amd._network_simplex.add_edge() - Code Metrics - Inspection of "1.2.3a1" - dwiddo/average-minimum-distance - Measure and Improve Code Quality continuously with Scrutinizer

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by Daniel

created 2022-06-13 12:02 UTC

amd._network_simplex.add_edge() A

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Complexity

Conditions

Size

Total Lines	25
Code Lines	17

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	17
dl	0
loc	25
rs	9.55
c	0
b	0
f	0
cc	3
nop	9

How to fix Many Parameters

"""An implementation of the Wasserstein metric between two compositional vectors.
Aka Earth Mover's distance, an appropriate metric for comparing two PDDs.

Copyright (C) 2020 Cameron Hargreaves. This code is part of the Element Movers
Distance project https://github.com/lrcfmd/ElMD.
"""

import numba
import numpy as np


@numba.njit(cache=True)
def network_simplex(source_demands, sink_demands, network_costs):
    """
    This is a port of the network simplex algorithm implented by Loïc Séguin-C
    for the networkx package to allow acceleration via the numba package.
    Copyright (C) 2010 Loïc Séguin-C. [email protected]
    All rights reserved.
    BSD license.

    References
    ----------
    [1] Z. Kiraly, P. Kovacs.
        Efficient implementation of minimum-cost flow algorithms.
        Acta Universitatis Sapientiae, Informatica 4(1):67--118. 2012.
    [2] R. Barr, F. Glover, D. Klingman.
        Enhancement of spanning tree labeling procedures for network
        optimization.
        INFOR 17(1):16--34. 1979.
    """

    n_sources, n_sinks = source_demands.shape[0], sink_demands.shape[0]
    network_costs = network_costs.ravel()
    n = n_sources + n_sinks
    e = n_sources * n_sinks
    B = np.int64(np.ceil(np.sqrt(e)))
    fp_multiplier = np.int64(1_000_000)

    # Add one additional node for a dummy source and sink
    source_d_int = (source_demands * fp_multiplier).astype(np.int64)
    sink_d_int = (sink_demands * fp_multiplier).astype(np.int64)

    # FP conversion error correction
    source_sum = np.sum(source_d_int)
    sink_sum = np.sum(sink_d_int)
    sink_source_sum_diff = sink_sum - source_sum

    if sink_source_sum_diff > 0:
        source_d_int[np.argmax(source_d_int)] += sink_source_sum_diff
    elif sink_source_sum_diff < 0:
        sink_d_int[np.argmax(sink_d_int)] -= sink_source_sum_diff

    demands = np.empty(n, dtype=np.int64)
    demands[:n_sources] = -source_d_int
    demands[n_sources:] = sink_d_int

    tails = np.empty(e + n, dtype=np.int64)
    heads = np.empty(e + n, dtype=np.int64)

    for i in range(n_sources):
        for j in range(n_sinks):
            ind = i * n_sinks + j
            tails[ind] = i
            heads[ind] = j + n_sources
    

    for i, demand in enumerate(demands):
        if demand > 0:
            tails[e + i] = -1
            heads[e + i] = -1
        else:
            tails[e + i] = i
            heads[e + i] = i

    # Create costs and capacities for the arcs between nodes
    network_costs = network_costs * fp_multiplier

    network_capac = np.array([min(source_demands[i], sink_demands[j])
                              for i in range(n_sources)
                              for j in range(n_sinks)],
                             dtype=np.float64) * fp_multiplier

    faux_inf = 3 * np.max(np.array((
                            np.sum(network_capac),

                            np.sum(np.abs(network_costs)),

                            np.amax(source_d_int),

                            np.amax(sink_d_int)),

                          dtype=np.int64))


