HemersonTacon

.var : New variable
    ANY   →   $var$ = $expr$

.str : Cast to string
    ANY   →   str($expr$)

.fstr : Apply f-string formatting
    ANY   →   f"{$expr$}"

.list : Cast to list

HemersonTacon

from string import ascii_lowercase
import argparse


def letter_frequency_builder(words_list):
    corpus_length = len(words_list)
    return {letter: sum(letter in word for word in words_list)/corpus_length for letter in ascii_lowercase}


def word_score(word, letter_frequency):

HemersonTacon

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 23 17:34:25 2020

@author: hemerson.tacon
"""

import re

HemersonTacon

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  9 22:51:09 2020
Given n non-negative integers representing an elevation map where the width
of each bar is 1, compute how much water it is able to trap after raining.

@author: Hemerson Tacon
"""

HemersonTacon

import os
import matplotlib.pyplot as plt

def plot_and_save(history, name, show_plots=False, folder="imgs"):
	"""
	 Create a charts comparing accuracy and loss of training and validation 
	 phases for each epoch 

	 Args:
	     history (Hisotry): The History object returned from a fit function

HemersonTacon

from keras.regularizers import l1_l2

def set_kernel_reg(model, lambdal1 = 0, lambdal2 = 0):
	"""
	 Apply kernel regularization to keras model	

	 Args:
	     model: Instance of `Model` not compiled yet
		 lambda1 (float): L1 regularization factor
		 lambda2 (float): L2 regularization factor