2740 lines
712 KiB
Text
2740 lines
712 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import os\n",
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"import xml.etree.ElementTree as ET\n",
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"from collections import Counter\n",
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"import math\n",
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"import warnings\n",
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"\n",
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"import json\n",
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"import argparse\n",
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"import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from pulp import LpMaximize, LpProblem, LpStatus, lpSum, LpVariable , LpMinimize ,PULP_CBC_CMD ,SCIP_CMD,SCIP_PY,GUROBI_CMD\n",
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"from LinProg_lib import *\n",
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"\n",
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"from itertools import combinations\n",
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"\n",
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"import matplotlib.cm as cm\n",
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"import matplotlib.colors as mcolors\n",
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"from matplotlib import colormaps\n",
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"\n",
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"debug=True\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"\n",
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" <iframe\n",
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" width=\"100%\"\n",
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" height=\"650\"\n",
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" src=\"http://127.0.0.1:8055/\"\n",
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" frameborder=\"0\"\n",
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" allowfullscreen\n",
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" \n",
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" ></iframe>\n",
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" "
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],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0x779594ca7b20>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# ======================================================================\n",
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"# Create the Graph for the NoC in this example 4x4 mesh\n",
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"# ======================================================================\n",
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"\n",
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"\n",
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"mesh_size = 4\n",
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"G_NoC = nx.DiGraph()\n",
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"\n",
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"# Add nodes\n",
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"for y in range(mesh_size):\n",
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" for x in range(mesh_size):\n",
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" G_NoC.add_node(coord_to_number(y, x,4))\n",
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"\n",
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"# mesh\n",
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"for y in range(mesh_size):\n",
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" for x in range(mesh_size):\n",
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" if y < mesh_size - 1: # Connect to the node below\n",
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" G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x,4))\n",
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" G_NoC.add_edge(coord_to_number(y + 1, x,4), coord_to_number(y, x,4))\n",
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" if x < mesh_size - 1: # Connect to the node to the right\n",
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" G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x + 1,4))\n",
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" G_NoC.add_edge(coord_to_number(y, x + 1,4), coord_to_number(y, x,4))\n",
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"\n",
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"\n",
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"# #folded torus\n",
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"# for y in range(mesh_size):\n",
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"# for x in range(mesh_size):\n",
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"# if y < mesh_size - 2: # Connect to the node below\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 2, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 2, x,4), coord_to_number(y, x,4))\n",
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"# if x < mesh_size - 2: # Connect to the node to the right\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x + 2,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x + 2,4), coord_to_number(y, x,4))\n",
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"\n",
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"# if y==0:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y +1, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y+1, x,4), coord_to_number(y, x,4))\n",
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"# if x==0:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x+1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x+1,4), coord_to_number(y, x,4))\n",
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"# if y==mesh_size-1:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y -1, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y-1, x,4), coord_to_number(y, x,4))\n",
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"# if x==mesh_size-1:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x-1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x-1,4), coord_to_number(y, x,4))\n",
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"\n",
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"\n",
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"#mesh with diagonal connections\n",
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"# for y in range(mesh_size):\n",
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"# for x in range(mesh_size):\n",
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"# if y < mesh_size - 1: # Connect to the node below\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x,4), coord_to_number(y, x,4))\n",
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"# if x < mesh_size - 1: # Connect to the node to the right\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x + 1,4), coord_to_number(y, x,4))\n",
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"# # Add diagonal connections (down-right and down-left)\n",
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"# if y < mesh_size - 1 and x < mesh_size - 1:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x + 1,4), coord_to_number(y, x,4))\n",
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"# if y < mesh_size - 1 and x > 0:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x - 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x - 1,4), coord_to_number(y, x,4))\n",
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"\n",
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"# if (y == 0 and x == 0) and y < mesh_size - 1 and x < mesh_size - 1:\n",
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"# # Top-left corner: down-right diagonal\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x + 1,4), coord_to_number(y, x,4))\n",
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"# if (y == 0 and x == mesh_size - 1) and y < mesh_size - 1 and x > 0:\n",
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"# # Top-right corner: down-left diagonal\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x - 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x - 1,4), coord_to_number(y, x,4))\n",
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"# if (y == mesh_size - 1 and x == 0) and y > 0 and x < mesh_size - 1:\n",
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"# # Bottom-left corner: up-right diagonal\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y - 1, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y - 1, x + 1,4), coord_to_number(y, x,4))\n",
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"# if (y == mesh_size - 1 and x == mesh_size - 1) and y > 0 and x > 0:\n",
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"# # Bottom-right corner: up-left diagonal\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y - 1, x - 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y - 1, x - 1,4), coord_to_number(y, x,4))\n",
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"\n",
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"\n",
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"# for y in range(mesh_size):\n",
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"# for x in range(mesh_size):\n",
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"# if y < mesh_size - 1: # Connect to the node below\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x,4), coord_to_number(y, x,4))\n",
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"# if x < mesh_size - 1: # Connect to the node to the right\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x + 1,4), coord_to_number(y, x,4))\n",
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"# # Add diagonal connections only in the corners, but not to the outermost diagonal\n",
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"# G_NoC.add_edge(1, 4)\n",
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"# G_NoC.add_edge(4, 1)\n",
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"\n",
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"# G_NoC.add_edge(2, 7)\n",
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"# G_NoC.add_edge(7, 2)\n",
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"\n",
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"# G_NoC.add_edge(8, 13)\n",
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"# G_NoC.add_edge(13, 8)\n",
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"\n",
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"# G_NoC.add_edge(14, 11)\n",
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"# G_NoC.add_edge(11, 14)\n",
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"\n",
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"#mesh with other diagonal connections\n",
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"# for y in range(mesh_size):\n",
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"# for x in range(mesh_size):\n",
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"# if y < mesh_size - 1 and (x==0 or x==mesh_size - 1): # Connect to the node below\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x,4), coord_to_number(y, x,4))\n",
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"# if x < mesh_size - 1 and (y==0 or y==mesh_size - 1): # Connect to the node to the right\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y, x + 1,4), coord_to_number(y, x,4))\n",
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"# # Add diagonal connections (down-right and down-left)\n",
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"# if y < mesh_size - 1 and x < mesh_size - 1:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x + 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x + 1,4), coord_to_number(y, x,4))\n",
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"# if y < mesh_size - 1 and x > 0:\n",
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"# G_NoC.add_edge(coord_to_number(y, x,4), coord_to_number(y + 1, x - 1,4))\n",
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"# G_NoC.add_edge(coord_to_number(y + 1, x - 1,4), coord_to_number(y, x,4))\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"if debug==True:\n",
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"\n",
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" elements = graph_to_cytoscape(G_NoC)\n",
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"\n",
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" # Dash App\n",
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" app = dash.Dash(__name__)\n",
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"\n",
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" app.layout = html.Div([\n",
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" html.H3(\"Drag and Move Nodes in Real Time\"),\n",
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" cyto.Cytoscape(\n",
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" id=\"cytoscape-graph\",\n",
