TomLisankie

# Was watching this (https://www.youtube.com/watch?v=ymsbTVLbyN4&t=3749s) and Abelson was talking about how you could just make a cons cell with a lambda. Remembered Python had lambdas (although they're one-line lambdas smh) and figured "why not"

def cons(a, b):
  return lambda x: a if (x == 1) else b

def car(cell):
  return cell(1)

def cdr(cell):
  return cell(2)

TomLisankie

lisp = input("Enter your Lisp code: ")

# splits Lisp expression into tokens and strips them of whitespace.
def makeTokens(lisp):
    lispTokens = lisp.replace("(", " ( ").replace(")", " ) ").split()
    return [token.strip() for token in lispTokens]

# if the token is a number, return it as the appropriate type (int or float).
# otherwise, return it as a string
def createAtom(token):

TomLisankie

"""
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)

TomLisankie

#fit_model code:

# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV

def fit_model(X, y):
    """ Performs grid search over the 'max_depth' parameter for a 
        decision tree regressor trained on the input data [X, y]. """

TomLisankie

Keybase proof

I hereby claim:

• I am tomlisankie on github.
• I am tomlisankie (https://keybase.io/tomlisankie) on keybase.
• I have a public key ASDtfZsxg2L_K02VywkZHxguyaA6ktChswzMW5rFWuwOrwo

To claim this, I am signing this object:

TomLisankie

//
//  Phoneme.swift


import Foundation

class Phoneme {
    
    private var isAVowelPhoneme = false;
    private var phoneme = "";