batuhan-basoglu/Vehicle-Anti-Theft-Face-Recognition-System
1
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Fixed database typo and removed unnecessary class identifier.

Browse source
This commit is contained in:
Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
parent 00ad49a143
commit 45fb349a7d
5098 changed files with 952558 additions and 85 deletions
Download patch file Download diff file Expand all files Collapse all files

13
venv/Lib/site-packages/networkx/linalg/__init__.py Normal file
View file

@ -0,0 +1,13 @@
from networkx.linalg.attrmatrix import *
import networkx.linalg.attrmatrix
from networkx.linalg.spectrum import *
import networkx.linalg.spectrum
from networkx.linalg.graphmatrix import *
import networkx.linalg.graphmatrix
from networkx.linalg.laplacianmatrix import *
import networkx.linalg.laplacianmatrix
from networkx.linalg.algebraicconnectivity import *
from networkx.linalg.modularitymatrix import *
import networkx.linalg.modularitymatrix
from networkx.linalg.bethehessianmatrix import *
import networkx.linalg.bethehessianmatrix

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/__init__.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/algebraicconnectivity.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/attrmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/bethehessianmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/graphmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/laplacianmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/modularitymatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/__pycache__/spectrum.cpython-36.pyc Normal file
View file

Binary file not shown.

600
venv/Lib/site-packages/networkx/linalg/algebraicconnectivity.py Normal file
View file

@ -0,0 +1,600 @@
"""
Algebraic connectivity and Fiedler vectors of undirected graphs.
"""
from functools import partial
import networkx as nx
from networkx.utils import not_implemented_for
from networkx.utils import reverse_cuthill_mckee_ordering
from networkx.utils import random_state
try:
from numpy import array, asarray, dot, ndarray, ones, sqrt, zeros, atleast_2d
from numpy.linalg import norm, qr
from scipy.linalg import eigh, inv
from scipy.sparse import csc_matrix, spdiags
from scipy.sparse.linalg import eigsh, lobpcg
__all__ = ["algebraic_connectivity", "fiedler_vector", "spectral_ordering"]
except ImportError:
__all__ = []
try:
from scipy.linalg.blas import dasum, daxpy, ddot
except ImportError:
if __all__:
# Make sure the imports succeeded.
# Use minimal replacements if BLAS is unavailable from SciPy.
dasum = partial(norm, ord=1)
ddot = dot
def daxpy(x, y, a):
y += a * x
return y
class _PCGSolver:
"""Preconditioned conjugate gradient method.
To solve Ax = b:
M = A.diagonal() # or some other preconditioner
solver = _PCGSolver(lambda x: A * x, lambda x: M * x)
x = solver.solve(b)
The inputs A and M are functions which compute
matrix multiplication on the argument.
A - multiply by the matrix A in Ax=b
M - multiply by M, the preconditioner surragate for A
Warning: There is no limit on number of iterations.
"""
def __init__(self, A, M):
self._A = A
self._M = M or (lambda x: x.copy())
def solve(self, B, tol):
B = asarray(B)
X = ndarray(B.shape, order="F")
for j in range(B.shape[1]):
X[:, j] = self._solve(B[:, j], tol)
return X
def _solve(self, b, tol):
A = self._A
M = self._M
tol *= dasum(b)
# Initialize.
x = zeros(b.shape)
r = b.copy()
z = M(r)
rz = ddot(r, z)
p = z.copy()
# Iterate.
while True:
Ap = A(p)
alpha = rz / ddot(p, Ap)
x = daxpy(p, x, a=alpha)
r = daxpy(Ap, r, a=-alpha)
if dasum(r) < tol:
return x
z = M(r)
beta = ddot(r, z)
beta, rz = beta / rz, beta
p = daxpy(p, z, a=beta)
class _CholeskySolver:
"""Cholesky factorization.
To solve Ax = b:
solver = _CholeskySolver(A)
x = solver.solve(b)
optional argument `tol` on solve method is ignored but included
to match _PCGsolver API.
"""
def __init__(self, A):
if not self._cholesky:
raise nx.NetworkXError("Cholesky solver unavailable.")
self._chol = self._cholesky(A)
def solve(self, B, tol=None):
return self._chol(B)
try:
from scikits.sparse.cholmod import cholesky
_cholesky = cholesky
except ImportError:
_cholesky = None
class _LUSolver:
"""LU factorization.
To solve Ax = b:
solver = _LUSolver(A)
x = solver.solve(b)
optional argument `tol` on solve method is ignored but included
to match _PCGsolver API.
"""
def __init__(self, A):
if not self._splu:
raise nx.NetworkXError("LU solver unavailable.")
self._LU = self._splu(A)
def solve(self, B, tol=None):
B = asarray(B)
X = ndarray(B.shape, order="F")
for j in range(B.shape[1]):
X[:, j] = self._LU.solve(B[:, j])
return X
try:
from scipy.sparse.linalg import splu
_splu = partial(
splu,
permc_spec="MMD_AT_PLUS_A",
diag_pivot_thresh=0.0,
options={"Equil": True, "SymmetricMode": True},
)
except ImportError:
_splu = None
def _preprocess_graph(G, weight):
"""Compute edge weights and eliminate zero-weight edges.
"""
if G.is_directed():
H = nx.MultiGraph()
H.add_nodes_from(G)
H.add_weighted_edges_from(
((u, v, e.get(weight, 1.0)) for u, v, e in G.edges(data=True) if u != v),
weight=weight,
)
G = H
if not G.is_multigraph():
edges = (
(u, v, abs(e.get(weight, 1.0))) for u, v, e in G.edges(data=True) if u != v
)
else:
edges = (
(u, v, sum(abs(e.get(weight, 1.0)) for e in G[u][v].values()))
for u, v in G.edges()
if u != v
)
H = nx.Graph()
H.add_nodes_from(G)
H.add_weighted_edges_from((u, v, e) for u, v, e in edges if e != 0)
return H
def _rcm_estimate(G, nodelist):
"""Estimate the Fiedler vector using the reverse Cuthill-McKee ordering.
"""
G = G.subgraph(nodelist)
order = reverse_cuthill_mckee_ordering(G)
n = len(nodelist)
index = dict(zip(nodelist, range(n)))
x = ndarray(n, dtype=float)
for i, u in enumerate(order):
x[index[u]] = i
x -= (n - 1) / 2.0
return x
def _tracemin_fiedler(L, X, normalized, tol, method):
"""Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.
The Fiedler vector of a connected undirected graph is the eigenvector
corresponding to the second smallest eigenvalue of the Laplacian matrix of
of the graph. This function starts with the Laplacian L, not the Graph.
Parameters
----------
L : Laplacian of a possibly weighted or normalized, but undirected graph
X : Initial guess for a solution. Usually a matrix of random numbers.
This function allows more than one column in X to identify more than
one eigenvector if desired.
normalized : bool
Whether the normalized Laplacian matrix is used.
tol : float
Tolerance of relative residual in eigenvalue computation.
Warning: There is no limit on number of iterations.
method : string
Should be 'tracemin_pcg', 'tracemin_chol' or 'tracemin_lu'.
Otherwise exception is raised.
Returns
-------
sigma, X : Two NumPy arrays of floats.
The lowest eigenvalues and corresponding eigenvectors of L.
The size of input X determines the size of these outputs.
As this is for Fiedler vectors, the zero eigenvalue (and
constant eigenvector) are avoided.
"""
n = X.shape[0]
if normalized:
# Form the normalized Laplacian matrix and determine the eigenvector of
# its nullspace.
e = sqrt(L.diagonal())
D = spdiags(1.0 / e, [0], n, n, format="csr")
L = D * L * D
e *= 1.0 / norm(e, 2)
if normalized:
def project(X):
"""Make X orthogonal to the nullspace of L.
"""
X = asarray(X)
for j in range(X.shape[1]):
X[:, j] -= dot(X[:, j], e) * e
else:
def project(X):
"""Make X orthogonal to the nullspace of L.
"""
X = asarray(X)
for j in range(X.shape[1]):
X[:, j] -= X[:, j].sum() / n
if method == "tracemin_pcg":
D = L.diagonal().astype(float)
solver = _PCGSolver(lambda x: L * x, lambda x: D * x)
elif method == "tracemin_chol" or method == "tracemin_lu":
# Convert A to CSC to suppress SparseEfficiencyWarning.
A = csc_matrix(L, dtype=float, copy=True)
# Force A to be nonsingular. Since A is the Laplacian matrix of a
# connected graph, its rank deficiency is one, and thus one diagonal
# element needs to modified. Changing to infinity forces a zero in the
# corresponding element in the solution.
i = (A.indptr[1:] - A.indptr[:-1]).argmax()
A[i, i] = float("inf")
if method == "tracemin_chol":
solver = _CholeskySolver(A)
else:
solver = _LUSolver(A)
else:
raise nx.NetworkXError("Unknown linear system solver: " + method)
# Initialize.
Lnorm = abs(L).sum(axis=1).flatten().max()
project(X)
W = ndarray(X.shape, order="F")
while True:
# Orthonormalize X.
X = qr(X)[0]
# Compute iteration matrix H.
W[:, :] = L @ X
H = X.T @ W
sigma, Y = eigh(H, overwrite_a=True)
# Compute the Ritz vectors.
X = X @ Y
# Test for convergence exploiting the fact that L * X == W * Y.
res = dasum(W @ Y[:, 0] - sigma[0] * X[:, 0]) / Lnorm