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" elements=elements,\n",
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" layout={\"name\": \"preset\"}, # Use preset positions\n",
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" style={\"width\": \"100%\", \"height\": \"600px\"},\n",
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" stylesheet=[\n",
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" {\"selector\": \"node\", \"style\": {\n",
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" \"content\": \"data(label)\",\n",
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" \"background-color\": \"lightblue\",\n",
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" \"width\": \"75px\", # Increase node size\n",
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" \"height\": \"75px\", # Increase node size\n",
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" \"font-size\": \"16px\", # Make labels larger\n",
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" \"text-valign\": \"center\",\n",
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" }},\n",
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" {\"selector\": \"edge\", \"style\": {\n",
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" \"curve-style\": \"bezier\",\n",
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" \"target-arrow-shape\": \"triangle\",\n",
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" \"label\": \"data(label)\",\n",
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" \"font-size\": \"12px\",\n",
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" \"color\": \"white\", # Make edge labels white\n",
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" }}\n",
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" ],\n",
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" ),\n",
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" ])\n",
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"\n",
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" if __name__ == \"__main__\":\n",
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" import socket\n",
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" port = 8050\n",
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" while True:\n",
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" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n",
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" if s.connect_ex((\"localhost\", port)) != 0: # Port is available\n",
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" break\n",
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" port += 1 # Try the next port\n",
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" app.run(debug=True, port=port)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[(3, 2.2), (3, 3.7)], [(3, 3.7), (3.2, 3.7)], [(3.2, 3.7), (3.2, 3.2)]]\n",
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"Edge: (11, 15), Start: (3, 2.2), End: (3, 3.7)\n",
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"Edge: (11, 15), Start: (3, 3.7), End: (3.2, 3.7)\n",
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"Edge: (11, 15), Start: (3.2, 3.7), End: (3.2, 3.2)\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 800x800 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"highlight_edges_with_colors = {\n",
|
|
" (1, 9): \"red\",\n",
|
|
" (11, 3): \"blue\",\n",
|
|
" (15, 11): \"green\",\n",
|
|
" (5, 7): \"green\",\n",
|
|
"}\n",
|
|
"\n",
|
|
"draw_folded_torus_noc(\n",
|
|
" mesh_size=4,\n",
|
|
" G_NoC=G_NoC,\n",
|
|
" highlight_edges_with_colors=highlight_edges_with_colors,\n",
|
|
" title=\"Folded Torus NoC (4x4)\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parallel paths between 0 and 53 are [['0', '4', '10', '11', '17', '35', '53'], ['0', '4', '11', '17', '35', '53']]\n",
|
|
"Nodes with unequal in and out degree: ['4', '11']\n",
|
|
"parallel_paths_edges: [['0', '4', '10', '11', '17', '35', '53'], ['0', '4', '11', '17', '35', '53'], ['53', '62'], ['62', '53']]\n",
|
|
"Nodes with unequal in and out degree: ['11', '4', '62', '53']\n",
|
|
"Updated parallel paths after splitting: [['0', '4'], ['4', '10', '11'], ['11', '17', '35', '53'], ['4', '11'], ['53', '62'], ['62', '53']]\n",
|
|
"Path: ['0', '4']\n",
|
|
"Number of hops: 1\n",
|
|
"Path: ['4', '10', '11']\n",
|
|
"Number of hops: 2\n",
|
|
"Path: ['11', '17', '35', '53']\n",
|
|
"Number of hops: 3\n",
|
|
"Path: ['4', '11']\n",
|
|
"Number of hops: 1\n",
|
|
"Path: ['53', '62']\n",
|
|
"Number of hops: 1\n",
|
|
"Path: ['62', '53']\n",
|
|
"Number of hops: 1\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# ======================================================================\n",
|
|
"# Use the PE graph to create inputs to be used by the NoC mapping LP\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"\n",
|
|
"import networkx as nx\n",
|
|
"from LinProg_lib import draw_networkx_graph\n",
|
|
"\n",
|
|
"G = nx.read_graphml(\"PE_graph.graphml\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Check for src and dst\n",
|
|
"# ======================================================================\n",
|
|
"nodes_with_src_true = [node for node, attrs in G.nodes(data=True) if attrs.get('src') == True]\n",
|
|
"nodes_with_dst_true = [node for node, attrs in G.nodes(data=True) if attrs.get('dst') == True]\n",
|
|
"\n",
|
|
"if len(nodes_with_src_true) == 1:\n",
|
|
" src= nodes_with_src_true[0]\n",
|
|
"else:\n",
|
|
" raise ValueError(\"There are multiple source nodes or no source nodes found.\")\n",
|
|
"if len(nodes_with_dst_true) == 1: \n",
|
|
" dst= nodes_with_dst_true[0]\n",
|
|
"else:\n",
|
|
" raise ValueError(\"There are multiple destination nodes or no destination nodes found.\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# Extract all parallel paths between src and dst\n",
|
|
"parallel_paths = list(nx.all_simple_paths(G, source=src, target=dst))\n",
|
|
"print(f\"Parallel paths between {src} and {dst} are {parallel_paths}\")\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# are there intermediate nodes which create new parallel paths and need\n",
|
|
"# to be split to multiple parallel paths (mid_start node)\n",
|
|
"# ======================================================================\n",
|
|
"nodes_with_unequal_io = []\n",
|
|
"for node in G.nodes:\n",
|
|
" in_degree = G.in_degree(node)\n",
|
|
" out_degree = G.out_degree(node)\n",
|
|
" # if in_degree != out_degree and node != src and node != dst:\n",
|
|
" # nodes_with_unequal_io.append(node)\n",
|
|
" if (in_degree >1 or out_degree>1) and node != src and node != dst:\n",
|
|
" nodes_with_unequal_io.append(node)\n",
|
|
"# nodes_with_unequal_io.append('14')\n",
|
|
"# # nodes_with_unequal_io.append('0')\n",
|
|
"# nodes_with_unequal_io.append('44')\n",
|
|
"print(f\"Nodes with unequal in and out degree: {nodes_with_unequal_io}\")\n",
|
|
"# ======================================================================\n",
|
|
"# edges which are not part of the parallel paths are paths not going \n",
|
|
"# from src to destination but the need to be added anyway\n",
|
|
"# ======================================================================\n",
|
|
"parallel_paths_edges=[]\n",
|
|
"for path in parallel_paths:\n",
|
|
" for idx,node in enumerate(path[:-1]):\n",
|
|
" parallel_paths_edges.append((node,path[idx+1]))\n",
|
|
"\n",
|
|
"\n",
|
|
"for edge in G.edges:\n",
|
|
" if edge not in parallel_paths_edges:\n",
|
|
" for path in parallel_paths:\n",
|
|
" if edge[0] in path:\n",
|
|
" nodes_with_unequal_io.append(edge[0])\n",
|
|
" parallel_paths.append([edge[0],edge[1]])\n",
|
|
" \n",
|
|
"nodes_with_unequal_io=list(set(nodes_with_unequal_io))\n",
|
|
"print(f\"parallel_paths_edges: {parallel_paths}\")\n",
|
|
"print(f\"Nodes with unequal in and out degree: {nodes_with_unequal_io}\")\n",
|
|
"# for path in parallel_paths:\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# paths with mid_start node need to be split up\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"\n",
|
|
"# Repeat the splitting process until no further splitting is required\n",
|
|
"paths_need_splitting = True\n",
|
|
"while paths_need_splitting:\n",
|
|
" paths_need_splitting = False # Assume no splitting is needed\n",
|
|
" new_parallel_paths = [] # Temporary list to store updated paths\n",
|
|
"\n",
|
|
" for path in parallel_paths:\n",
|
|
" split_occurred = False\n",
|
|
" for node in nodes_with_unequal_io:\n",
|
|
" if node in path and node != path[0] and node != path[-1]:\n",
|
|
" index = path.index(node)\n",
|
|
" part1 = path[:index + 1]\n",
|
|
" part2 = path[index:]\n",
|
|
" new_parallel_paths.append(part1)\n",
|
|
" new_parallel_paths.append(part2)\n",
|
|
" split_occurred = True\n",
|
|
" paths_need_splitting = True # Indicate that further splitting is needed\n",
|
|
" break # Stop checking this path further since it has been split\n",
|
|
" if not split_occurred:\n",
|
|
" new_parallel_paths.append(path) # Keep the path as is if no split occurred\n",
|
|
"\n",
|
|
" parallel_paths = new_parallel_paths # Update the paths with the newly split paths\n",
|
|
"# Remove duplicate paths from the final parallel paths\n",
|
|
"unique_parallel_paths = []\n",
|
|
"seen_paths = set()\n",
|
|
"for path in parallel_paths:\n",
|
|
" path_tuple = tuple(path) # Convert the path to a tuple for hashing\n",
|
|
" if path_tuple not in seen_paths:\n",
|
|
" seen_paths.add(path_tuple)\n",
|
|
" unique_parallel_paths.append(path) # Add the original list path to the unique list\n",
|
|
"\n",
|
|
"parallel_paths = unique_parallel_paths # Update the parallel_paths with unique paths\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"print(f\"Updated parallel paths after splitting: {parallel_paths}\")\n",
|
|
"# ======================================================================\n",
|
|
"# Get the number of hops for each path\n",
|
|
"# ======================================================================\n",
|
|
"hop_count = []\n",
|
|
"for path in parallel_paths:\n",
|
|
" print(f\"Path: {path}\")\n",
|
|
" hop_count.append(len(path) - 1)\n",
|
|
" print(f\"Number of hops: {len(path) - 1}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Combine src and dst nodes that are mapped to the same PE\n",
|
|
"# ======================================================================\n",
|
|
"src_dst_mapping = {}\n",
|
|
"\n",
|
|
"for idx, path in enumerate(parallel_paths):\n",
|
|
" src_key = f\"src_{idx}\"\n",
|
|
" src_dst_mapping[src_key] = path[0] \n",
|
|
"\n",
|
|
"for idx, path in enumerate(parallel_paths):\n",
|
|
" dst_key = f\"dst_{idx}\"\n",
|
|
" src_dst_mapping[dst_key] = path[-1] \n",
|
|
"\n",
|
|
"\n",
|
|
"# Group sources and destinations with the same values into sublists\n",
|
|
"same_value_groups = []\n",
|
|
"\n",
|
|
"for key, value in src_dst_mapping.items():\n",
|
|
" # Check if the value already exists in any group\n",
|
|
" added = False\n",
|
|
" for group in same_value_groups:\n",
|
|
" if value in [src_dst_mapping[item] for item in group]:\n",
|
|
" group.append(key)\n",
|
|
" added = True\n",
|
|
" break\n",
|
|
" # If the value is not in any group, create a new group\n",
|
|
" if not added:\n",
|
|
" same_value_groups.append([key])\n",
|
|
"\n",
|
|
" # Remove sublists with length less than 2\n",
|
|
"same_value_groups = [group for group in same_value_groups if len(group) >= 2]\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# needs to be tested with different graphs\n",
|
|
"# ======================================================================\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Set parameter Username\n",
|
|
"Set parameter LicenseID to value 2631888\n",
|
|
"Set parameter TimeLimit to value 60\n",
|
|
"Set parameter LogFile to value \"gurobi.log\"\n",
|
|
"Set parameter Threads to value 26\n",
|
|
"Using license file /home/sfischer/Software/gurobi12.0.1_linux64/gurobi1201/gurobi.lic\n",
|
|
"Academic license - for non-commercial use only - expires 2026-03-06\n",
|
|
"\n",
|
|
"Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (linux64 - \"Ubuntu 24.04.2 LTS\")\n",
|
|
"Copyright (c) 2025, Gurobi Optimization, LLC\n",
|
|
"\n",
|
|
"Read LP format model from file /tmp/d15c7784f22542d08e6079669283e281-pulp.lp\n",
|
|
"Reading time = 0.00 seconds\n",
|
|
"OBJ: 4724 rows, 1219 columns, 15780 nonzeros\n",
|
|
"\n",
|
|
"Using Gurobi shared library /home/sfischer/Software/gurobi12.0.1_linux64/gurobi1201/linux64/lib/libgurobi.so.12.0.1\n",
|
|
"\n",
|
|
"CPU model: Intel(R) Core(TM) i7-14700KF, instruction set [SSE2|AVX|AVX2]\n",
|
|
"Thread count: 28 physical cores, 28 logical processors, using up to 26 threads\n",
|
|
"\n",
|
|
"Non-default parameters:\n",
|
|
"TimeLimit 60\n",
|
|
"Threads 26\n",
|
|
"\n",
|
|
"Optimize a model with 4724 rows, 1219 columns and 15780 nonzeros\n",
|
|
"Model fingerprint: 0x7a0be82e\n",
|
|
"Variable types: 0 continuous, 1219 integer (784 binary)\n",
|
|
"Coefficient statistics:\n",
|
|
" Matrix range [1e+00, 1e+02]\n",
|
|
" Objective range [1e+00, 1e+00]\n",
|
|
