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Orbits/Code/Untitled1.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "geographic-wilderness",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import combined as c\n",
"import NeuralNetworkSpecifications as nns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "major-glucose",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[[4., 9., 6.]],\n",
"\n",
" [[0., 5., 4.]],\n",
"\n",
" [[3., 1., 9.]],\n",
"\n",
" [[6., 4., 8.]],\n",
"\n",
" [[8., 7., 6.]]], grad_fn=<CatBackward>)\n"
]
}
],
"source": [
"batch_size,states,choices = 5,3,1\n",
"constellations = states -1 #determined by debris tracking\n",
"max_start_state = 10\n",
"\n",
"stocks = torch.randint(max_start_state,(batch_size,1,constellations), dtype=torch.float32, requires_grad=True)\n",
"debris = torch.randint(max_start_state,(batch_size,1,1), dtype=torch.float32, requires_grad=True)\n",
"\n",
"s = c.States(stocks, debris)\n",
"\n",
"print(s.values)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "recognized-ability",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([5.6433e-07, 6.7631e-07], grad_fn=<MulBackward0>)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constellation_collision_risk = 1e-6 * torch.rand(constellations, requires_grad=True)\n",
"constellation_collision_risk"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "elect-float",
"metadata": {},
"outputs": [],
"source": [
"debris_decay_rate = 0.1\n",
"launch_debris = 0.05\n",
"debris_autocatalysis_rate = 1.4\n",
"\n",
"benefit_weight0 = torch.tensor([1.0,-0.02], requires_grad=True)\n",
"benefit_weight1 = torch.tensor([0.0,1.0], requires_grad=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "compressed-individual",
"metadata": {},
"outputs": [],
"source": [
"pm = c.PhysicalModel(10\n",
" , constellation_collision_risk #constellations_collision_risk #as tensor\n",
" , debris_decay_rate #debris_decay_rate\n",
" , launch_debris #launch_debris\n",
" , debris_autocatalysis_rate #debris_autocatalysis_rate\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "tracked-bachelor",
"metadata": {},
"outputs": [],
"source": [
"class LinearProfit():\n",
" \"\"\"\n",
" The simplest type of profit function available.\n",
" \"\"\"\n",
" def __init__(self, batch_size, constellation_number, discount_factor, benefit_weights, launch_cost, deorbit_cost=0, ):\n",
" self.batch_size = batch_size\n",
" \n",
" \n",
" #track which constellation this is.\n",
" self.constellation_number = constellation_number\n",
" \n",
" #get the number of constellations (pull from the benefit weight, in the dimension that counts across constellations)\n",
" self.number_of_constellations = benefit_weights.size()[0] -1\n",
"\n",
" #parameters describing the agent's situation\n",
" self.discount_factor = discount_factor\n",
" self.benefit_weights = benefit_weights\n",
" self.launch_cost = launch_cost\n",
" self.deorbit_cost = deorbit_cost\n",
" \n",
" def _period_benefit(self,stocks,debris,launches):\n",
" # multiply benefits times stocks\n",
" # sum across constellations\n",
" # reshape to standard dimensions\n",
" # subtract launch costs. \n",
" profit = torch.tensordot(self.benefit_weights,stocks, [[0],[1]])[:,self.constellation_number] \\\n",
" - (self.launch_cost * launches)[:,self.constellation_number,0]\n",
" return profit.view(batch_size,1)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "confidential-philippines",
"metadata": {},
"outputs": [],
"source": [
"launch_cost = 5\n",
"ea0 = LinearProfit(\n",
" batch_size\n",
" ,0 #constellation index\n",
" ,0.95 #discount\n",
" ,benefit_weight0\n",
" ,launch_cost #launch_cost\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "religious-georgia",
"metadata": {},
"outputs": [],
"source": [
"enn = nns.EstimandNN(batch_size\n",
" ,states\n",
" ,choices\n",
" ,constellations\n",
" ,12)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "painful-republican",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]]], grad_fn=<ReluBackward0>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"launch_decisions = enn.forward(s.values).choices\n",
"launch_decisions"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "equal-raising",
"metadata": {},
"outputs": [],
"source": [
"def test(stocks,launches):\n",
" # multiply benefits times stocks\n",
" # sum across constellations\n",
" # reshape to standard dimensions\n",
" # subtract launch costs. \n",
" profit = torch.tensordot(benefit_weight0,stocks, [[0],[1]])[:,0] - (launch_cost * launch_decisions)[:,0,0]\n",
" return profit.view(batch_size,1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "divine-editor",
"metadata": {},
"outputs": [],
"source": [
"t = LinearProfit(batch_size #batch_size\n",
" ,0 #constellation index\n",
" ,0.95 #discount\n",
" ,benefit_weight0\n",
" ,launch_cost #launch_cost\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "japanese-captain",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[3.9200],\n",
" [0.0000],\n",
" [2.9400],\n",
" [5.8800],\n",
" [7.8400]], grad_fn=<ViewBackward>)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test(stocks,launch_decisions)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "determined-difference",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[3.9200],\n",
" [0.0000],\n",
" [2.9400],\n",
" [5.8800],\n",
" [7.8400]], grad_fn=<ViewBackward>)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t._period_benefit(s.stocks,s.debris,launch_decisions)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "tribal-least",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([[[[[0.9800, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.0000, 0.0000]],\n",
" \n",
" [[0.9800, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.9800, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.9800, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.0000, 0.0000]],\n",
" \n",
" [[0.9800, 0.0000]]]]], grad_fn=<ViewBackward>),\n",
" tensor([[[[[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]],\n",
" \n",
" [[0.]]]]]),\n",
" tensor([[[[[-5.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-5.],\n",
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" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-5.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-5.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]]]],\n",
" \n",
" \n",
" \n",
" [[[[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-0.],\n",
" [ 0.]],\n",
" \n",
" [[-5.],\n",
" [ 0.]]]]]))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#in this case, the debris isn't tracked because it isn't included, and launch_decisions has a similar issue.\n",
"torch.autograd.functional.jacobian(t._period_benefit, (s.stocks,s.debris,launch_decisions), create_graph=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "statutory-lyric",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "naked-health",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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