koaning

import numpy as np
import matplotlib.pylab as plt

def run_with_samples(feats=1):
    n = 1000
    xs = np.random.uniform(0, 2, (n, feats))
    ys = 1.5 + xs.sum(axis=1) + np.random.normal(0, 1, (n,))
    
    size_subset = 500
    n_samples = 2000

koaning

koaning

import uuid
import json
import random

import keras
import numpy as np
import tensorflow as tf
import click

koaning

async def fetch(url, json_body, session):
    async with session.post(url, json=json_body) as response:
        return await response.read()

async def run(json_bodies, n_sim=1000):
    tasks = []
    url = make_url(n_sim=n_sim)
    # Fetch all responses within one Client session,
    # keep connection alive for all requests.
    async with ClientSession(connector=TCPConnector(limit=None), read_timeout=60000, conn_timeout=60000) as session:

koaning

import random
import math
from chalice import Chalice

app = Chalice(app_name='sushigo-algorithm')
app.debug = False

def sort_hand(hand, order):
    card_dict = {card: i for i, card in enumerate(order)}
    return sorted(hand, key=lambda x: card_dict[x])

koaning

import tensorflow as tf
import numpy as np
import os
import uuid

TENSORBOARD_PATH = "/tmp/tensorboard-switchpoint"
# tensorboard --logdir=/tmp/tensorboard-switchpoint

x1 = np.random.randn(35) - 1
x2 = np.random.randn(35) * 2 + 5

koaning

import tensorflow as tf
import numpy as np
import os
import uuid

TENSORBOARD_PATH = "/tmp/tensorboard-switchpoint"
# tensorboard --logdir=/tmp/tensorboard-switchpoint

x1 = np.random.randn(35)-1
x2 = np.random.randn(35)*2 + 5

koaning

from flask import Flask
app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello World!"

if __name__ == "__main__":
    app.run()

koaning

\begin{align*} 
E[\pmb{Ax + y}] &=  \pmb{A}(E[\pmb{x}]) + \pmb{y} \\ 
Cov[\pmb{Ax + y}] &= \pmb{A}(Cov[\pmb{x}])\pmb{A}^T
\end{align*}

koaning

import tensorflow as tf
import numpy as np
import uuid

x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)