" Bounds range [1e+00, 1e+00]\n",
|
|
" RHS range [1e+00, 1e+02]\n",
|
|
"Presolve removed 1513 rows and 125 columns\n",
|
|
"Presolve time: 0.02s\n",
|
|
"Presolved: 3211 rows, 1094 columns, 11851 nonzeros\n",
|
|
"Variable types: 0 continuous, 1094 integer (976 binary)\n",
|
|
"Found heuristic solution: objective 22.0000000\n",
|
|
"\n",
|
|
"Root relaxation: objective 9.000000e+00, 220 iterations, 0.00 seconds (0.01 work units)\n",
|
|
"\n",
|
|
" Nodes | Current Node | Objective Bounds | Work\n",
|
|
" Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n",
|
|
"\n",
|
|
" 0 0 9.00000 0 35 22.00000 9.00000 59.1% - 0s\n",
|
|
"H 0 0 15.0000000 9.00000 40.0% - 0s\n",
|
|
" 0 0 9.00000 0 62 15.00000 9.00000 40.0% - 0s\n",
|
|
"H 0 0 12.0000000 9.00000 25.0% - 0s\n",
|
|
" 0 0 9.00000 0 80 12.00000 9.00000 25.0% - 0s\n",
|
|
"H 0 0 10.0000000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 15 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 26 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 28 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 30 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 25 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 30 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 11 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 35 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 34 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 51 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 72 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 77 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 34 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 64 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 48 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 55 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 40 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 107 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 17 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 116 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 95 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 0 9.00000 0 74 10.00000 9.00000 10.0% - 0s\n",
|
|
" 0 2 9.00000 0 25 10.00000 9.00000 10.0% - 0s\n",
|
|
" 10311 358 9.00000 31 145 10.00000 9.00000 10.0% 103 5s\n",
|
|
" 21829 26 infeasible 14 10.00000 9.00000 10.0% 121 10s\n",
|
|
"\n",
|
|
"Cutting planes:\n",
|
|
" Learned: 816\n",
|
|
" Gomory: 3\n",
|
|
" Cover: 1645\n",
|
|
" Implied bound: 195\n",
|
|
" Clique: 37\n",
|
|
" MIR: 142\n",
|
|
" StrongCG: 15\n",
|
|
" Flow cover: 87\n",
|
|
" Inf proof: 79\n",
|
|
" Zero half: 26\n",
|
|
" RLT: 53\n",
|
|
" Relax-and-lift: 24\n",
|
|
" PSD: 1\n",
|
|
"\n",
|
|
"Explored 22445 nodes (2775185 simplex iterations) in 10.85 seconds (20.12 work units)\n",
|
|
"Thread count was 26 (of 28 available processors)\n",
|
|
"\n",
|
|
"Solution count 4: 10 12 15 22 \n",
|
|
"\n",
|
|
"Optimal solution found (tolerance 1.00e-04)\n",
|
|
"Best objective 1.000000000000e+01, best bound 1.000000000000e+01, gap 0.0000%\n",
|
|
"\n",
|
|
"Wrote result file '/tmp/d15c7784f22542d08e6079669283e281-pulp.sol'\n",
|
|
"\n",
|
|
"Objective value: 10.0\n",
|
|
"Optimal solution found:\n",
|
|
"Edge (4,0, 4) is part of the shortest path with value: 1.0\n",
|
|
"Edge (2,1, 0) is part of the shortest path with value: 1.0\n",
|
|
"Edge (2,2, 1) is part of the shortest path with value: 1.0\n",
|
|
"Edge (3,2, 3) is part of the shortest path with value: 1.0\n",
|
|
"Edge (2,3, 2) is part of the shortest path with value: 1.0\n",
|
|
"Edge (5,4, 0) is part of the shortest path with value: 1.0\n",
|
|
"Edge (3,6, 2) is part of the shortest path with value: 1.0\n",
|
|
"Edge (1,6, 7) is part of the shortest path with value: 1.0\n",
|
|
"Edge (1,7, 3) is part of the shortest path with value: 1.0\n",
|
|
"Edge (0,10, 6) is part of the shortest path with value: 1.0\n",
|
|
"Source node for src_0: 10\n",
|
|
"Source node for src_1: 6\n",
|
|
"Source node for src_2: 3\n",
|
|
"Source node for src_3: 6\n",
|
|
"Source node for src_4: 0\n",
|
|
"Source node for src_5: 4\n",
|
|
"Destination node for dst_0: 6\n",
|
|
"Destination node for dst_1: 3\n",
|
|
"Destination node for dst_2: 0\n",
|
|
"Destination node for dst_3: 3\n",
|
|
"Destination node for dst_4: 4\n",
|
|
"Destination node for dst_5: 0\n",
|
|
"Position of node (0, 0): 0.0\n",
|
|
"Position of node (1, 0): 0.0\n",
|
|
"Position of node (2, 0): 3.0\n",
|
|
"Position of node (3, 0): 0.0\n",
|
|
"Position of node (4, 0): 0.0\n",
|
|
"Position of node (5, 0): 1.0\n",
|
|
"Position of node (0, 1): 0.0\n",
|
|
"Position of node (1, 1): 0.0\n",
|
|
"Position of node (2, 1): 2.0\n",
|
|
"Position of node (3, 1): 0.0\n",
|
|
"Position of node (4, 1): 0.0\n",
|
|
"Position of node (5, 1): 0.0\n",
|
|
"Position of node (0, 2): 0.0\n",
|
|
"Position of node (1, 2): 0.0\n",
|
|
"Position of node (2, 2): 1.0\n",
|
|
"Position of node (3, 2): 1.0\n",
|
|
"Position of node (4, 2): 0.0\n",
|
|
"Position of node (5, 2): 0.0\n",
|
|
"Position of node (0, 3): 0.0\n",
|
|
"Position of node (1, 3): 2.0\n",
|
|
"Position of node (2, 3): 0.0\n",
|
|
"Position of node (3, 3): 2.0\n",
|
|
"Position of node (4, 3): 0.0\n",
|
|
"Position of node (5, 3): 0.0\n",
|
|
"Position of node (0, 4): 0.0\n",
|
|
"Position of node (1, 4): 0.0\n",
|
|
"Position of node (2, 4): 0.0\n",
|
|
"Position of node (3, 4): 0.0\n",
|
|
"Position of node (4, 4): 1.0\n",
|
|
"Position of node (5, 4): 0.0\n",
|
|
"Position of node (0, 5): 0.0\n",
|
|
"Position of node (1, 5): 0.0\n",
|
|
"Position of node (2, 5): 0.0\n",
|
|
"Position of node (3, 5): 0.0\n",
|
|
"Position of node (4, 5): 0.0\n",
|
|
"Position of node (5, 5): 0.0\n",
|
|
"Position of node (0, 6): 1.0\n",
|
|
"Position of node (1, 6): 0.0\n",
|
|
"Position of node (2, 6): 0.0\n",
|
|
"Position of node (3, 6): 0.0\n",
|
|
"Position of node (4, 6): 0.0\n",
|
|
"Position of node (5, 6): 0.0\n",
|
|
"Position of node (0, 7): 0.0\n",
|
|
"Position of node (1, 7): 1.0\n",
|
|
"Position of node (2, 7): 0.0\n",
|
|
"Position of node (3, 7): 0.0\n",
|
|
"Position of node (4, 7): 0.0\n",
|
|
"Position of node (5, 7): 0.0\n",
|
|
"Position of node (0, 8): 0.0\n",
|
|
"Position of node (1, 8): 0.0\n",
|
|
"Position of node (2, 8): 0.0\n",
|
|
"Position of node (3, 8): 0.0\n",
|
|
"Position of node (4, 8): 0.0\n",
|
|
"Position of node (5, 8): 0.0\n",
|
|
"Position of node (0, 9): 0.0\n",
|
|
"Position of node (1, 9): 0.0\n",
|
|
"Position of node (2, 9): 0.0\n",
|
|
"Position of node (3, 9): 0.0\n",
|
|
"Position of node (4, 9): 0.0\n",
|
|
"Position of node (5, 9): 0.0\n",
|
|
"Position of node (0, 10): 0.0\n",
|
|
"Position of node (1, 10): 0.0\n",
|
|
"Position of node (2, 10): 0.0\n",
|
|
"Position of node (3, 10): 0.0\n",
|
|
"Position of node (4, 10): 0.0\n",
|
|
"Position of node (5, 10): 0.0\n",
|
|
"Position of node (0, 11): 0.0\n",
|
|
"Position of node (1, 11): 0.0\n",
|
|
"Position of node (2, 11): 0.0\n",
|
|
"Position of node (3, 11): 0.0\n",
|
|
"Position of node (4, 11): 0.0\n",
|
|
"Position of node (5, 11): 0.0\n",
|
|
"Position of node (0, 12): 0.0\n",
|
|
"Position of node (1, 12): 0.0\n",
|
|
"Position of node (2, 12): 0.0\n",
|
|
"Position of node (3, 12): 0.0\n",
|
|
"Position of node (4, 12): 0.0\n",
|
|
"Position of node (5, 12): 0.0\n",
|
|
"Position of node (0, 13): 0.0\n",
|
|
"Position of node (1, 13): 0.0\n",
|
|
"Position of node (2, 13): 0.0\n",
|
|
"Position of node (3, 13): 0.0\n",
|
|
"Position of node (4, 13): 0.0\n",
|
|
"Position of node (5, 13): 0.0\n",
|
|
"Position of node (0, 14): 0.0\n",
|
|
"Position of node (1, 14): 0.0\n",
|
|
"Position of node (2, 14): 0.0\n",
|
|
"Position of node (3, 14): 0.0\n",
|
|
"Position of node (4, 14): 0.0\n",
|
|
"Position of node (5, 14): 0.0\n",
|
|
"Position of node (0, 15): 0.0\n",
|
|
"Position of node (1, 15): 0.0\n",
|
|
"Position of node (2, 15): 0.0\n",
|
|
"Position of node (3, 15): 0.0\n",
|
|
"Position of node (4, 15): 0.0\n",
|
|
"Position of node (5, 15): 0.0\n",
|
|
"Source position (src_0): 0.0\n",
|
|
"Source position (src_1): 0.0\n",
|
|
"Source position (src_2): 0.0\n",
|
|
"Source position (src_3): 0.0\n",
|
|
"Source position (src_4): 0.0\n",
|
|
"Source position (src_5): 0.0\n",
|
|
"Destination position (dst_0): 1.0\n",
|
|
"Destination position (dst_1): 2.0\n",
|
|
"Destination position (dst_2): 3.0\n",
|
|
"Destination position (dst_3): 2.0\n",
|
|
"Destination position (dst_4): 1.0\n",
|
|
"Destination position (dst_5): 1.0\n",
|
|
"choose_mid_0_node0 = 1.0\n",
|
|
"choose_mid_0_node1 = 1.0\n",
|
|
"choose_mid_0_node10 = 0.0\n",
|
|
"choose_mid_0_node11 = 0.0\n",
|
|
"choose_mid_0_node12 = 1.0\n",
|
|
"choose_mid_0_node13 = 1.0\n",
|
|
"choose_mid_0_node14 = 1.0\n",
|
|
"choose_mid_0_node15 = 1.0\n",
|
|
"choose_mid_0_node2 = 0.0\n",
|
|
"choose_mid_0_node3 = 1.0\n",
|
|
"choose_mid_0_node4 = 1.0\n",
|
|
"choose_mid_0_node5 = 1.0\n",
|
|
"choose_mid_0_node6 = 1.0\n",
|
|
"choose_mid_0_node7 = 1.0\n",
|
|
"choose_mid_0_node8 = 1.0\n",
|
|
"choose_mid_0_node9 = 1.0\n",
|
|
"choose_mid_1_node0 = 0.0\n",
|
|
"choose_mid_1_node1 = 0.0\n",
|
|
"choose_mid_1_node10 = 0.0\n",
|
|
"choose_mid_1_node11 = 0.0\n",
|
|
"choose_mid_1_node12 = 0.0\n",
|
|
"choose_mid_1_node13 = 0.0\n",
|
|
"choose_mid_1_node14 = 0.0\n",
|
|
"choose_mid_1_node15 = 0.0\n",
|
|
"choose_mid_1_node2 = 0.0\n",
|
|
"choose_mid_1_node3 = 0.0\n",
|
|
"choose_mid_1_node4 = 0.0\n",
|
|
"choose_mid_1_node5 = 0.0\n",
|
|
"choose_mid_1_node6 = 0.0\n",
|
|
"choose_mid_1_node7 = 0.0\n",
|
|
"choose_mid_1_node8 = 0.0\n",
|
|
"choose_mid_1_node9 = 0.0\n",
|
|
"choose_mid_2_node0 = 0.0\n",
|
|
"choose_mid_2_node1 = 0.0\n",
|
|
"choose_mid_2_node10 = 1.0\n",
|
|
"choose_mid_2_node11 = 1.0\n",
|
|
"choose_mid_2_node12 = 0.0\n",
|
|
"choose_mid_2_node13 = 0.0\n",
|
|
"choose_mid_2_node14 = 0.0\n",
|
|
"choose_mid_2_node15 = 0.0\n",
|
|
"choose_mid_2_node2 = 1.0\n",
|
|
"choose_mid_2_node3 = 0.0\n",
|
|
"choose_mid_2_node4 = 0.0\n",
|
|
"choose_mid_2_node5 = 0.0\n",
|
|
"choose_mid_2_node6 = 0.0\n",
|
|
"choose_mid_2_node7 = 0.0\n",
|
|
"choose_mid_2_node8 = 0.0\n",
|
|
"choose_mid_2_node9 = 0.0\n",
|
|
"choose_mid_3_node0 = 0.0\n",
|
|
"choose_mid_3_node1 = 0.0\n",
|
|
"choose_mid_3_node10 = 0.0\n",
|
|
"choose_mid_3_node11 = 0.0\n",
|
|
"choose_mid_3_node12 = 0.0\n",
|
|
"choose_mid_3_node13 = 0.0\n",
|
|
"choose_mid_3_node14 = 0.0\n",
|
|
"choose_mid_3_node15 = 0.0\n",
|
|
"choose_mid_3_node2 = 0.0\n",
|
|
"choose_mid_3_node3 = 0.0\n",
|
|
"choose_mid_3_node4 = 0.0\n",
|
|
"choose_mid_3_node5 = 0.0\n",
|
|
"choose_mid_3_node6 = 0.0\n",
|
|
"choose_mid_3_node7 = 0.0\n",
|
|
"choose_mid_3_node8 = 0.0\n",
|
|
"choose_mid_3_node9 = 0.0\n",
|
|
"choose_mid_4_node0 = 0.0\n",
|
|
"choose_mid_4_node1 = 0.0\n",
|
|
"choose_mid_4_node10 = 0.0\n",
|
|
"choose_mid_4_node11 = 0.0\n",
|
|
"choose_mid_4_node12 = 0.0\n",
|
|
"choose_mid_4_node13 = 0.0\n",
|
|
"choose_mid_4_node14 = 0.0\n",
|
|
"choose_mid_4_node15 = 0.0\n",
|
|
"choose_mid_4_node2 = 0.0\n",
|
|
"choose_mid_4_node3 = 0.0\n",
|
|
"choose_mid_4_node4 = 0.0\n",
|
|
"choose_mid_4_node5 = 0.0\n",
|
|
"choose_mid_4_node6 = 0.0\n",
|
|
"choose_mid_4_node7 = 0.0\n",
|
|
"choose_mid_4_node8 = 0.0\n",
|
|
"choose_mid_4_node9 = 0.0\n",
|
|
"choose_mid_5_node0 = 0.0\n",
|
|
"choose_mid_5_node1 = 0.0\n",
|
|
"choose_mid_5_node10 = 0.0\n",
|
|
"choose_mid_5_node11 = 0.0\n",
|
|
"choose_mid_5_node12 = 0.0\n",
|
|
"choose_mid_5_node13 = 0.0\n",
|
|
"choose_mid_5_node14 = 0.0\n",
|
|
"choose_mid_5_node15 = 0.0\n",
|
|
"choose_mid_5_node2 = 0.0\n",
|
|
"choose_mid_5_node3 = 0.0\n",
|
|
"choose_mid_5_node4 = 0.0\n",
|
|
"choose_mid_5_node5 = 0.0\n",
|
|
"choose_mid_5_node6 = 0.0\n",
|
|
"choose_mid_5_node7 = 0.0\n",
|
|
"choose_mid_5_node8 = 0.0\n",
|
|
"choose_mid_5_node9 = 0.0\n",
|
|
"dst_0_0 = 0.0\n",
|
|
"dst_0_1 = 0.0\n",
|
|
"dst_0_10 = 0.0\n",
|
|
"dst_0_11 = 0.0\n",
|
|
"dst_0_12 = 0.0\n",
|
|
"dst_0_13 = 0.0\n",
|
|
"dst_0_14 = 0.0\n",
|
|
"dst_0_15 = 0.0\n",
|
|
"dst_0_2 = 0.0\n",
|
|
"dst_0_3 = 0.0\n",
|
|
"dst_0_4 = 0.0\n",
|
|
"dst_0_5 = 0.0\n",
|
|
"dst_0_6 = 1.0\n",
|
|
"dst_0_7 = 0.0\n",
|
|
"dst_0_8 = 0.0\n",
|
|
"dst_0_9 = 0.0\n",
|
|
"dst_1_0 = 0.0\n",
|
|
"dst_1_1 = 0.0\n",
|
|
"dst_1_10 = 0.0\n",
|
|
"dst_1_11 = 0.0\n",
|
|
"dst_1_12 = 0.0\n",
|
|
"dst_1_13 = 0.0\n",
|
|
"dst_1_14 = 0.0\n",
|
|
"dst_1_15 = 0.0\n",
|
|
"dst_1_2 = 0.0\n",
|
|
"dst_1_3 = 1.0\n",
|
|
"dst_1_4 = 0.0\n",
|
|
"dst_1_5 = 0.0\n",
|
|
"dst_1_6 = 0.0\n",
|
|
"dst_1_7 = 0.0\n",
|
|
"dst_1_8 = 0.0\n",
|
|
"dst_1_9 = 0.0\n",
|
|
"dst_2_0 = 1.0\n",
|
|
"dst_2_1 = 0.0\n",
|
|
"dst_2_10 = 0.0\n",
|
|
"dst_2_11 = 0.0\n",
|
|
"dst_2_12 = 0.0\n",
|
|
"dst_2_13 = 0.0\n",
|
|
"dst_2_14 = 0.0\n",
|
|
"dst_2_15 = 0.0\n",
|
|
"dst_2_2 = 0.0\n",
|
|
"dst_2_3 = 0.0\n",
|
|
"dst_2_4 = 0.0\n",
|
|
"dst_2_5 = 0.0\n",
|
|
"dst_2_6 = 0.0\n",
|
|
"dst_2_7 = 0.0\n",
|
|
"dst_2_8 = 0.0\n",
|
|
"dst_2_9 = 0.0\n",
|
|
"dst_3_0 = 0.0\n",
|
|
"dst_3_1 = 0.0\n",
|
|
"dst_3_10 = 0.0\n",
|
|
"dst_3_11 = 0.0\n",
|
|
"dst_3_12 = 0.0\n",
|
|
"dst_3_13 = 0.0\n",
|
|
"dst_3_14 = 0.0\n",
|
|
"dst_3_15 = 0.0\n",
|
|
"dst_3_2 = 0.0\n",
|
|
"dst_3_3 = 1.0\n",
|
|
