ramonrosa

from functools import partial, wraps
from inspect import signature
from typing import Callable


def decorator_with_kwargs(decorator: Callable) -> Callable:
    """Decorator factory to give decorated decorators the skill to receive
    optional keyword arguments.

    If a decorator "some_decorator" is decorated with this function:

ramonrosa

y_pred = X.dot(theta_gd)

_, ax = plt.subplots(figsize=(8,8))
ax.scatter(y, y_pred, marker='o', alpha=0.5)
ax.set_xlabel('y', fontsize=16)
ax.set_ylabel('y predito', fontsize=16)
ax.tick_params(axis='both',labelsize=14)
ax.grid()

ramonrosa

def descida_de_gradiente(num_iteracoes, learning_rate, X, y):
    theta = np.random.uniform(size=(X.shape[1],))
    J_list = []

    for _ in range(num_iteracoes):
        J, gradiente = funcao_custo(theta, X, y)
        J_list.append(J)
        theta = theta - learning_rate*gradiente

    _, ax = plt.subplots(figsize=(10,6))

ramonrosa

def funcao_custo(theta, X, y):
    m = len(y)

    y_pred = X.dot(theta).flatten()
    J = (1/m)*np.sum((y-y_pred)**2)
    gradiente = -(2/m)*((X.T).dot(y - y_pred)).flatten()

    return J, gradiente

ramonrosa

import numpy as np
import matplotlib.pyplot as plt

def criar_dados_sinteticos():
    m = 10000     # numero de exemplos
    D = 5         # numero de dimensoes
    sigma=0.05    # desvio padrao da distribuicao do ruido

    theta_sintetico = [1.5, 2.2, 1.7, -3.0, 0.3]  # lista dos parâmetros theta (inventados)