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Solve the following python programming problem given the following code:
# %load adeline_sgd.py
"""
Implementation of ADAptive LInear NEuron (Adaline) algorithm from Chapter 2 of
"Python Machine Learning" adapted to use stochastic gradient descent instead of batch.
"""
import numpy as np

def train_adeline_sgd(observations, labels,
                      learning_rate=0.01, max_training_iterations=100,
                      shuffle=True):
    """
    Trains a (binary) perceptron, returning a function that can predict / classify
    given a new observations, as well as insight into how the training progressed via
    a log of the weights and squared errors for each iteration.

    :param observations: array of rows
    :param labels: correct label classification for each row: [1, -1, 1, 1, ...]
    :param learning_rate: how fast to update weights
    :param max_training_iterations: max number of times to iterate through observations
    :return: (prediction_fn, num_errors_log, final_weights)
    """
    the_weights = np.zeros(1 + observations.shape[1])
    num_errors_log = []

    def net_input(observations, weights):
        return np.dot(observations, weights[1:]) + weights[0]

    def quantized_output(output):
        return np.where(output >= 0.0, 1, -1)

    def predict(observations, weights=the_weights):
        return quantized_output(net_input(observations, weights))

    idxes = list(range(len(observations)))

    for _ in range(max_training_iterations):
        num_errors = 0
        if shuffle:
            np.random.shuffle(idxes)
        # for observation, correct_output in zip(observations, labels):
        for idx in idxes:
            observation = observations[idx]
            correct_output = labels[idx]
            raw_output = net_input(observation, the_weights)
            if quantized_output(raw_output) != correct_output:
                num_errors += 1
                weight_delta = learning_rate * (correct_output - raw_output)
                the_weights[1:] += weight_delta * observation
                the_weights[0] += weight_delta

       ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Stochaistic Gradient Descent
As the book states, sometimes updating the weights iteratively, training sample by training sample, can be more pragmatic than in batch, as it allows you to update the weights "online" and may converge faster with more frequent updates in the weights. 
Here's my implementation. My more functional approach admittedly is limited as it doesn't have an object to store the state in, and thus is missing the author's "partial_fit" method.
Note that the implementation does converge after a few iterations over the data set even without regularizing the parameters and there was no need to futz with the training rate.