"dst_3_4 = 0.0\n",
|
|
"dst_3_5 = 0.0\n",
|
|
"dst_3_6 = 0.0\n",
|
|
"dst_3_7 = 0.0\n",
|
|
"dst_3_8 = 0.0\n",
|
|
"dst_3_9 = 0.0\n",
|
|
"dst_4_0 = 0.0\n",
|
|
"dst_4_1 = 0.0\n",
|
|
"dst_4_10 = 0.0\n",
|
|
"dst_4_11 = 0.0\n",
|
|
"dst_4_12 = 0.0\n",
|
|
"dst_4_13 = 0.0\n",
|
|
"dst_4_14 = 0.0\n",
|
|
"dst_4_15 = 0.0\n",
|
|
"dst_4_2 = 0.0\n",
|
|
"dst_4_3 = 0.0\n",
|
|
"dst_4_4 = 1.0\n",
|
|
"dst_4_5 = 0.0\n",
|
|
"dst_4_6 = 0.0\n",
|
|
"dst_4_7 = 0.0\n",
|
|
"dst_4_8 = 0.0\n",
|
|
"dst_4_9 = 0.0\n",
|
|
"dst_5_0 = 1.0\n",
|
|
"dst_5_1 = 0.0\n",
|
|
"dst_5_10 = 0.0\n",
|
|
"dst_5_11 = 0.0\n",
|
|
"dst_5_12 = 0.0\n",
|
|
"dst_5_13 = 0.0\n",
|
|
"dst_5_14 = 0.0\n",
|
|
"dst_5_15 = 0.0\n",
|
|
"dst_5_2 = 0.0\n",
|
|
"dst_5_3 = 0.0\n",
|
|
"dst_5_4 = 0.0\n",
|
|
"dst_5_5 = 0.0\n",
|
|
"dst_5_6 = 0.0\n",
|
|
"dst_5_7 = 0.0\n",
|
|
"dst_5_8 = 0.0\n",
|
|
"dst_5_9 = 0.0\n",
|
|
"edge_0_0_1 = 0.0\n",
|
|
"edge_0_0_4 = 0.0\n",
|
|
"edge_0_10_11 = 0.0\n",
|
|
"edge_0_10_14 = 0.0\n",
|
|
"edge_0_10_6 = 1.0\n",
|
|
"edge_0_10_9 = 0.0\n",
|
|
"edge_0_11_10 = 0.0\n",
|
|
"edge_0_11_15 = 0.0\n",
|
|
"edge_0_11_7 = 0.0\n",
|
|
"edge_0_12_13 = 0.0\n",
|
|
"edge_0_12_8 = 0.0\n",
|
|
"edge_0_13_12 = 0.0\n",
|
|
"edge_0_13_14 = 0.0\n",
|
|
"edge_0_13_9 = 0.0\n",
|
|
"edge_0_14_10 = 0.0\n",
|
|
"edge_0_14_13 = 0.0\n",
|
|
"edge_0_14_15 = 0.0\n",
|
|
"edge_0_15_11 = 0.0\n",
|
|
"edge_0_15_14 = 0.0\n",
|
|
"edge_0_1_0 = 0.0\n",
|
|
"edge_0_1_2 = 0.0\n",
|
|
"edge_0_1_5 = 0.0\n",
|
|
"edge_0_2_1 = 0.0\n",
|
|
"edge_0_2_3 = 0.0\n",
|
|
"edge_0_2_6 = 0.0\n",
|
|
"edge_0_3_2 = 0.0\n",
|
|
"edge_0_3_7 = 0.0\n",
|
|
"edge_0_4_0 = 0.0\n",
|
|
"edge_0_4_5 = 0.0\n",
|
|
"edge_0_4_8 = 0.0\n",
|
|
"edge_0_5_1 = 0.0\n",
|
|
"edge_0_5_4 = 0.0\n",
|
|
"edge_0_5_6 = 0.0\n",
|
|
"edge_0_5_9 = 0.0\n",
|
|
"edge_0_6_10 = 0.0\n",
|
|
"edge_0_6_2 = 0.0\n",
|
|
"edge_0_6_5 = 0.0\n",
|
|
"edge_0_6_7 = 0.0\n",
|
|
"edge_0_7_11 = 0.0\n",
|
|
"edge_0_7_3 = 0.0\n",
|
|
"edge_0_7_6 = 0.0\n",
|
|
"edge_0_8_12 = 0.0\n",
|
|
"edge_0_8_4 = 0.0\n",
|
|
"edge_0_8_9 = 0.0\n",
|
|
"edge_0_9_10 = 0.0\n",
|
|
"edge_0_9_13 = 0.0\n",
|
|
"edge_0_9_5 = 0.0\n",
|
|
"edge_0_9_8 = 0.0\n",
|
|
"edge_1_0_1 = 0.0\n",
|
|
"edge_1_0_4 = 0.0\n",
|
|
"edge_1_10_11 = 0.0\n",
|
|
"edge_1_10_14 = 0.0\n",
|
|
"edge_1_10_6 = 0.0\n",
|
|
"edge_1_10_9 = 0.0\n",
|
|
"edge_1_11_10 = 0.0\n",
|
|
"edge_1_11_15 = 0.0\n",
|
|
"edge_1_11_7 = 0.0\n",
|
|
"edge_1_12_13 = 0.0\n",
|
|
"edge_1_12_8 = 0.0\n",
|
|
"edge_1_13_12 = 0.0\n",
|
|
"edge_1_13_14 = 0.0\n",
|
|
"edge_1_13_9 = 0.0\n",
|
|
"edge_1_14_10 = 0.0\n",
|
|
"edge_1_14_13 = 0.0\n",
|
|
"edge_1_14_15 = 0.0\n",
|
|
"edge_1_15_11 = 0.0\n",
|
|
"edge_1_15_14 = 0.0\n",
|
|
"edge_1_1_0 = 0.0\n",
|
|
"edge_1_1_2 = 0.0\n",
|
|
"edge_1_1_5 = 0.0\n",
|
|
"edge_1_2_1 = 0.0\n",
|
|
"edge_1_2_3 = 0.0\n",
|
|
"edge_1_2_6 = 0.0\n",
|
|
"edge_1_3_2 = 0.0\n",
|
|
"edge_1_3_7 = 0.0\n",
|
|
"edge_1_4_0 = 0.0\n",
|
|
"edge_1_4_5 = 0.0\n",
|
|
"edge_1_4_8 = 0.0\n",
|
|
"edge_1_5_1 = 0.0\n",
|
|
"edge_1_5_4 = 0.0\n",
|
|
"edge_1_5_6 = 0.0\n",
|
|
"edge_1_5_9 = 0.0\n",
|
|
"edge_1_6_10 = 0.0\n",
|
|
"edge_1_6_2 = 0.0\n",
|
|
"edge_1_6_5 = 0.0\n",
|
|
"edge_1_6_7 = 1.0\n",
|
|
"edge_1_7_11 = 0.0\n",
|
|
"edge_1_7_3 = 1.0\n",
|
|
"edge_1_7_6 = 0.0\n",
|
|
"edge_1_8_12 = 0.0\n",
|
|
"edge_1_8_4 = 0.0\n",
|
|
"edge_1_8_9 = 0.0\n",
|
|
"edge_1_9_10 = 0.0\n",
|
|
"edge_1_9_13 = 0.0\n",
|
|
"edge_1_9_5 = 0.0\n",
|
|
"edge_1_9_8 = 0.0\n",
|
|
"edge_2_0_1 = 0.0\n",
|
|
"edge_2_0_4 = 0.0\n",
|
|
"edge_2_10_11 = 0.0\n",
|
|
"edge_2_10_14 = 0.0\n",
|
|
"edge_2_10_6 = 0.0\n",
|
|
"edge_2_10_9 = 0.0\n",
|
|
"edge_2_11_10 = 0.0\n",
|
|
"edge_2_11_15 = 0.0\n",
|
|
"edge_2_11_7 = 0.0\n",
|
|
"edge_2_12_13 = 0.0\n",
|
|
"edge_2_12_8 = 0.0\n",
|
|
"edge_2_13_12 = 0.0\n",
|
|
"edge_2_13_14 = 0.0\n",
|
|
"edge_2_13_9 = 0.0\n",
|
|
"edge_2_14_10 = 0.0\n",
|
|
"edge_2_14_13 = 0.0\n",
|
|
"edge_2_14_15 = 0.0\n",
|
|
"edge_2_15_11 = 0.0\n",
|
|
"edge_2_15_14 = 0.0\n",
|
|
"edge_2_1_0 = 1.0\n",
|
|
"edge_2_1_2 = 0.0\n",
|
|
"edge_2_1_5 = 0.0\n",
|
|
"edge_2_2_1 = 1.0\n",
|
|
"edge_2_2_3 = 0.0\n",
|
|
"edge_2_2_6 = 0.0\n",
|
|
"edge_2_3_2 = 1.0\n",
|
|
"edge_2_3_7 = 0.0\n",
|
|
"edge_2_4_0 = 0.0\n",
|
|
"edge_2_4_5 = 0.0\n",
|
|
"edge_2_4_8 = 0.0\n",
|
|
"edge_2_5_1 = 0.0\n",
|
|
"edge_2_5_4 = 0.0\n",
|
|
"edge_2_5_6 = 0.0\n",
|
|
"edge_2_5_9 = 0.0\n",
|
|
"edge_2_6_10 = 0.0\n",
|
|
"edge_2_6_2 = 0.0\n",
|
|
"edge_2_6_5 = 0.0\n",
|
|
"edge_2_6_7 = 0.0\n",
|
|
"edge_2_7_11 = 0.0\n",
|
|
"edge_2_7_3 = 0.0\n",
|
|
"edge_2_7_6 = 0.0\n",
|
|
"edge_2_8_12 = 0.0\n",
|
|
"edge_2_8_4 = 0.0\n",
|
|
"edge_2_8_9 = 0.0\n",
|
|
"edge_2_9_10 = 0.0\n",
|
|
"edge_2_9_13 = 0.0\n",
|
|
"edge_2_9_5 = 0.0\n",
|
|
"edge_2_9_8 = 0.0\n",
|
|
"edge_3_0_1 = 0.0\n",
|
|
"edge_3_0_4 = 0.0\n",
|
|
"edge_3_10_11 = 0.0\n",
|
|
"edge_3_10_14 = 0.0\n",
|
|
"edge_3_10_6 = 0.0\n",
|
|
"edge_3_10_9 = 0.0\n",
|
|
"edge_3_11_10 = 0.0\n",
|
|
"edge_3_11_15 = 0.0\n",
|
|
"edge_3_11_7 = 0.0\n",
|
|
"edge_3_12_13 = 0.0\n",
|
|
"edge_3_12_8 = 0.0\n",
|
|
"edge_3_13_12 = 0.0\n",
|
|
"edge_3_13_14 = 0.0\n",
|
|
"edge_3_13_9 = 0.0\n",
|
|
"edge_3_14_10 = 0.0\n",
|
|
"edge_3_14_13 = 0.0\n",
|
|
"edge_3_14_15 = 0.0\n",
|
|
"edge_3_15_11 = 0.0\n",
|
|
"edge_3_15_14 = 0.0\n",
|
|
"edge_3_1_0 = 0.0\n",
|
|
"edge_3_1_2 = 0.0\n",
|
|
"edge_3_1_5 = 0.0\n",
|
|
"edge_3_2_1 = 0.0\n",
|
|
"edge_3_2_3 = 1.0\n",
|
|
"edge_3_2_6 = 0.0\n",
|
|
"edge_3_3_2 = 0.0\n",
|
|
"edge_3_3_7 = 0.0\n",
|
|
"edge_3_4_0 = 0.0\n",
|
|
"edge_3_4_5 = 0.0\n",
|
|
"edge_3_4_8 = 0.0\n",
|
|
"edge_3_5_1 = 0.0\n",
|
|
"edge_3_5_4 = 0.0\n",
|
|
"edge_3_5_6 = 0.0\n",
|
|
"edge_3_5_9 = 0.0\n",
|
|
"edge_3_6_10 = 0.0\n",
|
|
"edge_3_6_2 = 1.0\n",
|
|
"edge_3_6_5 = 0.0\n",
|
|
"edge_3_6_7 = 0.0\n",
|
|
"edge_3_7_11 = 0.0\n",
|
|
"edge_3_7_3 = 0.0\n",
|
|
"edge_3_7_6 = 0.0\n",
|
|
"edge_3_8_12 = 0.0\n",
|
|
"edge_3_8_4 = 0.0\n",
|
|
"edge_3_8_9 = 0.0\n",
|
|
"edge_3_9_10 = 0.0\n",
|
|
"edge_3_9_13 = 0.0\n",
|
|
"edge_3_9_5 = 0.0\n",
|
|
"edge_3_9_8 = 0.0\n",
|
|
"edge_4_0_1 = 0.0\n",
|
|
"edge_4_0_4 = 1.0\n",
|
|
"edge_4_10_11 = 0.0\n",
|
|
"edge_4_10_14 = 0.0\n",
|
|
"edge_4_10_6 = 0.0\n",
|
|
"edge_4_10_9 = 0.0\n",
|
|
"edge_4_11_10 = 0.0\n",
|
|
"edge_4_11_15 = 0.0\n",
|
|
"edge_4_11_7 = 0.0\n",
|
|
"edge_4_12_13 = 0.0\n",
|
|
"edge_4_12_8 = 0.0\n",
|
|
"edge_4_13_12 = 0.0\n",
|
|
"edge_4_13_14 = 0.0\n",
|
|
"edge_4_13_9 = 0.0\n",
|
|
"edge_4_14_10 = 0.0\n",
|
|
"edge_4_14_13 = 0.0\n",
|
|
"edge_4_14_15 = 0.0\n",
|
|
"edge_4_15_11 = 0.0\n",
|
|
"edge_4_15_14 = 0.0\n",
|
|
"edge_4_1_0 = 0.0\n",
|
|
"edge_4_1_2 = 0.0\n",
|
|
"edge_4_1_5 = 0.0\n",
|
|
"edge_4_2_1 = 0.0\n",
|
|
"edge_4_2_3 = 0.0\n",
|
|
"edge_4_2_6 = 0.0\n",
|
|
"edge_4_3_2 = 0.0\n",
|
|
"edge_4_3_7 = 0.0\n",
|
|
"edge_4_4_0 = 0.0\n",
|
|
"edge_4_4_5 = 0.0\n",
|
|
"edge_4_4_8 = 0.0\n",
|
|
"edge_4_5_1 = 0.0\n",
|
|
"edge_4_5_4 = 0.0\n",
|
|
"edge_4_5_6 = 0.0\n",
|
|
"edge_4_5_9 = 0.0\n",
|
|
"edge_4_6_10 = 0.0\n",
|
|
"edge_4_6_2 = 0.0\n",
|
|
"edge_4_6_5 = 0.0\n",
|
|
"edge_4_6_7 = 0.0\n",
|
|
"edge_4_7_11 = 0.0\n",
|
|
"edge_4_7_3 = 0.0\n",
|
|
"edge_4_7_6 = 0.0\n",
|
|
"edge_4_8_12 = 0.0\n",
|
|
"edge_4_8_4 = 0.0\n",
|
|
"edge_4_8_9 = 0.0\n",
|
|
"edge_4_9_10 = 0.0\n",
|
|
"edge_4_9_13 = 0.0\n",
|
|
"edge_4_9_5 = 0.0\n",
|
|
"edge_4_9_8 = 0.0\n",
|
|
"edge_5_0_1 = 0.0\n",
|
|
"edge_5_0_4 = 0.0\n",
|
|
"edge_5_10_11 = 0.0\n",
|
|
"edge_5_10_14 = 0.0\n",
|
|
"edge_5_10_6 = 0.0\n",
|
|
"edge_5_10_9 = 0.0\n",
|
|
"edge_5_11_10 = 0.0\n",
|
|
"edge_5_11_15 = 0.0\n",
|
|
"edge_5_11_7 = 0.0\n",
|
|
"edge_5_12_13 = 0.0\n",
|
|
"edge_5_12_8 = 0.0\n",
|
|
"edge_5_13_12 = 0.0\n",
|
|
"edge_5_13_14 = 0.0\n",
|
|
"edge_5_13_9 = 0.0\n",
|
|
"edge_5_14_10 = 0.0\n",
|
|
"edge_5_14_13 = 0.0\n",
|
|
"edge_5_14_15 = 0.0\n",
|
|
"edge_5_15_11 = 0.0\n",
|
|
"edge_5_15_14 = 0.0\n",
|
|
"edge_5_1_0 = 0.0\n",
|
|
"edge_5_1_2 = 0.0\n",
|
|
"edge_5_1_5 = 0.0\n",
|
|
"edge_5_2_1 = 0.0\n",
|
|
"edge_5_2_3 = 0.0\n",
|
|
"edge_5_2_6 = 0.0\n",
|
|
"edge_5_3_2 = 0.0\n",
|
|
"edge_5_3_7 = 0.0\n",
|
|
"edge_5_4_0 = 1.0\n",
|
|
"edge_5_4_5 = 0.0\n",
|
|
"edge_5_4_8 = 0.0\n",
|
|
"edge_5_5_1 = 0.0\n",
|
|
"edge_5_5_4 = 0.0\n",
|
|
"edge_5_5_6 = 0.0\n",
|
|
"edge_5_5_9 = 0.0\n",
|
|
"edge_5_6_10 = 0.0\n",
|
|
"edge_5_6_2 = 0.0\n",
|
|
"edge_5_6_5 = 0.0\n",
|
|
"edge_5_6_7 = 0.0\n",
|
|
"edge_5_7_11 = 0.0\n",
|
|
"edge_5_7_3 = 0.0\n",
|
|
"edge_5_7_6 = 0.0\n",
|
|
"edge_5_8_12 = 0.0\n",
|
|
"edge_5_8_4 = 0.0\n",
|
|
"edge_5_8_9 = 0.0\n",
|
|
"edge_5_9_10 = 0.0\n",
|
|
"edge_5_9_13 = 0.0\n",
|
|
"edge_5_9_5 = 0.0\n",
|
|
"edge_5_9_8 = 0.0\n",
|
|
"inout_layer0_node0 = 0.0\n",
|
|
"inout_layer0_node1 = 0.0\n",
|
|
"inout_layer0_node10 = 0.0\n",
|
|
"inout_layer0_node11 = 0.0\n",
|
|
"inout_layer0_node12 = 0.0\n",
|
|
"inout_layer0_node13 = 0.0\n",
|
|
"inout_layer0_node14 = 0.0\n",
|
|
"inout_layer0_node15 = 0.0\n",
|
|
"inout_layer0_node2 = 0.0\n",
|
|
"inout_layer0_node3 = 0.0\n",
|
|
"inout_layer0_node4 = 0.0\n",
|
|
"inout_layer0_node5 = 0.0\n",
|
|
"inout_layer0_node6 = 0.0\n",
|
|
"inout_layer0_node7 = 0.0\n",
|
|
"inout_layer0_node8 = 0.0\n",
|
|
"inout_layer0_node9 = 0.0\n",
|
|
"inout_layer1_node0 = 0.0\n",
|
|
"inout_layer1_node1 = 0.0\n",
|
|
"inout_layer1_node10 = 0.0\n",
|
|
"inout_layer1_node11 = 0.0\n",
|
|
"inout_layer1_node12 = 0.0\n",
|
|
"inout_layer1_node13 = 0.0\n",
|
|
"inout_layer1_node14 = 0.0\n",
|
|
"inout_layer1_node15 = 0.0\n",
|
|
"inout_layer1_node2 = 0.0\n",
|
|
"inout_layer1_node3 = 0.0\n",
|
|
"inout_layer1_node4 = 0.0\n",
|
|
"inout_layer1_node5 = 0.0\n",
|
|
"inout_layer1_node6 = 0.0\n",
|
|
"inout_layer1_node7 = 1.0\n",
|
|
"inout_layer1_node8 = 0.0\n",
|
|
"inout_layer1_node9 = 0.0\n",
|
|
"inout_layer2_node0 = 0.0\n",
|
|
"inout_layer2_node1 = 1.0\n",
|
|
"inout_layer2_node10 = 0.0\n",
|
|
"inout_layer2_node11 = 0.0\n",
|
|
"inout_layer2_node12 = 0.0\n",
|
|
"inout_layer2_node13 = 0.0\n",
|
|
"inout_layer2_node14 = 0.0\n",
|
|
"inout_layer2_node15 = 0.0\n",
|
|
"inout_layer2_node2 = 1.0\n",
|
|
"inout_layer2_node3 = 0.0\n",
|
|
"inout_layer2_node4 = 0.0\n",
|
|
"inout_layer2_node5 = 0.0\n",
|
|
"inout_layer2_node6 = 0.0\n",
|
|
"inout_layer2_node7 = 0.0\n",
|
|
"inout_layer2_node8 = 0.0\n",
|
|
"inout_layer2_node9 = 0.0\n",
|
|
"inout_layer3_node0 = 0.0\n",
|
|
"inout_layer3_node1 = 0.0\n",
|
|
"inout_layer3_node10 = 0.0\n",
|
|
"inout_layer3_node11 = 0.0\n",
|
|
"inout_layer3_node12 = 0.0\n",
|
|
"inout_layer3_node13 = 0.0\n",
|
|
"inout_layer3_node14 = 0.0\n",
|
|
"inout_layer3_node15 = 0.0\n",
|
|
"inout_layer3_node2 = 1.0\n",
|
|
"inout_layer3_node3 = 0.0\n",
|
|
"inout_layer3_node4 = 0.0\n",
|
|
"inout_layer3_node5 = 0.0\n",
|
|
"inout_layer3_node6 = 0.0\n",
|
|
"inout_layer3_node7 = 0.0\n",
|
|
"inout_layer3_node8 = 0.0\n",
|
|
"inout_layer3_node9 = 0.0\n",
|
|
"inout_layer4_node0 = 0.0\n",
|
|
"inout_layer4_node1 = 0.0\n",
|
|
"inout_layer4_node10 = 0.0\n",
|
|
"inout_layer4_node11 = 0.0\n",
|
|
"inout_layer4_node12 = 0.0\n",
|
|
"inout_layer4_node13 = 0.0\n",
|
|
"inout_layer4_node14 = 0.0\n",
|
|
"inout_layer4_node15 = 0.0\n",
|
|
"inout_layer4_node2 = 0.0\n",
|
|
"inout_layer4_node3 = 0.0\n",
|
|
"inout_layer4_node4 = 0.0\n",
|
|
"inout_layer4_node5 = 0.0\n",
|
|
"inout_layer4_node6 = 0.0\n",
|
|
"inout_layer4_node7 = 0.0\n",
|
|
"inout_layer4_node8 = 0.0\n",
|
|
"inout_layer4_node9 = 0.0\n",
|
|
"inout_layer5_node0 = 0.0\n",
|
|
"inout_layer5_node1 = 0.0\n",
|
|
"inout_layer5_node10 = 0.0\n",
|
|
"inout_layer5_node11 = 0.0\n",
|
|
"inout_layer5_node12 = 0.0\n",
|
|
"inout_layer5_node13 = 0.0\n",
|
|
"inout_layer5_node14 = 0.0\n",
|
|
"inout_layer5_node15 = 0.0\n",
|
|
"inout_layer5_node2 = 0.0\n",
|
|
"inout_layer5_node3 = 0.0\n",
|
|
"inout_layer5_node4 = 0.0\n",
|
|
"inout_layer5_node5 = 0.0\n",
|
|
"inout_layer5_node6 = 0.0\n",
|
|
"inout_layer5_node7 = 0.0\n",
|
|
"inout_layer5_node8 = 0.0\n",
|
|
"inout_layer5_node9 = 0.0\n",
|
|
"inout_total_node0 = 0.0\n",
|
|
"inout_total_node1 = 1.0\n",
|
|
"inout_total_node10 = 0.0\n",
|
|
"inout_total_node11 = 0.0\n",
|
|
"inout_total_node12 = 0.0\n",
|
|
"inout_total_node13 = 0.0\n",
|
|
"inout_total_node14 = 0.0\n",
|
|
"inout_total_node15 = 0.0\n",
|
|
"inout_total_node2 = 2.0\n",
|
|
"inout_total_node3 = 0.0\n",
|
|
"inout_total_node4 = 0.0\n",
|
|
"inout_total_node5 = 0.0\n",
|
|
"inout_total_node6 = 0.0\n",
|
|
"inout_total_node7 = 1.0\n",
|
|
"inout_total_node8 = 0.0\n",
|
|
"inout_total_node9 = 0.0\n",
|
|
"inout_total_norm0 = 0.0\n",
|
|
"inout_total_norm1 = 0.0\n",
|
|
"inout_total_norm10 = 0.0\n",
|
|
"inout_total_norm11 = 0.0\n",
|
|
"inout_total_norm12 = 0.0\n",
|
|
"inout_total_norm13 = 0.0\n",
|
|
"inout_total_norm14 = 0.0\n",
|
|
"inout_total_norm15 = 0.0\n",
|
|
"inout_total_norm2 = 1.0\n",
|
|
"inout_total_norm3 = 0.0\n",
|
|
"inout_total_norm4 = 0.0\n",
|
|
"inout_total_norm5 = 0.0\n",
|
|
"inout_total_norm6 = 0.0\n",
|
|
"inout_total_norm7 = 0.0\n",
|
|
"inout_total_norm8 = 0.0\n",
|
|
"inout_total_norm9 = 0.0\n",
|
|
"is_pos_nonzero_0_0 = 0.0\n",
|
|
"is_pos_nonzero_0_1 = 0.0\n",
|
|
"is_pos_nonzero_0_10 = 0.0\n",
|
|
"is_pos_nonzero_0_11 = 0.0\n",
|
|
"is_pos_nonzero_0_12 = 0.0\n",
|
|
