cemoody

from aa import config
import pandas as pd
import numpy as np
import os.path
import pickle

import chainer
import chainer.links as L
import chainer.functions as F

cemoody

from flask import Flask
from flask_restful import reqparse, abort, Api, Resource
from sqlitedict import SqliteDict
from flask import jsonify, request
import json

data = SqliteDict('./data.sqlite', autocommit=True)
app = Flask(__name__)
api = Api(app)

cemoody

# coding: utf-8

### Summary 

# This notebook presents the Barnes-Hut implementation of t-SNE. t-SNE is used to visualize high-dimensional data in a low dimensional space that attempts preserve the pairwise high-dimensional similarities in a low-dimensional embedding. The Barnes-Hut algorithm, which is used by astrophysicists to perform N-body simulations, allows the calculation of the t-SNE embedding in $O(N log N)$ time instead of $O(N^{2})$. This effectively allows us to learn embeddings of data sets with millions of elements instead of tens of thousands.

### Install

# To try out out the BH version of t-SNE, do the following:

cemoody