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##################### | |
# SETUP # | |
##################### | |
# imports | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import math | |
import struct |
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(({}+[])[4]+String(new Map([((Map) => {[String()]+{}})]))[8]+String(this)[([]+{}).charCodeAt()-({}+[[{Object}]])[8].charCodeAt()]+String.fromCharCode([Map+[this+[()=>{map(() => ([]+{}))}]],Object.keys([]+{})].map(() => (([[this],{this:[this]},{Map}+[]].map((l) => (l+[]+{})).join().match(/o/g).length+([]+{}).length)/(([[]+{},String([Map, {}+Object])]).length))-(([[]+{},String([Map, {}+Object])]).length)).join("")))+([Object.keys ? [[].fill]:{fill:fill(fill)[fill].fill(fill)}].join(([{},[]]) => {[[]+{}].keys})? String([unescape(unescape)].map(()=>unescape))[parseInt((((([]+[]/[]).length-[]/[]+{}).length)+[])[0]+((([[[([([].reduce)]*Object())]]+[].map((NaN)=>[[this],[]])].map(({})=>[[{}+[]],[()=>[].join(this)]]).map(({})=>[,,[,]])+[].length+",").length)-2))] : ([{_:[]}]).map(unescape)) |
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import random # imports | |
ITER = 100_000 # iteration count | |
a_guilty = b_guilty = 0 # initialize our counters | |
for _ in range(ITER): | |
# initialize our profiles | |
# [has blood type x, is guilty] | |
profiles = [[True, 1], [((random.randint(1,10)) == 1), 0]] |
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// ########################################################## | |
// pythonic list comprehension in js | |
// (as a toy task to test out github.com/metakirby5/codi.vim) | |
// ########################################################## | |
// #################################### | |
// first method: ~rust style. heavy WIP | |
// #################################### | |
String.prototype.for = function(inp) { | |
return inp.map(eval("x => " + this)) |
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" conditionally bind tab based on whether or not autosugestions are available | |
inoremap <expr> <Tab> pumvisible() ? "\<C-n>" : copilot#Accept("\<Tab>") | |
" reset the copilot tab map | |
let g:copilot_no_tab_map = v:true |
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# my love for math is driven by its beauty. | |
# but ultimately, i'm here to learn how to solve problems. | |
# sometimes, the best path forward is not the beautiful one -- and we have to learn how to follow that one too. | |
# what follows is the ugly solution, but frankly, a solution nonetheless. | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from tqdm import tqdm |
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[{"label":"UMAP small","isSelected":false,"tSNEIteration":2856,"tSNEPerplexity":22,"tSNELearningRate":1,"tSNEis3d":true,"umapIs3d":true,"umapNeighbors":15,"pcaComponentDimensions":[0,1,2],"projections":[{"pca-0":-0.905286967754364,"pca-1":0.3932833671569824,"pca-2":0.04721357300877571,"pca-3":-0.023566333577036858,"pca-4":0.009789039380848408,"pca-5":0.08993859589099884,"pca-6":-0.03183436393737793,"pca-7":-0.08828509598970413,"pca-8":0.05682482570409775,"pca-9":-0.01572861522436142,"tsne-0":19.323570375610835,"tsne-1":44.29724033227858,"tsne-2":-0.2908658269849631},{"pca-0":0.10668350756168365,"pca-1":-0.09059428423643112,"pca-2":-0.35334262251853943,"pca-3":-0.25714996457099915,"pca-4":-0.11577161401510239,"pca-5":0.5286425352096558,"pca-6":0.08626057207584381,"pca-7":0.120063915848732,"pca-8":0.34665390849113464,"pca-9":0.22104071080684662,"tsne-0":9.976823882328969,"tsne-1":-13.717373699700145,"tsne-2":-6.906102830290607,"umap-0":2.645669537615786,"umap-1":-1.051831590176074,"umap-2":-1.5504984237958803}, |