"is_pos_nonzero_0_13 = 0.0\n",
|
|
"is_pos_nonzero_0_14 = 0.0\n",
|
|
"is_pos_nonzero_0_15 = 0.0\n",
|
|
"is_pos_nonzero_0_2 = 0.0\n",
|
|
"is_pos_nonzero_0_3 = 0.0\n",
|
|
"is_pos_nonzero_0_4 = 0.0\n",
|
|
"is_pos_nonzero_0_5 = 0.0\n",
|
|
"is_pos_nonzero_0_6 = 1.0\n",
|
|
"is_pos_nonzero_0_7 = 0.0\n",
|
|
"is_pos_nonzero_0_8 = 0.0\n",
|
|
"is_pos_nonzero_0_9 = 0.0\n",
|
|
"is_pos_nonzero_1_0 = 0.0\n",
|
|
"is_pos_nonzero_1_1 = 0.0\n",
|
|
"is_pos_nonzero_1_10 = 0.0\n",
|
|
"is_pos_nonzero_1_11 = 0.0\n",
|
|
"is_pos_nonzero_1_12 = 0.0\n",
|
|
"is_pos_nonzero_1_13 = 0.0\n",
|
|
"is_pos_nonzero_1_14 = 0.0\n",
|
|
"is_pos_nonzero_1_15 = 0.0\n",
|
|
"is_pos_nonzero_1_2 = 0.0\n",
|
|
"is_pos_nonzero_1_3 = 1.0\n",
|
|
"is_pos_nonzero_1_4 = 0.0\n",
|
|
"is_pos_nonzero_1_5 = 0.0\n",
|
|
"is_pos_nonzero_1_6 = 0.0\n",
|
|
"is_pos_nonzero_1_7 = 1.0\n",
|
|
"is_pos_nonzero_1_8 = 0.0\n",
|
|
"is_pos_nonzero_1_9 = 0.0\n",
|
|
"is_pos_nonzero_2_0 = 1.0\n",
|
|
"is_pos_nonzero_2_1 = 1.0\n",
|
|
"is_pos_nonzero_2_10 = 0.0\n",
|
|
"is_pos_nonzero_2_11 = 0.0\n",
|
|
"is_pos_nonzero_2_12 = 0.0\n",
|
|
"is_pos_nonzero_2_13 = 0.0\n",
|
|
"is_pos_nonzero_2_14 = 0.0\n",
|
|
"is_pos_nonzero_2_15 = 0.0\n",
|
|
"is_pos_nonzero_2_2 = 1.0\n",
|
|
"is_pos_nonzero_2_3 = 0.0\n",
|
|
"is_pos_nonzero_2_4 = 0.0\n",
|
|
"is_pos_nonzero_2_5 = 0.0\n",
|
|
"is_pos_nonzero_2_6 = 0.0\n",
|
|
"is_pos_nonzero_2_7 = 0.0\n",
|
|
"is_pos_nonzero_2_8 = 0.0\n",
|
|
"is_pos_nonzero_2_9 = 0.0\n",
|
|
"is_pos_nonzero_3_0 = 0.0\n",
|
|
"is_pos_nonzero_3_1 = 0.0\n",
|
|
"is_pos_nonzero_3_10 = 0.0\n",
|
|
"is_pos_nonzero_3_11 = 0.0\n",
|
|
"is_pos_nonzero_3_12 = 0.0\n",
|
|
"is_pos_nonzero_3_13 = 0.0\n",
|
|
"is_pos_nonzero_3_14 = 0.0\n",
|
|
"is_pos_nonzero_3_15 = 0.0\n",
|
|
"is_pos_nonzero_3_2 = 1.0\n",
|
|
"is_pos_nonzero_3_3 = 1.0\n",
|
|
"is_pos_nonzero_3_4 = 0.0\n",
|
|
"is_pos_nonzero_3_5 = 0.0\n",
|
|
"is_pos_nonzero_3_6 = 0.0\n",
|
|
"is_pos_nonzero_3_7 = 0.0\n",
|
|
"is_pos_nonzero_3_8 = 0.0\n",
|
|
"is_pos_nonzero_3_9 = 0.0\n",
|
|
"is_pos_nonzero_4_0 = 0.0\n",
|
|
"is_pos_nonzero_4_1 = 0.0\n",
|
|
"is_pos_nonzero_4_10 = 0.0\n",
|
|
"is_pos_nonzero_4_11 = 0.0\n",
|
|
"is_pos_nonzero_4_12 = 0.0\n",
|
|
"is_pos_nonzero_4_13 = 0.0\n",
|
|
"is_pos_nonzero_4_14 = 0.0\n",
|
|
"is_pos_nonzero_4_15 = 0.0\n",
|
|
"is_pos_nonzero_4_2 = 0.0\n",
|
|
"is_pos_nonzero_4_3 = 0.0\n",
|
|
"is_pos_nonzero_4_4 = 1.0\n",
|
|
"is_pos_nonzero_4_5 = 0.0\n",
|
|
"is_pos_nonzero_4_6 = 0.0\n",
|
|
"is_pos_nonzero_4_7 = 0.0\n",
|
|
"is_pos_nonzero_4_8 = 0.0\n",
|
|
"is_pos_nonzero_4_9 = 0.0\n",
|
|
"is_pos_nonzero_5_0 = 1.0\n",
|
|
"is_pos_nonzero_5_1 = 0.0\n",
|
|
"is_pos_nonzero_5_10 = 0.0\n",
|
|
"is_pos_nonzero_5_11 = 0.0\n",
|
|
"is_pos_nonzero_5_12 = 0.0\n",
|
|
"is_pos_nonzero_5_13 = 0.0\n",
|
|
"is_pos_nonzero_5_14 = 0.0\n",
|
|
"is_pos_nonzero_5_15 = 0.0\n",
|
|
"is_pos_nonzero_5_2 = 0.0\n",
|
|
"is_pos_nonzero_5_3 = 0.0\n",
|
|
"is_pos_nonzero_5_4 = 0.0\n",
|
|
"is_pos_nonzero_5_5 = 0.0\n",
|
|
"is_pos_nonzero_5_6 = 0.0\n",
|
|
"is_pos_nonzero_5_7 = 0.0\n",
|
|
"is_pos_nonzero_5_8 = 0.0\n",
|
|
"is_pos_nonzero_5_9 = 0.0\n",
|
|
"max_pos_dst = 3.0\n",
|
|
"path_add_individual0_0 = 0.0\n",
|
|
"path_add_individual0_1 = 0.0\n",
|
|
"path_add_individual0_10 = 0.0\n",
|
|
"path_add_individual0_11 = 0.0\n",
|
|
"path_add_individual0_12 = 0.0\n",
|
|
"path_add_individual0_13 = 0.0\n",
|
|
"path_add_individual0_14 = 0.0\n",
|
|
"path_add_individual0_15 = 0.0\n",
|
|
"path_add_individual0_2 = 0.0\n",
|
|
"path_add_individual0_3 = 0.0\n",
|
|
"path_add_individual0_4 = 0.0\n",
|
|
"path_add_individual0_5 = 0.0\n",
|
|
"path_add_individual0_6 = 0.0\n",
|
|
"path_add_individual0_7 = 0.0\n",
|
|
"path_add_individual0_8 = 0.0\n",
|
|
"path_add_individual0_9 = 0.0\n",
|
|
"path_add_individual1_0 = 0.0\n",
|
|
"path_add_individual1_1 = 0.0\n",
|
|
"path_add_individual1_10 = 0.0\n",
|
|
"path_add_individual1_11 = 0.0\n",
|
|
"path_add_individual1_12 = 0.0\n",
|
|
"path_add_individual1_13 = 0.0\n",
|
|
"path_add_individual1_14 = 0.0\n",
|
|
"path_add_individual1_15 = 0.0\n",
|
|
"path_add_individual1_2 = 0.0\n",
|
|
"path_add_individual1_3 = 0.0\n",
|
|
"path_add_individual1_4 = 0.0\n",
|
|
"path_add_individual1_5 = 0.0\n",
|
|
"path_add_individual1_6 = 0.0\n",
|
|
"path_add_individual1_7 = 0.0\n",
|
|
"path_add_individual1_8 = 0.0\n",
|
|
"path_add_individual1_9 = 0.0\n",
|
|
"path_add_individual2_0 = 0.0\n",
|
|
"path_add_individual2_1 = 0.0\n",
|
|
"path_add_individual2_10 = 0.0\n",
|
|
"path_add_individual2_11 = 0.0\n",
|
|
"path_add_individual2_12 = 0.0\n",
|
|
"path_add_individual2_13 = 0.0\n",
|
|
"path_add_individual2_14 = 0.0\n",
|
|
"path_add_individual2_15 = 0.0\n",
|
|
"path_add_individual2_2 = 0.0\n",
|
|
"path_add_individual2_3 = 0.0\n",
|
|
"path_add_individual2_4 = 0.0\n",
|
|
"path_add_individual2_5 = 0.0\n",
|
|
"path_add_individual2_6 = 0.0\n",
|
|
"path_add_individual2_7 = 0.0\n",
|
|
"path_add_individual2_8 = 0.0\n",
|
|
"path_add_individual2_9 = 0.0\n",
|
|
"path_add_individual3_0 = 0.0\n",
|
|
"path_add_individual3_1 = 0.0\n",
|
|
"path_add_individual3_10 = 0.0\n",
|
|
"path_add_individual3_11 = 0.0\n",
|
|
"path_add_individual3_12 = 0.0\n",
|
|
"path_add_individual3_13 = 0.0\n",
|
|
"path_add_individual3_14 = 0.0\n",
|
|
"path_add_individual3_15 = 0.0\n",
|
|
"path_add_individual3_2 = 0.0\n",
|
|
"path_add_individual3_3 = 0.0\n",
|
|
"path_add_individual3_4 = 0.0\n",
|
|
"path_add_individual3_5 = 0.0\n",
|
|
"path_add_individual3_6 = 0.0\n",
|
|
"path_add_individual3_7 = 0.0\n",
|
|
"path_add_individual3_8 = 0.0\n",
|
|
"path_add_individual3_9 = 0.0\n",
|
|
"path_add_individual4_0 = 0.0\n",
|
|
"path_add_individual4_1 = 0.0\n",
|
|
"path_add_individual4_10 = 0.0\n",
|
|
"path_add_individual4_11 = 0.0\n",
|
|
"path_add_individual4_12 = 0.0\n",
|
|
"path_add_individual4_13 = 0.0\n",
|
|
"path_add_individual4_14 = 0.0\n",
|
|
"path_add_individual4_15 = 0.0\n",
|
|
"path_add_individual4_2 = 0.0\n",
|
|
"path_add_individual4_3 = 0.0\n",
|
|
"path_add_individual4_4 = 0.0\n",
|
|
"path_add_individual4_5 = 0.0\n",
|
|
"path_add_individual4_6 = 0.0\n",
|
|
"path_add_individual4_7 = 0.0\n",
|
|
"path_add_individual4_8 = 0.0\n",
|
|
"path_add_individual4_9 = 0.0\n",
|
|
"path_add_individual5_0 = 0.0\n",
|
|
"path_add_individual5_1 = 0.0\n",
|
|
"path_add_individual5_10 = 0.0\n",
|
|
"path_add_individual5_11 = 0.0\n",
|
|
"path_add_individual5_12 = 0.0\n",
|
|
"path_add_individual5_13 = 0.0\n",
|
|
"path_add_individual5_14 = 0.0\n",
|
|
"path_add_individual5_15 = 0.0\n",
|
|
"path_add_individual5_2 = 0.0\n",
|
|
"path_add_individual5_3 = 0.0\n",
|
|
"path_add_individual5_4 = 0.0\n",
|
|
"path_add_individual5_5 = 0.0\n",
|
|
"path_add_individual5_6 = 0.0\n",
|
|
"path_add_individual5_7 = 0.0\n",
|
|
"path_add_individual5_8 = 0.0\n",
|
|
"path_add_individual5_9 = 0.0\n",
|
|
"path_add_individual_2_0_0 = 0.0\n",
|
|
"path_add_individual_2_0_1 = 0.0\n",
|
|
"path_add_individual_2_0_10 = 0.0\n",
|
|
"path_add_individual_2_0_11 = 0.0\n",
|
|
"path_add_individual_2_0_12 = 0.0\n",
|
|
"path_add_individual_2_0_13 = 0.0\n",
|
|
"path_add_individual_2_0_14 = 0.0\n",
|
|
"path_add_individual_2_0_15 = 0.0\n",
|
|
"path_add_individual_2_0_2 = 0.0\n",
|
|
"path_add_individual_2_0_3 = 0.0\n",
|
|
"path_add_individual_2_0_4 = 0.0\n",
|
|
"path_add_individual_2_0_5 = 0.0\n",
|
|
"path_add_individual_2_0_6 = 0.0\n",
|
|
"path_add_individual_2_0_7 = 0.0\n",
|
|
"path_add_individual_2_0_8 = 0.0\n",
|
|
"path_add_individual_2_0_9 = 0.0\n",
|
|
"path_add_individual_2_1_0 = 0.0\n",
|
|
"path_add_individual_2_1_1 = 0.0\n",
|
|
"path_add_individual_2_1_10 = 0.0\n",
|
|
"path_add_individual_2_1_11 = 0.0\n",
|
|
"path_add_individual_2_1_12 = 0.0\n",
|
|
"path_add_individual_2_1_13 = 0.0\n",
|
|
"path_add_individual_2_1_14 = 0.0\n",
|
|
"path_add_individual_2_1_15 = 0.0\n",
|
|
"path_add_individual_2_1_2 = 0.0\n",
|
|
"path_add_individual_2_1_3 = 0.0\n",
|
|
"path_add_individual_2_1_4 = 0.0\n",
|
|
"path_add_individual_2_1_5 = 0.0\n",
|
|
"path_add_individual_2_1_6 = 0.0\n",
|
|
"path_add_individual_2_1_7 = 0.0\n",
|
|
"path_add_individual_2_1_8 = 0.0\n",
|
|
"path_add_individual_2_1_9 = 0.0\n",
|
|
"path_add_individual_2_2_0 = 0.0\n",
|
|
"path_add_individual_2_2_1 = 0.0\n",
|
|
"path_add_individual_2_2_10 = 0.0\n",
|
|
"path_add_individual_2_2_11 = 0.0\n",
|
|
"path_add_individual_2_2_12 = 0.0\n",
|
|
"path_add_individual_2_2_13 = 0.0\n",
|
|
"path_add_individual_2_2_14 = 0.0\n",
|
|
"path_add_individual_2_2_15 = 0.0\n",
|
|
"path_add_individual_2_2_2 = 1.0\n",
|
|
"path_add_individual_2_2_3 = 0.0\n",
|
|
"path_add_individual_2_2_4 = 0.0\n",
|
|
"path_add_individual_2_2_5 = 0.0\n",
|
|
"path_add_individual_2_2_6 = 0.0\n",
|
|
"path_add_individual_2_2_7 = 0.0\n",
|
|
"path_add_individual_2_2_8 = 0.0\n",
|
|
"path_add_individual_2_2_9 = 0.0\n",
|
|
"path_add_individual_2_3_0 = 0.0\n",
|
|
"path_add_individual_2_3_1 = 0.0\n",
|
|
"path_add_individual_2_3_10 = 0.0\n",
|
|
"path_add_individual_2_3_11 = 0.0\n",
|
|
"path_add_individual_2_3_12 = 0.0\n",
|
|
"path_add_individual_2_3_13 = 0.0\n",
|
|
"path_add_individual_2_3_14 = 0.0\n",
|
|
"path_add_individual_2_3_15 = 0.0\n",
|
|
"path_add_individual_2_3_2 = 1.0\n",
|
|
"path_add_individual_2_3_3 = 0.0\n",
|
|
"path_add_individual_2_3_4 = 0.0\n",
|
|
"path_add_individual_2_3_5 = 0.0\n",
|
|
"path_add_individual_2_3_6 = 0.0\n",
|
|
"path_add_individual_2_3_7 = 0.0\n",
|
|
"path_add_individual_2_3_8 = 0.0\n",
|
|
"path_add_individual_2_3_9 = 0.0\n",
|
|
"path_add_individual_2_4_0 = 0.0\n",
|
|
"path_add_individual_2_4_1 = 0.0\n",
|
|
"path_add_individual_2_4_10 = 0.0\n",
|
|
"path_add_individual_2_4_11 = 0.0\n",
|
|
"path_add_individual_2_4_12 = 0.0\n",
|
|
"path_add_individual_2_4_13 = 0.0\n",
|
|
"path_add_individual_2_4_14 = 0.0\n",
|
|
"path_add_individual_2_4_15 = 0.0\n",
|
|
"path_add_individual_2_4_2 = 0.0\n",
|
|
"path_add_individual_2_4_3 = 0.0\n",
|
|
"path_add_individual_2_4_4 = 0.0\n",
|
|
"path_add_individual_2_4_5 = 0.0\n",
|
|
"path_add_individual_2_4_6 = 0.0\n",
|
|
"path_add_individual_2_4_7 = 0.0\n",
|
|
"path_add_individual_2_4_8 = 0.0\n",
|
|
"path_add_individual_2_4_9 = 0.0\n",
|
|
"path_add_individual_2_5_0 = 0.0\n",
|
|
"path_add_individual_2_5_1 = 0.0\n",
|
|
"path_add_individual_2_5_10 = 0.0\n",
|
|
"path_add_individual_2_5_11 = 0.0\n",
|
|
"path_add_individual_2_5_12 = 0.0\n",
|
|
"path_add_individual_2_5_13 = 0.0\n",
|
|
"path_add_individual_2_5_14 = 0.0\n",
|
|
"path_add_individual_2_5_15 = 0.0\n",
|
|
"path_add_individual_2_5_2 = 0.0\n",
|
|
"path_add_individual_2_5_3 = 0.0\n",
|
|
"path_add_individual_2_5_4 = 0.0\n",
|
|
"path_add_individual_2_5_5 = 0.0\n",
|
|
"path_add_individual_2_5_6 = 0.0\n",
|
|
"path_add_individual_2_5_7 = 0.0\n",
|
|
"path_add_individual_2_5_8 = 0.0\n",
|
|
"path_add_individual_2_5_9 = 0.0\n",
|
|
"path_add_individual_3_0_0 = 0.0\n",
|
|
"path_add_individual_3_0_1 = 0.0\n",
|
|
"path_add_individual_3_0_10 = 0.0\n",
|
|
"path_add_individual_3_0_11 = 0.0\n",
|
|
"path_add_individual_3_0_12 = 0.0\n",
|
|
"path_add_individual_3_0_13 = 0.0\n",
|
|
"path_add_individual_3_0_14 = 0.0\n",
|
|
"path_add_individual_3_0_15 = 0.0\n",
|
|
"path_add_individual_3_0_2 = 0.0\n",
|
|
"path_add_individual_3_0_3 = 0.0\n",
|
|
"path_add_individual_3_0_4 = 0.0\n",
|
|
"path_add_individual_3_0_5 = 0.0\n",
|
|
"path_add_individual_3_0_6 = 0.0\n",
|
|
"path_add_individual_3_0_7 = 0.0\n",
|
|
"path_add_individual_3_0_8 = 0.0\n",
|
|
"path_add_individual_3_0_9 = 0.0\n",
|
|
"path_add_individual_3_1_0 = 0.0\n",
|
|
"path_add_individual_3_1_1 = 0.0\n",
|
|
"path_add_individual_3_1_10 = 0.0\n",
|
|
"path_add_individual_3_1_11 = 0.0\n",
|
|
"path_add_individual_3_1_12 = 0.0\n",
|
|
"path_add_individual_3_1_13 = 0.0\n",
|
|
"path_add_individual_3_1_14 = 0.0\n",
|
|
"path_add_individual_3_1_15 = 0.0\n",
|
|
"path_add_individual_3_1_2 = 0.0\n",
|
|
"path_add_individual_3_1_3 = 0.0\n",
|
|
"path_add_individual_3_1_4 = 0.0\n",
|
|
"path_add_individual_3_1_5 = 0.0\n",
|
|
"path_add_individual_3_1_6 = 0.0\n",
|
|
"path_add_individual_3_1_7 = 0.0\n",
|
|
"path_add_individual_3_1_8 = 0.0\n",
|
|
"path_add_individual_3_1_9 = 0.0\n",
|
|
"path_add_individual_3_2_0 = 0.0\n",
|
|
"path_add_individual_3_2_1 = 0.0\n",
|
|
"path_add_individual_3_2_10 = 0.0\n",
|
|
"path_add_individual_3_2_11 = 0.0\n",
|
|
"path_add_individual_3_2_12 = 0.0\n",
|
|
"path_add_individual_3_2_13 = 0.0\n",
|
|
"path_add_individual_3_2_14 = 0.0\n",
|
|
"path_add_individual_3_2_15 = 0.0\n",
|
|
"path_add_individual_3_2_2 = 0.0\n",
|
|
"path_add_individual_3_2_3 = 0.0\n",
|
|
"path_add_individual_3_2_4 = 0.0\n",
|
|
"path_add_individual_3_2_5 = 0.0\n",
|
|
"path_add_individual_3_2_6 = 0.0\n",
|
|
"path_add_individual_3_2_7 = 0.0\n",
|
|
"path_add_individual_3_2_8 = 0.0\n",
|
|
"path_add_individual_3_2_9 = 0.0\n",
|
|
"path_add_individual_3_3_0 = 0.0\n",
|
|
"path_add_individual_3_3_1 = 0.0\n",
|
|
"path_add_individual_3_3_10 = 0.0\n",
|
|
"path_add_individual_3_3_11 = 0.0\n",
|
|
"path_add_individual_3_3_12 = 0.0\n",
|
|
"path_add_individual_3_3_13 = 0.0\n",
|
|
"path_add_individual_3_3_14 = 0.0\n",
|
|
"path_add_individual_3_3_15 = 0.0\n",
|
|