    # allocate arrays
    costs = np.empty(e + n, dtype=np.int64)
    costs[:e] = network_costs
    costs[e:] = faux_inf

    capac = np.empty(e + n, dtype=np.int64)
    capac[:e] = network_capac
    capac[e:] = fp_multiplier

    flows = np.empty(e + n, dtype=np.int64)
    flows[:e] = 0
    flows[e:e+n_sources] = source_d_int
    flows[e+n_sources:] = sink_d_int

    potentials = np.empty(n, dtype=np.int64)
    demands_neg_mask = demands <= 0
    potentials[demands_neg_mask] = faux_inf
    potentials[~demands_neg_mask] = -faux_inf

    parent = np.empty(n + 1, dtype=np.int64)
    parent[:-1] = -1
    parent[-1] = -2

    size = np.empty(n + 1, dtype=np.int64)
    size[:-1] = 1
    size[-1] = n + 1

    next_node = np.arange(1, n + 2, dtype=np.int64)
    next_node[-2] = -1
    next_node[-1] = 0

    last_node = np.arange(n + 1, dtype=np.int64)
    last_node[-1] = n - 1

    prev_node = np.arange(-1, n, dtype=np.int64)
    edge = np.arange(e, e + n, dtype=np.int64)

    ### Pivot loop ###

    f = 0
    while True:
        i, p, q, f = find_entering_edges(B, e, f, tails, heads, costs, potentials, flows)
        if p == -1: # If no entering edges then the optimal score is found
            break

        cycle_nodes, cycle_edges = find_cycle(i, p, q, size, edge, parent)
        j, s, t = find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads)
        augment_flow(cycle_nodes, cycle_edges, residual_capacity(j, s, capac, flows, tails), tails, flows)

        if i != j:  # Do nothing more if the entering edge is the same as the leaving edge.
            if parent[t] != s:
                # Ensure that s is the parent of t.
                s, t = t, s

            # Ensure that q is in the subtree rooted at t.
            for val in cycle_edges:
                if val == j:

                    p, q = q, p
                    break
                elif val == i:
                    break

            remove_edge(s, t, size, prev_node, last_node, next_node, parent, edge)
            make_root(q, parent, size, last_node, prev_node, next_node, edge)
            add_edge(i, p, q, next_node, prev_node, last_node, size, parent, edge)
            update_potentials(i, p, q, heads, potentials, costs, last_node, next_node)

    final_flows = flows[:e] / fp_multiplier
    edge_costs = costs[:e] / fp_multiplier
    final = final_flows.dot(edge_costs)

    return final, final_flows.reshape((n_sources, n_sinks))


@numba.njit(cache=True)
def reduced_cost(i, costs, potentials, tails, heads, flows):

    """Return the reduced cost of an edge i.
    """
    c = costs[i] - potentials[tails[i]] + potentials[heads[i]]

    if flows[i] == 0:

        return c
    else:
        return -c


@numba.njit(cache=True)
def find_entering_edges(B, e, f, tails, heads, costs, potentials, flows):

    """Yield entering edges until none can be found.
    """
    # Entering edges are found by combining Dantzig's rule and Bland's
    # rule. The edges are cyclically grouped into blocks of size B. Within
    # each block, Dantzig's rule is applied to find an entering edge. The
    # blocks to search is determined following Bland's rule.

    M = (e + B - 1) // B    # number of blocks needed to cover all edges
    m = 0

    while m < M:
        # Determine the next block of edges.
        l = f + B
        if l <= e:
            edge_inds = np.arange(f, l)
        else:
            l -= e
            edge_inds = np.empty(e - f + l, dtype=np.int64)
            for i, v in enumerate(range(f, e)):
                edge_inds[i] = v
            for i in range(l):
                edge_inds[e - f + i] = i

        f = l

        # Find the first edge with the lowest reduced cost.
        i = edge_inds[0]
        c = reduced_cost(i, costs, potentials, tails, heads, flows)
        for ind in edge_inds[1:]:
            cost = reduced_cost(ind, costs, potentials, tails, heads, flows)
            if cost < c:
                c = cost
                i = ind

        p = q = -1

        if c >= 0:
            m += 1

        # Entering edge found.
        else:
            if flows[i] == 0:
                p = tails[i]
                q = heads[i]
            else:
                p = heads[i]
                q = tails[i]

            return i, p, q, f

    # All edges have nonnegative reduced costs. The flow is optimal.
    return -1, -1, -1, -1


@numba.njit(cache=True)
def find_apex(p, q, size, parent):
    """Find the lowest common ancestor of nodes p and q in the spanning tree.
    """
    size_p = size[p]
    size_q = size[q]

    while True:
        while size_p < size_q:
            p = parent[p]
            size_p = size[p]
        while size_p > size_q:
            q = parent[q]
            size_q = size[q]
        if size_p == size_q:
            if p != q:
                p = parent[p]
                size_p = size[p]
                q = parent[q]
                size_q = size[q]
            else:
                return p


