luthfianto

const sqlite3 = require("sqlite3")

class SqliteDF {
    table: any;
    created: boolean;
    con: any;
    max_index: number;
    _group_cols: any;
    verbose: boolean;
    _cache: any;

luthfianto

console.error(e);

let { message } = e;
if (e.sqlMessage) {
    message = e.sqlMessage.split(" for")[0];
}
throw new Error(message);

luthfianto

async function mergeSort<T>(arr: T[]): Promise<T[]> {
    if (arr.length < 2) {
        return arr;
    }

    const middle = Math.floor(arr.length / 2);
    const left = arr.slice(0, middle);
    const right = arr.slice(middle);

    const leftPromise = mergeSort(left);

luthfianto

/*
 * The MIT License
 * 
 *  Copyright (c) 2010 Mike de Boer
 * 
 *  Permission is hereby granted, free of charge, to any person obtaining a copy
 *  of this software and associated documentation files (the "Software"), to deal
 *  in the Software without restriction, including without limitation the rights
 *  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 *  copies of the Software, and to permit persons to whom the Software is

luthfianto

{
    "version": "0.2.0",
    "configurations": [
      {
          "type": "node",
          "request": "launch",
          "name": "Mocha All",
          "program": "${workspaceFolder}/node_modules/mocha/bin/_mocha",
          "args": [
                "--timeout",

luthfianto

SHELL=/bin/bash
VW=vw
python_exe=python2.7

train_data=wsj_train_subset
test_data=wsj_test_subset
tag=tags

.SECONDARY:

luthfianto

{
    "defaultSeverity": "error",
    "extends": [
        "tslint:all"
    ],
    "jsRules": {},
    "rules": {
        "array-type": false,
        "comment-format": false,
        "forin": false,

luthfianto

class FullScoreReturn {
    constructor(
        public readonly log_prob: number,
        public readonly ngram_length: number,
        public readonly oov: number,
    ) { }

    get prob() {
        return this.log_prob;
    }

luthfianto

import tensorflow as tf
import numpy as np
import keras
import keras.backend as K
from functools import reduce
from keras.models import Model
import keras.callbacks as cbks
from keras.applications import ResNet50
from keras.layers import Conv2D
import threading

luthfianto

from keras.layers.core import Layer  
from keras import initializers, regularizers, constraints  
from keras import backend as K

class Attention(Layer):
    def __init__(self,
                 kernel_regularizer=None, bias_regularizer=None,
                 kernel_constraint=None, bias_constraint=None,
                 use_bias=True, **kwargs):
        """