Module tf.core.otypefeature
Access to otype feature data.
In general, features are stored as dictionaries, but this specific feature has an optimized representation. Since it is a large feature and present in any TF dataset, this pays off.
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"""
# Access to *otype* feature data.
In general, features are stored as dictionaries, but this specific feature
has an optimized representation. Since it is a large feature and present
in any TF dataset, this pays off.
"""
class OtypeFeature:
def __init__(self, api, metaData, data):
self.api = api
self.meta = metaData
"""Metadata of the feature.
This is the information found in the lines starting with `@`
in the `.tf` feature file.
"""
self.data = data[0]
self.maxSlot = data[1]
"""Last slot node in the corpus."""
self.maxNode = data[2]
"""Last node node.in the corpus."""
self.slotType = data[3]
"""The name of the slot type."""
self.all = None
"""List of all node types from big to small."""
def items(self):
"""As in `tf.core.nodefeature.NodeFeature.items`.
"""
slotType = self.slotType
maxSlot = self.maxSlot
data = self.data
for n in range(1, maxSlot + 1):
yield (n, slotType)
maxNode = self.maxNode
shift = maxSlot + 1
for n in range(maxSlot + 1, maxNode + 1):
yield (n, data[n - shift])
def v(self, n):
"""Get the node type of a node.
Parameters
----------
n: integer
The node in question
Returns
-------
string
The node type of that node. All nodes have a node type, and it is
always a string.
"""
if n == 0:
return None
if n < self.maxSlot + 1:
return self.slotType
m = n - self.maxSlot
if m <= len(self.data):
return self.data[m - 1]
return None
def s(self, val):
"""Query all nodes having a specified node type.
This is an other way to walk through nodes than using
`tf.core.nodes.Nodes.walk`.
Parameters
----------
val: int | string
The node type that all resulting nodes have.
Returns
-------
tuple of int
All nodes that have this node type, sorted in the canonical order.
(`tf.core.nodes`)
"""
# NB: the support attribute has been added by precomputing __levels__
if val in self.support:
(b, e) = self.support[val]
return range(b, e + 1)
else:
return ()
def sInterval(self, val):
"""The interval of nodes having a specified node type.
The nodes are organized in intervals of nodes with the same type.
For each type there is only one such interval.
The first interval, `1:maxSlot + 1` is reserved for the slot type.
Parameters
----------
val: int | string
The node type in question.
Returns
-------
2-tuple of int
The start and end node of the interval of nodes with this type.
"""
# NB: the support attribute has been added by precomputing __levels__
if val in self.support:
return self.support[val]
else:
return ()Classes
class OtypeFeature (api, metaData, data)-
Expand source code Browse git
class OtypeFeature: def __init__(self, api, metaData, data): self.api = api self.meta = metaData """Metadata of the feature. This is the information found in the lines starting with `@` in the `.tf` feature file. """ self.data = data[0] self.maxSlot = data[1] """Last slot node in the corpus.""" self.maxNode = data[2] """Last node node.in the corpus.""" self.slotType = data[3] """The name of the slot type.""" self.all = None """List of all node types from big to small.""" def items(self): """As in `tf.core.nodefeature.NodeFeature.items`. """ slotType = self.slotType maxSlot = self.maxSlot data = self.data for n in range(1, maxSlot + 1): yield (n, slotType) maxNode = self.maxNode shift = maxSlot + 1 for n in range(maxSlot + 1, maxNode + 1): yield (n, data[n - shift]) def v(self, n): """Get the node type of a node. Parameters ---------- n: integer The node in question Returns ------- string The node type of that node. All nodes have a node type, and it is always a string. """ if n == 0: return None if n < self.maxSlot + 1: return self.slotType m = n - self.maxSlot if m <= len(self.data): return self.data[m - 1] return None def s(self, val): """Query all nodes having a specified node type. This is an other way to walk through nodes than using `tf.core.nodes.Nodes.walk`. Parameters ---------- val: int | string The node type that all resulting nodes have. Returns ------- tuple of int All nodes that have this node type, sorted in the canonical order. (`tf.core.nodes`) """ # NB: the support attribute has been added by precomputing __levels__ if val in self.support: (b, e) = self.support[val] return range(b, e + 1) else: return () def sInterval(self, val): """The interval of nodes having a specified node type. The nodes are organized in intervals of nodes with the same type. For each type there is only one such interval. The first interval, `1:maxSlot + 1` is reserved for the slot type. Parameters ---------- val: int | string The node type in question. Returns ------- 2-tuple of int The start and end node of the interval of nodes with this type. """ # NB: the support attribute has been added by precomputing __levels__ if val in self.support: return self.support[val] else: return ()Instance variables
var all-
List of all node types from big to small.
var maxNode-
Last node node.in the corpus.
var maxSlot-
Last slot node in the corpus.
var meta-
Metadata of the feature.
This is the information found in the lines starting with
@in the.tffeature file. var slotType-
The name of the slot type.
Methods
def items(self)-
As in
NodeFeature.items().Expand source code Browse git
def items(self): """As in `tf.core.nodefeature.NodeFeature.items`. """ slotType = self.slotType maxSlot = self.maxSlot data = self.data for n in range(1, maxSlot + 1): yield (n, slotType) maxNode = self.maxNode shift = maxSlot + 1 for n in range(maxSlot + 1, maxNode + 1): yield (n, data[n - shift]) def s(self, val)-
Query all nodes having a specified node type.
This is an other way to walk through nodes than using
Nodes.walk().Parameters
val:int | string- The node type that all resulting nodes have.
Returns
tupleofint- All nodes that have this node type, sorted in the canonical order.
(
tf.core.nodes)
Expand source code Browse git
def s(self, val): """Query all nodes having a specified node type. This is an other way to walk through nodes than using `tf.core.nodes.Nodes.walk`. Parameters ---------- val: int | string The node type that all resulting nodes have. Returns ------- tuple of int All nodes that have this node type, sorted in the canonical order. (`tf.core.nodes`) """ # NB: the support attribute has been added by precomputing __levels__ if val in self.support: (b, e) = self.support[val] return range(b, e + 1) else: return () def sInterval(self, val)-
The interval of nodes having a specified node type.
The nodes are organized in intervals of nodes with the same type. For each type there is only one such interval. The first interval,
1:maxSlot + 1is reserved for the slot type.Parameters
val:int | string- The node type in question.
Returns
2-tupleofint- The start and end node of the interval of nodes with this type.
Expand source code Browse git
def sInterval(self, val): """The interval of nodes having a specified node type. The nodes are organized in intervals of nodes with the same type. For each type there is only one such interval. The first interval, `1:maxSlot + 1` is reserved for the slot type. Parameters ---------- val: int | string The node type in question. Returns ------- 2-tuple of int The start and end node of the interval of nodes with this type. """ # NB: the support attribute has been added by precomputing __levels__ if val in self.support: return self.support[val] else: return () def v(self, n)-
Get the node type of a node.
Parameters
n:integer- The node in question
Returns
string- The node type of that node. All nodes have a node type, and it is always a string.
Expand source code Browse git
def v(self, n): """Get the node type of a node. Parameters ---------- n: integer The node in question Returns ------- string The node type of that node. All nodes have a node type, and it is always a string. """ if n == 0: return None if n < self.maxSlot + 1: return self.slotType m = n - self.maxSlot if m <= len(self.data): return self.data[m - 1] return None