Python collections.Sized() Examples

def to_players(self, *players):
		"""
		Set the destination of the chat message.

		:param players: Player instance(s) or player login string(s). Can be a list, or a single entry.
		:return: Self reference.
		:rtype: pyplanet.contrib.chat.query.ChatQuery
		"""
		# Unpack list in unpacked list if given.
		if len(players) == 1 and isinstance(players[0], collections.Iterable):
			players = players[0]

		# Replace logins.
		if isinstance(players, Player):
			self._logins = set()
			self._logins.add(players.login)
		elif isinstance(players, str):
			self._logins = set()
			self._logins.add(players)
		elif isinstance(players, collections.Iterable) and isinstance(players, collections.Sized):
			self._logins = set()
			self.add_to(players)
		return self 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [bytes(), str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_abc_registry(self):
        d = dict(a=1)

        self.assertIsInstance(d.viewkeys(), collections.KeysView)
        self.assertIsInstance(d.viewkeys(), collections.MappingView)
        self.assertIsInstance(d.viewkeys(), collections.Set)
        self.assertIsInstance(d.viewkeys(), collections.Sized)
        self.assertIsInstance(d.viewkeys(), collections.Iterable)
        self.assertIsInstance(d.viewkeys(), collections.Container)

        self.assertIsInstance(d.viewvalues(), collections.ValuesView)
        self.assertIsInstance(d.viewvalues(), collections.MappingView)
        self.assertIsInstance(d.viewvalues(), collections.Sized)

        self.assertIsInstance(d.viewitems(), collections.ItemsView)
        self.assertIsInstance(d.viewitems(), collections.MappingView)
        self.assertIsInstance(d.viewitems(), collections.Set)
        self.assertIsInstance(d.viewitems(), collections.Sized)
        self.assertIsInstance(d.viewitems(), collections.Iterable)
        self.assertIsInstance(d.viewitems(), collections.Container) 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_abc_registry(self):
        d = dict(a=1)

        self.assertIsInstance(d.viewkeys(), collections.KeysView)
        self.assertIsInstance(d.viewkeys(), collections.MappingView)
        self.assertIsInstance(d.viewkeys(), collections.Set)
        self.assertIsInstance(d.viewkeys(), collections.Sized)
        self.assertIsInstance(d.viewkeys(), collections.Iterable)
        self.assertIsInstance(d.viewkeys(), collections.Container)

        self.assertIsInstance(d.viewvalues(), collections.ValuesView)
        self.assertIsInstance(d.viewvalues(), collections.MappingView)
        self.assertIsInstance(d.viewvalues(), collections.Sized)

        self.assertIsInstance(d.viewitems(), collections.ItemsView)
        self.assertIsInstance(d.viewitems(), collections.MappingView)
        self.assertIsInstance(d.viewitems(), collections.Set)
        self.assertIsInstance(d.viewitems(), collections.Sized)
        self.assertIsInstance(d.viewitems(), collections.Iterable)
        self.assertIsInstance(d.viewitems(), collections.Container) 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def ensure_flatlist(x):
    """Flattens a multi-list x to a single index list."""
    if isinstance(x[0], Sized):
        return x[0]
    return x 

def is_singleton(x):
    """Checks if x is list-like."""
    return not isinstance(x, Sized) 

def partial_shape(addr, full_shape):
    """
    Calculate the size of the sub-array represented by `addr`
    """
    def size(x, max):
        if isinstance(x, (int, long, numpy.integer)):
            return None
        elif isinstance(x, slice):
            y = min(max, x.stop or max)  # slice limits can go past the bounds
            return 1 + (y - (x.start or 0) - 1) // (x.step or 1)
        elif isinstance(x, collections.Sized):
            if hasattr(x, 'dtype') and x.dtype == bool:
                return x.sum()
            else:
                return len(x)
        else:
            raise TypeError("Unsupported index type %s" % type(x))

    addr = full_address(addr, full_shape)
    if isinstance(addr, numpy.ndarray) and addr.dtype == bool:
        return (addr.sum(),)
    elif all(isinstance(x, collections.Sized) for x in addr):
        return (len(addr[0]),)
    else:
        shape = [size(x, max) for (x, max) in zip(addr, full_shape)]
        return tuple([x for x in shape if x is not None])  # remove empty dimensions 

