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maximum-weight-matching
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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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54
python/mwmatching.py
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@ -12,7 +12,7 @@ from typing import NamedTuple, Optional
def maximum_weight_matching(
edges: Sequence[tuple[int, int, int|float]]
edges: Sequence[tuple[int, int, float]]
) -> list[tuple[int, int]]:
"""Compute a maximum-weighted matching in the general undirected weighted
graph given by "edges".
@ -93,8 +93,8 @@ def maximum_weight_matching(
def adjust_weights_for_maximum_cardinality_matching(
edges: Sequence[tuple[int, int, int|float]]
) -> Sequence[tuple[int, int, int|float]]:
edges: Sequence[tuple[int, int, float]]
) -> Sequence[tuple[int, int, float]]:
"""Adjust edge weights such that the maximum-weight matching of
the adjusted graph is a maximum-cardinality matching, equal to
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:
return edges
delta: int|float
delta: float
if weight_range > 0:
# Increase weights to make minimum edge weight large enough
# to improve any non-maximum-cardinality matching.
@ -180,7 +180,7 @@ class MatchingError(Exception):
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
numerical ranges.
@ -227,7 +227,7 @@ def _check_input_types(edges: Sequence[tuple[int, int, int|float]]) -> None:
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
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(
edges: Sequence[tuple[int, int, int|float]]
) -> Sequence[tuple[int, int, int|float]]:
edges: Sequence[tuple[int, int, float]]
) -> Sequence[tuple[int, int, float]]:
"""Remove edges with negative weight.
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.
"""
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.
This function takes time O(n + m).
@ -296,7 +296,7 @@ class _GraphInfo:
# "w" is the edge weight.
#
# 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.
if edges:
@ -448,7 +448,7 @@ class _NonTrivialBlossom(_Blossom):
#
# "dual_var" is the current value of the dual variable.
# 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,
# "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.
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",
# "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.
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".
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_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".
This function takes time O(1) per call.
@ -625,7 +625,7 @@ class _MatchingContext:
if slack < best_slack:
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
any S-vertex and unlabeled vertex.
@ -637,7 +637,7 @@ class _MatchingContext:
or (-1, 0) if there is no suitable edge.
"""
best_index = -1
best_slack: int|float = 0
best_slack: float = 0
for x in range(self.graph.num_vertex):
if self.vertex_top_blossom[x].label == _LABEL_NONE:
@ -662,7 +662,7 @@ class _MatchingContext:
self,
blossom: _Blossom,
e: int,
slack: int|float
slack: float
) -> None:
"""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
# of the blossoms.
best_edge_to_blossom: list[int] = num_vertex * [-1]
zero_slack: int|float = 0
best_slack_to_blossom: list[int|float] = num_vertex * [zero_slack]
zero_slack: float = 0
best_slack_to_blossom: list[float] = num_vertex * [zero_slack]
# And find the overall least-slack edge to any other S-blossom.
best_edge = -1
best_slack: int|float = 0
best_slack: float = 0
# Add the least-slack edges of every S-sub-blossom.
for sub in blossom.subblossoms:
@ -774,7 +774,7 @@ class _MatchingContext:
# Keep the overall least-slack 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
any pair of top-level S-blossoms.
@ -786,7 +786,7 @@ class _MatchingContext:
or (-1, 0) if there is no suitable edge.
"""
best_index = -1
best_slack: int|float = 0
best_slack: float = 0
for blossom in self.trivial_blossom + self.nontrivial_blossom:
if (blossom.label == _LABEL_S) and (blossom.parent is None):
@ -1396,7 +1396,7 @@ class _MatchingContext:
def substage_calc_dual_delta(
self
) -> tuple[int, float|int, int, Optional[_NonTrivialBlossom]]:
) -> tuple[int, float, int, Optional[_NonTrivialBlossom]]:
"""Calculate a delta step in the dual LPP problem.
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)
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."""
num_vertex = self.graph.num_vertex
@ -1582,7 +1582,7 @@ class _MatchingContext:
def _verify_blossom_edges(
ctx: _MatchingContext,
blossom: _NonTrivialBlossom,
edge_slack_2x: list[int|float]
edge_slack_2x: list[float]
) -> None:
"""Descend down the blossom tree to find edges that are contained
in blossoms.
@ -1610,7 +1610,7 @@ def _verify_blossom_edges(
# Keep track of the sum of blossom duals at each depth along
# 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
# the current descent path.
@ -1759,7 +1759,7 @@ def _verify_optimum(ctx: _MatchingContext) -> None:
# Calculate the slack of each edge.
# 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
for (x, y, w) in ctx.graph.edges]