talolard

import requests
from functools import partial
class LTSession(requests.Session):
    def __init__(self, workspace:str,user:str,pwd:str,host:str=None):
        """[summary]
        Initializes a LightTag API session object
        Arguments:
            workspace {str} -- [The name of your workspace]
            user {str} -- [Your LightTag Username]
            pwd {str} -- [Your LightTag password]

talolard

@unpublished{LightTag,
    AUTHOR = {Perry, Tal },
    TITLE  = {{LightTag}: A platform for managing text annotation projects
    YEAR   = {2018},
    Note   = {To appear}
}

talolard

sudo yum update -y
sudo yum install -y docker
sudo usermod -a -G docker ec2-user
sudo curl -L https://github.com/docker/compose/releases/download/1.21.0/docker-compose-`uname -s`-`uname -m` | sudo tee /usr/local/bin/docker-compose > /dev/null
sudo chmod +x /usr/local/bin/docker-compose
sudo service docker start
sudo chkconfig docker on
sudo systemctl enable docker

talolard

talolard

talolard

      self.alphas= tf.contrib.layers.fully_connected(
          inputs=l2,
          num_outputs=num_stocks+1,
          activation_fn=tf.nn.relu,
          weights_initializer=tf.initializers.glorot_uniform)

      self.alphas +=1

      self.dirichlet  = tfp.distributions.Dirichlet(self.alphas)
      self.action = self.dirichlet._sample_n(1)

talolard

import numpy as np
from collections import  defaultdict

from env.priceGenerator import make_stock

costPerShare = 0 # 0.01
class Env:
    '''
    A simple environemnt for our agent,
    the action our agent gives is  weighting over the stocks + cash

talolard

import numpy as np
def make_stock(length=100, num_stocks=2):
    alpha = 0.9
    k = 2
    cov = np.random.normal(0, 5, [num_stocks, num_stocks])
    cov = cov.dot(cov.T) # This is a positive semidefinite matrix, e.g. a covariance matrix
    A = np.random.multivariate_normal(np.zeros(num_stocks), cov, size=[length]) # sample noise, with covariance 
    B = np.random.multivariate_normal(np.zeros(num_stocks), cov, size=[length]) # sample another noise, with covariance
    bs = [np.zeros(shape=num_stocks)] # 
    ps = [np.zeros(shape=num_stocks)] # The prices

talolard

import pprint
import boto3
import sys
repo_name = sys.argv[1]
print(repo_name)
client = boto3.client('ecr')
response = client.list_images(
    repositoryName=repo_name,
    filter={
        'tagStatus': 'TAGGED'

talolard

import pandas as pd
D = pd.read_csv('./my_csv_file.csv')
D.to_json('./my_new_json_file.json',orient="records")