john-bradshaw

import numpy as np

def intersect_indcs_two_arrays(x_array, y_array):
    """intersect_indcs_two_arrays(x, y) -> xm, ym
    x, y: 1-d arrays of unique values
    xm, ym: indices into x and y giving sorted intersection
    code taken from http://stackoverflow.com/questions/33529057/indices-that-intersect-and-sort-two-numpy-arrays?lq=1
    """
    assert np.all(np.logical_not(map(are_there_duplicates, [x_array, y_array]))), "Intersect assumes arrays are unique."
    # basic idea taken from numpy.lib.arraysetops.intersect1d

john-bradshaw

"""
Code for section 4.1 of:
Hall, R., Rinaldo, A. and Wasserman, L., 2013. Differential privacy for
functions and functional data. Journal of Machine Learning Research, 14(Feb), pp.703-727.


Coded up in a bit of a rush so apologies if there are bugs.
"""

john-bradshaw

"""
Code for Figure 1a of
Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
In Advances in Neural Information Processing Systems (pp. 267-273).
"""

import numpy as np
from scipy.integrate import odeint
from scipy.optimize import fsolve
from scipy import stats

john-bradshaw

"""
Code for Figure 1c of
Saul, L.K. and Jordan, M.I., 1997. A variational principle for model-based morphing.
In Advances in Neural Information Processing Systems (pp. 267-273).

Note not exactly the same setup and do not know settings of ell and variance to use
Also not sure how to deal with the change points

Written in a bit of a rush and not thoroughly tested so use with caution
"""

john-bradshaw

import os

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data as mnist_input_data
import numpy as np
from sklearn import cluster
from scipy.spatial import distance
import pandas as pd

john-bradshaw

import enum

import numpy as np
from scipy import stats
import fire
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from matplotlib.widgets import Button

john-bradshaw

import os

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data as mnist_input_data
import numpy as np
from sklearn import cluster
from scipy.spatial import distance
import pandas as pd

john-bradshaw

import numpy as np
import tensorflow as tf

def main1():
    x = tf.get_variable("x", initializer=np.float64(0))

    x_id = tf.identity(x)
    x_rd = x.read_value()
    x_p0 = x + 0
    with tf.control_dependencies([x_id, x_rd, x_p0]):

john-bradshaw

import tensorflow as tf

def main1():

    sess = tf.Session()
    x = tf.Variable(1.)
    sess.run(tf.initialize_variables([x]))

john-bradshaw

"""
Module for identifying for RDKit functions working with reaction SMILES strings.
"""


import collections
import itertools
import functools
import typing