if res < tol:
break
# Compute X = L \ X / (X' * (L \ X)).
# L \ X can have an arbitrary projection on the nullspace of L,
# which will be eliminated.
W[:, :] = solver.solve(X, tol)
X = (inv(W.T @ X) @ W.T).T # Preserves Fortran storage order.
project(X)
return sigma, asarray(X)
def _get_fiedler_func(method):
"""Returns a function that solves the Fiedler eigenvalue problem.
"""
if method == "tracemin": # old style keyword <v2.1
method = "tracemin_pcg"
if method in ("tracemin_pcg", "tracemin_chol", "tracemin_lu"):
def find_fiedler(L, x, normalized, tol, seed):
q = 1 if method == "tracemin_pcg" else min(4, L.shape[0] - 1)
X = asarray(seed.normal(size=(q, L.shape[0]))).T
sigma, X = _tracemin_fiedler(L, X, normalized, tol, method)
return sigma[0], X[:, 0]
elif method == "lanczos" or method == "lobpcg":
def find_fiedler(L, x, normalized, tol, seed):
L = csc_matrix(L, dtype=float)
n = L.shape[0]
if normalized:
D = spdiags(1.0 / sqrt(L.diagonal()), [0], n, n, format="csc")
L = D * L * D
if method == "lanczos" or n < 10:
# Avoid LOBPCG when n < 10 due to
# https://github.com/scipy/scipy/issues/3592
# https://github.com/scipy/scipy/pull/3594
sigma, X = eigsh(L, 2, which="SM", tol=tol, return_eigenvectors=True)
return sigma[1], X[:, 1]
else:
X = asarray(atleast_2d(x).T)
M = spdiags(1.0 / L.diagonal(), [0], n, n)
Y = ones(n)
if normalized:
Y /= D.diagonal()
sigma, X = lobpcg(
L, X, M=M, Y=atleast_2d(Y).T, tol=tol, maxiter=n, largest=False
)
return sigma[0], X[:, 0]
else:
raise nx.NetworkXError(f"unknown method '{method}'.")
return find_fiedler
@random_state(5)
@not_implemented_for("directed")
def algebraic_connectivity(
G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
):
"""Returns the algebraic connectivity of an undirected graph.
The algebraic connectivity of a connected undirected graph is the second
smallest eigenvalue of its Laplacian matrix.
Parameters
----------
G : NetworkX graph
An undirected graph.
weight : object, optional (default: None)
The data key used to determine the weight of each edge. If None, then
each edge has unit weight.
normalized : bool, optional (default: False)
Whether the normalized Laplacian matrix is used.
tol : float, optional (default: 1e-8)
Tolerance of relative residual in eigenvalue computation.
method : string, optional (default: 'tracemin_pcg')
Method of eigenvalue computation. It must be one of the tracemin
options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
or 'lobpcg' (LOBPCG).
The TraceMIN algorithm uses a linear system solver. The following
values allow specifying the solver to be used.
=============== ========================================
Value Solver
=============== ========================================
'tracemin_pcg' Preconditioned conjugate gradient method
'tracemin_chol' Cholesky factorization
'tracemin_lu' LU factorization
=============== ========================================
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Returns
-------
algebraic_connectivity : float
Algebraic connectivity.
Raises
------
NetworkXNotImplemented
If G is directed.
NetworkXError
If G has less than two nodes.
Notes
-----
Edge weights are interpreted by their absolute values. For MultiGraph's,
weights of parallel edges are summed. Zero-weighted edges are ignored.
To use Cholesky factorization in the TraceMIN algorithm, the
:samp:`scikits.sparse` package must be installed.
See Also
--------
laplacian_matrix
"""
if len(G) < 2:
raise nx.NetworkXError("graph has less than two nodes.")
G = _preprocess_graph(G, weight)
if not nx.is_connected(G):
return 0.0
L = nx.laplacian_matrix(G)
if L.shape[0] == 2:
return 2.0 * L[0, 0] if not normalized else 2.0
find_fiedler = _get_fiedler_func(method)
x = None if method != "lobpcg" else _rcm_estimate(G, G)
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
return sigma
@random_state(5)
@not_implemented_for("directed")
def fiedler_vector(
G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
):
"""Returns the Fiedler vector of a connected undirected graph.
The Fiedler vector of a connected undirected graph is the eigenvector
corresponding to the second smallest eigenvalue of the Laplacian matrix of
of the graph.
Parameters
----------
G : NetworkX graph
An undirected graph.
weight : object, optional (default: None)
The data key used to determine the weight of each edge. If None, then
each edge has unit weight.
normalized : bool, optional (default: False)
Whether the normalized Laplacian matrix is used.
tol : float, optional (default: 1e-8)
Tolerance of relative residual in eigenvalue computation.
method : string, optional (default: 'tracemin_pcg')
Method of eigenvalue computation. It must be one of the tracemin
options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
or 'lobpcg' (LOBPCG).
The TraceMIN algorithm uses a linear system solver. The following
values allow specifying the solver to be used.
=============== ========================================
Value Solver
=============== ========================================
'tracemin_pcg' Preconditioned conjugate gradient method
'tracemin_chol' Cholesky factorization
'tracemin_lu' LU factorization
=============== ========================================
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Returns
-------
fiedler_vector : NumPy array of floats.
Fiedler vector.
Raises
------
NetworkXNotImplemented
If G is directed.
NetworkXError
If G has less than two nodes or is not connected.
Notes
-----
Edge weights are interpreted by their absolute values. For MultiGraph's,
weights of parallel edges are summed. Zero-weighted edges are ignored.
To use Cholesky factorization in the TraceMIN algorithm, the
:samp:`scikits.sparse` package must be installed.
See Also
--------
laplacian_matrix
"""
if len(G) < 2:
raise nx.NetworkXError("graph has less than two nodes.")
G = _preprocess_graph(G, weight)
if not nx.is_connected(G):
raise nx.NetworkXError("graph is not connected.")
if len(G) == 2:
return array([1.0, -1.0])
find_fiedler = _get_fiedler_func(method)
L = nx.laplacian_matrix(G)
x = None if method != "lobpcg" else _rcm_estimate(G, G)
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
return fiedler
@random_state(5)
def spectral_ordering(
G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
):
"""Compute the spectral_ordering of a graph.
The spectral ordering of a graph is an ordering of its nodes where nodes
in the same weakly connected components appear contiguous and ordered by
their corresponding elements in the Fiedler vector of the component.
Parameters
----------
G : NetworkX graph
A graph.
weight : object, optional (default: None)
The data key used to determine the weight of each edge. If None, then
each edge has unit weight.
normalized : bool, optional (default: False)
Whether the normalized Laplacian matrix is used.
tol : float, optional (default: 1e-8)
Tolerance of relative residual in eigenvalue computation.
method : string, optional (default: 'tracemin_pcg')
Method of eigenvalue computation. It must be one of the tracemin
options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
or 'lobpcg' (LOBPCG).
The TraceMIN algorithm uses a linear system solver. The following
values allow specifying the solver to be used.
=============== ========================================
Value Solver
=============== ========================================
'tracemin_pcg' Preconditioned conjugate gradient method
'tracemin_chol' Cholesky factorization
'tracemin_lu' LU factorization
=============== ========================================
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Returns
-------
spectral_ordering : NumPy array of floats.
Spectral ordering of nodes.
Raises
------
NetworkXError
If G is empty.
Notes
-----
Edge weights are interpreted by their absolute values. For MultiGraph's,
weights of parallel edges are summed. Zero-weighted edges are ignored.
To use Cholesky factorization in the TraceMIN algorithm, the
:samp:`scikits.sparse` package must be installed.
See Also
--------
laplacian_matrix
"""
if len(G) == 0:
raise nx.NetworkXError("graph is empty.")
G = _preprocess_graph(G, weight)
find_fiedler = _get_fiedler_func(method)
order = []
for component in nx.connected_components(G):
size = len(component)
if size > 2:
L = nx.laplacian_matrix(G, component)
x = None if method != "lobpcg" else _rcm_estimate(G, component)
sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
sort_info = zip(fiedler, range(size), component)
order.extend(u for x, c, u in sorted(sort_info))
else:
order.extend(component)
return order

