The document provides instructions for implementing a method to generate new data points from the parameters estimated by a generative naive Bayes classifier. The method should sample x and y values based on the estimated priors, means, and covariance matrices to produce synthetic data that resembles the original training data. Hints are provided to refer to an existing data generation function.

1. Implement the generative naive Bayes classifier
In this task, you need to implement the generative process from the trained NBC model by
completing the generate_from_nbc() method. Given the estimated parameters, write a code
snippet to sample new data points (,) from the NBC model.
Hint: you may want to refer to the create_toy_data(mus, Sigmas, Ns) function above for some
details.
# REQUIREMENT: write a code snippet to generate new data pairs (x,y) from the estimated
parameters.
# The generated data points should look similar to the original data points.
def generate_from_nbc(estimated_pi, estimated_mu, estimated_Sigmas, N):
"""
Generate new data points from the estimated parameters.
estimated_pi: estimated_pi.shape == [num_classes]
estimated_mu: estimated_mu.shape == [num_classes, D]
estimated_Sigmas: estimated_Sigmas.shape == [num_classes, D, D]
N: the number of new data points to generate.
returns
x_generated: x_generated.shape == [N, D]
y_generated: y_generated.shape == [N], the integer class labels for each data point.
"""
#### Your code starts here #####
x_generated = None
y_generated = None
return x_generated, y_generated
#### Your code ends here #####