madisonmay

{"negative": [0.7401444395271548, 0.28408478157420025, 0.0, 0.06104001736239428, 0.052829689608438674, 0.9631350435736623, 0.5655241352304745, 0.3679754384302407, 0.0, 0.3010526604746881, 0.0, 0.049003323253499376, 0.0, 0.3623349850554368, 0.0, 0.10201364624894066, 0.3699260265480315, 0.0, 0.0, 0.2873237222303052, 0.0, 0.0, 0.22599129803989196, 0.090218287970653, 0.281288272371325, 0.3114189846769092, 0.2567905368634442, 0.2336614633799683, 0.0051746546399171756, 0.20433062590490317, 0.3567373180001101, 0.0, 0.0, 0.06136738950275861, 0.48047753484164246, 0.11005137789240804, 0.26666696243911037, 0.07755409812205752, 0.21090605870812276, 0.5047718563195722, 0.0, 0.13487756118596553, 0.20700081782289187, 0.11511649315792956, 0.4930083318674049, 0.46939962236241345, 0.4799421694511274, 0.4043727993172107, 0.21706460801023178, 0.4991243945059197], "positive": [1.0, 0.8094221524927675, 0.7589542882620298, 0.49541068835885216, 0.8574337542075641, 0.7191642685843241, 0.7265150611484562, 0.5180262959691256, 0.7425010

madisonmay

import numpy as np
from string import ascii_lowercase, ascii_uppercase

def _translate_axes_names(seq: str, translation_table: dict=None):
    if translation_table is None:
        translation_table = {}

    if not seq:
        return "", translation_table

madisonmay

$.ajax({
  url: "https://apiv2.indico.io/sentiment/batch?key=YOUR_API_KEY",
  type: "POST", 
  data: JSON.stringify({"data": ["text", "more text"]}), 
  success: function (data) {
    console.log(data);
  }, error: function(err) {
    console.log(err);
  }
})

madisonmay

!obj:pylearn2.train.Train {

  dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
    which_set: 'train',
    start: 0,
    stop: 50000
  },

  model: !obj:pylearn2.models.mlp.MLP {
    layers: [

madisonmay

from collections import defaultdict

from pylearn2.train_extensions import TrainExtension
import matplotlib.pyplot as plt

class PlotChannels(TrainExtension):

    def __init__(self, channels, legend_loc='upper right'):
        self.channels = channels
        self.legend_loc = legend_loc

madisonmay

import itertools, bisect

def weighted_choice(seq, weights):
  cumulative_dist = itertools.accumulate(weights)
  while True:
    x = random.random() * cumdist[-1]
    i = [bisect.bisect(cumulative_dist, x)]
    yield seq[i]

madisonmay

    Array.prototype.sortBy = function(p){
        //sorts an array of objects by property p
        return this.sort(function(a,b){
            return (a[p] > b[p]) ? 1 : (a[p] < b[p]) ? -1 : 0;
        });
    }