473
venv/Lib/site-packages/networkx/linalg/attrmatrix.py Normal file
View file

@ -0,0 +1,473 @@
"""
Functions for constructing matrix-like objects from graph attributes.
"""
__all__ = ["attr_matrix", "attr_sparse_matrix"]
def _node_value(G, node_attr):
"""Returns a function that returns a value from G.nodes[u].
We return a function expecting a node as its sole argument. Then, in the
simplest scenario, the returned function will return G.nodes[u][node_attr].
However, we also handle the case when `node_attr` is None or when it is a
function itself.
Parameters
----------
G : graph
A NetworkX graph
node_attr : {None, str, callable}
Specification of how the value of the node attribute should be obtained
from the node attribute dictionary.
Returns
-------
value : function
A function expecting a node as its sole argument. The function will
returns a value from G.nodes[u] that depends on `edge_attr`.
"""
if node_attr is None:
def value(u):
return u
elif not hasattr(node_attr, "__call__"):
# assume it is a key for the node attribute dictionary
def value(u):
return G.nodes[u][node_attr]
else:
# Advanced: Allow users to specify something else.
#
# For example,
# node_attr = lambda u: G.nodes[u].get('size', .5) * 3
#
value = node_attr
return value
def _edge_value(G, edge_attr):
"""Returns a function that returns a value from G[u][v].
Suppose there exists an edge between u and v. Then we return a function
expecting u and v as arguments. For Graph and DiGraph, G[u][v] is
the edge attribute dictionary, and the function (essentially) returns
G[u][v][edge_attr]. However, we also handle cases when `edge_attr` is None
and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
is a dictionary of all edges between u and v. In this case, the returned
function sums the value of `edge_attr` for every edge between u and v.
Parameters
----------
G : graph
A NetworkX graph
edge_attr : {None, str, callable}
Specification of how the value of the edge attribute should be obtained
from the edge attribute dictionary, G[u][v]. For multigraphs, G[u][v]
is a dictionary of all the edges between u and v. This allows for
special treatment of multiedges.
Returns
-------
value : function
A function expecting two nodes as parameters. The nodes should
represent the from- and to- node of an edge. The function will
return a value from G[u][v] that depends on `edge_attr`.
"""
if edge_attr is None:
# topological count of edges
if G.is_multigraph():
def value(u, v):
return len(G[u][v])
else:
def value(u, v):
return 1
elif not hasattr(edge_attr, "__call__"):
# assume it is a key for the edge attribute dictionary
if edge_attr == "weight":
# provide a default value
if G.is_multigraph():
def value(u, v):
return sum([d.get(edge_attr, 1) for d in G[u][v].values()])
else:
def value(u, v):
return G[u][v].get(edge_attr, 1)
else:
# otherwise, the edge attribute MUST exist for each edge
if G.is_multigraph():
def value(u, v):
return sum([d[edge_attr] for d in G[u][v].values()])
else:
def value(u, v):
return G[u][v][edge_attr]
else:
# Advanced: Allow users to specify something else.
#
# Alternative default value:
# edge_attr = lambda u,v: G[u][v].get('thickness', .5)
#
# Function on an attribute:
# edge_attr = lambda u,v: abs(G[u][v]['weight'])
#
# Handle Multi(Di)Graphs differently:
# edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
#
# Ignore multiple edges
# edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
#
value = edge_attr
return value
def attr_matrix(
G,
edge_attr=None,
node_attr=None,
normalized=False,
rc_order=None,
dtype=None,
order=None,
):
"""Returns a NumPy matrix using attributes from G.
If only `G` is passed in, then the adjacency matrix is constructed.
Let A be a discrete set of values for the node attribute `node_attr`. Then
the elements of A represent the rows and columns of the constructed matrix.
Now, iterate through every edge e=(u,v) in `G` and consider the value
of the edge attribute `edge_attr`. If ua and va are the values of the
node attribute `node_attr` for u and v, respectively, then the value of
the edge attribute is added to the matrix element at (ua, va).
Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy matrix.
edge_attr : str, optional
Each element of the matrix represents a running total of the
specified edge attribute for edges whose node attributes correspond
to the rows/cols of the matirx. The attribute must be present for
all edges in the graph. If no attribute is specified, then we
just count the number of edges whose node attributes correspond
to the matrix element.
node_attr : str, optional
Each row and column in the matrix represents a particular value
of the node attribute. The attribute must be present for all nodes
in the graph. Note, the values of this attribute should be reliably
hashable. So, float values are not recommended. If no attribute is
specified, then the rows and columns will be the nodes of the graph.
normalized : bool, optional
If True, then each row is normalized by the summation of its values.
rc_order : list, optional
A list of the node attribute values. This list specifies the ordering
of rows and columns of the array. If no ordering is provided, then
the ordering will be random (and also, a return value).
Other Parameters
----------------
dtype : NumPy data-type, optional
A valid NumPy dtype used to initialize the array. Keep in mind certain
dtypes can yield unexpected results if the array is to be normalized.
The parameter is passed to numpy.zeros(). If unspecified, the NumPy
default is used.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. This parameter is passed to
numpy.zeros(). If unspecified, the NumPy default is used.
Returns
-------
M : NumPy matrix
The attribute matrix.
ordering : list
If `rc_order` was specified, then only the matrix is returned.
However, if `rc_order` was None, then the ordering used to construct
the matrix is returned as well.
Examples
--------
Construct an adjacency matrix:
>>> G = nx.Graph()
>>> G.add_edge(0, 1, thickness=1, weight=3)
>>> G.add_edge(0, 2, thickness=2)
>>> G.add_edge(1, 2, thickness=3)
>>> nx.attr_matrix(G, rc_order=[0, 1, 2])
matrix([[0., 1., 1.],
[1., 0., 1.],
[1., 1., 0.]])
Alternatively, we can obtain the matrix describing edge thickness.
>>> nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
matrix([[0., 1., 2.],
[1., 0., 3.],
[2., 3., 0.]])
We can also color the nodes and ask for the probability distribution over
all edges (u,v) describing:
Pr(v has color Y | u has color X)
>>> G.nodes[0]["color"] = "red"
>>> G.nodes[1]["color"] = "red"
>>> G.nodes[2]["color"] = "blue"
>>> rc = ["red", "blue"]
>>> nx.attr_matrix(G, node_attr="color", normalized=True, rc_order=rc)
matrix([[0.33333333, 0.66666667],
[1. , 0. ]])
For example, the above tells us that for all edges (u,v):
Pr( v is red | u is red) = 1/3
Pr( v is blue | u is red) = 2/3
Pr( v is red | u is blue) = 1
Pr( v is blue | u is blue) = 0
Finally, we can obtain the total weights listed by the node colors.
>>> nx.attr_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
matrix([[3., 2.],
[2., 0.]])
Thus, the total weight over all edges (u,v) with u and v having colors:
(red, red) is 3 # the sole contribution is from edge (0,1)
(red, blue) is 2 # contributions from edges (0,2) and (1,2)
(blue, red) is 2 # same as (red, blue) since graph is undirected
(blue, blue) is 0 # there are no edges with blue endpoints
"""
try:
import numpy as np
except ImportError as e:
raise ImportError("attr_matrix() requires numpy: http://scipy.org/ ") from e
edge_value = _edge_value(G, edge_attr)
node_value = _node_value(G, node_attr)
if rc_order is None:
ordering = list({node_value(n) for n in G})
else:
ordering = rc_order
N = len(ordering)
undirected = not G.is_directed()
index = dict(zip(ordering, range(N)))
M = np.zeros((N, N), dtype=dtype, order=order)
seen = set()
for u, nbrdict in G.adjacency():
for v in nbrdict:
# Obtain the node attribute values.
i, j = index[node_value(u)], index[node_value(v)]
if v not in seen:
M[i, j] += edge_value(u, v)
if undirected:
M[j, i] = M[i, j]
if undirected:
seen.add(u)
if normalized:
M /= M.sum(axis=1).reshape((N, 1))
M = np.asmatrix(M)
if rc_order is None:
return M, ordering
else:
return M
def attr_sparse_matrix(
G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None
):
"""Returns a SciPy sparse matrix using attributes from G.
If only `G` is passed in, then the adjacency matrix is constructed.
Let A be a discrete set of values for the node attribute `node_attr`. Then
the elements of A represent the rows and columns of the constructed matrix.
Now, iterate through every edge e=(u,v) in `G` and consider the value
of the edge attribute `edge_attr`. If ua and va are the values of the
node attribute `node_attr` for u and v, respectively, then the value of
the edge attribute is added to the matrix element at (ua, va).
Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy matrix.
edge_attr : str, optional
Each element of the matrix represents a running total of the
specified edge attribute for edges whose node attributes correspond
to the rows/cols of the matirx. The attribute must be present for
all edges in the graph. If no attribute is specified, then we
just count the number of edges whose node attributes correspond
to the matrix element.
node_attr : str, optional
Each row and column in the matrix represents a particular value
of the node attribute. The attribute must be present for all nodes
in the graph. Note, the values of this attribute should be reliably
hashable. So, float values are not recommended. If no attribute is
specified, then the rows and columns will be the nodes of the graph.
normalized : bool, optional
If True, then each row is normalized by the summation of its values.
rc_order : list, optional
A list of the node attribute values. This list specifies the ordering
of rows and columns of the array. If no ordering is provided, then
the ordering will be random (and also, a return value).
Other Parameters
----------------
dtype : NumPy data-type, optional
A valid NumPy dtype used to initialize the array. Keep in mind certain
dtypes can yield unexpected results if the array is to be normalized.
The parameter is passed to numpy.zeros(). If unspecified, the NumPy
default is used.
Returns
-------
M : SciPy sparse matrix
The attribute matrix.
ordering : list
If `rc_order` was specified, then only the matrix is returned.
However, if `rc_order` was None, then the ordering used to construct
the matrix is returned as well.
Examples
--------
Construct an adjacency matrix:
>>> G = nx.Graph()
>>> G.add_edge(0, 1, thickness=1, weight=3)
>>> G.add_edge(0, 2, thickness=2)
>>> G.add_edge(1, 2, thickness=3)
>>> M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
>>> M.todense()
matrix([[0., 1., 1.],
[1., 0., 1.],
[1., 1., 0.]])
Alternatively, we can obtain the matrix describing edge thickness.
>>> M = nx.attr_sparse_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
>>> M.todense()
matrix([[0., 1., 2.],
[1., 0., 3.],
[2., 3., 0.]])
We can also color the nodes and ask for the probability distribution over
all edges (u,v) describing:
Pr(v has color Y | u has color X)
>>> G.nodes[0]["color"] = "red"
>>> G.nodes[1]["color"] = "red"
>>> G.nodes[2]["color"] = "blue"
>>> rc = ["red", "blue"]
>>> M = nx.attr_sparse_matrix(G, node_attr="color", normalized=True, rc_order=rc)
>>> M.todense()
matrix([[0.33333333, 0.66666667],
[1. , 0. ]])
For example, the above tells us that for all edges (u,v):
Pr( v is red | u is red) = 1/3
Pr( v is blue | u is red) = 2/3
Pr( v is red | u is blue) = 1
Pr( v is blue | u is blue) = 0
Finally, we can obtain the total weights listed by the node colors.
>>> M = nx.attr_sparse_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
>>> M.todense()
matrix([[3., 2.],
[2., 0.]])
Thus, the total weight over all edges (u,v) with u and v having colors:
(red, red) is 3 # the sole contribution is from edge (0,1)
(red, blue) is 2 # contributions from edges (0,2) and (1,2)
(blue, red) is 2 # same as (red, blue) since graph is undirected
(blue, blue) is 0 # there are no edges with blue endpoints
"""
try:
import numpy as np
from scipy import sparse
except ImportError as e:
raise ImportError(
"attr_sparse_matrix() requires scipy: " "http://scipy.org/ "
) from e
edge_value = _edge_value(G, edge_attr)
node_value = _node_value(G, node_attr)
if rc_order is None:
ordering = list({node_value(n) for n in G})
else:
ordering = rc_order
N = len(ordering)
undirected = not G.is_directed()
index = dict(zip(ordering, range(N)))
M = sparse.lil_matrix((N, N), dtype=dtype)
seen = set()
for u, nbrdict in G.adjacency():
for v in nbrdict:
# Obtain the node attribute values.
i, j = index[node_value(u)], index[node_value(v)]
if v not in seen:
M[i, j] += edge_value(u, v)
if undirected:
M[j, i] = M[i, j]
if undirected:
seen.add(u)
if normalized:
norms = np.asarray(M.sum(axis=1)).ravel()
for i, norm in enumerate(norms):
M[i, :] /= norm
if rc_order is None:
return M, ordering
else:
return M

78
venv/Lib/site-packages/networkx/linalg/bethehessianmatrix.py Normal file
View file