"path_add_individual_3_3_2 = 1.0\n",
|
|
"path_add_individual_3_3_3 = 0.0\n",
|
|
"path_add_individual_3_3_4 = 0.0\n",
|
|
"path_add_individual_3_3_5 = 0.0\n",
|
|
"path_add_individual_3_3_6 = 0.0\n",
|
|
"path_add_individual_3_3_7 = 0.0\n",
|
|
"path_add_individual_3_3_8 = 0.0\n",
|
|
"path_add_individual_3_3_9 = 0.0\n",
|
|
"path_add_individual_3_4_0 = 0.0\n",
|
|
"path_add_individual_3_4_1 = 0.0\n",
|
|
"path_add_individual_3_4_10 = 0.0\n",
|
|
"path_add_individual_3_4_11 = 0.0\n",
|
|
"path_add_individual_3_4_12 = 0.0\n",
|
|
"path_add_individual_3_4_13 = 0.0\n",
|
|
"path_add_individual_3_4_14 = 0.0\n",
|
|
"path_add_individual_3_4_15 = 0.0\n",
|
|
"path_add_individual_3_4_2 = 0.0\n",
|
|
"path_add_individual_3_4_3 = 0.0\n",
|
|
"path_add_individual_3_4_4 = 0.0\n",
|
|
"path_add_individual_3_4_5 = 0.0\n",
|
|
"path_add_individual_3_4_6 = 0.0\n",
|
|
"path_add_individual_3_4_7 = 0.0\n",
|
|
"path_add_individual_3_4_8 = 0.0\n",
|
|
"path_add_individual_3_4_9 = 0.0\n",
|
|
"path_add_individual_3_5_0 = 0.0\n",
|
|
"path_add_individual_3_5_1 = 0.0\n",
|
|
"path_add_individual_3_5_10 = 0.0\n",
|
|
"path_add_individual_3_5_11 = 0.0\n",
|
|
"path_add_individual_3_5_12 = 0.0\n",
|
|
"path_add_individual_3_5_13 = 0.0\n",
|
|
"path_add_individual_3_5_14 = 0.0\n",
|
|
"path_add_individual_3_5_15 = 0.0\n",
|
|
"path_add_individual_3_5_2 = 0.0\n",
|
|
"path_add_individual_3_5_3 = 0.0\n",
|
|
"path_add_individual_3_5_4 = 0.0\n",
|
|
"path_add_individual_3_5_5 = 0.0\n",
|
|
"path_add_individual_3_5_6 = 0.0\n",
|
|
"path_add_individual_3_5_7 = 0.0\n",
|
|
"path_add_individual_3_5_8 = 0.0\n",
|
|
"path_add_individual_3_5_9 = 0.0\n",
|
|
"path_add_total_0 = 0.0\n",
|
|
"path_add_total_1 = 0.0\n",
|
|
"path_add_total_2 = 0.0\n",
|
|
"path_add_total_3 = 1.0\n",
|
|
"path_add_total_4 = 0.0\n",
|
|
"path_add_total_5 = 0.0\n",
|
|
"pos_0_0 = 0.0\n",
|
|
"pos_0_1 = 0.0\n",
|
|
"pos_0_10 = 0.0\n",
|
|
"pos_0_11 = 0.0\n",
|
|
"pos_0_12 = 0.0\n",
|
|
"pos_0_13 = 0.0\n",
|
|
"pos_0_14 = 0.0\n",
|
|
"pos_0_15 = 0.0\n",
|
|
"pos_0_2 = 0.0\n",
|
|
"pos_0_3 = 0.0\n",
|
|
"pos_0_4 = 0.0\n",
|
|
"pos_0_5 = 0.0\n",
|
|
"pos_0_6 = 1.0\n",
|
|
"pos_0_7 = 0.0\n",
|
|
"pos_0_8 = 0.0\n",
|
|
"pos_0_9 = 0.0\n",
|
|
"pos_1_0 = 0.0\n",
|
|
"pos_1_1 = 0.0\n",
|
|
"pos_1_10 = 0.0\n",
|
|
"pos_1_11 = 0.0\n",
|
|
"pos_1_12 = 0.0\n",
|
|
"pos_1_13 = 0.0\n",
|
|
"pos_1_14 = 0.0\n",
|
|
"pos_1_15 = 0.0\n",
|
|
"pos_1_2 = 0.0\n",
|
|
"pos_1_3 = 2.0\n",
|
|
"pos_1_4 = 0.0\n",
|
|
"pos_1_5 = 0.0\n",
|
|
"pos_1_6 = 0.0\n",
|
|
"pos_1_7 = 1.0\n",
|
|
"pos_1_8 = 0.0\n",
|
|
"pos_1_9 = 0.0\n",
|
|
"pos_2_0 = 3.0\n",
|
|
"pos_2_1 = 2.0\n",
|
|
"pos_2_10 = 0.0\n",
|
|
"pos_2_11 = 0.0\n",
|
|
"pos_2_12 = 0.0\n",
|
|
"pos_2_13 = 0.0\n",
|
|
"pos_2_14 = 0.0\n",
|
|
"pos_2_15 = 0.0\n",
|
|
"pos_2_2 = 1.0\n",
|
|
"pos_2_3 = 0.0\n",
|
|
"pos_2_4 = 0.0\n",
|
|
"pos_2_5 = 0.0\n",
|
|
"pos_2_6 = 0.0\n",
|
|
"pos_2_7 = 0.0\n",
|
|
"pos_2_8 = 0.0\n",
|
|
"pos_2_9 = 0.0\n",
|
|
"pos_3_0 = 0.0\n",
|
|
"pos_3_1 = 0.0\n",
|
|
"pos_3_10 = 0.0\n",
|
|
"pos_3_11 = 0.0\n",
|
|
"pos_3_12 = 0.0\n",
|
|
"pos_3_13 = 0.0\n",
|
|
"pos_3_14 = 0.0\n",
|
|
"pos_3_15 = 0.0\n",
|
|
"pos_3_2 = 1.0\n",
|
|
"pos_3_3 = 2.0\n",
|
|
"pos_3_4 = 0.0\n",
|
|
"pos_3_5 = 0.0\n",
|
|
"pos_3_6 = 0.0\n",
|
|
"pos_3_7 = 0.0\n",
|
|
"pos_3_8 = 0.0\n",
|
|
"pos_3_9 = 0.0\n",
|
|
"pos_4_0 = 0.0\n",
|
|
"pos_4_1 = 0.0\n",
|
|
"pos_4_10 = 0.0\n",
|
|
"pos_4_11 = 0.0\n",
|
|
"pos_4_12 = 0.0\n",
|
|
"pos_4_13 = 0.0\n",
|
|
"pos_4_14 = 0.0\n",
|
|
"pos_4_15 = 0.0\n",
|
|
"pos_4_2 = 0.0\n",
|
|
"pos_4_3 = 0.0\n",
|
|
"pos_4_4 = 1.0\n",
|
|
"pos_4_5 = 0.0\n",
|
|
"pos_4_6 = 0.0\n",
|
|
"pos_4_7 = 0.0\n",
|
|
"pos_4_8 = 0.0\n",
|
|
"pos_4_9 = 0.0\n",
|
|
"pos_5_0 = 1.0\n",
|
|
"pos_5_1 = 0.0\n",
|
|
"pos_5_10 = 0.0\n",
|
|
"pos_5_11 = 0.0\n",
|
|
"pos_5_12 = 0.0\n",
|
|
"pos_5_13 = 0.0\n",
|
|
"pos_5_14 = 0.0\n",
|
|
"pos_5_15 = 0.0\n",
|
|
"pos_5_2 = 0.0\n",
|
|
"pos_5_3 = 0.0\n",
|
|
"pos_5_4 = 0.0\n",
|
|
"pos_5_5 = 0.0\n",
|
|
"pos_5_6 = 0.0\n",
|
|
"pos_5_7 = 0.0\n",
|
|
"pos_5_8 = 0.0\n",
|
|
"pos_5_9 = 0.0\n",
|
|
"pos_dst_0 = 1.0\n",
|
|
"pos_dst_1 = 2.0\n",
|
|
"pos_dst_2 = 3.0\n",
|
|
"pos_dst_3 = 2.0\n",
|
|
"pos_dst_4 = 1.0\n",
|
|
"pos_dst_5 = 1.0\n",
|
|
"pos_src_0 = 0.0\n",
|
|
"pos_src_1 = 0.0\n",
|
|
"pos_src_2 = 0.0\n",
|
|
"pos_src_3 = 0.0\n",
|
|
"pos_src_4 = 0.0\n",
|
|
"pos_src_5 = 0.0\n",
|
|
"src_0_0 = 0.0\n",
|
|
"src_0_1 = 0.0\n",
|
|
"src_0_10 = 1.0\n",
|
|
"src_0_11 = 0.0\n",
|
|
"src_0_12 = 0.0\n",
|
|
"src_0_13 = 0.0\n",
|
|
"src_0_14 = 0.0\n",
|
|
"src_0_15 = 0.0\n",
|
|
"src_0_2 = 0.0\n",
|
|
"src_0_3 = 0.0\n",
|
|
"src_0_4 = 0.0\n",
|
|
"src_0_5 = 0.0\n",
|
|
"src_0_6 = 0.0\n",
|
|
"src_0_7 = 0.0\n",
|
|
"src_0_8 = 0.0\n",
|
|
"src_0_9 = 0.0\n",
|
|
"src_1_0 = 0.0\n",
|
|
"src_1_1 = 0.0\n",
|
|
"src_1_10 = 0.0\n",
|
|
"src_1_11 = 0.0\n",
|
|
"src_1_12 = 0.0\n",
|
|
"src_1_13 = 0.0\n",
|
|
"src_1_14 = 0.0\n",
|
|
"src_1_15 = 0.0\n",
|
|
"src_1_2 = 0.0\n",
|
|
"src_1_3 = 0.0\n",
|
|
"src_1_4 = 0.0\n",
|
|
"src_1_5 = 0.0\n",
|
|
"src_1_6 = 1.0\n",
|
|
"src_1_7 = 0.0\n",
|
|
"src_1_8 = 0.0\n",
|
|
"src_1_9 = 0.0\n",
|
|
"src_2_0 = 0.0\n",
|
|
"src_2_1 = 0.0\n",
|
|
"src_2_10 = 0.0\n",
|
|
"src_2_11 = 0.0\n",
|
|
"src_2_12 = 0.0\n",
|
|
"src_2_13 = 0.0\n",
|
|
"src_2_14 = 0.0\n",
|
|
"src_2_15 = 0.0\n",
|
|
"src_2_2 = 0.0\n",
|
|
"src_2_3 = 1.0\n",
|
|
"src_2_4 = 0.0\n",
|
|
"src_2_5 = 0.0\n",
|
|
"src_2_6 = 0.0\n",
|
|
"src_2_7 = 0.0\n",
|
|
"src_2_8 = 0.0\n",
|
|
"src_2_9 = 0.0\n",
|
|
"src_3_0 = 0.0\n",
|
|
"src_3_1 = 0.0\n",
|
|
"src_3_10 = 0.0\n",
|
|
"src_3_11 = 0.0\n",
|
|
"src_3_12 = 0.0\n",
|
|
"src_3_13 = 0.0\n",
|
|
"src_3_14 = 0.0\n",
|
|
"src_3_15 = 0.0\n",
|
|
"src_3_2 = 0.0\n",
|
|
"src_3_3 = 0.0\n",
|
|
"src_3_4 = 0.0\n",
|
|
"src_3_5 = 0.0\n",
|
|
"src_3_6 = 1.0\n",
|
|
"src_3_7 = 0.0\n",
|
|
"src_3_8 = 0.0\n",
|
|
"src_3_9 = 0.0\n",
|
|
"src_4_0 = 1.0\n",
|
|
"src_4_1 = 0.0\n",
|
|
"src_4_10 = 0.0\n",
|
|
"src_4_11 = 0.0\n",
|
|
"src_4_12 = 0.0\n",
|
|
"src_4_13 = 0.0\n",
|
|
"src_4_14 = 0.0\n",
|
|
"src_4_15 = 0.0\n",
|
|
"src_4_2 = 0.0\n",
|
|
"src_4_3 = 0.0\n",
|
|
"src_4_4 = 0.0\n",
|
|
"src_4_5 = 0.0\n",
|
|
"src_4_6 = 0.0\n",
|
|
"src_4_7 = 0.0\n",
|
|
"src_4_8 = 0.0\n",
|
|
"src_4_9 = 0.0\n",
|
|
"src_5_0 = 0.0\n",
|
|
"src_5_1 = 0.0\n",
|
|
"src_5_10 = 0.0\n",
|
|
"src_5_11 = 0.0\n",
|
|
"src_5_12 = 0.0\n",
|
|
"src_5_13 = 0.0\n",
|
|
"src_5_14 = 0.0\n",
|
|
"src_5_15 = 0.0\n",
|
|
"src_5_2 = 0.0\n",
|
|
"src_5_3 = 0.0\n",
|
|
"src_5_4 = 1.0\n",
|
|
"src_5_5 = 0.0\n",
|
|
"src_5_6 = 0.0\n",
|
|
"src_5_7 = 0.0\n",
|
|
"src_5_8 = 0.0\n",
|
|
"src_5_9 = 0.0\n",
|
|
"src_dst_node0 = 1.0\n",
|
|
"src_dst_node1 = 0.0\n",
|
|
"src_dst_node10 = 1.0\n",
|
|
"src_dst_node11 = 0.0\n",
|
|
"src_dst_node12 = 0.0\n",
|
|
"src_dst_node13 = 0.0\n",
|
|
"src_dst_node14 = 0.0\n",
|
|
"src_dst_node15 = 0.0\n",
|
|
"src_dst_node2 = 0.0\n",
|
|
"src_dst_node3 = 1.0\n",
|
|
"src_dst_node4 = 1.0\n",
|
|
"src_dst_node5 = 0.0\n",
|
|
"src_dst_node6 = 1.0\n",
|
|
"src_dst_node7 = 0.0\n",
|
|
"src_dst_node8 = 0.0\n",
|
|
"src_dst_node9 = 0.0\n",
|
|
"[(4, 0, 4), (2, 1, 0), (2, 2, 1), (3, 2, 3), (2, 3, 2), (5, 4, 0), (3, 6, 2), (1, 6, 7), (1, 7, 3), (0, 10, 6)]\n",
|
|
"[[(0, 0), (0, -0.5)], [(0, -0.5), (0.2, -0.5)], [(0.2, -0.5), (0.2, 1)]]\n",
|
|
"Edge: (0, 4), Start: (0, 0), End: (0, -0.5)\n",
|
|
"Edge: (0, 4), Start: (0, -0.5), End: (0.2, -0.5)\n",
|
|
"Edge: (0, 4), Start: (0.2, -0.5), End: (0.2, 1)\n",
|
|
"[[(0, 0), (-0.5, 0)], [(-0.5, 0), (-0.5, 0.2)], [(-0.5, 0.2), (1, 0.2)]]\n",
|
|
"Edge: (0, 1), Start: (0, 0), End: (-0.5, 0)\n",
|
|
"Edge: (0, 1), Start: (-0.5, 0), End: (-0.5, 0.2)\n",
|
|
"Edge: (0, 1), Start: (-0.5, 0.2), End: (1, 0.2)\n",
|
|
"[[(1, 0.2), (2.5, 0.2)], [(2.5, 0.2), (2.5, 0)], [(2.5, 0), (2, 0)]]\n",
|
|
"Edge: (1, 2), Start: (1, 0.2), End: (2.5, 0.2)\n",
|
|
"Edge: (1, 2), Start: (2.5, 0.2), End: (2.5, 0)\n",
|
|
"Edge: (1, 2), Start: (2.5, 0), End: (2, 0)\n",
|
|
"[[(2, 0), (2, -0.5)], [(2, -0.5), (2.2, -0.5)], [(2.2, -0.5), (2.2, 1)]]\n",
|
|
"Edge: (2, 6), Start: (2, 0), End: (2, -0.5)\n",
|
|
"Edge: (2, 6), Start: (2, -0.5), End: (2.2, -0.5)\n",
|
|
"Edge: (2, 6), Start: (2.2, -0.5), End: (2.2, 1)\n",
|
|
"[[(2, 0), (3.5, 0)], [(3.5, 0), (3.5, 0.2)], [(3.5, 0.2), (3, 0.2)]]\n",
|
|
"Edge: (2, 3), Start: (2, 0), End: (3.5, 0)\n",
|
|
"Edge: (2, 3), Start: (3.5, 0), End: (3.5, 0.2)\n",
|
|
"Edge: (2, 3), Start: (3.5, 0.2), End: (3, 0.2)\n",
|
|
"[[(3, 0.2), (3, -0.30000000000000004)], [(3, -0.30000000000000004), (3.2, -0.30000000000000004)], [(3.2, -0.30000000000000004), (3.2, 1.2)]]\n",
|
|
"Edge: (3, 7), Start: (3, 0.2), End: (3, -0.30000000000000004)\n",
|
|
"Edge: (3, 7), Start: (3, -0.30000000000000004), End: (3.2, -0.30000000000000004)\n",
|
|
"Edge: (3, 7), Start: (3.2, -0.30000000000000004), End: (3.2, 1.2)\n",
|
|
"[[(2.2, 1), (2.2, 2.5)], [(2.2, 2.5), (2, 2.5)], [(2, 2.5), (2, 2)]]\n",
|
|
"Edge: (6, 10), Start: (2.2, 1), End: (2.2, 2.5)\n",
|
|
"Edge: (6, 10), Start: (2.2, 2.5), End: (2, 2.5)\n",
|
|
"Edge: (6, 10), Start: (2, 2.5), End: (2, 2)\n",
|
|
"[[(2.2, 1), (3.7, 1)], [(3.7, 1), (3.7, 1.2)], [(3.7, 1.2), (3.2, 1.2)]]\n",
|
|
"Edge: (6, 7), Start: (2.2, 1), End: (3.7, 1)\n",
|
|
"Edge: (6, 7), Start: (3.7, 1), End: (3.7, 1.2)\n",
|
|
"Edge: (6, 7), Start: (3.7, 1.2), End: (3.2, 1.2)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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",
|
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"text/plain": [
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"<Figure size 800x800 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x1000 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
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"<Figure size 600x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Plot saved to graph_plot.pkl\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
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"text/plain": [
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"<Figure size 800x800 with 1 Axes>"
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]
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
|
|
],
|
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"source": [
|
|
"import gurobipy as gp\n",
|
|
"from gurobipy import GRB\t\n",
|
|
"from pulp import GUROBI\n",
|
|
"\n",
|
|
"model = LpProblem(name='shortest_path', sense=LpMinimize)\n",
|
|
"# model= gp.Model(\"ShortestPath\")\n",
|
|
"# G =CreateNetworkXGraphManuelParallel()\n",
|
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"\n",
|
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"M = 100\n",
|
|
"dst_num=len(hop_count)\n",
|
|
"src_num=len(hop_count)\n",
|
|
"\n",
|
|
"layer_num=max(dst_num,src_num)\n",
|
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"\n",
|
|
"# length_list=[2,1,2,1,1,1]\n",
|
|
"# comb_groups = [[\"src_0\",\"src_1\",\"src_2\",\"src_3\",\"dst_4\"],[\"dst_1\",\"dst_0\",\"dst_2\",\"dst_5\"],[\"dst_3\",\"src_4\",\"src_5\"]]\n",
|
|
"length_list=hop_count\n",
|
|
"comb_groups=same_value_groups\n",
|
|
"# ======================================================================\n",
|
|
"# edges\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# create one variable for each edge of the graph/NoC\n",
|
|
"edges = { (k,i, j): LpVariable(f\"edge_{k}_{i}_{j}\", cat=\"Binary\") \n",
|
|
" for i, j in G_NoC.edges \n",
|
|
" for k in range(layer_num) }\n",
|
|
"\n",
|
|
"# edge can only be active in one layer\n",
|
|
"for i, j in G_NoC.edges:\n",
|
|
" model += lpSum(edges[k, i, j] for k in range(layer_num)) <= 1\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# variables to map destinations and sources to nodes\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# dst 0 mapped to layer 0, 1-->1, etc\n",
|
|
"dst_vars = {f\"dst_{i}\": {node: LpVariable(f\"dst_{i}_{node}\", cat=\"Binary\") for node in G_NoC.nodes} for i in range(dst_num)}\n",
|
|
"\n",
|
|
"\n",
|
|
"# dst (needs to be mapped to exactly one node)\n",
|
|
"for i in range(dst_num):\n",
|
|
" model += lpSum(dst_vars[f\"dst_{i}\"][node] for node in G_NoC.nodes) == 1\n",
|
|
"\n",
|
|
"\n",
|
|
"src_vars = {f\"src_{i}\": {node: LpVariable(f\"src_{i}_{node}\", cat=\"Binary\") for node in G_NoC.nodes} for i in range(src_num)}\n",
|
|
"\n",
|
|
"for i in range( src_num ):\n",
|
|
" model += lpSum(src_vars[f\"src_{i}\"][node] for node in G_NoC.nodes) == 1\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# position variables to enforce an order and intermediate nodes\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# var to indicate the position in the order of the nodes\n",
|
|
"pos = {(k,node): LpVariable(f\"pos_{k}_{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"\n",
|
|
"# Ensure that if an edge exists from node 1 to 2, then pos[2] = pos[1] + 1\n",
|
|
"for i, j in G_NoC.edges:\n",
|
|
" for k in range(layer_num):\n",
|
|
" model += pos[k,j]>=pos[k,i]-M*(1-edges[k,i,j]) + 1\n",
|
|
" model += pos[k,j]<=pos[k,i]+M*(1-edges[k,i,j]) + 1\n",
|
|
"\n",
|
|
"# Define the position of each dst and src variable \n",
|
|
