import tensorflow as tf
import numpy as np
from medusa.graph_theory import degree
def __density_gpu(W):
"""
Calculates the graph density using GPU.
Parameters
----------
W : numpy 2D matrix
Graph matrix. ChannelsXChannels.
Returns
-------
global_den : int
Global density.
nodal_den : numpy array
Nodal density.
"""
check_symmetry = tf.reduce_all(tf.math.equal(W,tf.transpose(W)))
check_symmetry = tf.cond(
tf.math.reduce_sum(tf.subtract(tf.linalg.band_part(W, -1, 0),tf.linalg.band_part(W, 0, 0))) == 0,
lambda: 1, lambda: check_symmetry)
check_symmetry = tf.cond(
tf.reduce_all(tf.math.equal(W,-tf.transpose(W))),
lambda: 2, lambda: check_symmetry)
N = W.shape[0]
deg = degree.degree(W,'GPU')
norm_value = tf.switch_case(tf.cast(check_symmetry,tf.int32),
branch_fns={0: lambda: tf.math.multiply(N,tf.math.subtract(N,1)),
1: lambda: tf.math.divide(tf.math.multiply(N,tf.math.subtract(N,1)),2),
2: lambda: tf.math.multiply(N,tf.math.subtract(N,1))})
nodal_den = tf.divide(deg,norm_value)
global_den = tf.divide(tf.math.reduce_sum(deg),norm_value)
return global_den,nodal_den
def __density_cpu(W):
"""
Calculates the graph density using CPU.
Parameters
----------
W : numpy 2D matrix
Graph matrix. ChannelsXChannels.
Returns
-------
global_den : int
Global density.
nodal_den : numpy array
Nodal density.
"""
check_symmetry = (W.transpose() == W).all() # if symmetric
if (W == np.triu(W)).all(): # if upper triangular
check_symmetry = 1
if (W.transpose() == -W).all(): # if anti-symmetric
check_symmetry = 2
N = W.shape[0]
deg = degree.degree(W,'CPU')
if check_symmetry == 0 or check_symmetry == 2:
norm_value = N*(N-1)
elif check_symmetry == 1:
norm_value = ((N*(N-1))/2)
nodal_den = np.divide(deg,norm_value)
global_den = np.divide(np.sum(deg),norm_value)
return global_den,nodal_den
[docs]def density(W,mode):
"""
Calculates the graph density.
Parameters
----------
W : numpy 2D matrix
Graph matrix. ChannelsXChannels.
mode : string
GPU or CPU
Returns
-------
global_den : int
Global density.
nodal_den : numpy array
Nodal density.
"""
if W.shape[0] is not W.shape[1]:
raise ValueError('W matrix must be square')
if not np.issubdtype(W.dtype, np.number):
raise ValueError('W matrix contains non-numeric values')
if mode == 'CPU':
global_den,nodal_den = __density_cpu(W)
elif mode == 'GPU':
global_den,nodal_den = __density_gpu(W)
else:
raise ValueError('Unknown mode')
return global_den, nodal_den