Kosti kgourgou

## Kozachenko_Leonenko_entropy_est.py
import jax
import jax.numpy as jnp


def ent(X):
    """Calculates the entropy of a dataset X."""

    N, D = X.shape  # Get the number of samples and dimensions

    # Compute pairwise squared distances

## .spacemacs
;; -*- mode: emacs-lisp -*-
;; This file is loaded by Spacemacs at startup.
;; It must be stored in your home directory.

(defun dotspacemacs/layers ()
  "Configuration Layers declaration.
You should not put any user code in this function besides modifying the variable
values."
  (setq-default
   ;; Base distribution to use. This is a layer contained in the directory

## cherry_tree.py
def tree_to_cherry_tree(A):
    """
    Given the adj matrix of a tree,
    produces a cherry t-tree that includes it.

    Arguments:
    - adj: numpy array or sparse array, the adjacency matrix of the tree.

    Returns:
    - new_adj: numpy array or sparse array, the adjacency matrix of the cherry tree.

## dict_clipper.py
from scipy import clip

def dict_clipper(dictionary):
    """
    Clips all the numeric values of a dictionary so
    that they lie within [0,1].

    It recurses within inner dictionaries,
    if any.


## spacemacs-cheshe.md

      
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                Spacemacs Cheat Sheet
              
          
    Useful Spacemacs commands


SPC q q - quit
SPC w / - split window vertically
SPC w - - split window horizontally
SPC 1   - switch to window 1
SPC 2   - switch to window 2
SPC w d - delete current window
SPC TAB - switch to previous buffer
SPC b b - switch buffers


## importance-sampling-example.py
#
import scipy as sc
from scipy.stats import norm

N=100
M = 1000
nsamp = sc.zeros(M)

for i in range(M):
        data=sc.randn(N)*0.1

## funm_randomized
function F = funm_randomized(A,fun)
%FUNM_RANDOMIZED  Evaluate general matrix function using
% randomized approximate diagonalization method of Davies (2007).
tol = 8*eps(A(1,1));  % Tolerance for single or double precision A.
E = randn(size(A));
[V,D] = eig(A + (tol*norm(A,'fro')/norm(E,'fro'))*E);
F = V*diag(fun(diag(D)))/V;

## hilary_trump_nyt.py
"""
A hilary-endorsing script that loads the predictions from the NYT.
"""
import requests
from dateutil.parser import parse

#  election forecast link
url = "https://intf.nyt.com/newsgraphics/2016/11-08-election-forecast/president.json"

req = requests.get(url)
	import jax
	import jax.numpy as jnp


	def ent(X):
	"""Calculates the entropy of a dataset X."""

	N, D = X.shape # Get the number of samples and dimensions

	# Compute pairwise squared distances
	;; -- mode: emacs-lisp --
	;; This file is loaded by Spacemacs at startup.
	;; It must be stored in your home directory.

	(defun dotspacemacs/layers ()
	"Configuration Layers declaration.
	You should not put any user code in this function besides modifying the variable
	values."
	(setq-default
	;; Base distribution to use. This is a layer contained in the directory
	def tree_to_cherry_tree(A):
	"""
	Given the adj matrix of a tree,
	produces a cherry t-tree that includes it.

	Arguments:
	- adj: numpy array or sparse array, the adjacency matrix of the tree.

	Returns:
	- new_adj: numpy array or sparse array, the adjacency matrix of the cherry tree.
	from scipy import clip

	def dict_clipper(dictionary):
	"""
	Clips all the numeric values of a dictionary so
	that they lie within [0,1].

	It recurses within inner dictionaries,
	if any.
	#
	import scipy as sc
	from scipy.stats import norm

	N=100
	M = 1000
	nsamp = sc.zeros(M)

	for i in range(M):
	data=sc.randn(N)*0.1
	function F = funm_randomized(A,fun)
	%FUNM_RANDOMIZED Evaluate general matrix function using
	% randomized approximate diagonalization method of Davies (2007).
	tol = 8*eps(A(1,1)); % Tolerance for single or double precision A.
	E = randn(size(A));
	[V,D] = eig(A + (tolnorm(A,'fro')/norm(E,'fro'))E);
	F = V*diag(fun(diag(D)))/V;
	"""
	A hilary-endorsing script that loads the predictions from the NYT.
	"""
	import requests
	from dateutil.parser import parse

	# election forecast link
	url = "https://intf.nyt.com/newsgraphics/2016/11-08-election-forecast/president.json"

	req = requests.get(url)