def test_linear_search(N, num_trials, ordered):
"""
Run a linear search experiment with the given configurations:
N: list size
num_trials: the number of times linear_search is called
ordered: whether ordered or un-ordered lists are used
"""
min_comps = math.inf # Initialize min as infinity
max_comps = -math.inf # Initialize max as -infinity
total_comps = 0
# Create a ListSearcher and generate (un)ordered data
s = ListSearcher(N)
s.generate_data(ordered)
# Run the trials
for _trial in range(num_trials):
s.reset_comparisons() # reset_comparison count
key = random.randrange(N) # generate a random key from 0 to N-1
s.linear_search(key) # call linear search
num_comps = s.comparisons # retrieve number of comparisons
# update max, min, and total number of comparisons
if (num_comps > max_comps):
max_comps = num_comps
if (num_comps < min_comps):
min_comps = num_comps
total_comps += num_comps
print("N=" + str(N) + ",", "best=" + str(min_comps), "(expected 1),",
"worst=" + str(max_comps), "(expected",
str(N) + "),", "avg=" + str(total_comps / num_trials), "(expected",
str(math.ceil(N / 2)) + ")")
def test_binary_search(N, num_trials):
"""
Run a binary search experiment with the given configurations:
N: list size
num_trials: the number of times binary_search is called
"""
min_comps = math.inf # Initialize min as infinity
max_comps = -math.inf # Initialize max as -infinity
total_comps = 0
# Creates a ListSearcher and generates ordered data
s = ListSearcher(N)
s.generate_data()
# Run the trials
for _trial in range(num_trials):
s.reset_comparisons()
key = random.randrange(N)
s.binary_search(key)
num_comps = s.comparisons
# update max, min, and total number of comparisons
if (num_comps > max_comps):
max_comps = num_comps
if (num_comps < min_comps):
min_comps = num_comps
total_comps += num_comps
print("N=" + str(N) + ",", "best=" + str(min_comps), "(expected 1),",
"worst=" + str(max_comps), "(expected",
str(math.ceil(math.log(N) / math.log(2.0))) + "),",
"avg=" + str(total_comps / num_trials), "(expected ???)")
def test_median(N, num_trials):
min_comps = math.inf # Initialize min as infinity
max_comps = -math.inf # Initialize max as -infinity
total_comps = 0
s = ListSearcher(N)
# Run the trials
for _trial in range(num_trials):
s.generate_data(ordered=False)
s.reset_comparisons()
s.find_median()
num_comps = s.comparisons
# update max, min, and total number of comparisons
if (num_comps > max_comps):
max_comps = num_comps
if (num_comps < min_comps):
min_comps = num_comps
total_comps += num_comps
print("N=" + str(N) + ",",
"best=" + str(min_comps) + ",",
"worst=" + str(max_comps) + ",",
"avg=" + str(total_comps/num_trials))