Midvel

AccumulatorMap new_Map(initialValue)
                   ("firstKey", firstValue)
                   ("secondKey", secondValue)
                   ("thirdKey", thirdValue);

Midvel

ConstructedObject GenerateObject(const Data& data)
{
    return AccumulatorMap(data)
                ("firstProperty", value1)
                ("secondProperty", value2);
}

Midvel

class AccumulatorMap
{
public:
    AccumulatorMap() = delete;
    explicit AccumulatorMap(double initialValue) : m_value(initialValue)
    {}

    AccumulatorMap& operator()(Operation op, double value)
    {
       m_value = m_calc[op].calculate(m_value, value);

Midvel

double CircleArea(double radius)
{
    return AccumulatorMap(radius)
                       (EXP, 2)
                       (MULTI, 3.14);
}
double SphereVolume(double radius)
{
    return AccumulatorMap(radius)
                       (EXP, 3)

Midvel

sms_data_clean['SMS'] = sms_data_clean['SMS'].str.replace('\W+', ' ').str.replace('\s+', ' ').str.strip()
sms_data_clean['SMS'] = sms_data_clean['SMS'].str.lower()
sms_data_clean['SMS'] = sms_data_clean['SMS'].str.split()

Midvel

train_data = sms_data_clean.sample(frac=0.8,random_state=1).reset_index(drop=True)
test_data = sms_data_clean.drop(train_data.index).reset_index(drop=True)
train_data = train_data.reset_index(drop=True)

sms_data_clean['Label'].value_counts() / sms_data.shape[0] * 100
'''
ham     86.593683
spam    13.406317
Name: Label, dtype: float64
'''

Midvel

vocabulary = list(set(train_data['SMS'].sum()))
word_counts_per_sms = pd.DataFrame([
    [row[1].count(word) for word in vocabulary]
    for _, row in train_data.iterrows()], columns=vocabulary)
train_data = pd.concat([train_data.reset_index(), word_counts_per_sms], axis=1).iloc[:,1:]

Midvel

def p_w_spam(word):
    if word in train_data.columns:
        return (train_data.loc[train_data['Label'] == 'spam', word].sum() + alpha) / (Nspam + alpha*Nvoc)
    else:
        return 1
      
def p_w_ham(word):
    if word in train_data.columns:
        return (train_data.loc[train_data['Label'] == 'ham', word].sum() + alpha) / (Nham + alpha*Nvoc)
    else:

Midvel

def classify(message):
    p_spam_given_message = Pspam
    p_ham_given_message = Pham
    for word in message:
        p_spam_given_message *= p_w_spam(word)
        p_ham_given_message *= p_w_ham(word)
    if p_ham_given_message > p_spam_given_message:
        return 'ham'
    elif p_ham_given_message < p_spam_given_message:
        return 'spam'

Midvel

import requests
import pandas as pd

def extract_categories(cat_list, cat):
    for element in cat:
        if 'categories' in element and len(element['categories']) > 0:
            extract_categories(cat_list, element['categories'])
        if('id' in element and 'name' in element):
            cat_list.append([element['name'], element['id']])