=True):
+        nl = max([len(str(x)) for x in self.names + ["Name"]])
+        sl = max([len(str(x)) for x in self._descs + ["Value"]])
+        cl = max([len(str(x)) if x else 0 for x in self._constrs  + ["Constraint"]])
+        format_spec = "  {{self.name:^{0}s}}  |  {{self._desc:^{1}s}}  |  {{self._constr:^{2}s}}  ".format(nl, sl, cl)
+        if header:
+            header = "  {{0:^{0}s}}  |  {{1:^{1}s}}  |  {{2:^{2}s}}  ".format(nl, sl, cl).format("Name", "Value", "Constraint")
+            header += '\n' + '-'*len(header)
+            return '\n'.join([header]+[x.__str__(format_spec=format_spec) for x in self._params])
+        return '\n'.join([x.__str__(format_spec=format_spec) for x in self._params])
+    pass
+
+class ParameterIndexing(object):
+    def __init__(self, corresponding_param):
+        self.properties = {}
+        self.param = corresponding_param
+    def add(self, prop, s):
+        if prop in self.properties.keys():
+            self.properties[prop] = self.combine_indices(self.properties[prop], s) 
+        else:
+            self.properties[prop] = [numpy.r_[st] for st in s]
+    def combine_indices(self, s1, s2):
+        return [numpy.union1d(numpy.r_[ar1], numpy.r_[ar2]) for ar1, ar2 in itertools.izip_longest(s1, s2)]
+
+class Parameter(object):
+    tied_to = []  # list of parameters this parameter is tied to
+    fixed = False  # if this parameter is fixed
+    
+    def __init__(self, name, value, constraint=None, *args, **kwargs):
+        self.name = name
+        self.constraints = ParameterIndexing(self)
+        self._value = value
+        self._current_slice = slice(None)
+
+        for name in dir(value):
+            if not hasattr(self, name):
+                self.__setattr__(name, value.__getattribute__(name))
+    
+    @property
+    def value(self):
+        return self._value[self._current_slice]
+    @value.setter
+    def value(self, value):
+        self._value[self._current_slice] = value
+    @property
+    def size(self):
+        return self.value.size
+    @property
+    def shape(self):
+        return self.value.shape
+    @property
+    def _desc(self):
+        if self.size <= 1:
+            return "%f"%self.value
+        else:
+            return self.shape
+    @property
+    def _constr(self):
+        return ' '.join([str(c) if c else '' for c in self.constraints.properties.keys()])
+
+    def _set_params(self, param):
+        self.value.flat = param
+
+    def _get_params(self):
+        return self.value.flat
+    
+    def _get_params_transformed(self):
+        params = self.value.copy()
+        import ipdb;ipdb.set_trace()
+        return  
+    
+    def constrain_positive(self):
+        import ipdb;ipdb.set_trace()
+        self.constraints.add(Logexp(), self._current_slice)
+        self._current_slice = slice(None)
+    
+    def __getitem__(self, s):
+        try:
+            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)]
+            return self
+        except IndexError as i:
+            raise i
+    
+    def __setitem__(self, s, value):
+        try:
+            self.value[s] = value
+            return self
+        except IndexError as i:
+            raise i
+
+        
+    def __repr__(self, *args, **kwargs):
+        view = repr(self.value)
+        self._current_slice = slice(None)
+        return view
+        
+    def __str__(self, format_spec=None):
+        if format_spec is None:
+            return str(self.value)
+        return format_spec.format(self=self)
+    
+    
+    
+if __name__ == '__main__':
+    X = numpy.random.randn(3,2)
+    p = Parameter("X", X)
+    p2 = Parameter("Y", numpy.random.randn(3,1))
+    p3 = Parameter("rbf_variance", numpy.random.rand(1))
+    p4 = Parameter("rbf_lengthscale", numpy.random.rand(2))
+    params = Parameters([p,p2,p3,p4])
+#     params.X[5].constrain_positive()
+    #params.X[1,1].constrain_positive()
+    

GPy/testing/parameter_testing.py

@@ -0,0 +1,72 @@
+'''
+Created on 4 Sep 2013
+
+@author: maxz
+'''
+import unittest
+from GPy.kern.constructors import rbf, linear, white
+from GPy.models.gp_regression import GPRegression
+import numpy
+from GPy.models.bayesian_gplvm import BayesianGPLVM
+from GPy.core.parameter import Parameter, Parameters
+
+
+class Test(unittest.TestCase):
+    N, D, Q = 100, 1, 2
+    def setUp(self):
+        self.rbf_variance = numpy.random.rand()
+        self.rbf_lengthscale = numpy.random.rand(self.Q)
+        self.linear_variance = numpy.random.rand(self.Q)
+        self.kern = (rbf(self.Q, self.rbf_variance, self.rbf_lengthscale, ARD=True)
+                     + linear(self.Q, self.linear_variance, ARD=True))
+        self.X = numpy.random.rand(self.N, self.Q) + 10
+        self.X_variance = numpy.random.rand(self.N, self.Q) * .2
+
+        K = self.kern.K(self.X)
+
+        self.Y = numpy.random.multivariate_normal(numpy.zeros(self.N), K + numpy.eye(self.N) * .2, self.D).T
+
+        self.bgplvm = BayesianGPLVM(self.Y, self.Q, self.X, self.X_variance, kernel=self.kern)
+        self.bgplvm.ensure_default_constraints()
+
+        self.parameter = Parameters([
+                                     Parameters([
+                                                 Parameter('X', self.X),
+                                                 Parameter('X_variance', self.X_variance),
+                                                 ],
+                                                prefix='X'),
+                                     Parameter('iip', self.bgplvm.Z),
+                                     Parameters([
+                                                 Parameter('rbf_variance', self.rbf_variance),
+                                                 Parameter('rbf_lengthscale', self.rbf_lengthscale)
+                                                 ],
+                                                'rbf'
+                                                ),
+                                     Parameter('linear_variance', self.linear_variance),
+                                     Parameter('noise_variance', self.linear_variance),
+                                     ])
+
+    def tearDown(self):
+        pass
+
+
+    def testGrepParamNames(self):
+        assert(self.bgplvm.grep_param_names('X_\d') == self.parameter.grep_param_names('X_\d'))
+        assert(self.bgplvm.grep_param_names('X_\d+_1') == self.parameter.grep_param_names('X_\d+_1'))
+        assert(self.bgplvm.grep_param_names('X_\d_1') == self.parameter.grep_param_names('X_\d_1'))
+        assert(self.bgplvm.grep_param_names('X_.+_1') == self.parameter.grep_param_names('X_.+_1'))
+        assert(self.bgplvm.grep_param_names('X_1_1') == self.parameter.grep_param_names('X_1_1'))
+        assert(self.bgplvm.grep_param_names('X') == self.parameter.grep_param_names('X'))
+        assert(self.bgplvm.grep_param_names('rbf') == self.parameter.grep_param_names('rbf'))
+        assert(self.bgplvm.grep_param_names('rbf_l.*_1') == self.parameter.grep_param_names('rbf_l.*_1'))
+        assert(self.bgplvm.grep_param_names('l') == self.parameter.grep_param_names('l'))
+        assert(self.bgplvm.grep_param_names('dont_match') == self.parameter.grep_param_names('dont_match'))
+        assert(self.bgplvm.grep_param_names('.*') == self.parameter.grep_param_names('.*'))
+
+    def testConstraints(self):
+        assert(self.bgplvm.constraints)
+
+
+if __name__ == "__main__":
+    # import sys;sys.argv = ['', 'Test.testName']
+    unittest.main()