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reviewed basic neural net (minor change) and changed the destruction (satellite loss) function in successfull recursion

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temporaryWork
youainti 5 years ago
parent de54b5767c
commit 3e8b8f5284
2 changed files with 132 additions and 37 deletions
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20
Code/BasicNeuralNet.ipynb
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@ -3,7 +3,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 16,
"id": "3867293b-8f5e-426a-a223-834c7e04daef", "id": "heavy-turkish",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -13,7 +13,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"id": "e73a6d0e-4db9-4b0d-ab8f-e421373f1944", "id": "ordered-disease",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -37,7 +37,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "2870086a-cb32-4f7e-9ee3-2e7a545c86cb", "id": "quiet-syria",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -58,7 +58,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 6,
"id": "fb6c7507-5abf-428e-8644-d0e42525368d", "id": "fleet-letter",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -68,7 +68,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 15,
"id": "b3244863-39f7-4fa6-a284-059b858569cf", "id": "provincial-crime",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -89,7 +89,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 12,
"id": "3480a0ec-7b5b-46d7-bdd2-631e57be9c85", "id": "particular-response",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -99,7 +99,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 11,
"id": "e1ace0be-9b05-4c77-b076-a05b68cf8f51", "id": "passing-termination",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -120,7 +120,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 13,
"id": "464ae8e4-f71e-494f-9845-fefe5c75a7b1", "id": "public-speaking",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -172,7 +172,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "04bca0f6-dfb9-4013-ac2a-f4efd2f93c15", "id": "original-warehouse",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
@ -194,7 +194,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.5" "version": "3.8.8"
} }
}, },
"nbformat": 4, "nbformat": 4,

147
Code/successful_recursion.ipynb
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@ -3,7 +3,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"id": "sustained-board", "id": "closed-glenn",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
@ -16,7 +16,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "numeric-victoria", "id": "naval-ivory",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Setup Functions\n", "# Setup Functions\n",
@ -26,7 +26,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"id": "virtual-arlington", "id": "italian-enforcement",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -39,17 +39,22 @@
" return lambda *args: f(g(*args))\n", " return lambda *args: f(g(*args))\n",
"\n", "\n",
"def compose_recursive_functions(fn,n):\n", "def compose_recursive_functions(fn,n):\n",
" #takes a function fn and composes it with itself n times\n",
" #Returns each of the iterations of the functions.\n",
" \n",
" #Set base conditions\n", " #Set base conditions\n",
" out_func = None\n", " out_func = None\n",
" out_func_list =[]\n", " out_func_list =[]\n",
"\n", "\n",
" #build the compositions of functions\n", " #build the compositions of functions\n",
" for f in itertools.repeat(fn, n):\n", " for f in itertools.repeat(fn, n):\n",
" #if first iteration\n",
" if out_func == None:\n", " if out_func == None:\n",
" out_func = f\n", " out_func = f\n",
" else:\n", " else:\n",
" out_func = compose(f,out_func)\n", " out_func = compose(f,out_func)\n",
"\n", "\n",
" #append the ou\n",
" out_func_list.append(out_func)\n", " out_func_list.append(out_func)\n",
" \n", " \n",
" return out_func_list" " return out_func_list"
@ -57,7 +62,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "wrapped-message", "id": "fancy-tucson",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Setup functions related to the problem" "## Setup functions related to the problem"
@ -65,8 +70,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 15,
"id": "neutral-vietnamese", "id": "outside-arrangement",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -77,17 +82,18 @@
