def get_running_time_binary(keys):
    """Returns the running time for the binary search function.
    Uses target of -1. 

    Args:
        keys (list): list of positive, consecutive integers (i.e. not
            containing -1)

    Returns:
        (float): time in seconds required for the function to execute
            once on the list keys.
    """
    target = -1
    start = time.time()
    sf.binary_search(keys, target, 0, len(keys) - 1)
    end = time.time()
    return end - start
def plot_times(min_length, max_length, step):
    """Produces a plot comparing the worst case running times for binary search
    and linear search on lists with length between min_length and max_length.

    Returns:
        None
    """

    # As the book does it, function is forced to round to zero, because the
    #   float returned by time.time() only has 6 or so decimal places.
    # Calling each more than once produces a better looking graph.

    # Binary search is so fast that the book's suggested method of speed-testing
    #   doesn't even work on a reasonably fast desktop...
    max_list = list(range(max_length))
    len_keys = []
    seconds_linear = []
    seconds_binary = []
    for i in range(min_length, max_length + 1, step):
        keys = max_list[:i]
        start = time.time()
        for i in range(10):
            sf.linear_search(keys, -1)
        end = time.time()
        seconds_linear.append(end - start)
        ##        print(f"Linear:\n\tstart was {start}\n\tend was {end}")

        start = time.time()
        for i in range(10):
            sf.binary_search(keys, -1, 0, len(keys))
        end = time.time()
        seconds_binary.append(end - start)

    # pyplot calls

    pyplot.plot(range(min_length, max_length + 1, step),
                seconds_linear,
                label='Linear search')
    pyplot.plot(range(min_length, max_length + 1, step),
                seconds_binary,
                label='Binary search')
    pyplot.legend(loc='upper center')
    pyplot.xlabel('len(keys)')
    pyplot.ylabel('Time in seconds')
    pyplot.show()
Beispiel #3
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def query_quakes(ids, data):
    prompt = 'Earthquake ID (q to quit): '
    key = input(prompt)
    while key.lower() != 'q':
        index = sf.binary_search(ids, key, 0, len(ids) - 1)
##        index = sf.linear_search(ids, key) # for comparison. Will exceed default cpython max recursion depth for worst case i.e. not-found.
        if index >=0:
            print('Location: ' + str(data[index][:2]) + '\n' +
                  'Magnitude: ' + str(data[index][3]) + '\n' +
                  'Depth : ' + str(data[index][2]) + '\n')
        else:
            print('An earthquake with that ID was not found.')
        key = input(prompt)
 def test_not_found_binary_search(self):
     """Test that target is NOT found by binary search."""
     pos = binary_search(self.my_list, -1)
     self.assertTrue(pos == None)
 def test_found_binary_search(self):
     """Test that target is found by binary search."""
     pos = binary_search(self.my_list, 3)
     self.assertEqual(pos, 1)