python code, need the graph like below

CODE

import copy import random import pandas as pd import time import matplotlib.pyplot as plt def display_hash(hash_table): for i in range(len(hash_table)): print(i, hash_table[i]) print() def hash_chaining_build(arr): size = int(len(arr) / 10) hash_table = [[] for _ in range(size)] for key in arr: hash_key = hash_function(key, size) hash_table[hash_key].append(key) # print(hash_table) return hash_table def hash_probing_build(arr): size = len(arr) * 2 hash_table = [None for _ in range(size)] for key in arr: hash_key = hash_function(key, size) i = 0 while hash_table[hash_key]: i += 1 hash_key = hash_function_probing(hash_key, size, i) hash_table[hash_key] = key return hash_table def hash_chaining_search(hash_table, arr): size = len(hash_table) found = [False] * len(arr) for j in range(len(arr)): key = arr[j] hash_key = hash_function(key, size) for item in hash_table[hash_key]: if item == key: found[j] = True break return found def hash_probing_search(hash_table, arr): size = len(hash_table) found = [False] * len(arr) # found = [False for i in range(size)] for j in range(len(arr)): key = arr[j] hash_key = hash_function(key, size) i = 0 while hash_table[hash_key] != key: i += 1 hash_key = hash_function_probing(hash_key, size, i) found[j] = True return found def hash_function(key, size): return key % size def hash_function_probing(hash_key, size, i): a, b, c = 5, 7, 9 hash_key = (hash_key + a * i ** 2 + b * i + c) % size return hash_key def random_list(size): arr = [i**3 for i in range(size)] random.shuffle(arr) return arr def run_algs(algs, sizes, trials): dict_algs = {} for alg in algs: dict_algs[alg[2]] = {} for size in sizes: for alg in algs: dict_algs[alg[2]][size] = 0 for trial in range(1, trials + 1): arr = random_list(size) arr_copy = copy.deepcopy(arr) random.shuffle(arr_copy) for alg in algs: start_time = time.time() build = alg[0] ds = build(arr) search = alg[1] found = search(ds, arr_copy) if size == sizes[0]: print(alg[2]) print(ds) print(found) end_time = time.time() net_time = end_time - start_time dict_algs[alg[2]][size] += 1000 * net_time return dict_algs def plot_times(dict_algs, sizes, trials, algs, title, file_name): alg_num = 0 plt.xticks([j for j in range(len(sizes))], [str(size) for size in sizes]) for alg in algs: alg_num += 1 d = dict_algs[alg.__name__] x_axis = [j + offset for j in range(len(sizes))] y_axis = [d[i] for i in sizes] plt.bar(x_axis, y_axis, width=width, alpha=0.75, label=alg.__name__) offset += width plt.legend() plt.title(title) plt.xlabel("Size") plt.ylabel("Time for " + str(trials) + " trials (ms)") plt.savefig(file_name) plt.show() def print_times(dict_algs): pd.set_option("display.max_rows", 500) pd.set_option("display.max_columns", 500) pd.set_option("display.width", 1000) df = pd.DataFrame.from_dict(dict_algs).T print(df) def big_test(): assn = "Assignment6" sizes = [10] algs = [(hash_probing_build, hash_probing_search, "Hash Probing"), (hash_chaining_build, hash_chaining_search, "Hash Chaining")] trials = 1 title = "Empirical Performance of Search Structures" dict_algs = run_algs(algs, sizes, trials) for alg in dict_algs: print(alg) for size in dict_algs[alg]: print(size, dict_algs[alg][size] / trials) print_times(dict_algs) plot_times(dict_algs, sizes, trials, algs, title, assn + '.png') def main(): # mini_test() big_test() if __name__ == "__main__": main()

Figure 1 Runtime of algorithms Number of elements