January 13, 2025
from typing import List
def duplicates(input_list: List[int]) -> bool:
"""Determine whether or not the input list contains a duplicate value."""
n = len(input_list)
for i in range(n):
for j in range(n):
if i != j and input_list[i] == input_list[j]:
return True
return False
assert(duplicates([1,2,6,3,4,5,6,7,8]))
assert(not duplicates([1,2,3,4]))
print(duplicates([1,2,6,3,4,5,6,7,8]))
print(not duplicates([1,2,3,4]))True
Trueduplicatesduplicatesfrom typing import Callable, List
import time
def timetrials(function: Callable, n: int, trials: int = 10) -> float:
"""Time a function with an input of size n for trials number of times."""
totaltime = 0
for _ in range(trials):
start = time.time()
function(list(range(n)))
totaltime += time.time() - start
print("Average time =%10.7f (s) for n = %d" % (totaltime/trials, n))
return totaltime/trialstimetrial: time a provided function with an input of size ntime.time function to compute start for each trialtotaltime += time.time() - startprint to display the average time for n and return the averageduplicates# conduct a doubling experiment for a provided function
timings = []
for n in [50, 100, 200, 400, 800, 1600, 3200]:
timings.append(timetrials(duplicates, n))Average time = 0.0000826 (s) for n = 50
Average time = 0.0003250 (s) for n = 100
Average time = 0.0012524 (s) for n = 200
Average time = 0.0054513 (s) for n = 400
Average time = 0.0242371 (s) for n = 800
Average time = 0.1023278 (s) for n = 1600
Average time = 0.4206432 (s) for n = 3200duplicates?duplicates?bash or zshpowerlevel10k or starshipfirefox or chromevim or emacsds2 package in donsheehy/datastructures book and package code
systemsense toolCommands
benchmarkinfo: Benchmark the system used for experiments.
completeinfo: Detect information about and then benchmark the system used for experiments.
systeminfo: Detect all relevant information about the system used for experiments.systeminfo: use packages like psutil to collect relevant information about the execution environment, suitable for characterizing and comparing local and cloud-based systemsbenchmarkinfo: use packages like timeit to run simple micro-benchmarks involving basic operations so as to characterize and compare baseline system performancepython, mise, asdf, and/or devenv, and poetrydef get_cpu() -> Dict[str, str]:
"""Return information about the current CPU in the system."""
# detect the name of the function in
# which this source code exists
function_name = inspect.stack()[0][3]
# parse out the second part of the name after
# the underscore character
function_name = function_name.split("_")[1]
# create a dictionary with the function's
# purpose as the key and the value as
# the return of the function that collects it
return {function_name: str(platform.machine())}inspect package to detect the name of the functionplatform package to detect the CPU architecturedef get_disk() -> Dict[str, str]:
"""Return disk space usage."""
function_name = inspect.stack()[0][3]
function_name = function_name.split("_")[1]
if platform.system() == constants.system.Windows:
total_disk = psutil.disk_usage("C:\\").total
used_disk = psutil.disk_usage("C:\\").used
else:
total_disk = psutil.disk_usage("/").total
used_disk = psutil.disk_usage("/").used
total_disk_gb = total_disk / (1024**3)
used_disk_gb = used_disk / (1024**3)
disk = f"Using {used_disk_gb:.2f} GB of {total_disk_gb:.2f} GB"
return {function_name: disk}psutil package to detect disk usage detailsdef time_benchmark_concatenation(
repeat: int = 3, number: int = 100000, size: int = 100
) -> Dict[str, str]:
"""Time the benchmark_concatenation function."""
function_name = inspect.stack()[0][3].split(_)[2]
performance_list = timeit.repeat(
"benchmark.benchmark_concatenation(size)",
repeat=repeat,
setup=f"""
from systemsense import benchmark
size = {size}
""",
number=number,
)
return {function_name: str(performance_list)}timeit to benchmark string concatenation functionmise or asdf to manage Python versionspoetry to manage Python packagesgit with instructor-provided repositoriesgit and GitHub flow on the course web site’s repositoryquarto to render a preview of the course web siteTips for effective algorithm engineering
Algorithmology