@numba.njit(cache=True)
def trace_path(p, w, edge, parent):
    """Return the nodes and edges on the path from node p to its ancestor w.
    """
    cycle_nodes = [p]
    cycle_edges = []

    while p != w:
        cycle_edges.append(edge[p])
        p = parent[p]
        cycle_nodes.append(p)

    return cycle_nodes, cycle_edges


@numba.njit(cache=True)
def find_cycle(i, p, q, size, edge, parent):

    """Return the nodes and edges on the cycle containing edge i == (p, q)
    when the latter is added to the spanning tree.
    The cycle is oriented in the direction from p to q.
    """

    w = find_apex(p, q, size, parent)
    cycle_nodes, cycle_edges = trace_path(p, w, edge, parent)
    cycle_nodes_rev, cycle_edges_rev = trace_path(q, w, edge, parent)
    append_i_to_edges = (len(cycle_edges) < 1 or cycle_edges[-1] != i)
    add_to_c_nodes = max(len(cycle_nodes_rev) - 1, 0)
    cycle_nodes_ = np.empty(len(cycle_nodes) + add_to_c_nodes, dtype=np.int64)

    for j in range(len(cycle_nodes)):
        cycle_nodes_[j] = cycle_nodes[-(j+1)]
    for j in range(add_to_c_nodes):
        cycle_nodes_[len(cycle_nodes) + j] = cycle_nodes_rev[j]

    if append_i_to_edges:
        cycle_edges_ = np.empty(len(cycle_edges) + len(cycle_edges_rev) + 1, dtype=np.int64)
        cycle_edges_[len(cycle_edges)] = i
    else:
        cycle_edges_ = np.empty(len(cycle_edges) + len(cycle_edges_rev), dtype=np.int64)

    for j in range(len(cycle_edges)):
        cycle_edges_[j] = cycle_edges[-(j+1)]
    for j in range(1, len(cycle_edges_rev) + 1):
        cycle_edges_[-j] = cycle_edges_rev[-j]

    return cycle_nodes_, cycle_edges_


@numba.njit(cache=True)
def residual_capacity(i, p, capac, flows, tails):
    """Return the residual capacity of an edge i in the direction away
    from its endpoint p.
    """
    if tails[i] == p:

        return capac[i] - flows[i]
    else:
        return flows[i]


@numba.njit(cache=True)
def find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads):

    """Return the leaving edge in a cycle represented by cycle_nodes and
    cycle_edges.
    """
    j, s = cycle_edges[0], cycle_nodes[0]
    res_caps_min = residual_capacity(j, s, capac, flows, tails)
    for ind in range(1, cycle_edges.shape[0]):
        j_, s_ = cycle_edges[ind], cycle_nodes[ind]
        res_cap = residual_capacity(j_, s_, capac, flows, tails)
        if res_cap < res_caps_min:
            res_caps_min = res_cap
            j, s = j_, s_

    t = heads[j] if tails[j] == s else tails[j]
    return j, s, t


@numba.njit(cache=True)
def augment_flow(cycle_nodes, cycle_edges, f, tails, flows):
    """Augment f units of flow along a cycle representing Wn with cycle_edges.
    """
    for i, p in zip(cycle_edges, cycle_nodes):
        if tails[i] == p:
            flows[i] += f
        else:
            flows[i] -= f


@numba.njit(cache=True)
def remove_edge(s, t, size, prev, last, next_node, parent, edge):

    """Remove an edge (s, t) where parent[t] == s from the spanning tree.
    """
    size_t = size[t]
    prev_t = prev[t]
    last_t = last[t]
    next_last_t = next_node[last_t]
    # Remove (s, t).
    parent[t] = -2
    edge[t] = -2
    # Remove the subtree rooted at t from the depth-first thread.
    next_node[prev_t] = next_last_t
    prev[next_last_t] = prev_t
    next_node[last_t] = t
    prev[t] = last_t