@ -0,0 +1,78 @@
"""Bethe Hessian or deformed Laplacian matrix of graphs."""
import networkx as nx
from networkx.utils import not_implemented_for
__all__ = ["bethe_hessian_matrix"]
@not_implemented_for("directed")
@not_implemented_for("multigraph")
def bethe_hessian_matrix(G, r=None, nodelist=None):
r"""Returns the Bethe Hessian matrix of G.
The Bethe Hessian is a family of matrices parametrized by r, defined as
H(r) = (r^2 - 1) I - r A + D where A is the adjacency matrix, D is the
diagonal matrix of node degrees, and I is the identify matrix. It is equal
to the graph laplacian when the regularizer r = 1.
The default choice of regularizer should be the ratio [2]
.. math::
r_m = \left(\sum k_i \right)^{-1}\left(\sum k_i^2 \right) - 1
Parameters
----------
G : Graph
A NetworkX graph
r : float
Regularizer parameter
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
Returns
-------
H : Numpy matrix
The Bethe Hessian matrix of G, with paramter r.
Examples
--------
>>> k = [3, 2, 2, 1, 0]
>>> G = nx.havel_hakimi_graph(k)
>>> H = nx.modularity_matrix(G)
See Also
--------
bethe_hessian_spectrum
to_numpy_array
adjacency_matrix
laplacian_matrix
References
----------
.. [1] A. Saade, F. Krzakala and L. Zdeborová
"Spectral clustering of graphs with the bethe hessian",
Advances in Neural Information Processing Systems. 2014.
.. [2] C. M. Lee, E. Levina
"Estimating the number of communities in networks by spectral methods"
arXiv:1507.00827, 2015.
"""
import scipy.sparse
if nodelist is None:
nodelist = list(G)
if r is None:
r = (
sum([d ** 2 for v, d in nx.degree(G)]) / sum([d for v, d in nx.degree(G)])
- 1
)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, format="csr")
n, m = A.shape
diags = A.sum(axis=1)
D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format="csr")
I = scipy.sparse.eye(m, n, format="csr")
return (r ** 2 - 1) * I - r * A + D

156
venv/Lib/site-packages/networkx/linalg/graphmatrix.py Normal file
View file

@ -0,0 +1,156 @@
"""
Adjacency matrix and incidence matrix of graphs.
"""
import networkx as nx
__all__ = ["incidence_matrix", "adj_matrix", "adjacency_matrix"]
def incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None):
"""Returns incidence matrix of G.
The incidence matrix assigns each row to a node and each column to an edge.
For a standard incidence matrix a 1 appears wherever a row's node is
incident on the column's edge. For an oriented incidence matrix each
edge is assigned an orientation (arbitrarily for undirected and aligning to
direction for directed). A -1 appears for the source (tail) of an edge and
1 for the destination (head) of the edge. The elements are zero otherwise.
Parameters
----------
G : graph
A NetworkX graph
nodelist : list, optional (default= all nodes in G)
The rows are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
edgelist : list, optional (default= all edges in G)
The columns are ordered according to the edges in edgelist.
If edgelist is None, then the ordering is produced by G.edges().
oriented: bool, optional (default=False)
If True, matrix elements are +1 or -1 for the head or tail node
respectively of each edge. If False, +1 occurs at both nodes.
weight : string or None, optional (default=None)
The edge data key used to provide each value in the matrix.
If None, then each edge has weight 1. Edge weights, if used,
should be positive so that the orientation can provide the sign.
Returns
-------
A : SciPy sparse matrix
The incidence matrix of G.
Notes
-----
For MultiGraph/MultiDiGraph, the edges in edgelist should be
(u,v,key) 3-tuples.
"Networks are the best discrete model for so many problems in
applied mathematics" [1]_.
References
----------
.. [1] Gil Strang, Network applications: A = incidence matrix,
http://academicearth.org/lectures/network-applications-incidence-matrix
"""
import scipy.sparse
if nodelist is None:
nodelist = list(G)
if edgelist is None:
if G.is_multigraph():
edgelist = list(G.edges(keys=True))
else:
edgelist = list(G.edges())
A = scipy.sparse.lil_matrix((len(nodelist), len(edgelist)))
node_index = {node: i for i, node in enumerate(nodelist)}
for ei, e in enumerate(edgelist):
(u, v) = e[:2]
if u == v:
continue # self loops give zero column
try:
ui = node_index[u]
vi = node_index[v]
except KeyError as e:
raise nx.NetworkXError(
f"node {u} or {v} in edgelist " f"but not in nodelist"
) from e
if weight is None:
wt = 1
else:
if G.is_multigraph():
ekey = e[2]
wt = G[u][v][ekey].get(weight, 1)
else:
wt = G[u][v].get(weight, 1)
if oriented:
A[ui, ei] = -wt
A[vi, ei] = wt
else:
A[ui, ei] = wt
A[vi, ei] = wt
return A.asformat("csc")
def adjacency_matrix(G, nodelist=None, weight="weight"):
"""Returns adjacency matrix of G.
Parameters
----------
G : graph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to provide each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
A : SciPy sparse matrix
Adjacency matrix representation of G.
Notes
-----
For directed graphs, entry i,j corresponds to an edge from i to j.
If you want a pure Python adjacency matrix representation try
networkx.convert.to_dict_of_dicts which will return a
dictionary-of-dictionaries format that can be addressed as a
sparse matrix.
For MultiGraph/MultiDiGraph with parallel edges the weights are summed.
See `to_numpy_array` for other options.
The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the edge weight attribute
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting Scipy sparse matrix can be modified as follows:
>>> import scipy as sp
>>> G = nx.Graph([(1, 1)])
>>> A = nx.adjacency_matrix(G)
>>> print(A.todense())
[[1]]
>>> A.setdiag(A.diagonal() * 2)
>>> print(A.todense())
[[2]]
See Also
--------
to_numpy_array
to_scipy_sparse_matrix
to_dict_of_dicts
adjacency_spectrum
"""
return nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight)
adj_matrix = adjacency_matrix

366
venv/Lib/site-packages/networkx/linalg/laplacianmatrix.py Normal file
View file

@ -0,0 +1,366 @@
"""Laplacian matrix of graphs.
"""
import networkx as nx
from networkx.utils import not_implemented_for
__all__ = [
"laplacian_matrix",
"normalized_laplacian_matrix",
"directed_laplacian_matrix",
"directed_combinatorial_laplacian_matrix",
]
@not_implemented_for("directed")
def laplacian_matrix(G, nodelist=None, weight="weight"):
"""Returns the Laplacian matrix of G.
The graph Laplacian is the matrix L = D - A, where
A is the adjacency matrix and D is the diagonal matrix of node degrees.
Parameters
----------
G : graph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
L : SciPy sparse matrix
The Laplacian matrix of G.
Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See Also
--------
to_numpy_array
normalized_laplacian_matrix
laplacian_spectrum
"""
import scipy.sparse
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
n, m = A.shape
diags = A.sum(axis=1)
D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format="csr")
return D - A
@not_implemented_for("directed")
def normalized_laplacian_matrix(G, nodelist=None, weight="weight"):
r"""Returns the normalized Laplacian matrix of G.
The normalized graph Laplacian is the matrix
.. math::
N = D^{-1/2} L D^{-1/2}
where `L` is the graph Laplacian and `D` is the diagonal matrix of
node degrees.
Parameters
----------
G : graph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
N : Scipy sparse matrix
The normalized Laplacian matrix of G.
Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.
If the Graph contains selfloops, D is defined as diag(sum(A,1)), where A is
the adjacency matrix [2]_.
See Also
--------
laplacian_matrix
normalized_laplacian_spectrum
References
----------
.. [1] Fan Chung-Graham, Spectral Graph Theory,
CBMS Regional Conference Series in Mathematics, Number 92, 1997.
.. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
March 2007.
"""
import numpy as np
import scipy
import scipy.sparse
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
n, m = A.shape
diags = A.sum(axis=1).flatten()
D = scipy.sparse.spdiags(diags, [0], m, n, format="csr")
L = D - A
with scipy.errstate(divide="ignore"):
diags_sqrt = 1.0 / np.sqrt(diags)
diags_sqrt[np.isinf(diags_sqrt)] = 0
DH = scipy.sparse.spdiags(diags_sqrt, [0], m, n, format="csr")
return DH.dot(L.dot(DH))
###############################################################################
# Code based on
# https://bitbucket.org/bedwards/networkx-community/src/370bd69fc02f/networkx/algorithms/community/
@not_implemented_for("undirected")
@not_implemented_for("multigraph")
def directed_laplacian_matrix(
G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
):
r"""Returns the directed Laplacian matrix of G.
The graph directed Laplacian is the matrix
.. math::
L = I - (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} ) / 2
where `I` is the identity matrix, `P` is the transition matrix of the
graph, and `\Phi` a matrix with the Perron vector of `P` in the diagonal and
zeros elsewhere.
Depending on the value of walk_type, `P` can be the transition matrix
induced by a random walk, a lazy random walk, or a random walk with
teleportation (PageRank).
Parameters
----------
G : DiGraph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
walk_type : string or None, optional (default=None)
If None, `P` is selected depending on the properties of the
graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
alpha : real
(1 - alpha) is the teleportation probability used with pagerank
Returns
-------
L : NumPy matrix
Normalized Laplacian of G.
Notes
-----
Only implemented for DiGraphs
See Also
--------
laplacian_matrix
References
----------
.. [1] Fan Chung (2005).
Laplacians and the Cheeger inequality for directed graphs.
Annals of Combinatorics, 9(1), 2005
"""
import numpy as np
from scipy.sparse import spdiags, linalg
P = _transition_matrix(
G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
)
n, m = P.shape
evals, evecs = linalg.eigs(P.T, k=1)
v = evecs.flatten().real
p = v / v.sum()
sqrtp = np.sqrt(p)
Q = spdiags(sqrtp, [0], n, n) * P * spdiags(1.0 / sqrtp, [0], n, n)
I = np.identity(len(G))
return I - (Q + Q.T) / 2.0
@not_implemented_for("undirected")
@not_implemented_for("multigraph")
def directed_combinatorial_laplacian_matrix(
G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
):
r"""Return the directed combinatorial Laplacian matrix of G.
The graph directed combinatorial Laplacian is the matrix
.. math::
L = \Phi - (\Phi P + P^T \Phi) / 2
where `P` is the transition matrix of the graph and and `\Phi` a matrix
with the Perron vector of `P` in the diagonal and zeros elsewhere.
Depending on the value of walk_type, `P` can be the transition matrix
induced by a random walk, a lazy random walk, or a random walk with
teleportation (PageRank).
Parameters
----------
G : DiGraph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
walk_type : string or None, optional (default=None)
If None, `P` is selected depending on the properties of the
graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
alpha : real
(1 - alpha) is the teleportation probability used with pagerank
Returns
-------
L : NumPy matrix
Combinatorial Laplacian of G.
Notes
-----
Only implemented for DiGraphs
See Also
--------
laplacian_matrix
References
----------
.. [1] Fan Chung (2005).
Laplacians and the Cheeger inequality for directed graphs.
Annals of Combinatorics, 9(1), 2005
"""
from scipy.sparse import spdiags, linalg
P = _transition_matrix(
G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
)
n, m = P.shape
evals, evecs = linalg.eigs(P.T, k=1)
v = evecs.flatten().real
p = v / v.sum()
Phi = spdiags(p, [0], n, n)
Phi = Phi.todense()
return Phi - (Phi * P + P.T * Phi) / 2.0
def _transition_matrix(G, nodelist=None, weight="weight", walk_type=None, alpha=0.95):
"""Returns the transition matrix of G.
This is a row stochastic giving the transition probabilities while
performing a random walk on the graph. Depending on the value of walk_type,
P can be the transition matrix induced by a random walk, a lazy random walk,
or a random walk with teleportation (PageRank).
Parameters
----------
G : DiGraph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
walk_type : string or None, optional (default=None)
If None, `P` is selected depending on the properties of the
graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
alpha : real
(1 - alpha) is the teleportation probability used with pagerank
Returns
-------
P : NumPy matrix
transition matrix of G.
Raises
------
NetworkXError
If walk_type not specified or alpha not in valid range
"""
import numpy as np
from scipy.sparse import identity, spdiags
if walk_type is None:
if nx.is_strongly_connected(G):
if nx.is_aperiodic(G):
walk_type = "random"
else:
walk_type = "lazy"
else:
walk_type = "pagerank"
M = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, dtype=float)
n, m = M.shape
if walk_type in ["random", "lazy"]:
DI = spdiags(1.0 / np.array(M.sum(axis=1).flat), [0], n, n)
if walk_type == "random":
P = DI * M
else:
I = identity(n)
P = (I + DI * M) / 2.0
elif walk_type == "pagerank":
if not (0 < alpha < 1):
raise nx.NetworkXError("alpha must be between 0 and 1")
# this is using a dense representation
M = M.todense()
# add constant to dangling nodes' row
dangling = np.where(M.sum(axis=1) == 0)
for d in dangling[0]:
M[d] = 1.0 / n
# normalize
M = M / M.sum(axis=1)
P = alpha * M + (1 - alpha) / n
else:
raise nx.NetworkXError("walk_type must be random, lazy, or pagerank")
return P

