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stackdump/knapsack_omition_count.ipynb

Last active January 9, 2025 17:34
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compare knapsack value optimization first setting rates to ask questions, then changing initial states
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knapsack_omition_count.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "dde326ae-f772-409b-ab84-7e2bb819969e",
"metadata": {},
"source": [
"[![pflow](https://pflow.dev/img/zb2rhhWUuHr2VcL9NyRTjReB4yKdRzCmhS7ZgSvHPSwX2CnnV.svg)](https://pflow.dev/p/zb2rhhWUuHr2VcL9NyRTjReB4yKdRzCmhS7ZgSvHPSwX2CnnV/)\n",
"\n",
"The design of this net is modified to add a means to omit N values from being added to the knapsack.\n",
"\n",
"We analyze the outcome of :value first by disallowing omition- setting omit_once=0 and setting the rate of each txn to 0 in turn.\n",
"\n",
"Then we change the initial values of omit_once to allow omition 1-N values.\n",
"\n",
"This demonstrates how we can use the design of the petri-net to ask questions from 'Within' the ODE system."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3bf0a6fa-6c34-4cee-b006-b1c09b52aa7e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"simulate_omitions (generic function with 1 method)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Petri\n",
"using LabelledArrays\n",
"using Plots\n",
"using OrdinaryDiffEq\n",
"\n",
"# Define the states (places in Petri net)\n",
"states = [\n",
" :item0, :item1, :item2, :item3, :weight, \n",
" :value, :capacity, :omit_once\n",
"]\n",
"\n",
"# Define the transitions (steps in Petri net)\n",
"transitions = LVector(\n",
" omit0=(LVector(item0=1, omit_once=1), LVector()),\n",
" omit1=(LVector(item1=1, omit_once=1), LVector()),\n",
" omit2=(LVector(item2=1, omit_once=1), LVector()),\n",
" omit3=(LVector(item3=1, omit_once=1), LVector()),\n",
" txn0=(LVector(item0=1, capacity=2), LVector(weight=2, value=10)),\n",
" txn1=(LVector(item1=1, capacity=4), LVector(weight=4, value=10)),\n",
" txn2=(LVector(item2=1, capacity=6), LVector(weight=6, value=12)),\n",
" txn3=(LVector(item3=1, capacity=9), LVector(weight=9, value=18))\n",
")\n",
"\n",
"# Create the Petri net model\n",
"knapsack_model = Petri.Model(states, transitions)\n",
"\n",
"# Define a function to create rates\n",
"function create_rates(success_rate, exclude=[])\n",
" rates = LVector(\n",
" omit0=success_rate,\n",
" omit1=success_rate,\n",
" omit2=success_rate,\n",
" omit3=success_rate,\n",
" txn0=success_rate,\n",
" txn1=success_rate, \n",
" txn2=success_rate,\n",
" txn3=success_rate\n",
" )\n",
" for key in exclude\n",
" rates[key] = 0\n",
" end\n",
" return rates\n",
"end\n",
"\n",
"\n",
"function create_state(omitions=0)\n",
" return LVector(\n",
" item0=1, item1=1, item2=1, item3=1, \n",
" weight=0, value=0, capacity=15, omit_once=omitions\n",
" )\n",
"end\n",
"\n",
"# Define the simulation function\n",
"function simulate_petri_net(model, initial_state, rates, time_max)\n",
" tspan = (0.0, time_max)\n",
" prob = ODEProblem(model, initial_state, tspan, rates)\n",
" return solve(prob, Tsit5(), saveat=0.1)\n",
"end\n",
"\n",
"# Simulation parameters\n",
"time_max = 5.0\n",
"\n",
"# exclude each transition by setting its rate to 0\n",
"# output shows that excluding txn1 will give the best result\n",
"function simulate_exclustions()\n",
" # Run simulations\n",
" solutions = Dict()\n",
"\n",
" # exclude each txn in turn\n",
" exclusions = [:none, :txn0, :txn1, :txn2, :txn3]\n",
"\n",
" inital_state = create_state()\n",
"\n",
" for exclusion in exclusions\n",
" if exclusion == :none\n",
" rates = create_rates(1)\n",
" else\n",
" rates = create_rates(1, [exclusion])\n",
" end\n",
" sol = simulate_petri_net(knapsack_model, inital_state, rates, time_max)\n",
" solutions[exclusion] = sol\n",
" end\n",
"\n",
" plot(title=\"Knapsack with Exclustions\", xlabel=\"Time\", ylabel=\"Tokens\", lw=2)\n",
" # Plot results\n",
" for (excluded, sol) in solutions\n",
" plot!(sol.t, sol[6, :], label=\"exclude: $excluded\", linestyle=:dash)\n",
" value = round(solutions[excluded][6, end], digits=3)\n",
" scatter!([4], [value], label=\"$excluded: $value\", markersize=5)\n",
" value = round(solutions[excluded][6, end], digits=3)\n",
" println(\"exclude_$excluded\\t$value\")\n",
" end\n",
"end\n",
"\n",
"# allow omitions in the model\n",
"# graph shows the effect of allowing number of omitions 0, 1, 2, 3\n",
"# output shows that omittion=1 will give the best result\n",
"function simulate_omitions()\n",
" # Run simulations\n",
" solutions = Dict()\n",
"\n",
" # control number of omitions\n",
" omitions = [1, 2, 3, 4]\n",
"\n",
" plot(title=\"Knapsack with Omitions\", xlabel=\"Time\", ylabel=\"Tokens\", lw=2)\n",
"\n",
" # solve for each omition\n",
" for omition in omitions\n",
" println(\"num_omitions_allowed: $omition\")\n",
" rates = create_rates(omition)\n",
" sol = simulate_petri_net(knapsack_model, create_state(omition), rates, time_max)\n",
" solutions[omition] = sol\n",
" end\n",
"\n",
" # Plot results\n",
" for (omition, sol) in solutions\n",
" plot!(sol.t, sol[6, :], label=\"exclude: $omition\", linestyle=:dash)\n",
" value = round(solutions[omition][6, end], digits=3)\n",
" scatter!([4], [value], label=\"omit_$omition: $value\", markersize=5)\n",
" idx = findfirst(x -> x == 4, sol.t)\n",
" value = round(sol[6, idx], digits=3)\n",
" println(\"omit_$omition\\t$value\")\n",
" end\n",
"end\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8ba1d446-a549-4f08-8488-2f4b9084b7f9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"exclude_none\t35.714\n",
"exclude_txn3\t32.0\n",
"exclude_txn2\t37.5\n",
"exclude_txn0\t31.579\n",
"exclude_txn1\t35.294\n"
]
},
{
"data": {
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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simulate_exclustions()\n",
"plot!()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcd7a783-b165-4da6-a016-456f0a20d3e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"num_omitions_allowed: 1\n",
"num_omitions_allowed: 2\n",
"num_omitions_allowed: 3\n"
]
}
],
"source": [
"simulate_omitions()\n",
"plot!()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8ecf25f6-8f34-4027-ad94-b32a93bb8ba1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.11.2",
"language": "julia",
"name": "julia-1.11"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@stackdump
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stackdump commented Jan 9, 2025 •
edited
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See the original analysis without the omit_once state
notebook: https://gist.github.com/stackdump/af151355d414491ac2eff9160e134892

pflow

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