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Jul 31st, 2022
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  1. Input shapes: (14,), (1,)
  2. Output shape: (14, 1)
  3.  
  4. Example input:
  5. rho_deltas = [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
  6. self.node_layers[i - 1].vals = [0.11032028]
  7. Expected Output:
  8. self.previous_edge_layer[i - 1] = [[0.]
  9. [0.]
  10. [0.]
  11. [0.]
  12. [0.]
  13. [0.]
  14. [0.]
  15. [0.]
  16. [0.]
  17. [0.]
  18. [0.]
  19. [0.]
  20. [0.]
  21. [0.]]
  22.  
  23.  
  24.  
  25.  
  26. Input shapes: (16,), (14,)
  27. Output shape: (16, 14)
  28.  
  29. Example input:
  30. rho_deltas = [ 3.02273024e-04 -4.09166629e-06 3.23958733e-04 1.11468952e-04
  31. 4.41575870e-04 7.46914839e-05 -1.24590666e-05 -2.44832862e-04
  32. -1.84525592e-04 -4.81304068e-04 3.81601611e-04 5.24932556e-04
  33. -2.06009549e-05 1.69839829e-04 4.98423775e-04 3.04959169e-04]
  34. self.node_layers[i - 1].vals = [0.09937776 0.09398928 0.0984087 0.09874022 0.12118162 0.12118162
  35. 0.09941601 0.09396888 0.09838702 0.10710283 0.09261984 0.09108975
  36. 0.10333051 0.10129038]
  37. Expected Output:
  38. self.previous_edge_layer[i - 1] = [[ 3.00392164e-05 2.84104229e-05 2.97462957e-05 2.98465054e-05
  39. 3.66299335e-05 3.66299335e-05 3.00507791e-05 2.84042561e-05
  40. 2.97397435e-05 3.23742959e-05 2.79964796e-05 2.75339731e-05
  41. 3.12340248e-05 3.06173495e-05]
  42. [-4.06620636e-07 -3.84572754e-07 -4.02655565e-07 -4.04012037e-07
  43. -4.95834733e-07 -4.95834733e-07 -4.06777152e-07 -3.84489279e-07
  44. -4.02566873e-07 -4.38229034e-07 -3.78969483e-07 -3.72708845e-07
  45. -4.22793952e-07 -4.14446434e-07]
  46. [ 3.21942936e-05 3.04486469e-05 3.18803581e-05 3.19877571e-05
  47. 3.92578427e-05 3.92578427e-05 3.22066858e-05 3.04420378e-05
  48. 3.18733359e-05 3.46968967e-05 3.00050065e-05 2.95093188e-05
  49. 3.34748201e-05 3.28139032e-05]
  50. [ 1.10775349e-05 1.04768862e-05 1.09695148e-05 1.10064690e-05
  51. 1.35079877e-05 1.35079877e-05 1.10817989e-05 1.04746121e-05
  52. 1.09670985e-05 1.19386401e-05 1.03242367e-05 1.01536786e-05
  53. 1.15181433e-05 1.12907325e-05]
  54. [ 4.38828214e-05 4.15033966e-05 4.34549078e-05 4.36012993e-05
  55. 5.35108775e-05 5.35108775e-05 4.38997128e-05 4.14943879e-05
  56. 4.34453361e-05 4.72940248e-05 4.08986871e-05 4.02230341e-05
  57. 4.56282585e-05 4.47273878e-05]
  58. [ 7.42267246e-06 7.02019854e-06 7.35029191e-06 7.37505368e-06
  59. 9.05123471e-06 9.05123471e-06 7.42552959e-06 7.01867474e-06
  60. 7.34867288e-06 7.99966921e-06 6.91791340e-06 6.80362833e-06
  61. 7.71790889e-06 7.56552880e-06]
  62. [-1.23815415e-06 -1.17101866e-06 -1.22608056e-06 -1.23021100e-06
  63. -1.50980982e-06 -1.50980982e-06 -1.23863074e-06 -1.17076448e-06
  64. -1.22581049e-06 -1.33440128e-06 -1.15395677e-06 -1.13489322e-06
  65. -1.28740167e-06 -1.26198359e-06]
  66. [-2.43309417e-05 -2.30116636e-05 -2.40936840e-05 -2.41748511e-05
