3e:null 3f:["$","title",null,{"children":"2x2 oscillator test (generator) | Dan Brickley | Observable"}] 45:T725,net = { const run_duration = 5000000; // 1000003; const net_size = 2; const step_size = 1; // why not 0.01 per Python, maths is off somewhere //0.01; // orig: 0.01 let weights = await tf.tensor2d([ [node0, 1], [-1, node1] ]); // 2x2 net: its self links are 4.5, and the crossover links are +/- 1 const taus = await tf.tensor1d([tau0, 1]); const gains = await tf.tensor1d([1, 1.1], tf.int32); const biases = await tf.tensor1d([-2.75, -1.75]); let ext_inputs = await tf.tensor1d([0, 0]); // let ext_inputs = tf.zeros([0, 2], tf.int32); let states = await tf.tensor1d([2, 3]); let outputs = await tf.tensor1d([0.2, 0.3]); let loopcount = 1; // todo: check the math // this steps more slowly than the py lib // let batches =[] // for (let ix = 0; i < 100; i++) { while (loopcount++ < run_duration) { // τy' = -y + Wσ(g(y+ θ)) + I /* tf.tody executes the provided function fn and after it is executed, cleans up all intermediate tensors allocated by fn except those returned by fn */ const calc = await tf.tidy(() => { let total_inputs = weights.dot(outputs); // Wo let biased_states = total_inputs.add(biases); // (Wo)+ϑ let gained_biased_states = biased_states.mul(gains); // g((Wo)+ϑ) // y' = -y +g(Wo+ϑ) let yprime = gained_biased_states.sub(states).div(taus); // Euler step: let delta = yprime.mul(tf.scalar(step_size)); let trash = states; states = states.add(delta); trash.dispose(); outputs = tf.sigmoid(states.transpose()); //σ return { states, outputs }; }); amon.log("loopcount: ", loopcount); yield { state: states, count: loopcount, weights: weights, outputs: outputs, inputs: ext_inputs }; } // } // end for batch }46:[] 43:{"id":"4957f64cf18f39b6","slug":null,"title":"2x2 oscillator test (generator)","thumbnail":"513ec7e6221e94792f862a771a8c6d727dcd9d7c5c77486bd57539bd9ff32e37","access_level":"public","create_time":"$D2021-09-26T18:20:42.070Z","update_time":"$D2021-10-02T23:08:57.878Z","view_time":null,"first_public_time":"$D2023-04-28T20:08:46.597Z","has_unpublished_edits":null,"fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"6aa75e0ed4c47ade","login":"mazeballsold","name":"Dan Brickley","avatar_url":"https://avatars-next.observableusercontent.com/c1e798353c1834a30de7804f61bbfd5116503c44db4803547a43228e89afc8cb","tier":"starter_2024","bio":"broken account, please ignore - migrating","following":false},"version":1556,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"44d3c304df1c5e78","login":"mazeballsold","name":"Dan Brickley","avatar_url":"https://avatars-next.observableusercontent.com/c1e798353c1834a30de7804f61bbfd5116503c44db4803547a43228e89afc8cb","tier":null,"bio":"broken account, please ignore - migrating","following":false}],"members":[{"type":"individual","id":"44d3c304df1c5e78","login":"mazeballsold","name":"Dan Brickley","avatar_url":"https://avatars-next.observableusercontent.com/c1e798353c1834a30de7804f61bbfd5116503c44db4803547a43228e89afc8cb","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":3,"collection":null,"type":"notebook","body":{"titling":"auto","title":"2x2 oscillator test (generator)","cells":[{"id":0,"value":"# 2x2 oscillator test (generator)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"Tests for CTRNN-in-js. For comparision, see 2x2 [in python](https://colab.research.google.com/drive/1n297cmIw0PIwQnIpe7faPl4MeNuASkHX#scrollTo=iT_wF70R_5ok). Twitter [thread](https://twitter.com/danbri/status/1376434000554786817?s=21). Eduardo Izquierdo [post](https://edizquierdo.wordpress.com/2006/12/09/generative-music-creative-dynamical-systems-and-ctrnn-activation-functions/). Randall Beer [paper](https://pubmed.ncbi.nlm.nih.gov/17052157/). See also [Transfer functions / Transfer functions / music work](https://www.researchgate.net/publication/332327760_Exploring_Transfer_Functions_in_Evolved_CTRNNs_for_Music_Generation).