def test_cp():
A = np.random.rand(10, 7, 5).astype(np.float32)
tensor = Tensor(A)
times = []
ers = []
times_bk = []
ers_bk = []
rks = [5, 10, 20]
for rk in rks:
print(rk)
start = time.time()
decomposed_cp = tensor.cp_als(rk, ret_tensors=False, maxiter=200)
times.append(time.time() - start)
er = tensor.relative_error(decomposed_cp) * 100
print(f'cp_als error: {er} %')
ers.append(er)
start = time.time()
decomposed_cp = tensor.cp_rand(rk, ret_tensors=False, maxiter=200)
times_bk.append(time.time() - start)
er = tensor.relative_error(decomposed_cp) * 100
print(f'cp_rand error: {er} %')
ers_bk.append(er)
plt.plot(rks, times_bk, sns.xkcd_rgb["amber"], label='ap', linewidth=3)
for p in range(len(rks)):
plt.plot([rks[p]], [times_bk[p]], 'o', color=sns.xkcd_rgb["amber"])
plt.plot(rks, times, sns.xkcd_rgb["dusty red"], label='ap', linewidth=3)
for p in range(len(rks)):
plt.plot([rks[p]], [times[p]], 'o', color=sns.xkcd_rgb["dusty red"])
p1 = mpatches.Patch(color=sns.xkcd_rgb["amber"], label='CPRAND')
p2 = mpatches.Patch(color=sns.xkcd_rgb["dusty red"], label='CP-ALS')
plt.xlabel('Ранг')
plt.ylabel('Время работы, с')
plt.legend(handles=[p1, p2])
plt.show()
plt.plot(rks, ers_bk, sns.xkcd_rgb["amber"], label='ap', linewidth=3)
for p in range(len(rks)):
plt.plot([rks[p]], [ers_bk[p]], 'o', color=sns.xkcd_rgb["amber"])
plt.plot(rks, ers, sns.xkcd_rgb["dusty red"], label='ap', linewidth=3)
for p in range(len(rks)):
plt.plot([rks[p]], [ers[p]], 'o', color=sns.xkcd_rgb["dusty red"])
plt.xlabel('Ранг')
plt.ylabel('Ошибка аппроксимации, %')
plt.legend(handles=[p1, p2])
plt.show()
def test_full_tt_svd():
np.random.seed(1234)
W = np.random.rand(4096, 4096).astype(np.float32)
tensor = Tensor(W, from_matrix=True, d=6)
Gs = tensor.tt_factorization(0.1)
sum = 1
for s in tensor.T.shape:
sum *= s
print('original parameters', sum)
sum = 0
for g in Gs:
sum_i = 1
for s in g.shape:
sum_i *= s
sum += sum_i
print(g.shape)
print('tt parameters', sum)
tt = Gs[0]
print(len(Gs))
for i in range(1, len(Gs)):
if i == 1 or i == 2:
r = max(Gs[i].shape) // 3
t = Tensor(Gs[i])
cp_rand = t.cp_rand(r, init='random', ret_tensors=False)
tt = np.tensordot(tt, cp_rand, [len(tt.shape) - 1, 0])
else:
tt = np.tensordot(tt, Gs[i], [len(tt.shape) - 1, 0])
print('tt-svd error', tensor.frobenius_norm(tensor.T - tt))
print('tt-svd error', tensor.relative_error(tt))