def _array_indices(self, addr):
        self.check_bounds(addr)

        def axis_indices(x, max):
            if isinstance(x, (int, long, numpy.integer)):
                return x
            elif isinstance(x, slice):  # need to handle negative values in slice
                return numpy.arange((x.start or 0),
                                    (x.stop or max),
                                    (x.step or 1),
                                    dtype=int)
            elif isinstance(x, collections.Sized):
                if hasattr(x, 'dtype') and x.dtype == bool:
                    return numpy.arange(max)[x]
                else:
                    return numpy.array(x)
            else:
                raise TypeError("Unsupported index type %s" % type(x))
        addr = self._full_address(addr)
        if isinstance(addr, numpy.ndarray) and addr.dtype == bool:
            if addr.ndim == 1:
                return (numpy.arange(self._shape[0])[addr],)
            else:
                raise NotImplementedError()
        elif all(isinstance(x, collections.Sized) for x in addr):
            indices = [numpy.array(x) for x in addr]
            return indices
        else:
            indices = [axis_indices(x, max) for (x, max) in zip(addr, self._shape)]
            if len(indices) == 1:
                return indices
            elif len(indices) == 2:
                if isinstance(indices[0], collections.Sized):
                    if isinstance(indices[1], collections.Sized):
                        mesh_xy = numpy.meshgrid(*indices)
                        return (mesh_xy[0].T, mesh_xy[1].T)  # meshgrid works on (x,y), not (i,j)
                return indices
            else:
                raise NotImplementedError("Only 1D and 2D arrays supported") 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def __instancecheck__(self, instance):
    return (isinstance(instance, collections.Iterable)
            and isinstance(instance, collections.Sized)
            and isinstance(instance, collections.Container)
            and all(isinstance(x, self._type) for x in instance)) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def _assert_is_collection(obj):
  assert not isinstance(obj, basestring)
  # Module 'collections' has no 'Iterable' member
  # pylint: disable=no-member
  if hasattr(collections, 'Iterable') and hasattr(collections, 'Sized'):
    assert (isinstance(obj, collections.Iterable) and
            isinstance(obj, collections.Sized)) 

def is_list(obj):
    return isinstance(obj, collections.Iterable) and \
           isinstance(obj, collections.Sized) and \
           not isinstance(obj, (str, collections.Mapping)) 

def _assert_is_collection(obj):
  assert not isinstance(obj, basestring)
  # Module 'collections' has no 'Iterable' member
  # pylint: disable=no-member
  if hasattr(collections, 'Iterable') and hasattr(collections, 'Sized'):
    assert (isinstance(obj, collections.Iterable) and
            isinstance(obj, collections.Sized)) 

def fit(self, X, y):
        """Actual fitting,  performing the search over parameters."""

        parameter_iterable = ParameterGrid(self.param_grid)

        estimator = self.estimator
        cv = self.cv

        n_samples = _num_samples(X)
        X, y = indexable(X, y)

        if y is not None:
            if len(y) != n_samples:
                raise ValueError('Target variable (y) has a different number '
                                 'of samples (%i) than data (X: %i samples)'
                                 % (len(y), n_samples))
        cv = check_cv(cv, X, y, classifier=is_classifier(estimator))

        if self.verbose > 0:
            if isinstance(parameter_iterable, Sized):
                n_candidates = len(parameter_iterable)
                print("Fitting {0} folds for each of {1} candidates, totalling"
                      " {2} fits".format(len(cv), n_candidates,
                                         n_candidates * len(cv)))

        base_estimator = clone(self.estimator)

        pre_dispatch = self.pre_dispatch

        out = Parallel(
            n_jobs=self.n_jobs, verbose=self.verbose,
            pre_dispatch=pre_dispatch
        )(delayed(cv_fit_and_score)(clone(base_estimator), X, y, self.scoring,
                                      parameters, cv=cv)
            for parameters in parameter_iterable)

        best = sorted(out, key=lambda x: x[0])[-1]
        self.best_params_ = best[1]
        self.best_score_ = best[0]

        if self.refit:
            # fit the best estimator using the entire dataset
            # clone first to work around broken estimators
            best_estimator = clone(base_estimator).set_params(
                **best[1])
            if y is not None:
                best_estimator.fit(X, y, **self.fit_params)
            else:
                best_estimator.fit(X, **self.fit_params)
            self.best_estimator_ = best_estimator

