kgourgou

"""
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)

kgourgou

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;

kgourgou

# 
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

kgourgou

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

kgourgou

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.
    

kgourgou

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.

kgourgou

;; -*- 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

kgourgou

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