    # Update the subtree sizes and last descendants of the (old) ancestors
    # of t.
    while s != -2:
        size[s] -= size_t
        if last[s] == last_t:
            last[s] = prev_t
        s = parent[s]


@numba.njit(cache=True)
def make_root(q, parent, size, last, prev, next_node, edge):

    """Make a node q the root of its containing subtree.
    """
    ancestors = []
    # -2 means node is checked
    while q != -2:
        ancestors.insert(0, q)
        q = parent[q]

    for p, q in zip(ancestors[:-1], ancestors[1:]):

        size_p = size[p]
        last_p = last[p]
        prev_q = prev[q]
        last_q = last[q]
        next_last_q = next_node[last_q]

        # Make p a child of q.
        parent[p] = q
        parent[q] = -2
        edge[p] = edge[q]
        edge[q] = -2
        size[p] = size_p - size[q]
        size[q] = size_p

        # Remove the subtree rooted at q from the depth-first thread.
        next_node[prev_q] = next_last_q
        prev[next_last_q] = prev_q
        next_node[last_q] = q
        prev[q] = last_q

        if last_p == last_q:
            last[p] = prev_q
            last_p = prev_q

        # Add the remaining parts of the subtree rooted at p as a subtree
        # of q in the depth-first thread.
        prev[p] = last_q
        next_node[last_q] = p
        next_node[last_p] = q
        prev[q] = last_p
        last[q] = last_p


@numba.njit(cache=True)
def add_edge(i, p, q, next_node, prev, last, size, parent, edge):

    """Add an edge (p, q) to the spanning tree where q is the root of a
    subtree.
    """
    last_p = last[p]
    next_last_p = next_node[last_p]
    size_q = size[q]
    last_q = last[q]
    # Make q a child of p.
    parent[q] = p
    edge[q] = i
    # Insert the subtree rooted at q into the depth-first thread.
    next_node[last_p] = q
    prev[q] = last_p
    prev[next_last_p] = last_q
    next_node[last_q] = next_last_p

    # Update the subtree sizes and last descendants of the (new) ancestors
    # of q.
    while p != -2:
        size[p] += size_q
        if last[p] == last_p:
            last[p] = last_q
        p = parent[p]


@numba.njit(cache=True)
def update_potentials(i, p, q, heads, potentials, costs, last_node, next_node):

    """Update the potentials of the nodes in the subtree rooted at a node
    q connected to its parent p by an edge i.
    """
    if q == heads[i]:
        d = potentials[p] - costs[i] - potentials[q]
    else:
        d = potentials[p] + costs[i] - potentials[q]

    potentials[q] += d
    l = last_node[q]
    while q != l:
        q = next_node[q]
        potentials[q] += d


