Untitled

from ipywidgets import interact, fixed
import ipywidgets as widgets

def plot_knn(k, points, classes): 
    """
    The function generates an interactive plot with a slider, 
    allowing users to adjust the number of nearest neighbors (k-NN) used for classification. 
    It classifies sample points based on the provided training points and their corresponding class labels. 
    """
    # Range of samples to take in the x and y direction
    N = 40

    # These lists will be filled with all the sample points and their classifications
    sample_points = []
    sample_classes = []

    # Loop over all x and y coordinates
    for x in range(N):
        for y in range(N):
            # Construct the new sample point
            sample = [x, y]
            
            #use the knn function from previous assignment: to determine the classification of the sample point
            sample_class = knn(sample, points, classes, k)
            
            #append (add) the sample points + classification to the right lists.
            sample_points.append(sample)
            sample_classes.append(sample_class)

    plot_points(sample_points, sample_classes)


# Enable the interactive plot with slider
interact(plot_knn, k=widgets.IntSlider(value=1, min=1, max=11, step=2, continuous_update=False),
         points=fixed(training_points), classes=fixed(training_classes));