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Clean up redundant type annotations "int|float"

This commit is contained in:
Joris van Rantwijk 2024-05-25 18:00:37 +02:00
parent c8a3f7f684
commit 91d4afb271
1 changed files with 27 additions and 27 deletions
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python/mwmatching.py
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@ -12,7 +12,7 @@ from typing import NamedTuple, Optional
def maximum_weight_matching( def maximum_weight_matching(
edges: Sequence[tuple[int, int, int|float]] edges: Sequence[tuple[int, int, float]]
) -> list[tuple[int, int]]: ) -> list[tuple[int, int]]:
"""Compute a maximum-weighted matching in the general undirected weighted """Compute a maximum-weighted matching in the general undirected weighted
graph given by "edges". graph given by "edges".
@ -93,8 +93,8 @@ def maximum_weight_matching(
def adjust_weights_for_maximum_cardinality_matching( def adjust_weights_for_maximum_cardinality_matching(
edges: Sequence[tuple[int, int, int|float]] edges: Sequence[tuple[int, int, float]]
) -> Sequence[tuple[int, int, int|float]]: ) -> Sequence[tuple[int, int, float]]:
"""Adjust edge weights such that the maximum-weight matching of """Adjust edge weights such that the maximum-weight matching of
the adjusted graph is a maximum-cardinality matching, equal to the adjusted graph is a maximum-cardinality matching, equal to
a matching in the original graph that has maximum weight out of all a matching in the original graph that has maximum weight out of all
@ -157,7 +157,7 @@ def adjust_weights_for_maximum_cardinality_matching(
if min_weight > 0 and min_weight >= num_vertex * weight_range: if min_weight > 0 and min_weight >= num_vertex * weight_range:
return edges return edges
delta: int|float delta: float
if weight_range > 0: if weight_range > 0:
# Increase weights to make minimum edge weight large enough # Increase weights to make minimum edge weight large enough
# to improve any non-maximum-cardinality matching. # to improve any non-maximum-cardinality matching.
@ -180,7 +180,7 @@ class MatchingError(Exception):
pass pass
def _check_input_types(edges: Sequence[tuple[int, int, int|float]]) -> None: def _check_input_types(edges: Sequence[tuple[int, int, float]]) -> None:
"""Check that the input consists of valid data types and valid """Check that the input consists of valid data types and valid
numerical ranges. numerical ranges.
@ -227,7 +227,7 @@ def _check_input_types(edges: Sequence[tuple[int, int, int|float]]) -> None:
f" less than {float_limit:g}") f" less than {float_limit:g}")
def _check_input_graph(edges: Sequence[tuple[int, int, int|float]]) -> None: def _check_input_graph(edges: Sequence[tuple[int, int, float]]) -> None:
"""Check that the input is a valid graph, without any multi-edges and """Check that the input is a valid graph, without any multi-edges and
without any self-edges. without any self-edges.
@ -259,8 +259,8 @@ def _check_input_graph(edges: Sequence[tuple[int, int, int|float]]) -> None:
def _remove_negative_weight_edges( def _remove_negative_weight_edges(
edges: Sequence[tuple[int, int, int|float]] edges: Sequence[tuple[int, int, float]]
) -> Sequence[tuple[int, int, int|float]]: ) -> Sequence[tuple[int, int, float]]:
"""Remove edges with negative weight. """Remove edges with negative weight.
This does not change the solution of the maximum-weight matching problem, This does not change the solution of the maximum-weight matching problem,
@ -278,7 +278,7 @@ class _GraphInfo:
These data remain unchanged while the algorithm runs. These data remain unchanged while the algorithm runs.
""" """
def __init__(self, edges: Sequence[tuple[int, int, int|float]]) -> None: def __init__(self, edges: Sequence[tuple[int, int, float]]) -> None:
"""Initialize the graph representation and prepare an adjacency list. """Initialize the graph representation and prepare an adjacency list.
This function takes time O(n + m). This function takes time O(n + m).
