Orbifold

library(stockPortfolio)
library(quadprog)
library(ggplot2)
stocks <- c("SPY",  "EFA",  "IWM",  "VWO",  "LQD",  "HYG")

returns <- getReturns(stocks, freq = "week")

eff.frontier <-  function (returns,
                           short = "no",
                           max.allocation = NULL,

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probabilityInsideCluster = 0.5
probabilityOutsideCluster = 0.2
probabilityNoise = 0.3
clusterBoundaries = c(10, 25, 56, 81, 151, 293)
clientTypeCount = c(0, 0, 0, 0)
library(dplyr)
library(rpart)
# this create a frame of clients
makeClientsFrame = function(attribCount = 1000,
                            clientCount = 1000,

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# if needed, install plotly via shell like so
%sh pip install plotly
#---
%pyspark

import plotly
import numpy as np
from plotly.graph_objs import Scatter, Layout

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%angular

<base href="http://demos.telerik.com/kendo-ui/gantt/index">
<style>html { font-size: 14px; font-family: Arial, Helvetica, sans-serif; }</style>
<title></title>
<link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.common-material.min.css" />
<link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.material.min.css" />
<link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.material.mobile.min.css" />

<script src="http://kendo.cdn.telerik.com/2016.3.1028/js/jquery.min.js"></script>

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from gensim.utils import simple_preprocess

tokenize = lambda x: simple_preprocess(x)
# tokenize("We can load the vocabulary from the JSON file, and generate a reverse mapping (from index to word, so that we can decode an encoded string if we want)?!")

import os
import json
import numpy as np
from gensim.models import Word2Vec

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require(mxnet)
batch.size = 32
seq.len = 32
num.hidden = 16
num.embed = 16
num.lstm.layer = 1
num.round = 1
learning.rate= 0.1
wd=0.00001
clip_gradient=1

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