\n\nTODO:\n - consider [batching](https://observablehq.com/@tmcw/generator-batching)\n - package core and make general, no hardcoded 2x2\n - [network view](https://livebook.manning.com/book/d3js-in-action-second-edition/chapter-7/)\n - Integrate cleaned up [API tests](https://observablehq.com/@mazeballs/testing-state-handling)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"viewof amon = new Monitor()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":919,"value":"{\n net;\n yield Plot.plot({\n x: {\n axis: null\n },\n marks: [\n Plot.lineY(col(results, 0), { stroke: \"steelblue\" }),\n Plot.lineY(col(results, 1), { stroke: \"orange\" })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":952,"value":"viewof node0 = Inputs.range([-10, 10], {\n value: 4.5,\n step: 0.1,\n label: \"Node 0 Feedback\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":953,"value":"viewof node1 = Inputs.range([-10, 10], {\n value: 4.5,\n step: 0.1,\n label: \"Node 1 Feedback\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1179,"value":"viewof tau0 = Inputs.range([0, 100], {\n value: 1,\n step: 0.1,\n label: \"Tau 0: \"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1209,"value":"cls = {\n clear;\n results.length = 0;\n //loopcount = 0;\n return html``;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1201,"value":"viewof clear = Inputs.button(\"Clear\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"$45","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":456,"value":"# Utilities and debugging","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1556,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1506,"value":"// TODO: this isn't working\n// attempting to slow down the generator from net\nacts_sample = {\n while (true) {\n yield Promises.delay(1000, activations); // or tick\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"/* rough cut at generating graphviz dot visualization\n based on network topology, weights, activation. For now, massively hardcoded. \n Could also use D3 force layout. */\n\nhardcodedGraphviz = {\n let n0 = ppfloat(activations[0]);\n let n1 = ppfloat(activations[1]);\n\n const steelblue_light = \"#e1eaf3\";\n const orange_light = \"#ffdead\";\n yield dot`digraph {\n graph [fontname = \"helvetica\"];\n node [fontname = \"helvetica\"];\n edge [fontname = \"helvetica\"];\n\nnode [style=filled]\n${n0}[fillcolor=\"${steelblue_light}\"]\n${n1}[fillcolor=\"${orange_light}\"]\n\n${n0} [color=lightgreen];\n${n0} -> ${n0} [color=blue penwidth=5]; \n${n0} -> ${n1} [color=blue penwidth=2]; \n${n1} -> ${n0} [arrowhead=tee color=red penwidth=2] ; \n${n1} -> ${n1} [color=blue penwidth=5];\n}`;\n}\n return Number.parseFloat(Number.parseFloat(x).toPrecision(2));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"viz = {\n results.push(net.outputs.dataSync());\n amon.log(\"numTensors:\", tf.memory().numTensors);\n amon.log(\"numBytes:\", tf.memory().numBytes);\n yield net;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1391,"value":"/* rough cut at generating graphviz dot visualization\n based on network topology, weights, activation. For now, massively hardcoded. \n Could also use D3 force layout. */\n\nhardcodedGraphviz = {\n let n0 = ppfloat(activations[0]);\n let n1 = ppfloat(activations[1]);\n\n const steelblue_light = \"#e1eaf3\";\n const orange_light = \"#ffdead\";\n yield dot`digraph {\n graph [fontname = \"helvetica\"];\n node [fontname = \"helvetica\"];\n edge [fontname = \"helvetica\"];\n\nnode [style=filled]\n${n0}[fillcolor=\"${steelblue_light}\"]\n${n1}[fillcolor=\"${orange_light}\"]\n\n${n0} [color=lightgreen];\n${n0} -> ${n0} [color=blue penwidth=5]; \n${n0} -> ${n1} [color=blue penwidth=2]; \n${n1} -> ${n0} [arrowhead=tee color=red penwidth=2] ; \n${n1} -> ${n1} [color=blue penwidth=5];\n}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1543,"value":"acts_sample","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"activations = {\n // we cleanup to avoid gpu leak here\n viz;\n //let viz = hardcodedGraphviz; // chain ourselves after last cell that needs what we'll dispose\n let activations = await viz.outputs.array();\n\n activations = activations.map((x) => {\n return ppfloat(x);\n });\n //activations[0] = ppfloat(activations[0]);\n net.outputs.dispose();\n yield activations;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1088,"value":"# Scrapbook (ignore)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":825,"value":"step0 = {\n const w = await tf.tensor2d([\n [4.5, 1],\n [-1, 4.5]\n ]);\n const s = await tf.tensor1d([2, 3]);\n return s.dot(w).dataSync();\n} // wtf...