# -*- coding: utf-8 -*-
"""Problem Sheet 2.

Stochastic Gradient Descent
"""
from helpers import batch_iter
from costs import compute_loss


def compute_stoch_gradient(y, tx, w):
    """Compute a stochastic gradient at w from just few examples n and their corresponding y_n labels.

    Args:
        y: shape=(N, )
        tx: shape=(N,2)
        w: shape=(2, ). The vector of model parameters.

    Returns:
        An array of shape (2, ) (same shape as w), containing the stochastic gradient of the loss at w.
    """

    ### SOLUTION
    err = y - tx.dot(w)
    grad = -tx.T.dot(err) / len(err)
    return grad, err

    ### TEMPLATE
    # # ***************************************************
    # # INSERT YOUR CODE HERE
    # # TODO: implement stochastic gradient computation. It's the same as the usual gradient.
    # # ***************************************************
    # raise NotImplementedError
    ### END SOLUTION


def stochastic_gradient_descent(y, tx, initial_w, batch_size, max_iters, gamma):
    """The Stochastic Gradient Descent algorithm (SGD).

    Args:
        y: shape=(N, )
        tx: shape=(N,2)
        initial_w: shape=(2, ). The initial guess (or the initialization) for the model parameters
        batch_size: a scalar denoting the number of data points in a mini-batch used for computing the stochastic gradient
        max_iters: a scalar denoting the total number of iterations of SGD
        gamma: a scalar denoting the stepsize

    Returns:
        losses: a list of length max_iters containing the loss value (scalar) for each iteration of SGD
        ws: a list of length max_iters containing the model parameters as numpy arrays of shape (2, ), for each iteration of SGD
    """

    # Define parameters to store w and loss
    ws = [initial_w]
    losses = []
    w = initial_w

    for n_iter in range(max_iters):

        ### SOLUTION
        for y_batch, tx_batch in batch_iter(
            y, tx, batch_size=batch_size, num_batches=1
        ):
            # compute a stochastic gradient and loss
            grad, _ = compute_stoch_gradient(y_batch, tx_batch, w)
            # update w through the stochastic gradient update
            w = w - gamma * grad
            # calculate loss
            loss = compute_loss(y, tx, w)
            # store w and loss
            ws.append(w)
            losses.append(loss)
        ### TEMPLATE
        # # ***************************************************
        # # INSERT YOUR CODE HERE
        # # TODO: implement stochastic gradient descent.
        # # ***************************************************
        # raise NotImplementedError
        ### END SOLUTION

        print(
            "SGD iter. {bi}/{ti}: loss={l}, w0={w0}, w1={w1}".format(
                bi=n_iter, ti=max_iters - 1, l=loss, w0=w[0], w1=w[1]
            )
        )
    return losses, ws