1			"""An implementation of the Wasserstein metric between two compositional vectors.
2			Aka Earth Mover's distance, an appropriate metric for comparing two PDDs.
3
4			Copyright (C) 2020 Cameron Hargreaves. This code is part of the Element Movers
5			Distance project https://github.com/lrcfmd/ElMD.
6			"""
7
8			import numba
9			import numpy as np
10
11
12			@numba.njit(cache=True)
13			def network_simplex(source_demands, sink_demands, network_costs):
14			"""
15			This is a port of the network simplex algorithm implented by Loïc Séguin-C
16			for the networkx package to allow acceleration via the numba package.
17			Copyright (C) 2010 Loïc Séguin-C. [email protected]
18			All rights reserved.
19			BSD license.
20
21			References
22			----------
23			[1] Z. Kiraly, P. Kovacs.
24			Efficient implementation of minimum-cost flow algorithms.
25			Acta Universitatis Sapientiae, Informatica 4(1):67--118. 2012.
26			[2] R. Barr, F. Glover, D. Klingman.
27			Enhancement of spanning tree labeling procedures for network
28			optimization.
29			INFOR 17(1):16--34. 1979.
30			"""
31
32			n_sources, n_sinks = source_demands.shape[0], sink_demands.shape[0]
33			network_costs = network_costs.ravel()
34			n = n_sources + n_sinks
35			e = n_sources * n_sinks
36			B = np.int64(np.ceil(np.sqrt(e)))
37			fp_multiplier = np.int64(1_000_000)
38
39			# Add one additional node for a dummy source and sink
40			source_d_int = (source_demands * fp_multiplier).astype(np.int64)
41			sink_d_int = (sink_demands * fp_multiplier).astype(np.int64)
42
43			# FP conversion error correction
44			source_sum = np.sum(source_d_int)
45			sink_sum = np.sum(sink_d_int)
46			sink_source_sum_diff = sink_sum - source_sum
47
48			if sink_source_sum_diff > 0:
49			source_d_int[np.argmax(source_d_int)] += sink_source_sum_diff
50			elif sink_source_sum_diff < 0:
51			sink_d_int[np.argmax(sink_d_int)] -= sink_source_sum_diff
52
53			demands = np.empty(n, dtype=np.int64)
54			demands[:n_sources] = -source_d_int
55			demands[n_sources:] = sink_d_int
56
57			tails = np.empty(e + n, dtype=np.int64)
58			heads = np.empty(e + n, dtype=np.int64)
59
60			for i in range(n_sources):
61			for j in range(n_sinks):
62			ind = i * n_sinks + j
63			tails[ind] = i
64			heads[ind] = j + n_sources
65
			0 ignored issues – show Coding Style introduced 2022-04-14 12:12 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
66			for i, demand in enumerate(demands):
67			if demand > 0:
68			tails[e + i] = -1
69			heads[e + i] = -1
70			else:
71			tails[e + i] = i
72			heads[e + i] = i
73
74			# Create costs and capacities for the arcs between nodes
75			network_costs = network_costs * fp_multiplier
76
77			network_capac = np.array([min(source_demands[i], sink_demands[j])
78			for i in range(n_sources)
79			for j in range(n_sinks)],
80			dtype=np.float64) * fp_multiplier
81
82			faux_inf = 3 * np.max(np.array((
83			np.sum(network_capac),
			0 ignored issues – show Coding Style introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Wrong hanging indentation (remove 20 spaces). Loading history...
84			np.sum(np.abs(network_costs)),
			0 ignored issues – show Coding Style introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Wrong hanging indentation (remove 20 spaces). Loading history...
85			np.amax(source_d_int),
			0 ignored issues – show Coding Style introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Wrong hanging indentation (remove 20 spaces). Loading history...
86			np.amax(sink_d_int)),
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87			dtype=np.int64))
			0 ignored issues – show Coding Style introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Wrong continued indentation (add 9 spaces). Loading history...
88
89			# allocate arrays
90			costs = np.empty(e + n, dtype=np.int64)
91			costs[:e] = network_costs
92			costs[e:] = faux_inf
93
94			capac = np.empty(e + n, dtype=np.int64)
95			capac[:e] = network_capac
96			capac[e:] = fp_multiplier
97
98			flows = np.empty(e + n, dtype=np.int64)
99			flows[:e] = 0
100			flows[e:e+n_sources] = source_d_int