158
venv/Lib/site-packages/networkx/linalg/modularitymatrix.py Normal file
View file

@ -0,0 +1,158 @@
"""Modularity matrix of graphs.
"""
import networkx as nx
from networkx.utils import not_implemented_for
__all__ = ["modularity_matrix", "directed_modularity_matrix"]
@not_implemented_for("directed")
@not_implemented_for("multigraph")
def modularity_matrix(G, nodelist=None, weight=None):
r"""Returns the modularity matrix of G.
The modularity matrix is the matrix B = A - <A>, where A is the adjacency
matrix and <A> is the average adjacency matrix, assuming that the graph
is described by the configuration model.
More specifically, the element B_ij of B is defined as
.. math::
A_{ij} - {k_i k_j \over 2 m}
where k_i is the degree of node i, and where m is the number of edges
in the graph. When weight is set to a name of an attribute edge, Aij, k_i,
k_j and m are computed using its value.
Parameters
----------
G : Graph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
Returns
-------
B : Numpy matrix
The modularity matrix of G.
Examples
--------
>>> k = [3, 2, 2, 1, 0]
>>> G = nx.havel_hakimi_graph(k)
>>> B = nx.modularity_matrix(G)
See Also
--------
to_numpy_array
modularity_spectrum
adjacency_matrix
directed_modularity_matrix
References
----------
.. [1] M. E. J. Newman, "Modularity and community structure in networks",
Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
"""
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
k = A.sum(axis=1)
m = k.sum() * 0.5
# Expected adjacency matrix
X = k * k.transpose() / (2 * m)
return A - X
@not_implemented_for("undirected")
@not_implemented_for("multigraph")
def directed_modularity_matrix(G, nodelist=None, weight=None):
"""Returns the directed modularity matrix of G.
The modularity matrix is the matrix B = A - <A>, where A is the adjacency
matrix and <A> is the expected adjacency matrix, assuming that the graph
is described by the configuration model.
More specifically, the element B_ij of B is defined as
.. math::
B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m
where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
of node j, with m the number of edges in the graph. When weight is set
to a name of an attribute edge, Aij, k_i, k_j and m are computed using
its value.
Parameters
----------
G : DiGraph
A NetworkX DiGraph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
Returns
-------
B : Numpy matrix
The modularity matrix of G.
Examples
--------
>>> G = nx.DiGraph()
>>> G.add_edges_from(
... (
... (1, 2),
... (1, 3),
... (3, 1),
... (3, 2),
... (3, 5),
... (4, 5),
... (4, 6),
... (5, 4),
... (5, 6),
... (6, 4),
... )
... )
>>> B = nx.directed_modularity_matrix(G)
Notes
-----
NetworkX defines the element A_ij of the adjacency matrix as 1 if there
is a link going from node i to node j. Leicht and Newman use the opposite
definition. This explains the different expression for B_ij.
See Also
--------
to_numpy_array
modularity_spectrum
adjacency_matrix
modularity_matrix
References
----------
.. [1] E. A. Leicht, M. E. J. Newman,
"Community structure in directed networks",
Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
"""
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
k_in = A.sum(axis=0)
k_out = A.sum(axis=1)
m = k_in.sum()
# Expected adjacency matrix
X = k_out * k_in / m
return A - X

166
venv/Lib/site-packages/networkx/linalg/spectrum.py Normal file
View file

@ -0,0 +1,166 @@
"""
Eigenvalue spectrum of graphs.
"""
import networkx as nx
__all__ = [
"laplacian_spectrum",
"adjacency_spectrum",
"modularity_spectrum",
"normalized_laplacian_spectrum",
"bethe_hessian_spectrum",
]
def laplacian_spectrum(G, weight="weight"):
"""Returns eigenvalues of the Laplacian of G
Parameters
----------
G : graph
A NetworkX graph
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
evals : NumPy array
Eigenvalues
Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.
See Also
--------
laplacian_matrix
"""
from scipy.linalg import eigvalsh
return eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())
def normalized_laplacian_spectrum(G, weight="weight"):
"""Return eigenvalues of the normalized Laplacian of G
Parameters
----------
G : graph
A NetworkX graph
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
evals : NumPy array
Eigenvalues
Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.
See Also
--------
normalized_laplacian_matrix
"""
from scipy.linalg import eigvalsh
return eigvalsh(nx.normalized_laplacian_matrix(G, weight=weight).todense())
def adjacency_spectrum(G, weight="weight"):
"""Returns eigenvalues of the adjacency matrix of G.
Parameters
----------
G : graph
A NetworkX graph
weight : string or None, optional (default='weight')
The edge data key used to compute each value in the matrix.
If None, then each edge has weight 1.
Returns
-------
evals : NumPy array
Eigenvalues
Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.
See Also
--------
adjacency_matrix
"""
from scipy.linalg import eigvals
return eigvals(nx.adjacency_matrix(G, weight=weight).todense())
def modularity_spectrum(G):
"""Returns eigenvalues of the modularity matrix of G.
Parameters
----------
G : Graph
A NetworkX Graph or DiGraph
Returns
-------
evals : NumPy array
Eigenvalues
See Also
--------
modularity_matrix
References
----------
.. [1] M. E. J. Newman, "Modularity and community structure in networks",
Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
"""
from scipy.linalg import eigvals
if G.is_directed():
return eigvals(nx.directed_modularity_matrix(G))
else:
return eigvals(nx.modularity_matrix(G))
def bethe_hessian_spectrum(G, r=None):
"""Returns eigenvalues of the Bethe Hessian matrix of G.
Parameters
----------
G : Graph
A NetworkX Graph or DiGraph
r : float
Regularizer parameter
Returns
-------
evals : NumPy array
Eigenvalues
See Also
--------
bethe_hessian_matrix
References
----------
.. [1] A. Saade, F. Krzakala and L. Zdeborová
"Spectral clustering of graphs with the bethe hessian",
Advances in Neural Information Processing Systems. 2014.
"""
from scipy.linalg import eigvalsh
return eigvalsh(nx.bethe_hessian_matrix(G, r).todense())

0
venv/Lib/site-packages/networkx/linalg/tests/__init__.py Normal file
View file

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/__init__.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_algebraic_connectivity.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_attrmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_bethehessian.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_graphmatrix.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_laplacian.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_modularity.cpython-36.pyc Normal file
View file

Binary file not shown.

BIN
venv/Lib/site-packages/networkx/linalg/tests/__pycache__/test_spectrum.cpython-36.pyc Normal file
View file

Binary file not shown.

