neurolang.datalog.basic_representation module

Compiler for the intermediate representation of a Datalog program. The DatalogProgram class processes the intermediate representation of a program and extracts the extensional, intensional, and builtin sets.

class neurolang.datalog.basic_representation.DatalogProgram(symbol_table=None)

Bases: DatalogProgramMixin, ExpressionWalker

Attributes:

included_constants

included_functions

patterns

Property holding an iterator of triplets (pattern, guard, action).

Methods

constant_equals
infer_iterable_type(iterable)	Infer the type of iterable elements without modifying the iterable.
match(expression)	Find the action for a given expression by going through the patterns.
pattern_match(pattern, expression)	Return True if pattern matches expression.
type
union_of_cq(expression)	This pattern is here to avoid processing the full program when one rule is rewritten.

add_extensional_predicate_from_tuples
add_included_constants_and_functions_to_symbol_table
builtins
extensional_database
fact
intensional_database
new_set
pattern_match_expression
pattern_match_expression_parameters
pattern_match_expression_tuple
pattern_match_tuple
pop_scope
predicate_terms
process_expression
process_iterable_argument
push_scope
statement_intensional
symbol
walk

protected_keywords = {}

type = typing.Any

class neurolang.datalog.basic_representation.Fact(consequent)

Bases: Implication

Attributes:

fact

Methods

__call__(*args, **kwargs)	Call self as a function.
apply(*args)	Builds a new expression using a tuple of its parameters
type
unapply()	Returns a tuple of parameters used to build the expression.

cast
change_type
get_wrapped_attribute

property fact

type = typing.Any

class neurolang.datalog.basic_representation.Implication(consequent, antecedent)

Bases: LogicOperator

Expression of the form P(x) ← Q(x)

Methods

__call__(*args, **kwargs)	Call self as a function.
apply(*args)	Builds a new expression using a tuple of its parameters
type
unapply()	Returns a tuple of parameters used to build the expression.

cast
change_type
get_wrapped_attribute

type = typing.Any

class neurolang.datalog.basic_representation.NullConstant(value, auto_infer_type=True, verify_type=True)

Bases: Constant

Methods

__call__(*args, **kwargs)	Call self as a function.
apply(*args)	Builds a new expression using a tuple of its parameters
type
unapply()	Returns a tuple of parameters used to build the expression.

cast
change_type
get_wrapped_attribute

type = typing.Any

class neurolang.datalog.basic_representation.Undefined(value, auto_infer_type=True, verify_type=True)

Bases: Constant

Methods

__call__(*args, **kwargs)	Call self as a function.
apply(*args)	Builds a new expression using a tuple of its parameters
type
unapply()	Returns a tuple of parameters used to build the expression.

cast
change_type
get_wrapped_attribute

type = typing.Any

class neurolang.datalog.basic_representation.UnionOfConjunctiveQueries

Bases: object

class neurolang.datalog.basic_representation.WrappedRelationalAlgebraSet(iterable=None, row_type=<class 'neurolang.type_system.Unknown'>, verify_row_type=True, **kwargs)

Bases: WrappedRelationalAlgebraSetMixin, RelationalAlgebraSet

Attributes:

arity

columns

row_type

Methods

clear()	This is slow (creates N new iterators!) but effective.
isdisjoint(other)	Return True if two sets have a null intersection.
pop()	Return the popped value.
projection(*columns)	Projects the set on the given list of columns.
remove(value)	Remove an element.
selection(select_criteria)	Select the elements based on the given selet_criteria.
selection_columns(select_criteria)	Select the elements where column pairs indicated as key, value items of the select_criteria are equal.

add
as_numpy_array
as_pandas_dataframe
copy
create_view_from
cross_product
dee
discard
dum
equijoin
fetch_one
groupby
is_constant_tuple_or_tuple_of_constants
is_dee
is_dum
is_empty
itervalues
unwrap
unwrapped_iter

unwrap()