joao141141

class Perceptron:
  def __init__(self, num_inputs=2, weights=[1,1]):
    self.num_inputs = num_inputs
    self.weights = weights
    
  def weighted_sum(self, inputs):
    weighted_sum = 0
    for i in range(self.num_inputs):
      weighted_sum += self.weights[i]*inputs[i]
    return weighted_sum

joao141141

from spam_data import training_spam_docs, training_doc_tokens, training_labels, training_docs
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

test_text = """
Play around with the spam classifier!
"""

bow_vectorizer = CountVectorizer()

joao141141

from spam_data import training_spam_docs, training_doc_tokens, training_labels, test_labels, test_spam_docs, training_docs, test_docs
from sklearn.naive_bayes import MultinomialNB

def create_features_dictionary(document_tokens):
  features_dictionary = {}
  index = 0
  for token in document_tokens:
    if token not in features_dictionary:
      features_dictionary[token] = index
      index += 1

joao141141

import re
import random

class AlienBot:
    def __init__(self):
        self.name = None
        self.alienbabble = {
            'describe_planet_intent': r'.*(how|tell.*about|interest.*in).*your.*planet',
            'answer_why_intent': r'why are you.*(here|asking me?\s?so?\s?many questions)\??',
            'cubed_intent': r'can.*you.*cube.*number (\d+)?',

joao141141

import re
import random

class AlienBot:
    def __init__(self):
        self.name = None
        self.alienbabble = {
            'describe_planet_intent': r'.*(how|tell.*about|interest.*in).*your.*planet',
            'answer_why_intent': r'why are you.*(here|asking me?\s?so?\s?many questions)\??',
            'cubed_intent': r'can.*you.*cube.*number (\d+)?',

joao141141

public class MadLibs {
  /*
  This program generates a mad libbed story.
  Author: João
  Date: 02/20/2024
  */
  	public static void main(String[] args){
      String name1 = "Carlos";
      String adjective1 = "good";
      String adjective2 = "bad";

joao141141

from nltk import pos_tag, RegexpParser
from tokenize_words import word_sentence_tokenize
from chunk_counters import np_chunk_counter, vp_chunk_counter

text = open("the_iliad.txt",encoding='utf-8').read().lower()



word_tokenized_text = word_sentence_tokenize(text)

joao141141

last_semester_gradebook = [["politics", 80], ["latin", 96], ["dance", 97], ["architecture", 65]]

# Your code below: 

subjects = ['physics', 'calculus', 'poetry', 'history']

grades = [98, 97, 85, 88]

gradebook = [[subjects[0], grades[0]], [subjects[1], grades[1]], [subjects[2], grades[2]], [subjects[3], grades[3]]]

joao141141

# Your code below: 
first_names = ['Ainsley', 'Ben', 'Chani', 'Depak']
preferred_size = ['Small', 'Large', 'Medium']
preferred_size.append('Medium')


customer_data = [['Ainsley', 'Small', True], ['Ben', 'Large', False], ['Chani', 'Medium', True], ['Depak', 'Medium', False]]

joao141141

# Define your functions
def coffee_bot():
    print('Welcome to the cafe!')

    size = get_size()
    print(size)

    drink_type = get_drink_type()
    print(drink_type)