365
venv/Lib/site-packages/networkx/linalg/tests/test_algebraic_connectivity.py Normal file
View file

@ -0,0 +1,365 @@
from math import sqrt
import pytest
numpy = pytest.importorskip("numpy")
numpy.linalg = pytest.importorskip("numpy.linalg")
scipy = pytest.importorskip("scipy")
scipy.sparse = pytest.importorskip("scipy.sparse")
import networkx as nx
from networkx.testing import almost_equal
try:
from scikits.sparse.cholmod import cholesky
_cholesky = cholesky
except ImportError:
_cholesky = None
if _cholesky is None:
methods = ("tracemin_pcg", "tracemin_lu", "lanczos", "lobpcg")
else:
methods = ("tracemin_pcg", "tracemin_chol", "tracemin_lu", "lanczos", "lobpcg")
def check_eigenvector(A, l, x):
nx = numpy.linalg.norm(x)
# Check zeroness.
assert not almost_equal(nx, 0)
y = A * x
ny = numpy.linalg.norm(y)
# Check collinearity.
assert almost_equal(numpy.dot(x, y), nx * ny)
# Check eigenvalue.
assert almost_equal(ny, l * nx)
class TestAlgebraicConnectivity:
@pytest.mark.parametrize("method", methods)
def test_directed(self, method):
G = nx.DiGraph()
pytest.raises(
nx.NetworkXNotImplemented, nx.algebraic_connectivity, G, method=method
)
pytest.raises(nx.NetworkXNotImplemented, nx.fiedler_vector, G, method=method)
@pytest.mark.parametrize("method", methods)
def test_null_and_singleton(self, method):
G = nx.Graph()
pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method=method)
pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
G.add_edge(0, 0)
pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method=method)
pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
@pytest.mark.parametrize("method", methods)
def test_disconnected(self, method):
G = nx.Graph()
G.add_nodes_from(range(2))
assert nx.algebraic_connectivity(G) == 0
pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
G.add_edge(0, 1, weight=0)
assert nx.algebraic_connectivity(G) == 0
pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
def test_unrecognized_method(self):
G = nx.path_graph(4)
pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method="unknown")
pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method="unknown")
@pytest.mark.parametrize("method", methods)
def test_two_nodes(self, method):
G = nx.Graph()
G.add_edge(0, 1, weight=1)
A = nx.laplacian_matrix(G)
assert almost_equal(nx.algebraic_connectivity(G, tol=1e-12, method=method), 2)
x = nx.fiedler_vector(G, tol=1e-12, method=method)
check_eigenvector(A, 2, x)
@pytest.mark.parametrize("method", methods)
def test_two_nodes_multigraph(self, method):
G = nx.MultiGraph()
G.add_edge(0, 0, spam=1e8)
G.add_edge(0, 1, spam=1)
G.add_edge(0, 1, spam=-2)
A = -3 * nx.laplacian_matrix(G, weight="spam")
assert almost_equal(
nx.algebraic_connectivity(G, weight="spam", tol=1e-12, method=method), 6
)
x = nx.fiedler_vector(G, weight="spam", tol=1e-12, method=method)
check_eigenvector(A, 6, x)
def test_abbreviation_of_method(self):
G = nx.path_graph(8)
A = nx.laplacian_matrix(G)
sigma = 2 - sqrt(2 + sqrt(2))
ac = nx.algebraic_connectivity(G, tol=1e-12, method="tracemin")
assert almost_equal(ac, sigma)
x = nx.fiedler_vector(G, tol=1e-12, method="tracemin")
check_eigenvector(A, sigma, x)
@pytest.mark.parametrize("method", methods)
def test_path(self, method):
G = nx.path_graph(8)
A = nx.laplacian_matrix(G)
sigma = 2 - sqrt(2 + sqrt(2))
ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
assert almost_equal(ac, sigma)
x = nx.fiedler_vector(G, tol=1e-12, method=method)
check_eigenvector(A, sigma, x)
@pytest.mark.parametrize("method", methods)
def test_problematic_graph_issue_2381(self, method):
G = nx.path_graph(4)
G.add_edges_from([(4, 2), (5, 1)])
A = nx.laplacian_matrix(G)
sigma = 0.438447187191
ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
assert almost_equal(ac, sigma)
x = nx.fiedler_vector(G, tol=1e-12, method=method)
check_eigenvector(A, sigma, x)
@pytest.mark.parametrize("method", methods)
def test_cycle(self, method):
G = nx.cycle_graph(8)
A = nx.laplacian_matrix(G)
sigma = 2 - sqrt(2)
ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
assert almost_equal(ac, sigma)
x = nx.fiedler_vector(G, tol=1e-12, method=method)
check_eigenvector(A, sigma, x)
@pytest.mark.parametrize("method", methods)
def test_seed_argument(self, method):
G = nx.cycle_graph(8)
A = nx.laplacian_matrix(G)
sigma = 2 - sqrt(2)
ac = nx.algebraic_connectivity(G, tol=1e-12, method=method, seed=1)
assert almost_equal(ac, sigma)
x = nx.fiedler_vector(G, tol=1e-12, method=method, seed=1)
check_eigenvector(A, sigma, x)
@pytest.mark.parametrize(
("normalized", "sigma", "laplacian_fn"),
(
(False, 0.2434017461399311, nx.laplacian_matrix),
(True, 0.08113391537997749, nx.normalized_laplacian_matrix),
),
)
@pytest.mark.parametrize("method", methods)
def test_buckminsterfullerene(self, normalized, sigma, laplacian_fn, method):
G = nx.Graph(
[
(1, 10),
(1, 41),
(1, 59),
(2, 12),
(2, 42),
(2, 60),
(3, 6),
(3, 43),
(3, 57),
(4, 8),
(4, 44),
(4, 58),
(5, 13),
(5, 56),
(5, 57),
(6, 10),
(6, 31),
(7, 14),
(7, 56),
(7, 58),
(8, 12),
(8, 32),
(9, 23),
(9, 53),
(9, 59),
(10, 15),
(11, 24),
(11, 53),
(11, 60),
(12, 16),
(13, 14),
(13, 25),
(14, 26),
(15, 27),
(15, 49),
(16, 28),
(16, 50),
(17, 18),
(17, 19),
(17, 54),
(18, 20),
(18, 55),
(19, 23),
(19, 41),
(20, 24),
(20, 42),
(21, 31),
(21, 33),
(21, 57),
(22, 32),
(22, 34),
(22, 58),
(23, 24),
(25, 35),
(25, 43),
(26, 36),
(26, 44),
(27, 51),
(27, 59),
(28, 52),
(28, 60),
(29, 33),
(29, 34),
(29, 56),
(30, 51),
(30, 52),
(30, 53),
(31, 47),
(32, 48),
(33, 45),
(34, 46),
(35, 36),
(35, 37),
(36, 38),
(37, 39),
(37, 49),
(38, 40),
(38, 50),
(39, 40),
(39, 51),
(40, 52),
(41, 47),
(42, 48),
(43, 49),
(44, 50),
(45, 46),
(45, 54),
(46, 55),
(47, 54),
(48, 55),
]
)
A = laplacian_fn(G)
try:
assert almost_equal(
nx.algebraic_connectivity(
G, normalized=normalized, tol=1e-12, method=method
),
sigma,
)
x = nx.fiedler_vector(G, normalized=normalized, tol=1e-12, method=method)
check_eigenvector(A, sigma, x)
except nx.NetworkXError as e:
if e.args not in (
("Cholesky solver unavailable.",),
("LU solver unavailable.",),
):
raise
class TestSpectralOrdering:
_graphs = (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
@pytest.mark.parametrize("graph", _graphs)
def test_nullgraph(self, graph):
G = graph()
pytest.raises(nx.NetworkXError, nx.spectral_ordering, G)
@pytest.mark.parametrize("graph", _graphs)
def test_singleton(self, graph):
G = graph()
G.add_node("x")
assert nx.spectral_ordering(G) == ["x"]
G.add_edge("x", "x", weight=33)
G.add_edge("x", "x", weight=33)
assert nx.spectral_ordering(G) == ["x"]
def test_unrecognized_method(self):
G = nx.path_graph(4)
pytest.raises(nx.NetworkXError, nx.spectral_ordering, G, method="unknown")
@pytest.mark.parametrize("method", methods)
def test_three_nodes(self, method):
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2), (2, 3, 1)], weight="spam")
order = nx.spectral_ordering(G, weight="spam", method=method)
assert set(order) == set(G)
assert {1, 3} in (set(order[:-1]), set(order[1:]))
@pytest.mark.parametrize("method", methods)
def test_three_nodes_multigraph(self, method):
G = nx.MultiDiGraph()
G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2), (2, 3, 1), (2, 3, 2)])
order = nx.spectral_ordering(G, method=method)
assert set(order) == set(G)
assert {2, 3} in (set(order[:-1]), set(order[1:]))
@pytest.mark.parametrize("method", methods)
def test_path(self, method):
# based on setup_class numpy is installed if we get here
from numpy.random import shuffle
path = list(range(10))
shuffle(path)
G = nx.Graph()
nx.add_path(G, path)
order = nx.spectral_ordering(G, method=method)
assert order in [path, list(reversed(path))]
@pytest.mark.parametrize("method", methods)
def test_seed_argument(self, method):
# based on setup_class numpy is installed if we get here
from numpy.random import shuffle
path = list(range(10))
shuffle(path)
G = nx.Graph()
nx.add_path(G, path)
order = nx.spectral_ordering(G, method=method, seed=1)
assert order in [path, list(reversed(path))]
@pytest.mark.parametrize("method", methods)
def test_disconnected(self, method):
G = nx.Graph()
nx.add_path(G, range(0, 10, 2))
nx.add_path(G, range(1, 10, 2))
order = nx.spectral_ordering(G, method=method)
assert set(order) == set(G)
seqs = [
list(range(0, 10, 2)),
list(range(8, -1, -2)),
list(range(1, 10, 2)),
list(range(9, -1, -2)),
]
assert order[:5] in seqs
assert order[5:] in seqs
@pytest.mark.parametrize(
("normalized", "expected_order"),
(
(False, [[1, 2, 0, 3, 4, 5, 6, 9, 7, 8], [8, 7, 9, 6, 5, 4, 3, 0, 2, 1]]),
(True, [[1, 2, 3, 0, 4, 5, 9, 6, 7, 8], [8, 7, 6, 9, 5, 4, 0, 3, 2, 1]]),
),
)
@pytest.mark.parametrize("method", methods)
def test_cycle(self, normalized, expected_order, method):
path = list(range(10))
G = nx.Graph()
nx.add_path(G, path, weight=5)
G.add_edge(path[-1], path[0], weight=1)
A = nx.laplacian_matrix(G).todense()
try:
order = nx.spectral_ordering(G, normalized=normalized, method=method)
except nx.NetworkXError as e:
if e.args not in (
("Cholesky solver unavailable.",),
("LU solver unavailable.",),
):
raise
else:
assert order in expected_order

108
venv/Lib/site-packages/networkx/linalg/tests/test_attrmatrix.py Normal file
View file

