Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/sitepackages/networkx/algorithms/centrality/flow_matrix.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author  shellac 

date  Mon, 22 Mar 2021 18:12:50 +0000 
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0
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1 # Helpers for currentflow betweenness and currentflow closness 
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2 # Lazy computations for inverse Laplacian and flowmatrix rows. 
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3 import networkx as nx 
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4 
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5 
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6 def flow_matrix_row(G, weight=None, dtype=float, solver="lu"): 
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7 # Generate a row of the currentflow matrix 
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8 import numpy as np 
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9 
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10 solvername = { 
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11 "full": FullInverseLaplacian, 
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12 "lu": SuperLUInverseLaplacian, 
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13 "cg": CGInverseLaplacian, 
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14 } 
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15 n = G.number_of_nodes() 
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16 L = laplacian_sparse_matrix( 
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17 G, nodelist=range(n), weight=weight, dtype=dtype, format="csc" 
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18 ) 
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19 C = solvername[solver](L, dtype=dtype) # initialize solver 
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20 w = C.w # w is the Laplacian matrix width 
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21 # rowbyrow flow matrix 
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22 for u, v in sorted(sorted((u, v)) for u, v in G.edges()): 
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23 B = np.zeros(w, dtype=dtype) 
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24 c = G[u][v].get(weight, 1.0) 
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25 B[u % w] = c 
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26 B[v % w] = c 
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27 # get only the rows needed in the inverse laplacian 
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28 # and multiply to get the flow matrix row 
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29 row = np.dot(B, C.get_rows(u, v)) 
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30 yield row, (u, v) 
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31 
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32 
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33 # Class to compute the inverse laplacian only for specified rows 
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34 # Allows computation of the currentflow matrix without storing entire 
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35 # inverse laplacian matrix 
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36 class InverseLaplacian: 
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37 def __init__(self, L, width=None, dtype=None): 
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38 global np 
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39 import numpy as np 
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40 
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41 (n, n) = L.shape 
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42 self.dtype = dtype 
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43 self.n = n 
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44 if width is None: 
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45 self.w = self.width(L) 
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46 else: 
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47 self.w = width 
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48 self.C = np.zeros((self.w, n), dtype=dtype) 
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49 self.L1 = L[1:, 1:] 
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50 self.init_solver(L) 
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51 
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52 def init_solver(self, L): 
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53 pass 
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54 
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55 def solve(self, r): 
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56 raise nx.NetworkXError("Implement solver") 
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57 
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58 def solve_inverse(self, r): 
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59 raise nx.NetworkXError("Implement solver") 
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60 
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61 def get_rows(self, r1, r2): 
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62 for r in range(r1, r2 + 1): 
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63 self.C[r % self.w, 1:] = self.solve_inverse(r) 
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64 return self.C 
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65 
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66 def get_row(self, r): 
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67 self.C[r % self.w, 1:] = self.solve_inverse(r) 
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68 return self.C[r % self.w] 
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69 
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70 def width(self, L): 
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71 m = 0 
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72 for i, row in enumerate(L): 
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73 w = 0 
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74 x, y = np.nonzero(row) 
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75 if len(y) > 0: 
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76 v = y  i 
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77 w = v.max()  v.min() + 1 
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78 m = max(w, m) 
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79 return m 
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80 
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81 
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82 class FullInverseLaplacian(InverseLaplacian): 
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83 def init_solver(self, L): 
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84 self.IL = np.zeros(L.shape, dtype=self.dtype) 
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85 self.IL[1:, 1:] = np.linalg.inv(self.L1.todense()) 
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86 
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87 def solve(self, rhs): 
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88 s = np.zeros(rhs.shape, dtype=self.dtype) 
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89 s = np.dot(self.IL, rhs) 
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90 return s 
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91 
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92 def solve_inverse(self, r): 
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93 return self.IL[r, 1:] 
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94 
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95 
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96 class SuperLUInverseLaplacian(InverseLaplacian): 
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97 def init_solver(self, L): 
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98 from scipy.sparse import linalg 
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99 
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100 self.lusolve = linalg.factorized(self.L1.tocsc()) 
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101 
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102 def solve_inverse(self, r): 
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103 rhs = np.zeros(self.n, dtype=self.dtype) 
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104 rhs[r] = 1 
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105 return self.lusolve(rhs[1:]) 
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106 
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107 def solve(self, rhs): 
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108 s = np.zeros(rhs.shape, dtype=self.dtype) 
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109 s[1:] = self.lusolve(rhs[1:]) 
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110 return s 
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111 
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112 
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113 class CGInverseLaplacian(InverseLaplacian): 
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114 def init_solver(self, L): 
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115 global linalg 
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116 from scipy.sparse import linalg 
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117 
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118 ilu = linalg.spilu(self.L1.tocsc()) 
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119 n = self.n  1 
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120 self.M = linalg.LinearOperator(shape=(n, n), matvec=ilu.solve) 
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121 
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122 def solve(self, rhs): 
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123 s = np.zeros(rhs.shape, dtype=self.dtype) 
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124 s[1:] = linalg.cg(self.L1, rhs[1:], M=self.M, atol=0)[0] 
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125 return s 
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126 
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127 def solve_inverse(self, r): 
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128 rhs = np.zeros(self.n, self.dtype) 
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129 rhs[r] = 1 
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130 return linalg.cg(self.L1, rhs[1:], M=self.M, atol=0)[0] 
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131 
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132 
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133 # graph laplacian, sparse version, will move to linalg/laplacianmatrix.py 
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134 def laplacian_sparse_matrix(G, nodelist=None, weight=None, dtype=None, format="csr"): 
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135 import numpy as np 
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136 import scipy.sparse 
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137 
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138 A = nx.to_scipy_sparse_matrix( 
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139 G, nodelist=nodelist, weight=weight, dtype=dtype, format=format 
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140 ) 
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141 (n, n) = A.shape 
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142 data = np.asarray(A.sum(axis=1).T) 
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143 D = scipy.sparse.spdiags(data, 0, n, n, format=format) 
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144 return D  A 