fehiepsi

tik0

talperetz

mandieq

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff
import plotly.offline as offline
import pandas as pd

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

kauffmanes

Thanks everyone for commenting/contributing! I made this in college for a class and I no longer really use the technology. I encourage you all to help each other, but I probably won't be answering questions anymore.

This article is also on my blog, too.

Note: $ denotes the start of a command. Don't actually type this.

Steps to Install Anaconda on Windows Ubuntu Terminal

1. Install WSL (Ubuntu for Windows - can be found in Windows Store). I recommend the latest version (I'm using 18.04) because there are some bugs they worked out during 14/16 (microsoft/WSL#785)
2. Go to https://repo.continuum.io/archive to find the list of Anaconda releases
3. Select the release you want. I have a 64-bit computer, so I chose the latest release ending in x86_64.sh. If I had a 32-bit computer, I'd select the x86.sh version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose `Ana

mwaskom

tigershen23

def model(data):
  alpha = torch.tensor(6.0)
  beta = torch.tensor(10.0)
  pay_probs = pyro.sample('pay_probs', dist.Beta(alpha, beta).expand(3).independent(1))
  normalized_pay_probs = pay_probs / torch.sum(pay_probs)

  with pyro.iarange('data_loop', len(data)):
    pyro.sample('obs', dist.Categorical(probs=normalized_pay_probs), obs=data)

rauschma

Strings

String.prototype.*

None of the string methods modify this – they always return fresh strings.

• charAt(pos: number): string ^ES1

  Returns the character at index pos, as a string (JavaScript does not have a datatype for characters). str[i] is equivalent to str.charAt(i) and more concise (caveat: may not work on old engines).

ByungSunBae

import tensorflow as tf
from tensorflow import layers
from tensorflow.contrib.layers import xavier_initializer

import gym
import numpy as np

from collections import deque
from collections import namedtuple

calclavia

import gym
import numpy as np

from keras.models import Model
from keras.layers import *
from keras import backend as K

from collections import deque

def one_hot(index, categories):