@ -0,0 +1,108 @@
import pytest
np = pytest.importorskip("numpy")
import numpy.testing as npt
import networkx as nx
def test_attr_matrix():
G = nx.Graph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
def node_attr(u):
return G.nodes[u].get("size", 0.5) * 3
def edge_attr(u, v):
return G[u][v].get("thickness", 0.5)
M = nx.attr_matrix(G, edge_attr=edge_attr, node_attr=node_attr)
npt.assert_equal(M[0], np.array([[6.0]]))
assert M[1] == [1.5]
def test_attr_matrix_directed():
G = nx.DiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 1., 1.],
[0., 0., 1.],
[0., 0., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
def test_attr_matrix_multigraph():
G = nx.MultiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 3., 1.],
[3., 0., 1.],
[1., 1., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
M = nx.attr_matrix(G, edge_attr="weight", rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 9., 1.],
[9., 0., 1.],
[1., 1., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
M = nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 3., 2.],
[3., 0., 3.],
[2., 3., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
def test_attr_sparse_matrix():
pytest.importorskip("scipy")
G = nx.Graph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_sparse_matrix(G)
mtx = M[0]
data = np.ones((3, 3), float)
np.fill_diagonal(data, 0)
npt.assert_equal(mtx.todense(), np.array(data))
assert M[1] == [0, 1, 2]
def test_attr_sparse_matrix_directed():
G = nx.DiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 1., 1.],
[0., 0., 1.],
[0., 0., 0.]]
)
# fmt: on
npt.assert_equal(M.todense(), np.array(data))

42
venv/Lib/site-packages/networkx/linalg/tests/test_bethehessian.py Normal file
View file

@ -0,0 +1,42 @@
import pytest
np = pytest.importorskip("numpy")
npt = pytest.importorskip("numpy.testing")
sp = pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestBetheHessian:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.P = nx.path_graph(3)
def test_bethe_hessian(self):
"Bethe Hessian matrix"
# fmt: off
H = np.array([[4, -2, 0],
[-2, 5, -2],
[0, -2, 4]])
# fmt: on
permutation = [2, 0, 1]
# Bethe Hessian gives expected form
npt.assert_equal(nx.bethe_hessian_matrix(self.P, r=2).todense(), H)
# nodelist is correctly implemented
npt.assert_equal(
nx.bethe_hessian_matrix(self.P, r=2, nodelist=permutation).todense(),
H[np.ix_(permutation, permutation)],
)
# Equal to Laplacian matrix when r=1
npt.assert_equal(
nx.bethe_hessian_matrix(self.G, r=1).todense(),
nx.laplacian_matrix(self.G).todense(),
)
# Correct default for the regularizer r
npt.assert_equal(
nx.bethe_hessian_matrix(self.G).todense(),
nx.bethe_hessian_matrix(self.G, r=1.25).todense(),
)

293
venv/Lib/site-packages/networkx/linalg/tests/test_graphmatrix.py Normal file
View file

@ -0,0 +1,293 @@
import pytest
np = pytest.importorskip("numpy")
npt = pytest.importorskip("numpy.testing")
scipy = pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
from networkx.exception import NetworkXError
def test_incidence_matrix_simple():
deg = [3, 2, 2, 1, 0]
G = havel_hakimi_graph(deg)
deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
MG = nx.random_clustered_graph(deg, seed=42)
I = nx.incidence_matrix(G).todense().astype(int)
# fmt: off
expected = np.array(
[[1, 1, 1, 0],
[0, 1, 0, 1],
[1, 0, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]]
)
# fmt: on
npt.assert_equal(I, expected)
I = nx.incidence_matrix(MG).todense().astype(int)
# fmt: off
expected = np.array(
[[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 1]]
)
# fmt: on
npt.assert_equal(I, expected)
with pytest.raises(NetworkXError):
nx.incidence_matrix(G, nodelist=[0, 1])
class TestGraphMatrix:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
# fmt: off
cls.OI = np.array(
[[-1, -1, -1, 0],
[1, 0, 0, -1],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]]
)
cls.A = np.array(
[[0, 1, 1, 1, 0],
[1, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.WG = havel_hakimi_graph(deg)
cls.WG.add_edges_from(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
# fmt: off
cls.WA = np.array(
[[0, 0.5, 0.5, 0.5, 0],
[0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0],
[0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.MG = nx.MultiGraph(cls.G)
cls.MG2 = cls.MG.copy()
cls.MG2.add_edge(0, 1)
# fmt: off
cls.MG2A = np.array(
[[0, 2, 1, 1, 0],
[2, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
cls.MGOI = np.array(
[[-1, -1, -1, -1, 0],
[1, 1, 0, 0, -1],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {"weight": 8})])
cls.no_edges_A = np.array([[0, 0], [0, 0]])
def test_incidence_matrix(self):
"Conversion to incidence matrix"
I = (
nx.incidence_matrix(
self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=True,
)
.todense()
.astype(int)
)
npt.assert_equal(I, self.OI)
I = (
nx.incidence_matrix(
self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=False,
)
.todense()
.astype(int)
)
npt.assert_equal(I, np.abs(self.OI))
I = (
nx.incidence_matrix(
self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=True,
)
.todense()
.astype(int)
)
npt.assert_equal(I, self.OI)
I = (
nx.incidence_matrix(
self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=False,
)
.todense()
.astype(int)
)
npt.assert_equal(I, np.abs(self.OI))
I = (
nx.incidence_matrix(
self.MG2,
nodelist=sorted(self.MG2),
edgelist=sorted(self.MG2.edges()),
oriented=True,
)
.todense()
.astype(int)
)
npt.assert_equal(I, self.MGOI)
I = (
nx.incidence_matrix(
self.MG2,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG2.edges()),
oriented=False,
)
.todense()
.astype(int)
)
npt.assert_equal(I, np.abs(self.MGOI))
def test_weighted_incidence_matrix(self):
I = (
nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
)
.todense()
.astype(int)
)
npt.assert_equal(I, self.OI)
I = (
nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False,
)
.todense()
.astype(int)
)
npt.assert_equal(I, np.abs(self.OI))
# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,
# weight='weight').todense(),0.5*self.OI)
# npt.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(),
# np.abs(0.5*self.OI))
# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(),
# 0.3*self.OI)
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight="weight",
).todense()
npt.assert_equal(I, 0.5 * self.OI)
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False,
weight="weight",
).todense()
npt.assert_equal(I, np.abs(0.5 * self.OI))
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight="other",
).todense()
npt.assert_equal(I, 0.3 * self.OI)
# WMG=nx.MultiGraph(self.WG)
# WMG.add_edge(0,1,weight=0.5,other=0.3)
# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(),
# np.abs(0.5*self.MGOI))
# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(),
# 0.5*self.MGOI)
# npt.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(),
# 0.3*self.MGOI)
WMG = nx.MultiGraph(self.WG)
WMG.add_edge(0, 1, weight=0.5, other=0.3)
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight="weight",
).todense()
npt.assert_equal(I, 0.5 * self.MGOI)
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=False,
weight="weight",
).todense()
npt.assert_equal(I, np.abs(0.5 * self.MGOI))
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight="other",
).todense()
npt.assert_equal(I, 0.3 * self.MGOI)
def test_adjacency_matrix(self):
"Conversion to adjacency matrix"
npt.assert_equal(nx.adj_matrix(self.G).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG2).todense(), self.MG2A)
npt.assert_equal(
nx.adj_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2]
)
npt.assert_equal(nx.adj_matrix(self.WG).todense(), self.WA)
npt.assert_equal(nx.adj_matrix(self.WG, weight=None).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG2, weight=None).todense(), self.MG2A)
npt.assert_equal(
nx.adj_matrix(self.WG, weight="other").todense(), 0.6 * self.WA
)
npt.assert_equal(
nx.adj_matrix(self.no_edges_G, nodelist=[1, 3]).todense(), self.no_edges_A
)

231
venv/Lib/site-packages/networkx/linalg/tests/test_laplacian.py Normal file
View file