101			flows[e+n_sources:] = sink_d_int
102
103			potentials = np.empty(n, dtype=np.int64)
104			demands_neg_mask = demands <= 0
105			potentials[demands_neg_mask] = faux_inf
106			potentials[~demands_neg_mask] = -faux_inf
107
108			parent = np.empty(n + 1, dtype=np.int64)
109			parent[:-1] = -1
110			parent[-1] = -2
111
112			size = np.empty(n + 1, dtype=np.int64)
113			size[:-1] = 1
114			size[-1] = n + 1
115
116			next_node = np.arange(1, n + 2, dtype=np.int64)
117			next_node[-2] = -1
118			next_node[-1] = 0
119
120			last_node = np.arange(n + 1, dtype=np.int64)
121			last_node[-1] = n - 1
122
123			prev_node = np.arange(-1, n, dtype=np.int64)
124			edge = np.arange(e, e + n, dtype=np.int64)
125
126			### Pivot loop ###
127
128			f = 0
129			while True:
130			i, p, q, f = find_entering_edges(B, e, f, tails, heads, costs, potentials, flows)
131			if p == -1: # If no entering edges then the optimal score is found
132			break
133
134			cycle_nodes, cycle_edges = find_cycle(i, p, q, size, edge, parent)
135			j, s, t = find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads)
136			augment_flow(cycle_nodes, cycle_edges, residual_capacity(j, s, capac, flows, tails), tails, flows)
137
138			if i != j: # Do nothing more if the entering edge is the same as the leaving edge.
139			if parent[t] != s:
140			# Ensure that s is the parent of t.
141			s, t = t, s
142
143			# Ensure that q is in the subtree rooted at t.
144			for val in cycle_edges:
145			if val == j:
			0 ignored issues – show Unused Code introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Unnecessary "elif" after "break" Loading history...
146			p, q = q, p
147			break
148			elif val == i:
149			break
150
151			remove_edge(s, t, size, prev_node, last_node, next_node, parent, edge)
152			make_root(q, parent, size, last_node, prev_node, next_node, edge)
153			add_edge(i, p, q, next_node, prev_node, last_node, size, parent, edge)
154			update_potentials(i, p, q, heads, potentials, costs, last_node, next_node)
155
156			final_flows = flows[:e] / fp_multiplier
157			edge_costs = costs[:e] / fp_multiplier
158			final = final_flows.dot(edge_costs)
159
160			return final, final_flows.reshape((n_sources, n_sinks))
161
162
163			@numba.njit(cache=True)
164			def reduced_cost(i, costs, potentials, tails, heads, flows):
			0 ignored issues – show best-practice introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many arguments (6/5) Loading history...
165			"""Return the reduced cost of an edge i.
166			"""
167			c = costs[i] - potentials[tails[i]] + potentials[heads[i]]
168
169			if flows[i] == 0:
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170			return c
171			else:
172			return -c
173
174
175			@numba.njit(cache=True)
176			def find_entering_edges(B, e, f, tails, heads, costs, potentials, flows):
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177			"""Yield entering edges until none can be found.
178			"""
179			# Entering edges are found by combining Dantzig's rule and Bland's
180			# rule. The edges are cyclically grouped into blocks of size B. Within
181			# each block, Dantzig's rule is applied to find an entering edge. The
182			# blocks to search is determined following Bland's rule.
183
184			M = (e + B - 1) // B # number of blocks needed to cover all edges
185			m = 0
186
187			while m < M:
188			# Determine the next block of edges.
189			l = f + B
190			if l <= e:
191			edge_inds = np.arange(f, l)
192			else:
193			l -= e
194			edge_inds = np.empty(e - f + l, dtype=np.int64)
195			for i, v in enumerate(range(f, e)):
196			edge_inds[i] = v
197			for i in range(l):
198			edge_inds[e - f + i] = i
199
200			f = l
201
202			# Find the first edge with the lowest reduced cost.
203			i = edge_inds[0]
204			c = reduced_cost(i, costs, potentials, tails, heads, flows)
205			for ind in edge_inds[1:]:
206			cost = reduced_cost(ind, costs, potentials, tails, heads, flows)
207			if cost < c:
208			c = cost
209			i = ind
210
211			p = q = -1
212
213			if c >= 0:
214			m += 1
215
216			# Entering edge found.
217			else:
218			if flows[i] == 0:
219			p = tails[i]
220			q = heads[i]
