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| # Impor Library: | |
| import pandas as pd # pandas as pd: Untuk manipulasi data dalam bentuk DataFrame. | |
| import numpy as np # numpy as np: Untuk operasi numerik dan matriks. | |
| from collections import defaultdict # collections.defaultdict: Untuk membuat kamus yang secara otomatis menginisialisasi nilai kunci baru dengan nilai default. | |
| import spacy # spacy: Untuk pemrosesan bahasa, khususnya untuk tokenisasi. | |
| import pickle # pickle: Untuk menyimpan dan mengambil objek Python, seperti DataFrame PMI. | |
| from data_utils import * # data_utils: Ini adalah file terpisah yang berisi fungsi tambahan untuk pemrosesan data (tidak disertakan dalam kode ini). | |
| # from nltk.corpus import stopwords # nltk.corpus.stopwords: Untuk menghilangkan kata-kata yang umum seperti "the", "a", "and" (dikomentari dalam kode). | |
| # Fungsi Co-Occurence |
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| # Impor librari yang diperlukan | |
| import numpy as np # numpy: Digunakan untuk operasi numerik dan manipulasi matriks. | |
| import torch.nn as nn # torch.nn: Digunakan untuk mendefinisikan arsitektur jaringan saraf. | |
| import torch # torch: Digunakan untuk operasi tensor dan komputasi deep learning. | |
| import pickle # pickle: Digunakan untuk menyimpan dan memuat objek Python. | |
| from transformers import BertModel,BertTokenizer # transformers: Digunakan untuk mengimpor model BERT dan tokenizer. | |
| # Kelas Tokenizer digunakan untuk mengubah teks menjadi urutan token numerik. | |
| class Tokenizer(object): | |
| def __init__(self, word2idx=None): |
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| # Daftar librari yang dipakai | |
| import os # os: Librari ini digunakan untuk berinteraksi dengan sistem operasi, seperti membuat direktori. | |
| import math # math: Librari ini menyediakan fungsi matematika seperti sqrt yang digunakan untuk inisialisasi model. | |
| import argparse # argparse: Librari ini digunakan untuk memproses argumen baris perintah, memungkinkan pengguna untuk menyesuaikan parameter model. | |
| import random # random: Librari ini menyediakan fungsi untuk menghasilkan angka acak. | |
| import numpy # numpy: Librari ini digunakan untuk manipulasi array multidimensi. | |
| import torch # torch: Librari PyTorch, dasar untuk membangun model deep learning. | |
| import torch.nn as nn # torch.nn: Submodul PyTorch yang menyediakan kelas dan fungsi untuk membangun arsitektur jaringan saraf. | |
| from sklearn import metrics # sklearn: Librari scikit-learn, yang menyediakan fungsi untuk metrik evaluasi seperti f1_score. | |
| from data_utils import DDIDatesetReader # data_utils: Modul yang didefinisikan sendiri, berisi kelas DDIDatesetReader yan |
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| # RegCN | |
| import math # Untuk fungsi matematika dasar | |
| import torch # Librari utama untuk komputasi tensor dalam PyTorch | |
| import torch.nn as nn # Modul untuk membangun jaringan saraf | |
| import torch.nn.functional as F # Fungsi aktivasi dan operasi umum dalam jaringan saraf | |
| from transformers import BertModel # Model BERT dari pustaka Transformers, untuk embedding teks | |
| # Modul DynamicLSTM yang didefinisikan secara terpisah, mungkin untuk menangani panjang urutan yang variabel | |
| from layers.dynamic_rnn import DynamicLSTM |
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| # Dependency Graph | |
| import numpy as np # Mengimpor library NumPy dan menyingkatnya sebagai np untuk penggunaan selanjutnya. NumPy digunakan untuk manipulasi array dan matriks | |
| import spacy # Mengimpor library spaCy, yang merupakan library Pemrosesan Bahasa Alami (NLP) yang kuat untuk pengolahan teks | |
| import pickle # Mengimpor library pickle, yang digunakan untuk menyimpan objek Python dalam file biner, yang memungkinkan pemuatan kembali objek tersebut nanti | |
| # Memuat model bahasa spaCy yang dilatih sebelumnya untuk bahasa Inggris | |
| # Model ini akan digunakan untuk menganalisis sintaksis teks. | |
| nlp = spacy.load('en_core_web_sm') |