@ -0,0 +1,231 @@
import pytest
np = pytest.importorskip("numpy")
npt = pytest.importorskip("numpy.testing")
pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
from networkx.generators.expanders import margulis_gabber_galil_graph
class TestLaplacian:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.WG = nx.Graph(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
cls.WG.add_node(4)
cls.MG = nx.MultiGraph(cls.G)
# Graph with clsloops
cls.Gsl = cls.G.copy()
for node in cls.Gsl.nodes():
cls.Gsl.add_edge(node, node)
def test_laplacian(self):
"Graph Laplacian"
# fmt: off
NL = np.array([[3, -1, -1, -1, 0],
[-1, 2, -1, 0, 0],
[-1, -1, 2, 0, 0],
[-1, 0, 0, 1, 0],
[0, 0, 0, 0, 0]])
# fmt: on
WL = 0.5 * NL
OL = 0.3 * NL
npt.assert_equal(nx.laplacian_matrix(self.G).todense(), NL)
npt.assert_equal(nx.laplacian_matrix(self.MG).todense(), NL)
npt.assert_equal(
nx.laplacian_matrix(self.G, nodelist=[0, 1]).todense(),
np.array([[1, -1], [-1, 1]]),
)
npt.assert_equal(nx.laplacian_matrix(self.WG).todense(), WL)
npt.assert_equal(nx.laplacian_matrix(self.WG, weight=None).todense(), NL)
npt.assert_equal(nx.laplacian_matrix(self.WG, weight="other").todense(), OL)
def test_normalized_laplacian(self):
"Generalized Graph Laplacian"
# fmt: off
G = np.array([[ 1. , -0.408, -0.408, -0.577, 0.],
[-0.408, 1. , -0.5 , 0. , 0.],
[-0.408, -0.5 , 1. , 0. , 0.],
[-0.577, 0. , 0. , 1. , 0.],
[ 0. , 0. , 0. , 0. , 0.]])
GL = np.array([[1.00, -0.408, -0.408, -0.577, 0.00],
[-0.408, 1.00, -0.50, 0.00, 0.00],
[-0.408, -0.50, 1.00, 0.00, 0.00],
[-0.577, 0.00, 0.00, 1.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00]])
Lsl = np.array([[0.75, -0.2887, -0.2887, -0.3536, 0.],
[-0.2887, 0.6667, -0.3333, 0., 0.],
[-0.2887, -0.3333, 0.6667, 0., 0.],
[-0.3536, 0., 0., 0.5, 0.],
[0., 0., 0., 0., 0.]])
# fmt: on
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.G, nodelist=range(5)).todense(),
G,
decimal=3,
)
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.G).todense(), GL, decimal=3
)
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.MG).todense(), GL, decimal=3
)
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.WG).todense(), GL, decimal=3
)
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.WG, weight="other").todense(),
GL,
decimal=3,
)
npt.assert_almost_equal(
nx.normalized_laplacian_matrix(self.Gsl).todense(), Lsl, decimal=3
)
def test_directed_laplacian(self):
"Directed Laplacian"
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". The graph contains dangling nodes, so
# the pagerank random walk is selected by directed_laplacian
G = nx.DiGraph()
G.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
# fmt: off
GL = np.array([[0.9833, -0.2941, -0.3882, -0.0291, -0.0231, -0.0261],
[-0.2941, 0.8333, -0.2339, -0.0536, -0.0589, -0.0554],
[-0.3882, -0.2339, 0.9833, -0.0278, -0.0896, -0.0251],
[-0.0291, -0.0536, -0.0278, 0.9833, -0.4878, -0.6675],
[-0.0231, -0.0589, -0.0896, -0.4878, 0.9833, -0.2078],
[-0.0261, -0.0554, -0.0251, -0.6675, -0.2078, 0.9833]])
# fmt: on
L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
npt.assert_almost_equal(L, GL, decimal=3)
# Make the graph strongly connected, so we can use a random and lazy walk
G.add_edges_from(((2, 5), (6, 1)))
# fmt: off
GL = np.array([[1., -0.3062, -0.4714, 0., 0., -0.3227],
[-0.3062, 1., -0.1443, 0., -0.3162, 0.],
[-0.4714, -0.1443, 1., 0., -0.0913, 0.],
[0., 0., 0., 1., -0.5, -0.5],
[0., -0.3162, -0.0913, -0.5, 1., -0.25],
[-0.3227, 0., 0., -0.5, -0.25, 1.]])
# fmt: on
L = nx.directed_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="random"
)
npt.assert_almost_equal(L, GL, decimal=3)
# fmt: off
GL = np.array([[0.5, -0.1531, -0.2357, 0., 0., -0.1614],
[-0.1531, 0.5, -0.0722, 0., -0.1581, 0.],
[-0.2357, -0.0722, 0.5, 0., -0.0456, 0.],
[0., 0., 0., 0.5, -0.25, -0.25],
[0., -0.1581, -0.0456, -0.25, 0.5, -0.125],
[-0.1614, 0., 0., -0.25, -0.125, 0.5]])
# fmt: on
L = nx.directed_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="lazy"
)
npt.assert_almost_equal(L, GL, decimal=3)
def test_directed_combinatorial_laplacian(self):
"Directed combinatorial Laplacian"
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". The graph contains dangling nodes, so
# the pagerank random walk is selected by directed_laplacian
G = nx.DiGraph()
G.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
# fmt: off
GL = np.array([[0.0366, -0.0132, -0.0153, -0.0034, -0.0020, -0.0027],
[-0.0132, 0.0450, -0.0111, -0.0076, -0.0062, -0.0069],
[-0.0153, -0.0111, 0.0408, -0.0035, -0.0083, -0.0027],
[-0.0034, -0.0076, -0.0035, 0.3688, -0.1356, -0.2187],
[-0.0020, -0.0062, -0.0083, -0.1356, 0.2026, -0.0505],
[-0.0027, -0.0069, -0.0027, -0.2187, -0.0505, 0.2815]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
npt.assert_almost_equal(L, GL, decimal=3)
# Make the graph strongly connected, so we can use a random and lazy walk
G.add_edges_from(((2, 5), (6, 1)))
# fmt: off
GL = np.array([[0.1395, -0.0349, -0.0465, 0, 0, -0.0581],
[-0.0349, 0.0930, -0.0116, 0, -0.0465, 0],
[-0.0465, -0.0116, 0.0698, 0, -0.0116, 0],
[0, 0, 0, 0.2326, -0.1163, -0.1163],
[0, -0.0465, -0.0116, -0.1163, 0.2326, -0.0581],
[-0.0581, 0, 0, -0.1163, -0.0581, 0.2326]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="random"
)
npt.assert_almost_equal(L, GL, decimal=3)
# fmt: off
GL = np.array([[0.0698, -0.0174, -0.0233, 0, 0, -0.0291],
[-0.0174, 0.0465, -0.0058, 0, -0.0233, 0],
[-0.0233, -0.0058, 0.0349, 0, -0.0058, 0],
[0, 0, 0, 0.1163, -0.0581, -0.0581],
[0, -0.0233, -0.0058, -0.0581, 0.1163, -0.0291],
[-0.0291, 0, 0, -0.0581, -0.0291, 0.1163]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="lazy"
)
npt.assert_almost_equal(L, GL, decimal=3)
E = nx.DiGraph(margulis_gabber_galil_graph(2))
L = nx.directed_combinatorial_laplacian_matrix(E)
# fmt: off
expected = np.array(
[[ 0.16666667, -0.08333333, -0.08333333, 0. ],
[-0.08333333, 0.16666667, 0. , -0.08333333],
[-0.08333333, 0. , 0.16666667, -0.08333333],
[ 0. , -0.08333333, -0.08333333, 0.16666667]]
)
# fmt: on
npt.assert_almost_equal(L, expected, decimal=6)
with pytest.raises(nx.NetworkXError):
nx.directed_combinatorial_laplacian_matrix(
G, walk_type="pagerank", alpha=100
)
with pytest.raises(nx.NetworkXError):
nx.directed_combinatorial_laplacian_matrix(G, walk_type="silly")

86
venv/Lib/site-packages/networkx/linalg/tests/test_modularity.py Normal file
View file

@ -0,0 +1,86 @@
import pytest
np = pytest.importorskip("numpy")
npt = pytest.importorskip("numpy.testing")
scipy = pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestModularity:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
cls.DG = nx.DiGraph()
cls.DG.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
def test_modularity(self):
"Modularity matrix"
# fmt: off
B = np.array([[-1.125, 0.25, 0.25, 0.625, 0.],
[0.25, -0.5, 0.5, -0.25, 0.],
[0.25, 0.5, -0.5, -0.25, 0.],
[0.625, -0.25, -0.25, -0.125, 0.],
[0., 0., 0., 0., 0.]])
# fmt: on
permutation = [4, 0, 1, 2, 3]
npt.assert_equal(nx.modularity_matrix(self.G), B)
npt.assert_equal(
nx.modularity_matrix(self.G, nodelist=permutation),
B[np.ix_(permutation, permutation)],
)
def test_modularity_weight(self):
"Modularity matrix with weights"
# fmt: off
B = np.array([[-1.125, 0.25, 0.25, 0.625, 0.],
[0.25, -0.5, 0.5, -0.25, 0.],
[0.25, 0.5, -0.5, -0.25, 0.],
[0.625, -0.25, -0.25, -0.125, 0.],
[0., 0., 0., 0., 0.]])
# fmt: on
G_weighted = self.G.copy()
for n1, n2 in G_weighted.edges():
G_weighted.edges[n1, n2]["weight"] = 0.5
# The following test would fail in networkx 1.1
npt.assert_equal(nx.modularity_matrix(G_weighted), B)
# The following test that the modularity matrix get rescaled accordingly
npt.assert_equal(nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B)
def test_directed_modularity(self):
"Directed Modularity matrix"
# fmt: off
B = np.array([[-0.2, 0.6, 0.8, -0.4, -0.4, -0.4],
[0., 0., 0., 0., 0., 0.],
[0.7, 0.4, -0.3, -0.6, 0.4, -0.6],
[-0.2, -0.4, -0.2, -0.4, 0.6, 0.6],
[-0.2, -0.4, -0.2, 0.6, -0.4, 0.6],
[-0.1, -0.2, -0.1, 0.8, -0.2, -0.2]])
# fmt: on
node_permutation = [5, 1, 2, 3, 4, 6]
idx_permutation = [4, 0, 1, 2, 3, 5]
mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
npt.assert_equal(mm, B)
npt.assert_equal(
nx.directed_modularity_matrix(self.DG, nodelist=node_permutation),
B[np.ix_(idx_permutation, idx_permutation)],
)

72
venv/Lib/site-packages/networkx/linalg/tests/test_spectrum.py Normal file
View file

@ -0,0 +1,72 @@
import pytest
np = pytest.importorskip("numpy")
npt = pytest.importorskip("numpy.testing")
scipy = pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestSpectrum:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.P = nx.path_graph(3)
cls.WG = nx.Graph(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
cls.WG.add_node(4)
cls.DG = nx.DiGraph()
nx.add_path(cls.DG, [0, 1, 2])
def test_laplacian_spectrum(self):
"Laplacian eigenvalues"
evals = np.array([0, 0, 1, 3, 4])
e = sorted(nx.laplacian_spectrum(self.G))
npt.assert_almost_equal(e, evals)
e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
npt.assert_almost_equal(e, evals)
e = sorted(nx.laplacian_spectrum(self.WG))
npt.assert_almost_equal(e, 0.5 * evals)
e = sorted(nx.laplacian_spectrum(self.WG, weight="other"))
npt.assert_almost_equal(e, 0.3 * evals)
def test_normalized_laplacian_spectrum(self):
"Normalized Laplacian eigenvalues"
evals = np.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
e = sorted(nx.normalized_laplacian_spectrum(self.G))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight="other"))
npt.assert_almost_equal(e, evals)
def test_adjacency_spectrum(self):
"Adjacency eigenvalues"
evals = np.array([-np.sqrt(2), 0, np.sqrt(2)])
e = sorted(nx.adjacency_spectrum(self.P))
npt.assert_almost_equal(e, evals)
def test_modularity_spectrum(self):
"Modularity eigenvalues"
evals = np.array([-1.5, 0.0, 0.0])
e = sorted(nx.modularity_spectrum(self.P))
npt.assert_almost_equal(e, evals)
# Directed modularity eigenvalues
evals = np.array([-0.5, 0.0, 0.0])
e = sorted(nx.modularity_spectrum(self.DG))
npt.assert_almost_equal(e, evals)
def test_bethe_hessian_spectrum(self):
"Bethe Hessian eigenvalues"
evals = np.array([0.5 * (9 - np.sqrt(33)), 4, 0.5 * (9 + np.sqrt(33))])
e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
npt.assert_almost_equal(e, evals)
# Collapses back to Laplacian:
e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
e2 = sorted(nx.laplacian_spectrum(self.P))
npt.assert_almost_equal(e1, e2)