itdxer

# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch

# This is a summary file containing the main takeaways from chapter 6.

import urllib.request
import zipfile
import os

itdxer

import math
from functools import reduce
from collections import namedtuple

def multiply_all(numbers):
    return reduce(int.__mul__, numbers)

# 1. Highly composite number consists only of the consecutive primes
# 2. Larger primes cannot have larger powers, since the number can always be reduced without effecting
#    number of divisors

itdxer

import numpy as np
from itertools import combinations

primes = [2, 2, 2, 5, 5, 7]
number = 1400
assert np.prod(primes) == number  # sanity check

observed_sets = set()
n_hips = 0

itdxer

import random

n_start_milk_choc = 2
n_start_black_choc = 8

n_trials = 1000000
n_milk_last = 0

for _ in range(n_trials):
    prev_pulled_choc = None

itdxer

import numpy as np

n_rolls = 10000000
n_sides = 20
min_dice_value = 8

rolls_1 = np.random.randint(n_sides, size=(n_rolls, 2)) + 1
rolls_2 = np.random.randint(n_sides, size=(n_rolls, 2)) + 1

print('-' * 30)

itdxer

import numpy as np

n_rolls = 10000000
n_sides = 20

rolls_1 = np.random.randint(n_sides, size=(n_rolls, 2)) + 1
rolls_2 = np.random.randint(n_sides, size=(n_rolls, 2)) + 1

print('-' * 30)
print('advantage of disadvantages')

itdxer

import numpy as np
import tensorflow as tf

np.random.seed(0)

w1 = np.random.random((3, 3, 1, 100))
w2 = np.random.random((3, 3, 1, 100))

x1 = np.random.random((1, 256, 256, 1))
x2 = np.random.random((1, 256, 256, 1))

itdxer

from collections import namedtuple

def flatten_tuples(values):
    for value in values:
        if isinstance(value, tuple):
            for inner_value in flatten_tuples(value):
                yield inner_value
        else:
            yield value

itdxer

(defn replace-values 
    [array current-value new-value]
    (assoc array (.indexOf array current-value) new-value))

(defn swap-balls-in-boxes
    [box-a box-b]
    (let [random-ball-a (rand-nth box-a)
          random-ball-b (rand-nth box-b)]
        [(replace-values box-a random-ball-a random-ball-b)
         (replace-values box-b random-ball-b random-ball-a)]))

itdxer

import theano
import theano.tensor as T
import numpy as np


def asfloat(value):
    """ Convert variable to float type configured by theano
    floatX variable.

    Parameters