        return self 

def _assert_is_collection(obj):
  assert not isinstance(obj, basestring)
  # Module 'collections' has no 'Iterable' member
  # pylint: disable=no-member
  if hasattr(collections, 'Iterable') and hasattr(collections, 'Sized'):
    assert (isinstance(obj, collections.Iterable) and
            isinstance(obj, collections.Sized)) 

def trigger(self, channels, event_name, data, socket_id=None):
        '''
        Trigger an event on one or more channels, see:

        http://pusher.com/docs/rest_api#method-post-event
        '''

        if isinstance(channels, six.string_types):
            channels = [channels]

        if isinstance(channels, dict) or not isinstance(channels, (collections.Sized, collections.Iterable)):
            raise TypeError("Expected a single or a list of channels")

        if len(channels) > 10:
            raise ValueError("Too many channels")

        channels = list(map(validate_channel, channels))

        event_name = ensure_text(event_name, "event_name")

        if len(event_name) > 200:
            raise ValueError("event_name too long")

        if isinstance(data, six.string_types):
            data = ensure_text(data, "data")
        else:
            data = json.dumps(data, cls=self._json_encoder)

        if len(data) > 10240:
            raise ValueError("Too much data")

        params = {
            'name': event_name,
            'channels': channels,
            'data': data
        }
        if socket_id:
            params['socket_id'] = validate_socket_id(socket_id)

        return Request(self, POST, "/apps/%s/events" % self.app_id, params) 

def test_parameter_grid():
    # Test basic properties of ParameterGrid.
    params1 = {"foo": [1, 2, 3]}
    grid1 = ParameterGrid(params1)
    assert_true(isinstance(grid1, Iterable))
    assert_true(isinstance(grid1, Sized))
    assert_equal(len(grid1), 3)
    assert_grid_iter_equals_getitem(grid1)

    params2 = {"foo": [4, 2],
               "bar": ["ham", "spam", "eggs"]}
    grid2 = ParameterGrid(params2)
    assert_equal(len(grid2), 6)

    # loop to assert we can iterate over the grid multiple times
    for i in xrange(2):
        # tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
        points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
        assert_equal(points,
                     set(("bar", x, "foo", y)
                         for x, y in product(params2["bar"], params2["foo"])))
    assert_grid_iter_equals_getitem(grid2)

    # Special case: empty grid (useful to get default estimator settings)
    empty = ParameterGrid({})
    assert_equal(len(empty), 1)
    assert_equal(list(empty), [{}])
    assert_grid_iter_equals_getitem(empty)
    assert_raises(IndexError, lambda: empty[1])

    has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
    assert_equal(len(has_empty), 4)
    assert_equal(list(has_empty), [{'C': 1}, {'C': 10}, {}, {'C': .5}])
    assert_grid_iter_equals_getitem(has_empty) 

def test_parameter_grid():
    # Test basic properties of ParameterGrid.
    params1 = {"foo": [1, 2, 3]}
    grid1 = ParameterGrid(params1)
    assert_true(isinstance(grid1, Iterable))
    assert_true(isinstance(grid1, Sized))
    assert_equal(len(grid1), 3)
    assert_grid_iter_equals_getitem(grid1)

    params2 = {"foo": [4, 2],
               "bar": ["ham", "spam", "eggs"]}
    grid2 = ParameterGrid(params2)
    assert_equal(len(grid2), 6)

    # loop to assert we can iterate over the grid multiple times
    for i in xrange(2):
        # tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
        points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
        assert_equal(points,
                     set(("bar", x, "foo", y)
                         for x, y in product(params2["bar"], params2["foo"])))

    assert_grid_iter_equals_getitem(grid2)

    # Special case: empty grid (useful to get default estimator settings)
    empty = ParameterGrid({})
    assert_equal(len(empty), 1)
    assert_equal(list(empty), [{}])
    assert_grid_iter_equals_getitem(empty)
    assert_raises(IndexError, lambda: empty[1])

    has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
    assert_equal(len(has_empty), 4)
    assert_equal(list(has_empty), [{'C': 1}, {'C': 10}, {}, {'C': .5}])
    assert_grid_iter_equals_getitem(has_empty) 

def __instancecheck__(self, instance):
    return (isinstance(instance, collections.Iterable)
            and isinstance(instance, collections.Sized)
            and isinstance(instance, collections.Container)
            and all(isinstance(x, self._type) for x in instance)) 

def __init__(self, value, shape=None, dtype=None):
        """
        Create a new lazy array.

