evanthebouncy

import numpy as np

np.random.seed(0)

def generate_pair():
    # make two random numbers x and y
    # make x first randomly from 1 to 10
    x = np.random.randint(1, 11)
    y = np.random.randint(1, 11)
    x_y = x + y

evanthebouncy

track = track4()
car = newCar(track)
startDisplay(track, car)

def control(sd, pastsd):
    # average these values to be more smooth
    leftt = sd[0] + pastsd[0];
    mid = sd[1] + pastsd[1];
    # this bit of assymetry is actually very clever, because 
    # "intuitively" assymetrical things has more information on it

evanthebouncy

import numpy as np
import random
from queue import PriorityQueue


# here is the task
# we want to construct a goal number, starting from a template of expression
# i.e. (E * E) + E = 11
# we can expand the expression E node further following the grammar
# E -> E + E | E * E | -3 | -2 | -1 | 1 | 2 | 3

evanthebouncy

import numpy as np
import random
from queue import PriorityQueue


# here is the task
# we want to construct a goal number, starting from a template of expression
# i.e. (E * E) + E = 11
# we can expand the expression E node further following the grammar
# E -> E + E | E * E | -3 | -2 | -1 | 1 | 2 | 3

evanthebouncy

(declare-sort Person)
(declare-fun ancestor (Person Person) Bool)

;; anti symmetry
(assert (forall ((x Person) (y Person))
                (=> (ancestor x y) (not (ancestor y x)))))

;; transitivity
(assert (forall ((x Person) (y Person) (z Person))
  (=> (and (ancestor x y) (ancestor y z))

evanthebouncy

import numpy as np

# open whichever w2v file you want
fd = open("glove.6B/glove.6B.50d.txt").readlines()

# return a list of keys (words) and the w2v matrix Nxd
def to_numpy(lines):
    keys = []
    ary = []
    for l in lines:

evanthebouncy

import random
import numpy as np

# there are 10 casinos
# each casino_i initially has 0 arms, however
# each casino_i is equipted with a slot-machine maker 
# assume the slot machine maker is Unif(a_i, opt_i)
# where a_i < opt_i < 1

# you can take 2 kinds of actions:

evanthebouncy

def normalise(mat, axis):
    if axis == 0:
        row_sums = mat.sum(axis=1)
        new_matrix = mat / row_sums[:, np.newaxis]
        return new_matrix
    if axis == 1:
        col_sums = mat.sum(axis=0)
        new_matrix = mat / col_sums[np.newaxis, :]
        return new_matrix
      

evanthebouncy

import numpy as np
from numpy import array
from scipy.misc import imresize
import copy
from scipy.ndimage.filters import gaussian_filter
import random

from keras.datasets import mnist
import matplotlib.pyplot as plt

evanthebouncy

%%
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%% lgrind.dtx  (with options: `package')
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%% languages for LaTeX.