import std
import statistics as stat
check_num = [1, 2, 4, 7]
test1 = std.standard_dev(check_num)
test2 = stat.stdev(check_num)
print(f"Stats std num {test1}")
print(f"C --- std num {test2}")
import sumC
sumC.uni_reg([1, 2, 3, 4], [1, 2, 3])
sumC.uni_reg([1, 2, 3, 4], [1, 2, 3, 4])
sumC.uni_reg([1, 2, 3, 4], [1, 2, 3, 4], eta=10, epochs=100)
sumC.uni_reg([1, 2, 3, 4], [1, 2, 3, 4], eta=-1, epochs=100)
sumC.uni_reg([1, 2, 3, 4], [1, 2, 3, 4], eta=0.05, epochs=1000)
lens = range(10, 300, 10)
py_time = []
np_time = []
c_time = []
for l in lens:
rands = [random.random() for _ in range(0, l)]
numpy_rands = np.array(rands)
py_time = np.append(
py_time,
timeit.timeit(lambda: standard_deviation(rands), number=10000))
np_time = np.append(
np_time, timeit.timeit(lambda: np.std(numpy_rands), number=10000))
c_time = np.append(
c_time, timeit.timeit(lambda: std.standard_dev(rands),
number=10000))
data = np.array(
[np.transpose(py_time),
np.transpose(np_time),
np.transpose(c_time)])
df = pd.DataFrame(data.transpose(),
index=lens,
columns=['Python', 'Numpy', 'C++'])
plt.figure()
df.plot()
plt.legend(loc='best')
plt.ylabel('Time (Seconds)')
plt.xlabel('Number of Elements')
plt.title('10k Runs of Standard Deviation')
import std
import stdcy
import stdcyc
import stdcyt
import stdpy
import numpy as np
import random
random.seed(100)
rands = [random.random() for _ in range(0, 5)]
cyc_rands = stdcyc.pystd(rands)
cyt_rands = stdcyt.pystd(rands)
print('std: ', std.standard_dev(rands))
print('stdcy: ', stdcy.standard_dev(rands))
print('stdcyc: ', cyc_rands.standard_dev())
print('stdcyt: ', cyt_rands.standard_dev())
print('stdpy: ', stdpy.standard_dev(rands))
print('numpy: ', np.std(rands))
cy_time = []
cyc_time = []
pybind_time = []
for l in lens:
rands = [random.random() for _ in range(0, l)]
numpy_rands = np.array(
rands) # If you don't do this, It will convert every time
# and there will be a some performance issues.
cyc_rands = stdcyc.pystd(rands)
np_time = np.append(
np_time, timeit.timeit(lambda: np.std(numpy_rands), number=100))
c_time = np.append(
c_time, timeit.timeit(lambda: std.standard_dev(rands), number=100))
cy_time = np.append(
cy_time,
timeit.timeit(lambda: stdcy.standard_dev(rands), number=100))
cyc_time = np.append(
cyc_time,
timeit.timeit(lambda: cyc_rands.standard_dev(), number=100))
pybind_time = np.append(
pybind_time,
timeit.timeit(lambda: stdpy.standard_dev(rands), number=100))
if include_pure_py:
py_time = np.append(
py_time,
timeit.timeit(lambda: standard_deviation(rands), number=100))
import random
import timeit
import math
import sys
import std
if __name__ == '__main__':
lens = range(10, 300, 10)
c_time = []
for l in lens:
rands = [random.random() for _ in range(0, l)]
execution_time = timeit.timeit(lambda: std.standard_dev(rands), number=10000)
c_time.append(execution_time)
print("array_size: %3d, execution_time:%f" % (l, execution_time))
with open("data/time_per_array_size_c.dat", "w") as f:
for i in range(len(c_time)):
f.write("%d %f\n" % (lens[i], c_time[i]))