collisions

import numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeansfrom sklearn.cluster import DBSCAN from sklearn.datasets import make_blobs n_samples = [750,750,750] cluster_std = 1 random_state = 200 X, Y_true = make_blobs(n_samples=n_samples, random_state=random_state, cluster_std=cluster_std) plt.scatter(X[:, 0], X[:, 1], marker='.', c=Y_true) #DBSCAN model, setting up required parameters eps = 0.4min_Samples = 10db = DBSCAN(eps=eps, min_samples=min_Samples).fit(X)labels = db.labels_labels # To count number of clusters in labels, ignoring noise if present.n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)n_noise_ = list(labels).count(-1) print('Estimated number of clusters: %d' % n_clusters_)print('Estimated number of noise points: %d' % n_noise_) # Plot result# Use black to label noise points.unique_labels = set(labels)colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels))) core_samples_mask = np.zeros_like(db.labels_,…

def upgrade_stations(threshold: int, num_bikes: int,                     stations: List["Station"]) -> int:    """Modify each station in stations that has a capacity that is less than    threshold by adding num_bikes to the capacity and bikes available counts.    Modify each station at most once.     Return the total number of bikes that were added to the bike share network.     Precondition: num_bikes >= 0     >>> handout_copy = [HANDOUT_STATIONS[0][:], HANDOUT_STATIONS[1][:]]    >>> upgrade_stations(25, 5, handout_copy)    5    >>> handout_copy[0] == HANDOUT_STATIONS[0]    True    >>> handout_copy[1] == [7001, 'Lower Jarvis St SMART / The Esplanade', \                            43.647992, -79.370907, 20, 10, 10]    True    """

Write a script that creates a dictionary of name-age pair. Add some entries to your dictionary. Add a menu to support ADD, REMOVE, SERACH, AGE INCREASE, PRINT operations.The “Age Increase” option increases the age by 1 for all the entries in the dictionary.Test the four operations