" #Gompertz distribution for simplicity\n", " #Gompertz distribution for simplicity\n",
" #commonly used with saturation\n", " #commonly used with saturation\n",
" #TODO: ACTUALLY DERIVE A SURVIVAL FUNCTION. THIS IS JUST A PLACEHOLDER. PROBABLY SHOULD BE AN EXPONENTIAL DISTRIBUTION\n", " #TODO: ACTUALLY DERIVE A SURVIVAL FUNCTION. THIS IS JUST A PLACEHOLDER. PROBABLY SHOULD BE AN EXPONENTIAL DISTRIBUTION\n",
" eta = 1.0/(SCALING@stock)\n",
" b = 1/debris\n",
" \n", " \n",
" return 1 - ( b*eta*torch.exp(eta+b*stock-eta*torch.exp(b*stock)))\n", " #eta = 1.0/(SCALING@stock)\n",
" #b = 1/debris\n",
" #return 1 - ( b*eta*torch.exp(eta+b*stock-eta*torch.exp(b*stock)))\n",
" return 1 - torch.exp(-SCALING * stock-debris)\n",
"\n", "\n",
"def test_launch(stock, debris):\n", "def test_launch(stock, debris):\n",
" return torch.ones(5, requires_grad=True)\n", " return torch.ones(5, requires_grad=True)\n",
"\n", "\n",
"def laws_of_motion(stock, debris, launches):\n", "def laws_of_motion(stock, debris, launches):\n",
" \n", " \n",
" new_stock = stock*survival(stock,debris) + launches(stock,debris) #TODO: Launches will become a function (neural network)\n", " new_stock = stock*survival(stock,debris) + launches#(stock,debris) #TODO: Launches will become a function (neural network)\n",
" \n", " \n",
" #TODO: Currently Ignoring autocatalysis\n", " #TODO: Currently Ignoring autocatalysis\n",
" new_debris = (1-DELTA)*debris + LAUNCH_DEBRIS_RATE * launches.sum() + COLLISION_DEBRIS_RATE*(1-survival(stock,debris)) @ stock\n", " new_debris = (1-DELTA)*debris + LAUNCH_DEBRIS_RATE * launches.sum() + COLLISION_DEBRIS_RATE*(1-survival(stock,debris)) @ stock\n",
@ -101,8 +107,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 16,
"id": "considered-configuration", "id": "romance-generation",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -128,7 +134,10 @@
" #This gets the transition of the value function derivatives over time.\n", " #This gets the transition of the value function derivatives over time.\n",
" transitioned = single_transition(\n", " transitioned = single_transition(\n",
" item_to_transition, #item to iterate, i.e. the derivatives of the value function\n", " item_to_transition, #item to iterate, i.e. the derivatives of the value function\n",
" laws_motion, profit, launch, #functions #TODO: reimplement with launch as a function\n", " #functions\n",
" laws_motion, \n",
" profit, \n",
" launch, #TODO: reimplement with launch as a function\n",
" stocks, debris #states\n", " stocks, debris #states\n",
" )\n", " )\n",
" \n", " \n",
@ -140,7 +149,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "premium-lesbian", "id": "fluid-parks",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Actual calculations" "# Actual calculations"
@ -148,8 +157,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 17,
"id": "suffering-google", "id": "changing-january",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -180,32 +189,118 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 19,
"id": "checked-medication", "id": "dominant-boost",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"tensor([-0.1543, 1.3888, 1.1316, 1.1316, 1.1553], grad_fn=<MvBackward>)\n", "tensor([0.0000, 1.2632, 1.0526, 1.0526, 1.0892], grad_fn=<MvBackward>)\n",
"tensor([-1.2150, 1.6724, 1.1912, 1.1912, 1.2161], grad_fn=<MvBackward>)\n", "tensor([-0.9519, 1.3928, 1.0020, 1.0020, 1.0575], grad_fn=<MvBackward>)\n",
"tensor([-2.3316, 1.9710, 1.2539, 1.2539, 1.2801], grad_fn=<MvBackward>)\n", "tensor([-1.8565, 1.5150, 0.9530, 0.9530, 0.9882], grad_fn=<MvBackward>)\n",
"tensor([nan, nan, nan, nan, nan], grad_fn=<MvBackward>)\n", "tensor([-2.8103, 1.6872, 0.9376, 0.9376, 0.9474], grad_fn=<MvBackward>)\n",
"tensor([nan, nan, nan, nan, nan], grad_fn=<MvBackward>)\n" "tensor([-3.8626, 1.9131, 0.9505, 0.9505, 0.9408], grad_fn=<MvBackward>)\n",
"tensor([-5.0235, 2.1830, 0.9819, 0.9819, 0.9598], grad_fn=<MvBackward>)\n",
"tensor([-6.2860, 2.4869, 1.0247, 1.0247, 0.9951], grad_fn=<MvBackward>)\n",
"tensor([-7.6403, 2.8175, 1.0746, 1.0746, 1.0403], grad_fn=<MvBackward>)\n",
"tensor([-9.0802, 3.1712, 1.1293, 1.1293, 1.0918], grad_fn=<MvBackward>)\n",