  67. -2.96692419e-05 -2.96692419e-05 -2.43403072e-05 -2.30066687e-05
  68. -2.40883769e-05 -2.62222921e-05 -2.26763809e-05 -2.23017633e-05
  69. -2.52987037e-05 -2.47992137e-05]
  70. [-1.83377402e-05 -1.73434269e-05 -1.81589238e-05 -1.82200979e-05
  71. -2.23611094e-05 -2.23611094e-05 -1.83447988e-05 -1.73396623e-05
  72. -1.81549240e-05 -1.97632129e-05 -1.70907311e-05 -1.68083894e-05
  73. -1.90671230e-05 -1.86906674e-05]
  74. [-4.78309208e-05 -4.52374212e-05 -4.73645082e-05 -4.75240704e-05
  75. -5.83252047e-05 -5.83252047e-05 -4.78493318e-05 -4.52276019e-05
  76. -4.73540753e-05 -5.15490272e-05 -4.45783065e-05 -4.38418655e-05
  77. -4.97333933e-05 -4.87514720e-05]
  78. [ 3.79227138e-05 3.58664594e-05 3.75529189e-05 3.76794277e-05
  79. 4.62430999e-05 4.62430999e-05 3.79373110e-05 3.58586742e-05
  80. 3.75446472e-05 4.08706120e-05 3.53438807e-05 3.47599940e-05
  81. 3.94310879e-05 3.86525722e-05]
  82. [ 5.21666223e-05 4.93380312e-05 5.16579311e-05 5.18319570e-05
  83. 6.36121754e-05 6.36121754e-05 5.21867022e-05 4.93273219e-05
  84. 5.16465524e-05 5.62217617e-05 4.86191702e-05 4.78159735e-05
  85. 5.42415471e-05 5.31706182e-05]
  86. [-2.04727678e-06 -1.93626885e-06 -2.02731322e-06 -2.03414286e-06
  87. -2.49645701e-06 -2.49645701e-06 -2.04806482e-06 -1.93584856e-06
  88. -2.02686666e-06 -2.20642055e-06 -1.90805718e-06 -1.87653576e-06
  89. -2.12870711e-06 -2.08667856e-06]
  90. [ 1.68783020e-05 1.59631226e-05 1.67137169e-05 1.67700223e-05
  91. 2.05814649e-05 2.05814649e-05 1.68847987e-05 1.59596577e-05
  92. 1.67100354e-05 1.81903261e-05 1.57305380e-05 1.54706669e-05
  93. 1.75496356e-05 1.72031408e-05]
  94. [ 4.95322390e-05 4.68464901e-05 4.90492363e-05 4.92144741e-05
  95. 6.03997985e-05 6.03997985e-05 4.95513049e-05 4.68363216e-05
  96. 4.90384323e-05 5.33825963e-05 4.61639311e-05 4.54012953e-05
  97. 5.15023814e-05 5.04855337e-05]
  98. [ 3.03061595e-05 2.86628916e-05 3.00106357e-05 3.01117359e-05
  99. 3.69554448e-05 3.69554448e-05 3.03178249e-05 2.86566701e-05
  100. 3.00040253e-05 3.26619896e-05 2.82452699e-05 2.77786533e-05
  101. 3.15115855e-05 3.08894301e-05]]
  102.  
  103.  
  104.  
  105. Input shapes: (1,), (16,)
  106. Output shape: (1, 16)
  107.  
  108. Example input:
  109. rho_deltas = [-0.00093641]
  110. self.node_layers[i - 1].vals = [-0.04904765 0.0102615 -0.0288954 -0.01562272 -0.05148525 -0.03598049
  111. 0.00804836 0.04560111 0.01275833 0.0269781 -0.02977255 -0.04021762
  112. -0.03309063 -0.04302922 -0.06740782 -0.04750709]
  113. Expected Output:
  114. self.previous_edge_layer[i - 1] = [[ 4.59286857e-05 -9.60896394e-06 2.70579239e-05 1.46292634e-05
  115. 4.82112762e-05 3.36924713e-05 -7.53655849e-06 -4.27013109e-05
  116. -1.19470250e-05 -2.52625461e-05 2.78793006e-05 3.76601607e-05
  117. 3.09863838e-05 4.02929729e-05 6.31213243e-05 4.44860925e-05]]
  118.  
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