        `value` : may be an int, long, float, bool, NumPy array, iterator,
                  generator or a function, `f(i)` or `f(i,j)`, depending on the
                  dimensions of the array.

        `f(i,j)` should return a single number when `i` and `j` are integers,
        and a 1D array when either `i` or `j` or both is a NumPy array (in the
        latter case the two arrays must have equal lengths).
        """

        self.dtype = dtype
        self.operations = []
        if isinstance(value, basestring):
            raise TypeError("An larray cannot be created from a string")
        elif isinstance(value, larray):
            if shape is not None and value.shape is not None:
                assert shape == value.shape
            self._shape = shape or value.shape
            self.base_value = value.base_value
            self.dtype = dtype or value.dtype
            self.operations = value.operations  # should deepcopy?
      
        elif isinstance(value, collections.Sized):  # False for numbers, generators, functions, iterators
            if have_scipy and sparse.issparse(value):  # For sparse matrices
                self.dtype = dtype or value.dtype                           
            elif not isinstance(value, numpy.ndarray):  
                value = numpy.array(value, dtype=dtype)
            elif dtype is not None:
               assert numpy.can_cast(value.dtype, dtype, casting='safe')  # or could convert value to the provided dtype
            if shape and value.shape != shape:
                raise ValueError("Array has shape %s, value has shape %s" % (shape, value.shape))
            self._shape = value.shape
            self.base_value = value
    
        else:
            assert numpy.isreal(value)  # also True for callables, generators, iterators
            self._shape = shape
            if dtype is None or isinstance(value, dtype):
                self.base_value = value
            else:
                try:
                    self.base_value = dtype(value)
                except TypeError:
                    self.base_value = value 

def check_bounds(self, addr):
        """
        Check whether the given address is within the array bounds.
        """
        def is_boolean_array(arr):
            return hasattr(arr, 'dtype') and arr.dtype == bool

        def check_axis(x, size):
            if isinstance(x, (int, long, numpy.integer)):
                lower = upper = x
            elif isinstance(x, slice):
                lower = x.start or 0
                upper = min(x.stop or size - 1, size - 1)  # slices are allowed to go past the bounds
            elif isinstance(x, collections.Sized):
                if is_boolean_array(x):
                    lower = 0
                    upper = x.size - 1
                else:
                    if len(x) == 0:
                        raise ValueError("Empty address component (address was %s)" % str(addr))
                    if hasattr(x, "min"):
                        lower = x.min()
                    else:
                        lower = min(x)
                    if hasattr(x, "max"):
                        upper = x.max()
                    else:
                        upper = max(x)
            else:
                raise TypeError("Invalid array address: %s (element of type %s)" % (str(addr), type(x)))
            if (lower < -size) or (upper >= size):
                raise IndexError("Index out of bounds")
        full_addr = self._full_address(addr)
        if isinstance(addr, numpy.ndarray) and addr.dtype == bool:
            if len(addr.shape) > len(self._shape):
                raise IndexError("Too many indices for array")
            for xmax, size in zip(addr.shape, self._shape):
                upper = xmax - 1
                if upper >= size:
                    raise IndexError("Index out of bounds")
        else:
            for i, size in zip(full_addr, self._shape):
                check_axis(i, size) 

def __init__(self, iterable=None):
        r"""Create a new, empty Multiset object.

        And if given, initialize with elements from input iterable.
        Or, initialize from a mapping of elements to their multiplicity.