221			else:
222			p = heads[i]
223			q = tails[i]
224
225			return i, p, q, f
226
227			# All edges have nonnegative reduced costs. The flow is optimal.
228			return -1, -1, -1, -1
229
230
231			@numba.njit(cache=True)
232			def find_apex(p, q, size, parent):
233			"""Find the lowest common ancestor of nodes p and q in the spanning tree.
234			"""
235			size_p = size[p]
236			size_q = size[q]
237
238			while True:
239			while size_p < size_q:
240			p = parent[p]
241			size_p = size[p]
242			while size_p > size_q:
243			q = parent[q]
244			size_q = size[q]
245			if size_p == size_q:
246			if p != q:
247			p = parent[p]
248			size_p = size[p]
249			q = parent[q]
250			size_q = size[q]
251			else:
252			return p
253
254
255			@numba.njit(cache=True)
256			def trace_path(p, w, edge, parent):
257			"""Return the nodes and edges on the path from node p to its ancestor w.
258			"""
259			cycle_nodes = [p]
260			cycle_edges = []
261
262			while p != w:
263			cycle_edges.append(edge[p])
264			p = parent[p]
265			cycle_nodes.append(p)
266
267			return cycle_nodes, cycle_edges
268
269
270			@numba.njit(cache=True)
271			def find_cycle(i, p, q, size, edge, parent):
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272			"""Return the nodes and edges on the cycle containing edge i == (p, q)
273			when the latter is added to the spanning tree.
274			The cycle is oriented in the direction from p to q.
275			"""
276
277			w = find_apex(p, q, size, parent)
278			cycle_nodes, cycle_edges = trace_path(p, w, edge, parent)
279			cycle_nodes_rev, cycle_edges_rev = trace_path(q, w, edge, parent)
280			append_i_to_edges = (len(cycle_edges) < 1 or cycle_edges[-1] != i)
281			add_to_c_nodes = max(len(cycle_nodes_rev) - 1, 0)
282			cycle_nodes_ = np.empty(len(cycle_nodes) + add_to_c_nodes, dtype=np.int64)
283
284			for j in range(len(cycle_nodes)):
285			cycle_nodes_[j] = cycle_nodes[-(j+1)]
286			for j in range(add_to_c_nodes):
287			cycle_nodes_[len(cycle_nodes) + j] = cycle_nodes_rev[j]
288
289			if append_i_to_edges:
290			cycle_edges_ = np.empty(len(cycle_edges) + len(cycle_edges_rev) + 1, dtype=np.int64)
291			cycle_edges_[len(cycle_edges)] = i
292			else:
293			cycle_edges_ = np.empty(len(cycle_edges) + len(cycle_edges_rev), dtype=np.int64)
294
295			for j in range(len(cycle_edges)):
296			cycle_edges_[j] = cycle_edges[-(j+1)]
297			for j in range(1, len(cycle_edges_rev) + 1):
298			cycle_edges_[-j] = cycle_edges_rev[-j]
299
300			return cycle_nodes_, cycle_edges_
301
302
303			@numba.njit(cache=True)
304			def residual_capacity(i, p, capac, flows, tails):
305			"""Return the residual capacity of an edge i in the direction away
306			from its endpoint p.
307			"""
308			if tails[i] == p:
			0 ignored issues – show unused-code introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Unnecessary "else" after "return" Loading history...
309			return capac[i] - flows[i]
310			else:
311			return flows[i]
312
313
314			@numba.njit(cache=True)
315			def find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads):
			0 ignored issues – show best-practice introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many arguments (6/5) Loading history...
316			"""Return the leaving edge in a cycle represented by cycle_nodes and
317			cycle_edges.
318			"""
319			j, s = cycle_edges[0], cycle_nodes[0]
320			res_caps_min = residual_capacity(j, s, capac, flows, tails)
321			for ind in range(1, cycle_edges.shape[0]):
322			j_, s_ = cycle_edges[ind], cycle_nodes[ind]
323			res_cap = residual_capacity(j_, s_, capac, flows, tails)
324			if res_cap < res_caps_min:
325			res_caps_min = res_cap
326			j, s = j_, s_
327
328			t = heads[j] if tails[j] == s else tails[j]
329			return j, s, t
330
331
332			@numba.njit(cache=True)
333			def augment_flow(cycle_nodes, cycle_edges, f, tails, flows):
334			"""Augment f units of flow along a cycle representing Wn with cycle_edges.
335			"""
336			for i, p in zip(cycle_edges, cycle_nodes):
337			if tails[i] == p:
338			flows[i] += f
339			else:
340			flows[i] -= f
341
342
343			@numba.njit(cache=True)
