Nguyen Huynh huynhnguyen

## privacy_recommendation.py
print('install splotlight if needed')
#!pip install  git+https://github.com/maciejkula/spotlight.git@master#egg=spotlight


# # movielense data
# - Download the 100k version from https://grouplens.org/datasets/movielens/
# - extract to folder './ml-100k/'

import numpy as np
from spotlight.interactions import Interactions

## install note
#annoying babel error
npm install -D babel-loader @babel/core @babel/preset-env webpack

## jupyter_custom.js
element.append(`<button type="button"
                   onclick='$("div.input").toggle(1000)'
                   class="btn btn-primary active">toggle code</button>`)

## ldd
p = np.random.randint(0,10,[n_topics, n_words])*1.0
sum_over_topic = np.sum(p, axis=1)
dists = np.divide(p.T,sum_over_topic).T
n_topics = 3
n_words = 10
docs_topic = [0, 1, 2, 2, 1, 2, 0, 1, 2]
docs = []
for d_topic in docs_topic:
    _pvals = dists[d_topic]
#     print(np.sum(_pvals[:,-1]))

## tank_mh.py
'''
This is the edward implementation of German tank problem
link: https://en.wikipedia.org/wiki/German_tank_problem
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import edward as ed
import numpy as np

## orthogonal_initialize_demo_code.py
import numpy as np
def orthogonal_initializer(scale=1.0, seed=None, dtype=tf.float32):
    def _initializer(shape, dtype=dtype, partition_info=None):
        flat = (shape[0], np.prod(shape[1:]))
        a = np.random.normal(0.0, 1.0, flat)
        u, _, v = np.linalg.svd(a, full_matrices=False)
        q = (u if u.shape == flat else v).reshape(shape)
        return tf.constant(scale * q[:shape[0], :shape[1]], dtype=dtype)
    return _initializer

## BayesianSearchOptimization.py
from scipy import minimum,maximum
from scipy.optimize import minimize
import time
from sklearn.gaussian_process import GaussianProcess
from scipy.stats import norm,entropy
Bounds = [[-16,16]]
#target function
def f(x):
    y = x**2*np.sin(x)
    return y

## bayesianprior.m
%% Baseball Batting Average Shrinkage Estimates
% Reproduce example from
% "Data Analysis Using Stein's Estimator and its Generalizations"
% Bradley Efron; Carl Morris JASA Vol. 70, No. 350. (Jun., 1975), pp.
% 311-319.
%% Data
% 1970 batting averages for 18 major league players.
% first column = batting average for first 45 at bats
% 2nd column   = batting everage for remainder of season

## index.html
<!doctype html>
<html>
  <head>
    <meta charset="utf-8" />
    <script src="script.js"></script>
  </head>
  <body><body>
</html>
	print('install splotlight if needed')
	#!pip install git+https://github.com/maciejkula/spotlight.git@master#egg=spotlight


	# # movielense data
	# - Download the 100k version from https://grouplens.org/datasets/movielens/
	# - extract to folder './ml-100k/'

	import numpy as np
	from spotlight.interactions import Interactions
	#annoying babel error
	npm install -D babel-loader @babel/core @babel/preset-env webpack
	element.append(`<button type="button"
	onclick='$("div.input").toggle(1000)'
	class="btn btn-primary active">toggle code</button>`)
	p = np.random.randint(0,10,[n_topics, n_words])*1.0
	sum_over_topic = np.sum(p, axis=1)
	dists = np.divide(p.T,sum_over_topic).T
	n_topics = 3
	n_words = 10
	docs_topic = [0, 1, 2, 2, 1, 2, 0, 1, 2]
	docs = []
	for d_topic in docs_topic:
	_pvals = dists[d_topic]
	# print(np.sum(_pvals[:,-1]))
	'''
	This is the edward implementation of German tank problem
	link: https://en.wikipedia.org/wiki/German_tank_problem
	'''
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import edward as ed
	import numpy as np
	import numpy as np
	def orthogonal_initializer(scale=1.0, seed=None, dtype=tf.float32):
	def _initializer(shape, dtype=dtype, partition_info=None):
	flat = (shape[0], np.prod(shape[1:]))
	a = np.random.normal(0.0, 1.0, flat)
	u, _, v = np.linalg.svd(a, full_matrices=False)
	q = (u if u.shape == flat else v).reshape(shape)
	return tf.constant(scale * q[:shape[0], :shape[1]], dtype=dtype)
	return _initializer
	from scipy import minimum,maximum
	from scipy.optimize import minimize
	import time
	from sklearn.gaussian_process import GaussianProcess
	from scipy.stats import norm,entropy
	Bounds = [[-16,16]]
	#target function
	def f(x):
	y = x*2np.sin(x)
	return y
	%% Baseball Batting Average Shrinkage Estimates
	% Reproduce example from
	% "Data Analysis Using Stein's Estimator and its Generalizations"
	% Bradley Efron; Carl Morris JASA Vol. 70, No. 350. (Jun., 1975), pp.
	% 311-319.
	%% Data
	% 1970 batting averages for 18 major league players.
	% first column = batting average for first 45 at bats
	% 2nd column = batting everage for remainder of season
	<!doctype html>
	<html>
	<head>
	<meta charset="utf-8" />
	<script src="script.js"></script>
	</head>
	<body><body>
	</html>