@ -296,7 +296,7 @@ class _GraphInfo:
# "w" is the edge weight. # "w" is the edge weight.
# #
# These data remain unchanged while the algorithm runs. # These data remain unchanged while the algorithm runs.
self.edges: Sequence[tuple[int, int, int|float]] = edges self.edges: Sequence[tuple[int, int, float]] = edges
# num_vertex = the number of vertices. # num_vertex = the number of vertices.
if edges: if edges:
@ -448,7 +448,7 @@ class _NonTrivialBlossom(_Blossom):
# #
# "dual_var" is the current value of the dual variable. # "dual_var" is the current value of the dual variable.
# New blossoms start with dual variable 0. # New blossoms start with dual variable 0.
self.dual_var: int|float = 0 self.dual_var: float = 0
# For a non-trivial, top-level S-blossom, # For a non-trivial, top-level S-blossom,
# "best_edge_set" is a list of least-slack edges between this blossom # "best_edge_set" is a list of least-slack edges between this blossom
@ -538,7 +538,7 @@ class _MatchingContext:
# #
# Vertex duals are initialized to half the maximum edge weight. # Vertex duals are initialized to half the maximum edge weight.
max_weight = max(w for (_x, _y, w) in graph.edges) max_weight = max(w for (_x, _y, w) in graph.edges)
self.vertex_dual_2x: list[int|float] = num_vertex * [max_weight] self.vertex_dual_2x: list[float] = num_vertex * [max_weight]
# For each T-vertex or unlabeled vertex "x", # For each T-vertex or unlabeled vertex "x",
# "vertex_best_edge[x]" is the edge index of the least-slack edge # "vertex_best_edge[x]" is the edge index of the least-slack edge
@ -548,7 +548,7 @@ class _MatchingContext:
# Queue of S-vertices to be scanned. # Queue of S-vertices to be scanned.
self.queue: collections.deque[int] = collections.deque() self.queue: collections.deque[int] = collections.deque()
def edge_slack_2x(self, e: int) -> int|float: def edge_slack_2x(self, e: int) -> float:
"""Return 2 times the slack of the edge with index "e". """Return 2 times the slack of the edge with index "e".
The result is only valid for edges that are not between vertices The result is only valid for edges that are not between vertices
@ -611,7 +611,7 @@ class _MatchingContext:
blossom.best_edge = -1 blossom.best_edge = -1
blossom.best_edge_set = None blossom.best_edge_set = None
def lset_add_vertex_edge(self, y: int, e: int, slack: int|float) -> None: def lset_add_vertex_edge(self, y: int, e: int, slack: float) -> None:
"""Add edge "e" from an S-vertex to unlabeled vertex or T-vertex "y". """Add edge "e" from an S-vertex to unlabeled vertex or T-vertex "y".
This function takes time O(1) per call. This function takes time O(1) per call.
@ -625,7 +625,7 @@ class _MatchingContext:
if slack < best_slack: if slack < best_slack:
self.vertex_best_edge[y] = e self.vertex_best_edge[y] = e
def lset_get_best_vertex_edge(self) -> tuple[int, int|float]: def lset_get_best_vertex_edge(self) -> tuple[int, float]:
"""Return the index and slack of the least-slack edge between """Return the index and slack of the least-slack edge between
any S-vertex and unlabeled vertex. any S-vertex and unlabeled vertex.
@ -637,7 +637,7 @@ class _MatchingContext:
or (-1, 0) if there is no suitable edge. or (-1, 0) if there is no suitable edge.
""" """
best_index = -1 best_index = -1
best_slack: int|float = 0 best_slack: float = 0
for x in range(self.graph.num_vertex): for x in range(self.graph.num_vertex):
if self.vertex_top_blossom[x].label == _LABEL_NONE: if self.vertex_top_blossom[x].label == _LABEL_NONE:
@ -662,7 +662,7 @@ class _MatchingContext:
self, self,
blossom: _Blossom, blossom: _Blossom,
e: int, e: int,
slack: int|float slack: float
) -> None: ) -> None:
"""Add edge "e" between the specified S-blossom and another S-blossom. """Add edge "e" between the specified S-blossom and another S-blossom.