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| var m,_,afState={arrLen:{},form:{}},{stringify,parse}=JSON,autoForm=opts=>({view:()=>{var withAs=(obj,cb)=>cb(obj),ifit=(obj,cb)=>Boolean(obj)&&cb(obj),ors=array=>array.find(Boolean),ands=array=>array.reduce((a,b)=>a&&b,true),normal=name=>name.replace(/\d/g,'$'),fileData=(key,val)=>{var form=new FormData();form.append(key,val);return form},dateValue=(timestamp,hour)=>{var date=new Date(timestamp),zeros=num=>num<10?'0'+num:''+num,dateStamp=[date.getFullYear(),zeros(date.getMonth()+1),zeros(date.getDate())].join('-'),hourStamp=['T',zeros(date.getHours()),':',zeros(date.getMinutes())].join('');return!hour?dateStamp:dateStamp+hourStamp},linearize=obj=>{var recurse=doc=>withAs(doc[_.keys(doc)[0]],value=>typeof(value)==='object'?_.map(value,(val,key)=>recurse({[_.keys(doc)[0]+'.'+key]:val})):doc);return _.fromPairs(_.flattenDeep(recurse({doc:obj})).map(i=>[_.keys(i)[0].substr(4),_.values(i)[0]]))}afState.form[opts.id]=opts.doc?_.assign(afState.form[opts.id],linearize(opts.doc)):afState.form[opts.id];var attr={form: |
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| var m,_,mgState={},comps={},state={},withAs=(opts,cb)=>cb(opts),ifit=(opts,cb)=>Boolean(opts)&&cb(opts),ands=array=>array.reduce((a,b)=>a&&b,true),makeArray=n=>[...Array(n).keys()],makeIconLabel=(icon,label)=>[icon&&m('span.icon',m('i.fas.fa-'+icon)),m('span',label)],poster=(url,body,cb)=>fetch(url,{headers:{'Content-Type':'application/json'},method:'post',body:JSON.stringify(body)}).then(res=>res.json()).then(cb),mitGen=opts=>({view:()=>m('div',{class:'has-background-light'},opts.theme&&m('link',{rel:'stylesheet',href:`https://unpkg.com/bulmaswatch/${opts.theme }/bulmaswatch.min.css`}),m('nav.navbar.is-primary.is-fixed-top',m('.navbar-brand',m('a.navbar-item',{onclick:()=>[_.assign(mgState,{comp:undefined}),m.redraw()]},opts.brand.full||opts.brand.name),m('.navbar-burger',{role:'button',class:mgState.burgerMenu&&'is-active',onclick:()=>mgState.burgerMenu=!mgState.burgerMenu},_.range(3).map(i=>m('span',{'aria-hidden':true})))),m('.navbar-menu',{class:mgState.burgerMenu&&'is-active'},m('.navbar-start',_.map(op |
rikyperdana
/ autoTable.js
Created
May 14, 2024 01:12
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| var atState={},ors=array=>array.find(Boolean),ands=array=>array.reduce((a,b)=>a&&b,true),withAs=(obj,cb)=>cb(obj),ifit=(obj,cb)=>Boolean(obj)&&cb(obj),timestamp=str=> +(new Date(str)),atModify=(rows,opts)=>rows .filter(i=>withAs({start:_.get(atState,[opts.id,'start_range']),end:_.get(atState,[opts.id,'end_range']),func:eval(_.get(atState,[opts.id,'rangeFunc']))},({start,end,func})=>ands([start,end,timestamp(start)<timestamp(end)])?func(i,timestamp(start),timestamp(end)):i)).filter(i=>ands(_.map(_.get(atState,[opts.id,'filters']),(val,key)=>opts.filters[key].find(j=>j.label===val)).map(j=>j.func(i.data)))).filter(i=>_.values(i.row).map(_.lowerCase).join('').includes(_.get(atState,[opts.id,'search'])||'')).sort((a,b)=>_.get(atState,[opts.id,'sortBy'])&&_[_.get(atState,[opts.id,'sortWay'])?'gt':'lt'](a.row[_.get(atState,[opts.id,'sortBy'])],b.row[_.get(atState,[opts.id,'sortBy'])])?-1:1).slice((_.get(atState,[opts.id,'activeStep'])||0)*(_.get(atState,[opts.id,'pagination'])||0),((_.get(atState,[opts.id,'activeSt |
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| var m,_,afState={arrLen:{},form:{}},{stringify,parse}=JSON,autoForm=opts=>({view:()=>{var withAs=(obj,cb)=>cb(obj),ifit=(obj,cb)=>Boolean(obj)&&cb(obj),ors=array=>array.find(Boolean),ands=array=>array.reduce((a,b)=>a&&b,true),normal=name=>name.replace(/\d/g,'$'),fileData=(key,val)=>{var form=new FormData();form.append(key,val);return form},dateValue=(timestamp,hour)=>{var date=new Date(timestamp),zeros=num=>num<10?'0'+num:''+num,dateStamp=[date.getFullYear(),zeros(date.getMonth()+1),zeros(date.getDate())].join('-'),hourStamp=['T',zeros(date.getHours()),':',zeros(date.getMinutes())].join('');return!hour?dateStamp:dateStamp+hourStamp},linearize=obj=>{var recurse=doc=>withAs(doc[_.keys(doc)[0]],value=>typeof(value)==='object'?_.map(value,(val,key)=>recurse({[_.keys(doc)[0]+'.'+key]:val})):doc);return _.fromPairs(_.flattenDeep(recurse({doc:obj})).map(i=>[_.keys(i)[0].substr(4),_.values(i)[0]]))}afState.form[opts.id]=opts.doc?_.assign(afState.form[opts.id],linearize(opts.doc)):afState.form[opts.id];var attr={form: |
rikyperdana
/ pathFinder.js
Created
April 28, 2024 09:44
Pathfinder Algorithm and Vehicle Routing Problem in Functional Javascript
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| cityMap = { | |
| a: {b: 20, d: 80, g: 90}, | |
| b: {f: 10}, | |
| c: {f: 50, h: 20, d: 10}, | |
| d: {g: 20, c: 10}, | |
| e: {b: 50, g: 30}, | |
| f: {c: 10, d: 40}, | |
| g: {a: 20}, | |
| h: {h: 0} | |
| } |
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