In many machine learning applications, data handling is done using lists, which are then converted into numpy arrays. This practice can be memory inefficient, especially when dealing with large datasets. Below, I illustrate the problem and provide a solution using numpy pre-allocation.
Let's take an example of data pre-processing with a list:
pre_processed = []
| /* eslint-disable */ | |
| declare module '@elastic/app-search-node' { | |
| export = AppSearchClient; | |
| declare class AppSearchClient { | |
| /** | |
| * Creates a jwt search key that can be used for authentication to enforce a set of required search options. | |
| * | |
| * @param {String} apiKey the API Key used to sign the search key | |
| * @param {String} apiKeyName the unique name for the API Key | |
| * @param {Object} options Object see the <a href="https://swiftype.com/documentation/app-search/authentication#signed">App Search API</a> for supported search options |
| const axios = require("axios"); | |
| const Spotify = require("node-spotify-api"); | |
| const moment = require("moment"); | |
| /** | |
| * | |
| * @param searchType {{'concert' | 'movie' | 'spotify'}} | |
| * @param spotifyCredentials {Object=} | |
| * @param spotifyCredentials.id {string} | |
| * @param spotifyCredentials.secret {string} |
| import Paper from "@material-ui/core/Paper"; | |
| import { withStyles } from "@material-ui/core/styles"; | |
| import { graphql } from "react-apollo"; | |
| import { compose } from 'recompose'; | |
| import getTree from './queries/getTree'; | |
| import styles from './styles'; | |
| const Canvas = ({ | |
| classes, | |
| // activeItem, |