        Example:

        >>> ms = Multiset()                 # a new, empty multiset
        >>> ms = Multiset('abc')            # a new multiset from an iterable
        >>> ms = Multiset({'a': 4, 'b': 2}) # a new multiset from a mapping

        Args:
            iterable:
                An optional iterable of elements or mapping of elements to multiplicity to
                initialize the multiset from.
        """
        if isinstance(iterable, BaseMultiset):
            self._elements = iterable._elements.copy()
            self._total = iterable._total
        else:
            self._elements = _elements = defaultdict(int)
            _total = 0
            if iterable is not None:
                if isinstance(iterable, _sequence_types):
                    for element in iterable:
                        _elements[element] += 1
                    _total = len(iterable)
                elif isinstance(iterable, dict):
                    for element, multiplicity in iterable.items():
                        if multiplicity > 0:
                            _elements[element] = multiplicity
                            _total += multiplicity
                elif isinstance(iterable, _iter_types):
                    for element in iterable:
                        _elements[element] += 1
                        _total += 1
                elif isinstance(iterable, Mapping):
                    for element, multiplicity in iterable.items():
                        if multiplicity > 0:
                            _elements[element] = multiplicity
                            _total += multiplicity
                elif isinstance(iterable, Sized):
                    for element in iterable:
                        _elements[element] += 1
                    _total = len(iterable)
                else:
                    for element in iterable:
                        _elements[element] += 1
                        _total += 1
            self._total = _total 

def fit(self, X, y):
        """Actual fitting,  performing the search over parameters."""

        parameter_iterable = ParameterSampler(self.param_distributions,
                                              self.n_iter,
                                              random_state=self.random_state)
        estimator = self.estimator
        cv = self.cv

        n_samples = _num_samples(X)
        X, y = indexable(X, y)

        if y is not None:
            if len(y) != n_samples:
                raise ValueError('Target variable (y) has a different number '
                                 'of samples (%i) than data (X: %i samples)'
                                 % (len(y), n_samples))
        cv = check_cv(cv, X, y, classifier=is_classifier(estimator))

        if self.verbose > 0:
            if isinstance(parameter_iterable, Sized):
                n_candidates = len(parameter_iterable)
                print("Fitting {0} folds for each of {1} candidates, totalling"
                      " {2} fits".format(len(cv), n_candidates,
                                         n_candidates * len(cv)))

        base_estimator = clone(self.estimator)

        pre_dispatch = self.pre_dispatch

        out = Parallel(
            n_jobs=self.n_jobs, verbose=self.verbose,
            pre_dispatch=pre_dispatch
        )(
            delayed(cv_fit_and_score)(clone(base_estimator), X, y, self.scoring,
                                      parameters, cv=cv)
            for parameters in parameter_iterable)

        best = sorted(out, reverse=True)[0]
        self.best_params_ = best[1]
        self.best_score_ = best[0]

        if self.refit:
            # fit the best estimator using the entire dataset
            # clone first to work around broken estimators
            best_estimator = clone(base_estimator).set_params(
                **best[1])
            if y is not None:
                best_estimator.fit(X, y, **self.fit_params)
            else:
                best_estimator.fit(X, **self.fit_params)
            self.best_estimator_ = best_estimator

        return self 

def __call__(self, x):
        """
        Evaluates the piecewise linear function using
        interpolation. This method supports vectorized
        function calls as the interpolation process can be
        expensive for high dimensional data.

        For the case when a single point is provided, the
        argument x should be a (D,) shaped numpy array or
        list, where D is the dimension of points in the
        triangulation.

        For the vectorized case, the argument x should be
        a (n,D)-shaped numpy array.
        """
        assert isinstance(x, collections.Sized)
        if isinstance(x, pyomo.core.kernel.component_piecewise.\
                      util.numpy.ndarray):
            if x.shape != self._tri.points.shape[1:]:
                multi = True
                assert x.shape[1:] == self._tri.points[0].shape, \
                    "%s[1] != %s" % (x.shape, self._tri.points[0].shape)
            else:
                multi = False
        else:
            multi = False
        _, ndim = self._tri.points.shape
        i = self._tri.find_simplex(x)
        if multi:
            Tinv = self._tri.transform[i,:ndim]
            r = self._tri.transform[i,ndim]
            b = pyomo.core.kernel.component_piecewise.util.\
                numpy.einsum('ijk,ik->ij', Tinv, x-r)
            b = pyomo.core.kernel.component_piecewise.util.\
                numpy.c_[b, 1 - b.sum(axis=1)]
            s = self._tri.simplices[i]
            return (b*self._values[s]).sum(axis=1)
        else:
            b = self._tri.transform[i,:ndim,:ndim].dot(
                x - self._tri.transform[i,ndim,:])
            s = self._tri.simplices[i]
            val = b.dot(self._values[s[:ndim]])
            val += (1-b.sum())*self._values[s[ndim]]
            return val