def compareCholeskyCustomVSNumpy():
maxSize = 500
nbOfPoints = 200
nbValuesForAverage = 2
x, yCustom, yNumpy = [], [], []
for size in [round(i) for i in numpy.linspace(5, maxSize, nbOfPoints)]:
# Computes matrix density
x.append(size)
# Computes average execution times on nbValuesForAverage calls
# on different matrix
customTime, numpyTime = 0, 0
for j in range(nbValuesForAverage):
M = matgen.symmetricPositiveDefinite(size)
wrapped1 = wrapper(cholesky.completeCholesky, M)
customTime += timeit.timeit(wrapped1, number=1)
wrapped2 = wrapper(numpy.linalg.cholesky, M)
numpyTime += timeit.timeit(wrapped2, number=1)
yCustom.append(customTime/nbValuesForAverage)
yNumpy.append(numpyTime/nbValuesForAverage)
p1 = plt.plot(x, yCustom, 'b', marker='o')
p2 = plt.plot(x, yNumpy, 'g', marker='o')
plt.title("Average execution time for the Cholesky factorization (custom and Numpy's) in function of size\n")
plt.legend(["New custom Cholesky factorization", "Numpy Cholesky factorization"], loc=2)
plt.ylabel("Execution time (s)")
plt.xlabel("size")
plt.show()
def test_cholesky():
random.seed()
for i in range(10):
print(".", end="", flush= True)
size = random.randint(100, 200)
M = matgen.symmetricPositiveDefinite(size)
T = cholesky.completeCholesky(M)
if not numpy.allclose(M, numpy.dot(T, T.transpose())):
return False
return True