"pos_dst = {f\"dst_{i}\": LpVariable(f\"pos_dst_{i}\", cat=\"Integer\") for i in range(dst_num)}\n",
|
|
"\n",
|
|
"# If dst i is mapped to node j, then pos_dst[i] = pos[j]\n",
|
|
"for i in range(dst_num):\n",
|
|
" for node in G_NoC.nodes:\n",
|
|
" model += pos_dst[f\"dst_{i}\"] >= pos[i,node] - M * (1 - dst_vars[f\"dst_{i}\"][node])\n",
|
|
" model += pos_dst[f\"dst_{i}\"] <= pos[i,node] + M * (1 - dst_vars[f\"dst_{i}\"][node])\n",
|
|
"\n",
|
|
"pos_src = {f\"src_{i}\": LpVariable(f\"pos_src_{i}\", cat=\"Integer\") for i in range(src_num)}\n",
|
|
"\n",
|
|
"for i in range(src_num):\n",
|
|
" for node in G_NoC.nodes:\n",
|
|
" model += pos_src[f\"src_{i}\"] >= pos[i,node] - M * (1 - src_vars[f\"src_{i}\"][node])\n",
|
|
" model += pos_src[f\"src_{i}\"] <= pos[i,node] + M * (1 - src_vars[f\"src_{i}\"][node])\n",
|
|
"\n",
|
|
"# all src positions should be 0\n",
|
|
"for i in range(src_num):\n",
|
|
" model += pos_src[f\"src_{i}\"] == 0\n",
|
|
"\n",
|
|
"# if a node has no inputs or outputs its pos should be 0 so it has no effect on sum(pos)\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" model += pos[k,node] <= M*(lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) + lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node))\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Define groups of destinations and sources that should be mapped to the same node\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# comb_groups = [[\"src_0\", \"src_3\"],[\"src_1\",\"src_2\",\"dst_0\"],[\"dst_1\",\"dst_2\",\"dst_3\"]]\n",
|
|
"# comb_groups = [[]]\n",
|
|
"# comb_groups = [[\"src_0\",\"src_1\",\"src_2\",\"src_3\",\"src_5\",\"dst_4\"],[\"dst_0\",\"dst_1\",\"dst_2\",\"dst_5\"],[\"dst_3\",\"src_4\"]]\n",
|
|
"# comb_groups = [[\"src_0\",\"src_1\"],[\"dst_1\",\"dst_0\"]]\n",
|
|
"comb_groups = [list(set(group)) for group in comb_groups] # Ensure each object in the comb_groups list is unique\n",
|
|
"\n",
|
|
"# The while loop iterates through the `comb_groups` list and merges any groups that share common elements.\n",
|
|
"i = 0\n",
|
|
"while i < len(comb_groups):\n",
|
|
" j = i + 1\n",
|
|
" while j < len(comb_groups):\n",
|
|
" # Check if the two groups share any common elements\n",
|
|
" if any(item in comb_groups[i] for item in comb_groups[j]):\n",
|
|
" # Merge the two groups\n",
|
|
" comb_groups[i] = list(set(comb_groups[i] + comb_groups[j]))\n",
|
|
" # Remove the merged group\n",
|
|
" del comb_groups[j]\n",
|
|
" else:\n",
|
|
" j += 1\n",
|
|
" i += 1\n",
|
|
"\n",
|
|
"# All elements in the same group should be mapped to the same node\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for comb_group in comb_groups:\n",
|
|
" for idx,entry1 in enumerate(comb_group[:-1]):\n",
|
|
" entry2=comb_group[idx + 1]\n",
|
|
" if \"src\" in entry1 and \"src\" in entry2:\n",
|
|
" model += src_vars[entry1][node] == src_vars[entry2][node]\n",
|
|
" elif \"dst\" in entry1 and \"dst\" in entry2:\n",
|
|
" model += dst_vars[entry1][node] == dst_vars[entry2][node]\n",
|
|
" elif \"src\" in entry1 and \"dst\" in entry2:\n",
|
|
" model += src_vars[entry1][node] == dst_vars[entry2][node]\n",
|
|
" elif \"dst\" in entry1 and \"src\" in entry2:\n",
|
|
" model += dst_vars[entry1][node] == src_vars[entry2][node]\n",
|
|
" else:\n",
|
|
" raise ValueError(f\"Invalid entry in comb_group: {entry1}, {entry2}\")\n",
|
|
"\n",
|
|
"# all elements not in the same group should be mapped to different nodes\n",
|
|
"not_in_group_pairs = []\n",
|
|
"all_sources = [f\"src_{i}\" for i in range(src_num)]\n",
|
|
"all_destinations = [f\"dst_{i}\" for i in range(dst_num)]\n",
|
|
"all_entries = all_sources + all_destinations\n",
|
|
"\n",
|
|
"for entry1, entry2 in combinations(all_entries, 2):\n",
|
|
" in_same_group = any(entry1 in group and entry2 in group for group in comb_groups)\n",
|
|
" if not in_same_group:\n",
|
|
" not_in_group_pairs.append((entry1, entry2))\n",
|
|
"\n",
|
|
"\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for pair in not_in_group_pairs:\n",
|
|
" entry1, entry2 = pair\n",
|
|
" if \"src\" in entry1 and \"src\" in entry2:\n",
|
|
" model += src_vars[entry1][node] + src_vars[entry2][node]<=1\n",
|
|
" elif \"dst\" in entry1 and \"dst\" in entry2:\n",
|
|
" model += dst_vars[entry1][node] + dst_vars[entry2][node]<=1\n",
|
|
" elif \"src\" in entry1 and \"dst\" in entry2:\n",
|
|
" model += src_vars[entry1][node] + dst_vars[entry2][node]<=1\n",
|
|
" elif \"dst\" in entry1 and \"src\" in entry2:\n",
|
|
" model += dst_vars[entry1][node] + src_vars[entry2][node]<=1\n",
|
|
" else:\n",
|
|
" raise ValueError(f\"Invalid entry in pair: {entry1}, {entry2}\")\n",
|
|
"\n",
|
|
"# model +=dst_vars[\"dst_0\"][11] == 1\n",
|
|
"# model +=dst_vars[\"dst_1\"][2] == 1\n",
|
|
"\n",
|
|
"# model +=src_vars[\"src_0\"][8] == 1\n",
|
|
"# model +=src_vars[\"src_1\"][14] == 1\n",
|
|
"# ======================================================================\n",
|
|
"# Flow conditions to src, dst and intermediate nodes\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" # Balance condition (same number out as in) for all not specific nodes (nodes which are not src or dst)\n",
|
|
" model += (lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) - lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node)) <= 0 + M * (src_vars[f\"src_{k}\"][node]+ dst_vars[f\"dst_{k}\"][node])\n",
|
|
" model += (lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) - lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node)) >= 0 - M * (src_vars[f\"src_{k}\"][node]+ dst_vars[f\"dst_{k}\"][node])\n",
|
|
" \n",
|
|
" #intermediate nodes can have max one input and one output because they are considered as real intermediate nodes of the task graph. Two paths can share an intermediate task.\n",
|
|
" #intermediate nodes always have to be considered as a real task node. No \"just passing\" nodes are possible\n",
|
|
" # model += (lpSum(x[i, j] for i, j in G_NoC.edges if i == node) - lpSum(x[i, j] for i, j in G_NoC.edges if j == node)) <= 1\n",
|
|
"\n",
|
|
" dst_var = dst_vars[f\"dst_{k}\"]\n",
|
|
" \n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node) <= 1 + M * (1 - dst_var[node]) # One incoming edge\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node) >= 1 - M * (1 - dst_var[node])\n",
|
|
"\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) <= 0 + M * (1 - dst_var[node]) # No outgoing edge\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) >= 0 - M * (1 - dst_var[node])\n",
|
|
"\n",
|
|
"\n",
|
|
" src_var = src_vars[f\"src_{k}\"]\n",
|
|
" \n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node) <= 0 + M * (1 - src_var[node]) # No incoming edge\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if j == node) >= 0 - M * (1 - src_var[node])\n",
|
|
"\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) <= 1 + M * (1 - src_var[node]) # One outgoing edge\n",
|
|
" model += lpSum(edges[k,i, j] for i, j in G_NoC.edges if i == node) >= 1 - M * (1 - src_var[node])\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Nodes can not be used twice as intermediate nodes\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# is a src or dst mapped to this node\n",
|
|
"src_dst = {node: LpVariable(f\"src_dst_node{node}\", cat=\"Binary\") for node in G_NoC.nodes}\n",
|
|
"\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for m in range(dst_num):\n",
|
|
" model +=src_dst[node] >=dst_vars[f\"dst_{m}\"][node]\n",
|
|
" model +=src_dst[node] >=src_vars[f\"src_{m}\"][node]\n",
|
|
" model += src_dst[node] <= lpSum(dst_vars[f\"dst_{m}\"][node] for m in range(dst_num)) + lpSum(src_vars[f\"src_{m}\"][node] for m in range(src_num))\n",
|
|
"\n",
|
|
"\n",
|
|
"# is there an input AND an output for this node (is it an intermediate node)\n",
|
|
"in_out = {(k, node): LpVariable(f\"inout_layer{k}_node{node}\", cat=\"Binary\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" model += in_out[k, node] >= lpSum(edges[k, i, j] for i, j in G_NoC.edges if i == node) + lpSum(edges[k, i, j] for i, j in G_NoC.edges if j == node) - 1\n",
|
|
" model += in_out[k, node] <= lpSum(edges[k, i, j] for i, j in G_NoC.edges if i == node) + lpSum(edges[k, i, j] for i, j in G_NoC.edges if j == node)\n",
|
|
" \n",
|
|
"\n",
|
|
"# (input or output) AND (src or dst) -> path_add_individual\n",
|
|
"# use a src or dst node as an intermediate node -> can not use it as normal intermediate node -> total hops +1\n",
|
|
"path_add_individual = {(k,node): LpVariable(f\"path_add_individual{k}_{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" model += path_add_individual[k,node] >= in_out[k, node] + src_dst[node] - 1\n",
|
|
" model += path_add_individual[k,node] <= in_out[k, node]\n",
|
|
" model += path_add_individual[k,node] <= src_dst[node]\n",
|
|
"\n",
|
|
"\n",
|
|
"# how often is a node used as an intermediate node (intermediate node if in one path the node has both input and output)\n",
|
|
"in_out_total={node: LpVariable(f\"inout_total_node{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" model += in_out_total[node] == lpSum(in_out[k, node] for k in range(layer_num))\n",
|
|
"\n",
|
|
"# The node is used as an intermediate node in at least 2 paths (if a>1: b=1 elif a=<1: b=0)\n",
|
|
"in_out_total_norm={node: LpVariable(f\"inout_total_norm{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" model += in_out_total[node] -1 <= M*in_out_total_norm[node]\n",
|
|
" model += in_out_total[node] -1 >= -M*(1-in_out_total_norm[node])\n",
|
|
"\n",
|
|
"\n",
|
|
"# is this node an intermediate node and used in at least 2 paths\n",
|
|
"multi_use_node = {(k,node): LpVariable(f\"path_add_individual_2_{k}_{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" # check if something is mapped to this node\n",
|
|
" is_pos_nonzero = LpVariable(f\"is_pos_nonzero_{k}_{node}\", cat=\"Binary\")\n",
|
|
" model += pos[k, node] <= is_pos_nonzero * M # Upper bound\n",
|
|
" model += pos[k, node] >= 1 - (1 - is_pos_nonzero) * M # Lower bound\n",
|
|
" \n",
|
|
" # something is mapped to the node and it is an intermediate node in at least 2 paths and it it no destination node (because the penalty for using a destination node as a intermediate node is already applied)\n",
|
|
" model += multi_use_node[k, node] <= is_pos_nonzero\n",
|
|
" model += multi_use_node[k, node] <= in_out_total_norm[node]\n",
|
|
" model += multi_use_node[k, node] <= 1 - dst_vars[f\"dst_{k}\"][node]\n",
|
|
" model += multi_use_node[k, node] >= is_pos_nonzero + in_out_total_norm[node] - dst_vars[f\"dst_{k}\"][node] -1\n",
|
|
"\n",
|
|
"\n",
|
|
"# choose which path is allowed to use the multiple times used intermediate node\n",
|
|
"choose_mid_node = {(k,node): LpVariable(f\"choose_mid_{k}_node{node}\", cat=\"Binary\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" model += lpSum(choose_mid_node[k,node] for k in range(layer_num)) == 1\n",
|
|
"\n",
|
|
"# path_add_individual2 is the additional cost/length that is added to a path if it uses one of the multi use hops. If this path is chosen we do not need to add anything (because if multiple paths want to use one intermediate node, one path is allowed to do so)\n",
|
|
"path_add_individual2 = {(k,node): LpVariable(f\"path_add_individual_3_{k}_{node}\", lowBound=0, cat=\"Integer\") for node in G_NoC.nodes for k in range(layer_num)}\n",
|
|
"for node in G_NoC.nodes:\n",
|
|
" for k in range(layer_num):\n",
|
|
" model +=path_add_individual2[k, node] <= multi_use_node[k, node]\n",
|
|
" model +=path_add_individual2[k, node] <= 1-choose_mid_node[k, node]\n",
|
|
" model +=path_add_individual2[k, node] >= multi_use_node[k, node] - choose_mid_node[k, node]\n",
|
|
"\n",
|
|
"\n",
|
|
"# total number of hops needed to add to each path\n",
|
|
"path_add_total = {k: LpVariable(f\"path_add_total_{k}\", lowBound=0, cat=\"Integer\") for k in range(layer_num)}\n",
|
|
"\n",
|
|
"for k in range(layer_num):\n",
|
|
" model += path_add_total[k] == lpSum(path_add_individual[k, node] for node in G_NoC.nodes) + lpSum(path_add_individual2[k, node] for node in G_NoC.nodes)\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# set length of the paths (number of hops) with positions of destination\n",
|
|
"# ======================================================================\n",
|
|
"# length_list=[1,2,3,2]\n",
|
|
"# length_list=[2,1,2,1,1,1]\n",
|
|
"# length_list=[2,1]\n",
|
|
"# model += pos_dst[f\"dst_1\"] ==2\n",
|
|
"for i in range(dst_num):\n",
|
|
" model += pos_dst[f\"dst_{i}\"] >= length_list[i] +path_add_total[i]\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Optimization objectives\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"# Define variables for the maximum pos_dst -> minimize longest path\n",
|
|
"max_pos_dst = LpVariable(\"max_pos_dst\", lowBound=0, cat=\"Integer\")\n",
|
|
"\n",
|
|
"for dst_key, var in pos_dst.items():\n",
|
|
" model += max_pos_dst >= var\n",
|
|
"\n",
|
|
"# sum of positions of all nodes including all intermediate nodes\n",
|
|
"# model += lpSum(edges[k, i, j] for i, j in G_NoC.edges for k in range(layer_num))\n",
|
|
"\n",
|
|
"\n",
|
|
"# model += pos_dst[\"dst_0\"]\n",