"tensor([-10.6035, 3.5462, 1.1879, 1.1879, 1.1477],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-12.2108, 3.9422, 1.2501, 1.2501, 1.2075],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-13.9045, 4.3597, 1.3157, 1.3157, 1.2708],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-15.6882, 4.7995, 1.3849, 1.3849, 1.3375],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-17.5662, 5.2625, 1.4577, 1.4577, 1.4079],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-19.5432, 5.7500, 1.5345, 1.5345, 1.4820],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-21.6244, 6.2631, 1.6152, 1.6152, 1.5600],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-23.8151, 6.8033, 1.7002, 1.7002, 1.6421],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-26.1212, 7.3719, 1.7897, 1.7897, 1.7285],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-28.5486, 7.9704, 1.8839, 1.8839, 1.8195],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-31.1038, 8.6004, 1.9831, 1.9831, 1.9152],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-33.7934, 9.2636, 2.0874, 2.0874, 2.0160],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-36.6247, 9.9617, 2.1973, 2.1973, 2.1221],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-39.6049, 10.6965, 2.3129, 2.3129, 2.2338],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-42.7420, 11.4700, 2.4347, 2.4347, 2.3514],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-46.0442, 12.2842, 2.5628, 2.5628, 2.4751],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-49.5202, 13.1413, 2.6977, 2.6977, 2.6054],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-53.1792, 14.0435, 2.8397, 2.8397, 2.7425],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-57.0307, 14.9931, 2.9891, 2.9891, 2.8869],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-61.0850, 15.9928, 3.1465, 3.1465, 3.0388],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-65.3526, 17.0450, 3.3121, 3.3121, 3.1988],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-69.8449, 18.1526, 3.4864, 3.4864, 3.3671],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-74.5736, 19.3186, 3.6699, 3.6699, 3.5443],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-79.5511, 20.5459, 3.8630, 3.8630, 3.7309],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-84.7907, 21.8378, 4.0664, 4.0664, 3.9272],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-90.3060, 23.1976, 4.2804, 4.2804, 4.1339],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-96.1115, 24.6291, 4.5057, 4.5057, 4.3515],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-102.2227, 26.1359, 4.7428, 4.7428, 4.5805],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-108.6555, 27.7220, 4.9924, 4.9924, 4.8216],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-115.4268, 29.3916, 5.2552, 5.2552, 5.0754],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-122.5545, 31.1490, 5.5318, 5.5318, 5.3425],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-130.0574, 32.9990, 5.8229, 5.8229, 5.6237],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-137.9552, 34.9463, 6.1294, 6.1294, 5.9197],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-146.2686, 36.9961, 6.4520, 6.4520, 6.2313],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-155.0196, 39.1538, 6.7916, 6.7916, 6.5592],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-164.2312, 41.4251, 7.1490, 7.1490, 6.9044],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-173.9275, 43.8158, 7.5253, 7.5253, 7.2678],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-184.1343, 46.3325, 7.9213, 7.9213, 7.6503],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-194.8782, 48.9815, 8.3382, 8.3382, 8.0530],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-206.1876, 51.7701, 8.7771, 8.7771, 8.4768],\n",
" grad_fn=<MvBackward>)\n",
"tensor([-218.0922, 54.7053, 9.2391, 9.2391, 8.9230],\n",
" grad_fn=<MvBackward>)\n"
] ]
} }
], ],
"source": [ "source": [
"#calculate results for first 5 iterations\n", "#calculate results for first 5 iterations\n",
"for f in compose_recursive_functions(transition_wrapper,5):\n", "for f in compose_recursive_functions(transition_wrapper,50):\n",
" result = f(base_data)\n", " result = f(base_data)\n",
" print(result[5])" " print(result[5])"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "wanted-principal", "id": "unnecessary-architect",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Note how this fails on the last few iterations.\n", "Note how this fails on the last few iterations.\n",
@ -220,7 +315,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "solid-correlation", "id": "varying-organization",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []

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