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Implements linear search and probabilistic search to analyse time complexity.
import random
from matplotlib import pyplot as plt
def linear_search(value, list):
Searches the index of the value in the list.
Returns the number of iterations.
for index, val in enumerate(list):
if value == val:
return index
return index
def prob_search(value, l):
Searches a value in a list using a probabilistic search.
Returns the number of iterations.
indexes = list(range(len(l)))
for iteration, index in enumerate(indexes):
if l[index] == value:
return iteration
return iteration
def compare_methods(size, n=100):
Returns the average number of iterations for each method to find
a random value in a random list.
values_linear = []
values_prob = []
for _ in range(n):
# Random list
list = random.sample(range(size * 2), size)
# Random value from list
value = random.choice(list)
values_linear.append(linear_search(value, list))
values_prob.append(prob_search(value, list))
return (
sum(values_linear) / len(values_linear),
sum(values_prob) / len(values_prob)
def plot_time_complexity(a=10, b=100, step=10, n=100):
Plots time complexity comparison of both search methods for lists with size
between 'a' and 'b'. Each value is an average of 'n' algorithm runs.
values = [(size, compare_methods(size, n)) for size in range(a, b, step)]
steps = [value[0] for value in values]
values_linear = [value[1][0] for value in values]
values_prob = [value[1][1] for value in values]
plt.plot(steps, values_linear, 'b-', label='Linear search')
plt.plot(steps, values_prob, 'r-', label='Probabilistic search')
plt.title('Average number of iterations by list size')
plt.xlabel('List size')
plt.ylabel('Num. iterations')
plt.legend(loc='upper left')
if __name__ == '__main__':
plot_time_complexity(10, 1000, 10)
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