sararob

Integrations

• Go package
• Ruby: script for caching a user's submissions on disk

Apps

Angular

sararob

{
    "rules": {
        ".read": true,
        "articles": {
          "$article": {
            "title": {
              ".write": "auth != null",
              ".validate": "newData.isString() && newData.val() != ''"
            },
            "url": {

sararob

SELECT 
  COUNT(*) as c, adjective, subject
FROM 
  JS(
    (SELECT tokens FROM [sara-bigquery:syntax.election_tweets]),
    tokens,
    "[{ name: 'adjective', type: 'string'}, 
      {name: 'subject', type: 'string'}
     ]",
    "function(row, emit) { 

sararob

import * as tf from ‘@tensorflow/tfjs’;
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [4], units: 100}));
model.add(tf.layers.dense({units: 4}));
model.compile({loss: ‘categoricalCrossentropy’, optimizer: ‘sgd’});

sararob

await model.fit(
  xData, yData, {
    batchSize: batchSize,
    epochs: epochs
});

sararob

var App = Ember.Application.create({});

var Promise = Ember.RSVP.Promise;

// ADAPTER

App.ApplicationAdapter = DS.FirebaseAdapter.extend({
  firebase: new Firebase("https://<your-firebase-name>.firebaseio.com/")
});

sararob

# Enable Standard SQL to run this query
# More on BigQuery here: https://cloud.google.com/bigquery/docs/quickstarts
SELECT 
  REGEXP_REPLACE(starttime, '([0-9]{1,2})/([0-9]{1,2})/([0-9]{4})', '\\3-\\1-\\2') as starttime, 
  from_station_lat, 
  from_station_lng 
FROM `sara-bigquery.nyc_citibike.trips_*`
WHERE cast(bikeid as int64) = 17526 # This is the bike with the most trips (found from another query)
order by starttime

sararob

#!/bin/bash

# SETUP
# 1) This requires having SoX (http://sox.sourceforge.net/) installed. On a Mac, you can do this with `brew install sox --with-flac`
# 2) You'll also need an API key for your Google Cloud Platform project: https://support.google.com/cloud/answer/6158862

# Create a request file with our JSON request in the current directory
FILENAME="request-"`date +"%s".json`
cat <<EOF > $FILENAME
{

sararob

'use strict';

const fs = require('fs');
const ndjson = require('ndjson');
const request = require('request');

fs.createReadStream('reddit-comments.json') // Newline delimited JSON file
  .pipe(ndjson.parse())
  .on('data', function(obj) {

sararob

// Get measurements for a new flower to generate a prediction
// The first argument is the data, and the second is the shape.
const inputData = tf.tensor2d([[4.8, 3.0, 1.4, 0.1]], [1, 4]);

// Get the highest confidence prediction from our model
const result = model.predict(inputData);
const winner = irisClasses[result.argMax().dataSync()[0]];

// Display the winner
console.log(winner);