@ -708,12 +708,12 @@ class _MatchingContext:
# each top-level S-blossom. This array is indexed by the base vertex # each top-level S-blossom. This array is indexed by the base vertex
# of the blossoms. # of the blossoms.
best_edge_to_blossom: list[int] = num_vertex * [-1] best_edge_to_blossom: list[int] = num_vertex * [-1]
zero_slack: int|float = 0 zero_slack: float = 0
best_slack_to_blossom: list[int|float] = num_vertex * [zero_slack] best_slack_to_blossom: list[float] = num_vertex * [zero_slack]
# And find the overall least-slack edge to any other S-blossom. # And find the overall least-slack edge to any other S-blossom.
best_edge = -1 best_edge = -1
best_slack: int|float = 0 best_slack: float = 0
# Add the least-slack edges of every S-sub-blossom. # Add the least-slack edges of every S-sub-blossom.
for sub in blossom.subblossoms: for sub in blossom.subblossoms:
@ -774,7 +774,7 @@ class _MatchingContext:
# Keep the overall least-slack edge. # Keep the overall least-slack edge.
blossom.best_edge = best_edge blossom.best_edge = best_edge
def lset_get_best_blossom_edge(self) -> tuple[int, int|float]: def lset_get_best_blossom_edge(self) -> tuple[int, float]:
"""Return the index and slack of the least-slack edge between """Return the index and slack of the least-slack edge between
any pair of top-level S-blossoms. any pair of top-level S-blossoms.
@ -786,7 +786,7 @@ class _MatchingContext:
or (-1, 0) if there is no suitable edge. or (-1, 0) if there is no suitable edge.
""" """
best_index = -1 best_index = -1
best_slack: int|float = 0 best_slack: float = 0
for blossom in self.trivial_blossom + self.nontrivial_blossom: for blossom in self.trivial_blossom + self.nontrivial_blossom:
if (blossom.label == _LABEL_S) and (blossom.parent is None): if (blossom.label == _LABEL_S) and (blossom.parent is None):
@ -1396,7 +1396,7 @@ class _MatchingContext:
def substage_calc_dual_delta( def substage_calc_dual_delta(
self self
) -> tuple[int, float|int, int, Optional[_NonTrivialBlossom]]: ) -> tuple[int, float, int, Optional[_NonTrivialBlossom]]:
"""Calculate a delta step in the dual LPP problem. """Calculate a delta step in the dual LPP problem.
This function returns the minimum of the 4 types of delta values, This function returns the minimum of the 4 types of delta values,
@ -1460,7 +1460,7 @@ class _MatchingContext:
return (delta_type, delta_2x, delta_edge, delta_blossom) return (delta_type, delta_2x, delta_edge, delta_blossom)
def substage_apply_delta_step(self, delta_2x: int|float) -> None: def substage_apply_delta_step(self, delta_2x: float) -> None:
"""Apply a delta step to the dual LPP variables.""" """Apply a delta step to the dual LPP variables."""
num_vertex = self.graph.num_vertex num_vertex = self.graph.num_vertex
@ -1582,7 +1582,7 @@ class _MatchingContext:
def _verify_blossom_edges( def _verify_blossom_edges(
ctx: _MatchingContext, ctx: _MatchingContext,
blossom: _NonTrivialBlossom, blossom: _NonTrivialBlossom,
edge_slack_2x: list[int|float] edge_slack_2x: list[float]
) -> None: ) -> None:
"""Descend down the blossom tree to find edges that are contained """Descend down the blossom tree to find edges that are contained
in blossoms. in blossoms.
@ -1610,7 +1610,7 @@ def _verify_blossom_edges(
# Keep track of the sum of blossom duals at each depth along # Keep track of the sum of blossom duals at each depth along
# the current descent path. # the current descent path.
path_sum_dual: list[int|float] = [0] path_sum_dual: list[float] = [0]
# Keep track of the number of matched edges at each depth along # Keep track of the number of matched edges at each depth along
# the current descent path. # the current descent path.
@ -1759,7 +1759,7 @@ def _verify_optimum(ctx: _MatchingContext) -> None:
# Calculate the slack of each edge. # Calculate the slack of each edge.
# A correction will be needed for edges inside blossoms. # A correction will be needed for edges inside blossoms.
edge_slack_2x: list[int|float] = [ edge_slack_2x: list[float] = [
ctx.vertex_dual_2x[x] + ctx.vertex_dual_2x[y] - 2 * w ctx.vertex_dual_2x[x] + ctx.vertex_dual_2x[y] - 2 * w
for (x, y, w) in ctx.graph.edges] for (x, y, w) in ctx.graph.edges]