human
Solve the following python programming problem given the following code:
# %load perceptron.py
"""
Implementation of perceptron algorithm from Chapter 2 of "Python Machine Learning"
"""
import numpy as np

def train_perceptron(observations, labels, learning_rate=0.1, max_training_iterations=10):
    """
    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 number of 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, weights_log, errors_log)
    """
    the_weights = np.zeros(1 + observations.shape[1])
    weights_log = []
    errors_log = []

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

    def predict(observation, weights=the_weights):
        return np.where(net_input(observation, weights) >= 0.0, 1, -1)

    for _ in range(max_training_iterations):
        errors = 0
        weights_log.append(np.copy(the_weights))
        for observation, correct_output in zip(observations, labels):
            weight_delta = learning_rate * (correct_output - predict(observation))
            the_weights[1:] += weight_delta * observation
            the_weights[0] += weight_delta
            errors += int(weight_delta != 0.0)
        errors_log.append(errors)
        if errors == 0:
            break

    return predict, weights_log, errors_log

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

Training the model
Let's implement the perceptron. We want to see how the weights and error rate progresses during iterations of training so our training function will also provide insight into that.
Note: I diverged from the author's implementation to a perhaps cleaner solution using a higher order function instead of a class, and also instrumented it to return a log of the weights across iterations so we can visualize how the fit improved with each iteration. I also break once there are no errors found instead of continuing to iterate mindlessly after converging.