cigrainger

import PropTypes from "prop-types";
import { PureComponent } from "react";

class Delayed extends PureComponent {
  constructor(props) {
    super(props);
    this.state = { hidden: true };
  }

  componentDidMount() {

cigrainger

model = Sequential()
model.add(Dense(5, input_shape = (10,)))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer= 'sgd',
              metrics=['binary_accuracy', 'fmeasure', 'precision', 'recall'])

history = model.fit(X_train, Y_train,

cigrainger

ebony	chris
gotta go	okay i
ok i	en.wikipedia.org wiki
my boss	https en.wikipedia.org
my mum	my mom
ok i'm	okay i'm
this shit	wonderful human
https youtu.be	ebony i
my bro	mean that's

cigrainger

ebony	chris
gotta go	okay i
ok i	en.wikipedia.org wiki
my boss	https en.wikipedia.org
my mum	my mom
ok i'm	okay i'm
this shit	wonderful human
https youtu.be	ebony i
my bro	mean that's

cigrainger

ebony	chris
in other news	https en.wikipedia.org wiki
in the states	on my way
ok i'm going	yeah i mean
gotta go x	wonderful human being
told me to	the same way
i actually don't	i mean that's
i feel so	but i also
tell you about	my god i

cigrainger

data <- readLines('~/Downloads/_chat.txt')
remove <- vector(mode = 'logical', length = length(data))
for (i in 1:length(data)){
  if (!str_detect(data[i], ': Christopher Grainger: |: Ebony Ruggero: ')) {
    data[i-1] <- str_c(data[i-1], data[i], sep = ' ')
    remove[i] <- TRUE
  } else {
    remove[i] <- FALSE
  }
}

cigrainger

i <- seq(1, numberguesses)
result <- apply(i, function(x){
        a = random(1,10000000)
        b = random(a,10000001)
        cond_exp <- CONEXP(a,b)
        cond_exp <- cond_exp[cond_exp is within .1% of actual expected value]
        return (cond_exp)
  }
)

cigrainger

library(readr)
library(dplyr)
df <- read_csv('~/desktop/guns.csv')

library(ggplot2)
library(ggthemes)
df %>%
  arrange(desc(guns_per_100_people)) %>% 
  top_n(n = 20, wt = guns_per_100_people) %>% 
  ggplot(aes(x = country, 

cigrainger

library(quanteda)
library(dplyr)
library(tidyr)
library(networkD3)

load(url("http://www.kenbenoit.net/files/presDebateCorpus2016seg.RData"))
candidates <- subset(presDebateCorpus2016seg, speakertype == 'candidate')
dfm <- dfm(candidates, groups = c("tag"), ngrams = 1:3,
           ignoredFeatures = c('people','go','going','will','know','think',
                               'country','get','applause','want','need',

cigrainger

temp_precip = []
for i,p in enumerate(correct_precip):
    if i % 6 == 0:
        temp_precip.append(p)
    else:
        temp_precip.append(p-correct_precip[i-1])
    if i % 10000 == 0:
        print i