|
|
"# model +=max_pos_dst\n",
|
|
"\n",
|
|
"# sum of all path lengths\n",
|
|
"model += lpSum(pos_dst[f\"dst_{i}\"] for i in range(dst_num))\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# status = model.solve(GUROBI_CMD(threads=26, options=[(\"IIS\", 1), (\"IISFile\", \"/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/infeasible.ilp\")]))\n",
|
|
"status = model.solve(GUROBI_CMD(threads=26, timeLimit=60))\n",
|
|
"# status = model.solve(GUROBI(Threads=26, TimeLimit=600,ImprovementTime=30))\n",
|
|
"\n",
|
|
"# status = model.solve(GUROBI_CMD(threads=26))\n",
|
|
"\n",
|
|
"# model.computeIIS()\n",
|
|
"# model.write('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/infeasible.ilp')\n",
|
|
"model.writeLP(\"/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/model.lp\")\n",
|
|
"# ======================================================================\n",
|
|
"# Debug information\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"print(f\"Objective value: {model.objective.value()}\")\n",
|
|
"\n",
|
|
"if debug==True:\n",
|
|
" edge_list = []\n",
|
|
" if status == 1: # Check if the solution is optimal\n",
|
|
" print(\"Optimal solution found:\")\n",
|
|
" for (k, i, j), var in edges.items():\n",
|
|
" if var.value() > 0: # Only print edges that are part of the solution\n",
|
|
" print(f\"Edge ({k},{i}, {j}) is part of the shortest path with value: {var.value()}\")\n",
|
|
" edge_list.append((k,i,j))\n",
|
|
" else:\n",
|
|
" print(f\"Problem status: {LpStatus[status]}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" # Print all source nodes from src_vars\n",
|
|
" for src_key, src_dict in src_vars.items():\n",
|
|
" for node, var in src_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" print(f\"Source node for {src_key}: {node}\")\n",
|
|
"\n",
|
|
" # Print all destination nodes from dst_vars\n",
|
|
" for dst_key, dst_dict in dst_vars.items():\n",
|
|
" for node, var in dst_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" print(f\"Destination node for {dst_key}: {node}\")\n",
|
|
"\n",
|
|
"\n",
|
|
" for node, var in pos.items():\n",
|
|
" print(f\"Position of node {node}: {var.value()}\")\n",
|
|
"\n",
|
|
" # Print the positions of all source nodes\n",
|
|
" for src_key, var in pos_src.items():\n",
|
|
" print(f\"Source position ({src_key}): {var.value()}\")\n",
|
|
"\n",
|
|
" # Print the positions of all destination nodes\n",
|
|
" for dst_key, var in pos_dst.items():\n",
|
|
" print(f\"Destination position ({dst_key}): {var.value()}\")\n",
|
|
"\n",
|
|
"\n",
|
|
" # Print all variables and their values\n",
|
|
" for v in model.variables():\n",
|
|
" print(f\"{v.name} = {v.value()}\")\n",
|
|
" \n",
|
|
" print(edge_list)\n",
|
|
"\n",
|
|
" highlight_edges_with_colors = {\n",
|
|
" (1, 9): \"red\",\n",
|
|
" (11, 3): \"blue\",\n",
|
|
" (5, 7): \"green\"\n",
|
|
" }\n",
|
|
"\n",
|
|
" draw_folded_torus_noc(\n",
|
|
" mesh_size=4,\n",
|
|
" G_NoC=G_NoC,\n",
|
|
" highlight_edges_with_colors=convert_edge_list_to_highlight_dict(edge_list),\n",
|
|
" title=\"Folded Torus NoC (4x4)\"\n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
" plot_nx4x4_grid_with_highlighted_edges(edge_list)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# import gurobipy as gp\n",
|
|
"# from gurobipy import GRB\n",
|
|
"\n",
|
|
"# # Create model\n",
|
|
"# model = gp.Model(\"shortest_path\")\n",
|
|
"\n",
|
|
"# M = 100\n",
|
|
"# dst_num = len(hop_count)\n",
|
|
"# src_num = len(hop_count)\n",
|
|
"# layer_num = max(dst_num, src_num)\n",
|
|
"# length_list = hop_count\n",
|
|
"# comb_groups = same_value_groups\n",
|
|
"\n",
|
|
"# # Variables\n",
|
|
"# edges = model.addVars(\n",
|
|
"# [(k, i, j) for i, j in G_NoC.edges for k in range(layer_num)],\n",
|
|
"# vtype=GRB.BINARY,\n",
|
|
"# name=\"edge\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# dst_vars = model.addVars(\n",
|
|
"# [(i, node) for i in range(dst_num) for node in G_NoC.nodes],\n",
|
|
"# vtype=GRB.BINARY,\n",
|
|
"# name=\"dst\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# src_vars = model.addVars(\n",
|
|
"# [(i, node) for i in range(src_num) for node in G_NoC.nodes],\n",
|
|
"# vtype=GRB.BINARY,\n",
|
|
"# name=\"src\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# pos = model.addVars(\n",
|
|
"# [(k, node) for node in G_NoC.nodes for k in range(layer_num)],\n",
|
|
"# vtype=GRB.INTEGER,\n",
|
|
"# lb=0,\n",
|
|
"# name=\"pos\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# pos_dst = model.addVars(\n",
|
|
"# [i for i in range(dst_num)],\n",
|
|
"# vtype=GRB.INTEGER,\n",
|
|
"# name=\"pos_dst\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# pos_src = model.addVars(\n",
|
|
"# [i for i in range(src_num)],\n",
|
|
"# vtype=GRB.INTEGER,\n",
|
|
"# name=\"pos_src\"\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# # ... (define all other variables similarly using model.addVars or model.addVar) ...\n",
|
|
"\n",
|
|
"# # Constraints\n",
|
|
"# # Example: edge can only be active in one layer\n",
|
|
"# for i, j in G_NoC.edges:\n",
|
|
"# model.addConstr(gp.quicksum(edges[k, i, j] for k in range(layer_num)) <= 1)\n",
|
|
"\n",
|
|
"# # Example: dst (needs to be mapped to exactly one node)\n",
|
|
"# for i in range(dst_num):\n",
|
|
"# model.addConstr(gp.quicksum(dst_vars[i, node] for node in G_NoC.nodes) == 1)\n",
|
|
"\n",
|
|
"# # Example: src (needs to be mapped to exactly one node)\n",
|
|
"# for i in range(src_num):\n",
|
|
"# model.addConstr(gp.quicksum(src_vars[i, node] for node in G_NoC.nodes) == 1)\n",
|
|
"\n",
|
|
"# # ... (translate all other constraints using model.addConstr or model.addConstrs) ...\n",
|
|
"\n",
|
|
"# # Objective\n",
|
|
"# model.setObjective(gp.quicksum(pos_dst[i] for i in range(dst_num)), GRB.MINIMIZE)\n",
|
|
"\n",
|
|
"# # Set Gurobi parameters\n",
|
|
"# model.setParam(\"Threads\", 26)\n",
|
|
"# model.setParam(\"TimeLimit\", 600)\n",
|
|
"# model.setParam(\"ImprovementTime\", 30)\n",
|
|
"\n",
|
|
"# # Optimize\n",
|
|
"# model.optimize()\n",
|
|
"\n",
|
|
"# # Debug information\n",
|
|
"# if model.status == GRB.OPTIMAL or model.status == GRB.TIME_LIMIT:\n",
|
|
"# print(f\"Objective value: {model.ObjVal}\")\n",
|
|
"# # Print solution values as needed\n",
|
|
"# for v in model.getVars():\n",
|
|
"# print(f\"{v.VarName} = {v.X}\")\n",
|
|
"# else:\n",
|
|
"# print(f\"Problem status: {model.Status}\")\n",
|
|
"\n",
|
|
"# # Save model to file\n",
|
|
"# # model.write(\"/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/model.lp\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# ======================================================================\n",
|
|
"# Convert result into information usable to generate mapping files\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Where are the src and dst mapped to\n",
|
|
"# ======================================================================\n",
|
|
"src_dst_nodes = set()\n",
|
|
"all_nodes= set()\n",
|
|
"not_mapped=set()\n",
|
|
"for src_key, src_dict in src_vars.items():\n",
|
|
" for node, var in src_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" src_dst_nodes.add(src_dst_mapping[src_key])\n",
|
|
"\n",
|
|
"for dst_key, dst_dict in dst_vars.items():\n",
|
|
" for node, var in dst_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" src_dst_nodes.add(src_dst_mapping[dst_key])\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# What tasks are mapped together\n",
|
|
"# ======================================================================\n",
|
|
"combined_mapping_dict = {}\n",
|
|
"\n",
|
|
"with open(\"CombinedMapping.txt\", \"r\") as file:\n",
|
|
" for line in file:\n",
|
|
" if line.strip(): # Ignore empty lines\n",
|
|
" # Split the line into router and tasks\n",
|
|
" router, tasks = line.split(\":\")\n",
|
|
" router = router.strip().replace(\"Router \", \"\") # Extract router number\n",
|
|
" tasks = tasks.strip().replace(\"Tasks [\", \"\").replace(\"]\", \"\") # Extract tasks\n",
|
|
" task_list = [int(task.strip()) for task in tasks.split(\",\")] # Convert tasks to a list of integers\n",
|
|
" combined_mapping_dict[int(router)] = task_list\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# What nodes are not mapped yet (intermediate nodes)\n",
|
|
"# ======================================================================\n",
|
|
"for key,value in combined_mapping_dict.items():\n",
|
|
" all_nodes.add(key)\n",
|
|
"\n",
|
|
"not_mapped = all_nodes - set(map(int, src_dst_nodes))\n",
|
|
"not_mapped_dict = {}\n",
|
|
"\n",
|
|
"for node in not_mapped:\n",
|
|
" not_mapped_dict[node] = {\n",
|
|
" \"path\": None, # Placeholder for path value\n",
|
|
" \"mapping\": None # Placeholder for mapping value\n",
|
|
" }\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Create a graph from the edges to easily work with paths\n",
|
|
"# ======================================================================\n",
|
|
"shortest_path=[]\n",
|
|
"for k in range(layer_num):\n",
|
|
" G_layer = nx.DiGraph()\n",
|
|
" active_edges = []\n",
|
|
" for (layer, i, j), var in edges.items():\n",
|
|
" if layer == k and var.value() == 1:\n",
|
|
" active_edges.append((i, j))\n",
|
|
" G_layer.add_edge(i, j)\n",
|
|
"\n",
|
|
" src_node_layer = [node for node in G_layer.nodes if G_layer.in_degree(node) == 0 and G_layer.out_degree(node) > 0]\n",
|
|
" dst_node_layer = [node for node in G_layer.nodes if G_layer.out_degree(node) == 0 and G_layer.in_degree(node) > 0]\n",
|
|
" \n",
|
|
" shortest_path.append(nx.shortest_path(G_layer, source=src_node_layer[0], target=dst_node_layer[0]))\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# Where can the intermediate nodes be mapped to (check chose var)\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"available_nodes = {}\n",
|
|
"\n",
|
|
"for i in range(layer_num):\n",
|
|
" for m in shortest_path[i][1:-1]:\n",
|
|
" if i not in available_nodes:\n",
|
|
" available_nodes[i] = []\n",
|
|
" if in_out_total_norm[m].value()==1 and choose_mid_node[i,m].value()==1:\n",
|
|
" available_nodes[i].append(m)\n",
|
|
" if in_out_total_norm[m].value()==0:\n",
|
|
" available_nodes[i].append(m)\n",
|
|
"\n",
|
|
"\n",
|
|
"split_list = []\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# choose the value closest to the src so that multiple intermediate nodes are mapped in the right order\n",
|
|
"# ======================================================================\n",
|
|
"mapping={}\n",
|
|
"for node,atr in not_mapped_dict.items():\n",
|
|
" for idx,i in enumerate(parallel_paths):\n",
|
|
" if str(node) in i:\n",
|
|
" atr[\"path\"]=idx\n",
|
|
" chosen_value = available_nodes[idx][0]\n",
|
|
" atr[\"mapping\"] = chosen_value\n",
|
|
" mapping[str(node)] =chosen_value\n",
|
|
" available_nodes[idx].remove(chosen_value)\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# combine the mapping of intermediate node and src + dst\n",
|
|
"# ======================================================================\n",
|
|
"\n",
|
|
"for dst_key, dst_dict in dst_vars.items():\n",
|
|
" for node, var in dst_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" mapping[src_dst_mapping[dst_key]] = node\n",
|
|
"\n",
|
|
"for src_key, src_dict in src_vars.items():\n",
|
|
" for node, var in src_dict.items():\n",
|
|
" if var.value() == 1:\n",
|
|
" mapping[src_dst_mapping[src_key]] = node\n",
|
|
"\n",
|
|
"\n",
|
|
"# ======================================================================\n",
|
|
"# For the circuit switched configuration each connection between two nodes\n",
|
|
"# needs to be a separate path -> split the original path depending on intermediate nodes\n",
|
|
"# ======================================================================\n",
|
|
"split_points=[]\n",
|
|
"\n",
|
|
"for m in range(len(shortest_path)):\n",
|
|
" split_points.append([])\n",
|
|
" for node,atr in not_mapped_dict.items():\n",
|
|
"\n",
|
|
" if int(atr[\"path\"])==m:\n",
|
|
" split_points[m].append(int(atr[\"mapping\"]))\n",
|
|
"\n",
|
|
"\n",
|
|
"result_list = []\n",
|
|
"for idx,value in enumerate(shortest_path):\n",
|
|
" original_list = value\n",
|
|
" split_point=split_points[idx]\n",
|
|
" result = []\n",
|
|
" current_sublist = []\n",
|
|
"\n",
|
|
" for item in original_list:\n",
|
|
" current_sublist.append(item)\n",
|
|
" if item in split_point:\n",
|
|
" result.append(current_sublist)\n",
|
|
" current_sublist = [item]\n",
|
|
"\n",
|
|
" if current_sublist:\n",
|
|
" result.append(current_sublist)\n",
|
|
"\n",
|
|
" result_list+=result\n",
|
|
"shortest_path=result_list"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import json\n",
|
|
"\n",
|
|
"# Data to save\n",
|
|
"data_to_save = {\n",
|
|
" \"combined_mapping_dict\": combined_mapping_dict,\n",
|
|
" \"mapping\": mapping,\n",
|
|
" \"shortest_path\": shortest_path\n",
|
|
"}\n",
|
|
"\n",
|
|
"# Save to a JSON file\n",
|
|
"with open(\"LinProgResults.json\", \"w\") as json_file:\n",
|
|
" json.dump(data_to_save, json_file, indent=4)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "env",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|