| /* globals localStorage */ | |
| import { onSnapshot, applySnapshot } from 'mobx-state-tree'; | |
| import Storage from './storage'; | |
| export const persist = (name, store, options, schema = {}) => { | |
| let hydrated = false; | |
| let storage = options.storage; | |
| if (typeof localStorage !== 'undefined' && localStorage === storage) { |
| git rm -r --cached node_modules | |
| git commit -m 'Remove the now ignored directory node_modules' | |
| git push origin master |
| from nltk.tokenize import word_tokenize | |
| import pickle | |
| import pprint | |
| import json | |
| """ | |
| (heads, descs, keywords) = ([headline], [description], ) | |
| """ |
| function flatten(data){ | |
| return [].concat.apply([],[].concat.apply([],data)) | |
| } | |
| var result = flatten([[1,2,[3]],4]); | |
| console.log(result); |
What follows is a technical test for this job offer at CARTO: https://boards.greenhouse.io/cartodb/jobs/705852#.WSvORxOGPUI
Build the following and make it run as fast as you possibly can using Python 3 (vanilla). The faster it runs, the more you will impress us!
Your code should:
| MOVIMENTO;AMOUNT;DATETIME;PAYOUT;SHORTCODE;TRANSACTION TIME | |
| SIGNAL_100;100.00;25/10/17 21:00;100.00;CALL_FRXUSDJPY_100_1508964887_1508965247_S0P_0;1508965253 | |
| -;0.00;25/10/17 21:00;20.00;PUT_FRXUSDJPY_20_1508964887_1508965247_S0P_0;1508965253 | |
| NEURAL_NEUTRO;10.00;25/10/17 20:55;10.00;PUT_FRXUSDJPY_10_1508964577_1508964937_S0P_0;1508964941 | |
| -;0.00;25/10/17 20:50;10.00;PUT_FRXUSDJPY_10_1508964262_1508964622_S0P_0;1508964624 | |
| SIGNAL_100;100.00;25/10/17 18:50;100.00;PUT_FRXUSDJPY_100_1508957070_1508957430_S0P_0;1508957432 | |
| NEURAL;20.00;25/10/17 18:50;20.00;PUT_FRXUSDJPY_20_1508957070_1508957430_S0P_0;1508957432 | |
| NEURAL;20.00;25/10/17 18:50;20.00;PUT_FRXUSDJPY_20_1508957070_1508957430_S0P_0;1508957432 | |
| SIGNAL_100;100.00;25/10/17 18:50;100.00;PUT_FRXUSDJPY_100_1508957070_1508957430_S0P_0;1508957432 | |
| NEURAL_NEUTRO;10.00;25/10/17 18:40;10.00;PUT_FRXUSDJPY_10_1508956452_1508956812_S0P_0;1508956814 |
| Privacy Policy | |
| João Gabriel built the Mobilito app as a Free app. This SERVICE is provided by João Gabriel at no cost and is intended for use as is. | |
| This page is used to inform website visitors regarding my policies with the collection, use, and disclosure of Personal Information if anyone decided to use my Service. | |
| If you choose to use my Service, then you agree to the collection and use of information in relation to this policy. The Personal Information that I collect is used for providing and improving the Service. I will not use or share your information with anyone except as described in this Privacy Policy. | |
| The terms used in this Privacy Policy have the same meanings as in our Terms and Conditions, which is accessible at Mobilito unless otherwise defined in this Privacy Policy. |
| ============================================================== | |
| Function | Description | |
| ============================================================== | |
| startAt | todos os valores a partir de um índice específico | |
| endAt | todos os valores terminando em um índice particular | |
| after | todos os valores após um índice particular. | |
| before | todos os valores antes de um índice particular. | |
| between | todos os valores entre dois índices | |
| ============================================================== |
| var subset = dataFrame.skip(10).take(15); | |
| console.log(subset.toString()); |
Convolutional neural networks for emotion classification from facial images as described in the following work:
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
If you find our models useful, please add suitable reference to our paper in your work.