sanealytics

import React, { Component } from 'react';
import { StyleSheet, View, Image } from 'react-native';
import {
  PanGestureHandler,
  State,
  PinchGestureHandler,
  TapGestureHandler,
} from 'react-native-gesture-handler';

import Animated, { Easing } from 'react-native-reanimated';

sanealytics

import React, { Component } from 'react';
import { StyleSheet, View, Image, } from 'react-native';
import {
  PanGestureHandler,
  State,
  PinchGestureHandler,
  TapGestureHandler,
} from 'react-native-gesture-handler';

import Animated, { Easing } from 'react-native-reanimated';

sanealytics

shinyServer(function(input, output, session) {

  # Session level
  idx <- round(runif(1, 1, 2500)) # random place to start

  state <- reactive({
    if(input$submit > 0) { # Not on first page load
      isolate({ # Don't get reactive on me now
        answer <- input$yesorno

sanealytics

library(shiny)

shinyUI(fluidPage(

  # Application title
  titlePanel("Verify Auto-tagged dresses from Instagram"),

  # Sidebar with a slider input for number of bins
  sidebarLayout(
    sidebarPanel(

sanealytics

library(mongolite)

# Connections to mongo
instagram <- mongo(collection = "InstagramV4", db = "DeepDress", url = "mongodb://localhost", verbose = FALSE)
matches <- mongo(collection = "MatchesV4", db = "DeepDress", url = "mongodb://localhost", verbose = FALSE)
corrections <- mongo(collection = "CorrectionsV4", db = "DeepDress", url = "mongodb://localhost", verbose = FALSE)

# Get Instagram data we saved earlier
ids.df <- instagram$find(fields = '{"_id" : 1, "payload.created_time" : 1}')
ids.df.simple <- data.frame(id = ids.df$`_id`,

sanealytics

import urllib.request
import json
from pymongo import MongoClient
import time
from multiprocessing import Pool

DEEP_DRESS_WGET = 'http://localhost/wget/?url='
DEEP_DRESS_PREDICT = 'http://localhost/predict/'

def wget_deep_dress(url):

sanealytics

import urllib.request
import json
from pymongo import MongoClient
import time

def fetchAndStash(url, client):
  try:
    response = urllib.request.urlopen(url + '&count=100')
    f = response.read()
    payload = f.decode('utf-8')

sanealytics

data(MovieLense)

scheme <- evaluationScheme(MovieLense, method = "split", train = .9,
                           k = 1, given = 10, goodRating = 4)

scheme

# register recommender
recommenderRegistry$set_entry(
  method="RSVD", dataType = "realRatingMatrix", fun=REAL_RSVD,

sanealytics

require(recommenderlab) # Install this if you don't have it already
require(devtools) # Install this if you don't have this already
# Get additional recommendation algorithms
install_github("sanealytics", "recommenderlabrats")

data(MovieLense) # Get data

# Divvy it up
scheme <- evaluationScheme(MovieLense, method = "split", train = .9,
                           k = 1, given = 10, goodRating = 4) 

sanealytics

unroll_Vecs <- function (params, Y, R, num_users, num_movies, num_features) {
  # Unrolls vector into X and Theta
  # Also calculates difference between preduction and actual 
  
  endIdx <- num_movies * num_features
  
  X     <- matrix(params[1:endIdx], nrow = num_movies, ncol = num_features)
  Theta <- matrix(params[(endIdx + 1): (endIdx + (num_users * num_features))], 
                  nrow = num_users, ncol = num_features)