\n\n// see also https://js.tensorflow.org/api/latest/#layers.multiply","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"[(2 + -2.75) * 1, (3 - 1.75) * 1]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"abc = {\n const gains = await tf.tensor1d([1, 2], tf.int32);\n const biases = await tf.tensor1d([-2.75, -1.75]);\n let states = await tf.tensor1d([2, 3]);\n let biased_states = await states.add(biases);\n let weighted_biased_states = await biased_states.mul(gains);\n\n return {\n states: states,\n statesshape: states.shape,\n biased_states: biased_states,\n biased_statesshape: biased_states.shape,\n weighted_biased_states: weighted_biased_states,\n weighted_biased_statesshape: weighted_biased_states.shape\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1079,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"elementwisemultiple101 = {\n const arr1 = tf.tensor1d([10, 20, 30, 40, 50]);\n const arr2 = tf.tensor1d([5, 10, 15, 20, 25]);\n return arr1.mul(arr2).shape;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":857,"value":"elementwisemultiplyv2 = {\n const arr1 = tf.tensor1d([10, 20, 30, 40, 50]);\n const arr2 = tf.tensor1d([5, 10, 15, 20, 25]);\n return tf.layers.multiply().apply([arr1, arr2]).shape;\n // return arr1.mul(arr2).data();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"checkmaths = {\n const a = tf\n .tensor2d([\n [4.5, -1],\n [1, 4.5]\n ])\n .transpose();\n const b = tf.tensor1d([5, 100]);\n yield await b.dot(a).array();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":491,"value":"mutable results = [] // = [\"A\", \"B\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":522,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":575,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":580,"value":"function col(data, n) {\n return data.map(function (value, index) {\n return value[n];\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"testgpusize = {\n //\n //yield await tf.ones([10, 10000000], tf.int32).data();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"testmul = {\n const rot90 = await tf.tensor2d([\n [0, 1],\n [-1, 0]\n ]);\n\n //let theta = 0.2;\n const rotate = await tf\n .tensor2d([\n [Math.sin(theta), -Math.cos(theta)],\n [Math.cos(theta), Math.sin(theta)]\n ])\n .transpose();\n\n let A = await tf.tensor2d([\n [4.5, 1],\n [-1, 4.5]\n ]);\n\n // let node_outputs = await tf.tensor1d([-0.3, 0.23]);\n\n let node_outputs = await tf.tensor1d([1, 2]);\n\n yield await rotate.dot(node_outputs).data();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":212,"value":"viewof theta = Inputs.range([0, Math.PI * 4], {\n value: Math.PI * 0.01,\n step: 0.1,\n label: \"Theta\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":452,"value":"# Setup and Libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"tf = require(\"@tensorflow/tfjs\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"class Monitor {\n constructor() {\n this._items = new Map();\n this._list = html` `;\n //\n\n const view = html`

${this._list}`;\n view.value = this;\n return view;\n }\n\n clear(prefix = \"\") {\n for (let [key, item] of this._items.entries()) {\n if (key.substr(0, prefix.length) !== prefix) continue;\n if (item.node.parentNode) item.node.parentNode.removeChild(item.node);\n this._items.delete(key);\n }\n }\n\n _initItem(name) {\n const label = html`${label}${value}`;\n this._list.appendChild(node);\n this._items.set(name, { label, value, node });\n }\n\n log(key, value) {\n if (!this._items.has(key)) this._initItem(key);\n return (this._items.get(key).value.textContent = value);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W46","stdlib":"1"}} 44:"$43:body"

${DOM.text(name)}`;\n const value = html`

`;\n const node = html`