ijkilchenko

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

ijkilchenko

import random
import copy


def make_exchange_matrix(N=3, seed=2017):
    '''In this function, we make an exchange matrix
    for testing purposes by randomly picking exchange 
    rates from the first currency to all the other
    currencies. '''

ijkilchenko

from itertools import combinations
from collections import defaultdict, Counter


def to_multiset(word):
	return frozenset(Counter(word).items())


def find_longer_words(dictionary, words):
	for num_words in range(2, min(len(words), 10)):

ijkilchenko

import keyboard
import time

if __name__ == '__main__':
  frames = [0]

  def forward():
    global last_frame_index
    while True:
      if not is_paused:

ijkilchenko

import random
from math import sqrt, floor

"""
Guessing game. When playing, the game picks a random value from i to j
and each time a guess g is made, g is added to the previous running cost
of playing this game.
"""
cache = {}
class Game:

ijkilchenko

from math import log10, floor

def r(num):
    num_digits = floor(log10(num) + 1)
    return num//10 + (num % 10)*(10**(num_digits-1))

def does_a_divide_b(a, b):
    if b % a == 0:
        return True
    else:

ijkilchenko

from sklearn.datasets import make_classification
from sklearn.cross_validation import train_test_split
from itertools import product
import numpy as np
import pandas as pd


class NeuralNetwork:
    g = np.vectorize(lambda z: 1 / (1 + np.exp(-z)))
    log = np.vectorize(lambda x: np.log(x))

ijkilchenko

syntax enable
colorscheme monokai

set number
set ruler

set swapfile
set dir=~/tmp

set tabstop=4

ijkilchenko

import random
import numpy as np
#from cvxpy import *

def p(M):
    """Method to pretty print our matrix. """
    for i in range(len(M)):
        print(' '.join([str(num) for num in M[i]]))

def generate_M(N=3, max_touches=2):

ijkilchenko

import random

class Node():
    def __init__(self, data=None):
        self.data = data
        self.parent = None
        self.left = None
        self.right = None
        self.load = 1 # Assumed to be a leaf when initialized.