344			def remove_edge(s, t, size, prev, last, next_node, parent, edge):
			0 ignored issues – show best-practice introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many arguments (8/5) Loading history...
345			"""Remove an edge (s, t) where parent[t] == s from the spanning tree.
346			"""
347			size_t = size[t]
348			prev_t = prev[t]
349			last_t = last[t]
350			next_last_t = next_node[last_t]
351			# Remove (s, t).
352			parent[t] = -2
353			edge[t] = -2
354			# Remove the subtree rooted at t from the depth-first thread.
355			next_node[prev_t] = next_last_t
356			prev[next_last_t] = prev_t
357			next_node[last_t] = t
358			prev[t] = last_t
359
360			# Update the subtree sizes and last descendants of the (old) ancestors
361			# of t.
362			while s != -2:
363			size[s] -= size_t
364			if last[s] == last_t:
365			last[s] = prev_t
366			s = parent[s]
367
368
369			@numba.njit(cache=True)
370			def make_root(q, parent, size, last, prev, next_node, edge):
			0 ignored issues – show best-practice introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many arguments (7/5) Loading history...
371			"""Make a node q the root of its containing subtree.
372			"""
373			ancestors = []
374			# -2 means node is checked
375			while q != -2:
376			ancestors.insert(0, q)
377			q = parent[q]
378
379			for p, q in zip(ancestors[:-1], ancestors[1:]):
			0 ignored issues – show unused-code introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Redefining argument with the local name 'q' Loading history...
380			size_p = size[p]
381			last_p = last[p]
382			prev_q = prev[q]
383			last_q = last[q]
384			next_last_q = next_node[last_q]
385
386			# Make p a child of q.
387			parent[p] = q
388			parent[q] = -2
389			edge[p] = edge[q]
390			edge[q] = -2
391			size[p] = size_p - size[q]
392			size[q] = size_p
393
394			# Remove the subtree rooted at q from the depth-first thread.
395			next_node[prev_q] = next_last_q
396			prev[next_last_q] = prev_q
397			next_node[last_q] = q
398			prev[q] = last_q
399
400			if last_p == last_q:
401			last[p] = prev_q
402			last_p = prev_q
403
404			# Add the remaining parts of the subtree rooted at p as a subtree
405			# of q in the depth-first thread.
406			prev[p] = last_q
407			next_node[last_q] = p
408			next_node[last_p] = q
409			prev[q] = last_p
410			last[q] = last_p
411
412
413			@numba.njit(cache=True)
414			def add_edge(i, p, q, next_node, prev, last, size, parent, edge):
			0 ignored issues – show best-practice introduced 2022-04-14 08:53 UTC by Report Bug Copy Issue Report Too many arguments (9/5) Loading history...
415			"""Add an edge (p, q) to the spanning tree where q is the root of a
416			subtree.
417			"""
418			last_p = last[p]
419			next_last_p = next_node[last_p]
420			size_q = size[q]
421			last_q = last[q]
422			# Make q a child of p.
423			parent[q] = p
424			edge[q] = i
425			# Insert the subtree rooted at q into the depth-first thread.
426			next_node[last_p] = q
427			prev[q] = last_p
428			prev[next_last_p] = last_q
429			next_node[last_q] = next_last_p
430
431			# Update the subtree sizes and last descendants of the (new) ancestors
432			# of q.
433			while p != -2:
434			size[p] += size_q
435			if last[p] == last_p:
436			last[p] = last_q
437			p = parent[p]
438
439
440			@numba.njit(cache=True)
441			def update_potentials(i, p, q, heads, potentials, costs, last_node, next_node):
			0 ignored issues – show best-practice introduced 2022-06-13 12:09 UTC by Report Bug Copy Issue Report Too many arguments (8/5) Loading history...
442			"""Update the potentials of the nodes in the subtree rooted at a node
443			q connected to its parent p by an edge i.
444			"""
445			if q == heads[i]:
446			d = potentials[p] - costs[i] - potentials[q]
447			else:
448			d = potentials[p] + costs[i] - potentials[q]
449
450			potentials[q] += d
451			l = last_node[q]
452			while q != l:
453			q = next_node[q]
454			potentials[q] += d
455

dwiddo / average-minimum-distance

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amd._network_simplex.add_edge() A

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