def TestSubAdd():
ten1 = tensor(np.arange(10))
ten2 = tensor(np.arange(10))
ten = base.tensor_sub(ten1, ten2)
print(ten.data)
ten = base.tensor_add(ten1, ten2)
print(ten.data)
def random_tensor(shape=None,
sparsity=0.5,
seed=1234,
distributed='normal',
normal=None,
uniform=None,
plot=True):
np.random.seed(seed)
tensor_size = prod(shape)
if distributed == 'normal':
mean, std = normal
data = np.random.normal(mean, std, tensor_size)
if plot == True:
n, bins, patches = plt.hist(data,
30,
normed=True,
facecolor='blue',
alpha=0.5)
y = mlab.normpdf(bins, mean, std)
plt.plot(bins, y, 'r--')
plt.xlabel('Expectation')
plt.ylabel('Probability')
plt.title('Histogram of Normal Distribution:$\mu =' + str(mean) +
'$, $\sigma=' + str(std) + '$')
plt.axvline(norm.ppf(sparsity) * std + mean)
plt.show()
data[data <= norm.ppf(sparsity) * std + mean] = 0
if distributed == 'uniform':
low, high = uniform
data = np.random.uniform(low, high, size=tensor_size)
if plot == True:
n, bins, patches = plt.hist(data,
30,
normed=True,
facecolor='blue',
alpha=0.5)
plt.xlabel('Expectation')
plt.ylabel('Probability')
plt.title('Histogram of Uniform Distribution:$low =' + str(low) +
'$, $high =' + str(high) + '$')
plt.axvline((high - low) * sparsity)
plt.show()
data[data <= (high - low) * sparsity] = 0
return tensor(data.reshape(shape))
def tensor_times_mat(ten, mat, mode):
shp = ten.shape
ndim = ten.ndims
order = float2front(ten.order, mode)
newdata = np.dot(mat, unfold(X, mode).data)
p = mat.shape[0]
newshp = [p]
newshp.extend(shp[0:mode])
newshp.extend(shp[mode + 1:ndim])
T = newdata.reshape(newshp)
T = np.transpose(T, [order.index(i) for i in range(len(order))])
return tensor(T)
def tensor_times_mat(X,mat,mode):
shp = X.shape
ndim = X.ndims()
order = []
order.extend([mode])
order.extend(range(0, mode))
order.extend(range(mode + 1, ndim))
newdata = np.dot(mat,unfold(X,mode).data)
p = mat.shape[0]
newshp = [p]
newshp.extend(shp[0:mode])
newshp.extend(shp[mode+1:ndim])
T = newdata.reshape(newshp)
T = np.transpose(T,[order.index(i) for i in range(len(order))])
return tl.tensor(T)
def TestNorm():
ten1 = tensor(np.arange(3))
print(ten1.data)
print(base.tennorm(ten1)**2)
from skimage.measure import compare_psnr
im = Image.open("Lenna.png")
print('Image Mode and Size：' + str(im.mode) + ',' + str(im.size))
data = np.array(im)
print('Image Shape：' + str(data.shape))
data_float = data.astype(np.float32)
time0 = time.time()
tucker_ranks = [200, 200, 3]
tensor_data = tensor(data)
U, core = tk.hooi(tensor_data, ranks=tucker_ranks, tol=10e-8, print_enable=True, init='hosvd', plot_enable=True)
data_reconstruction = tk.tucker2tensor(U, core)
print('Image Reconstruction Shape：' + str(data_reconstruction.shape))
def convert2uin8(tensor):
im = tensor.data
im -= im.min()
im /= im.max()
im *= 255
return im.astype(np.uint8)
im_reconstruction = convert2uin8(data_reconstruction)
import numpy as np
from tensorcomlib.decomposition import tucker as tk
from tensorcomlib import base
from tensorcomlib import tensor
def TestHooI(data):
#eps: range(0,1)
U, core = tk.hooi(data, eps=0.08, tol=1e-10, plot=True)
ndim = data.ndims()
modelist = list(range(ndim))
data1 = base.tensor_multi_times_mat(core, U, modelist=modelist, transpose=False)
print("Original tensor:",data.data.reshape(1024)[1:10])
print("Tucker tensor:",data1.data.reshape(1024)[1:10])
if __name__ == '__main__':
np.random.seed(5)
B = np.round(np.random.randint(1, 10, (8, 16, 8)))
print(B.shape)
data = tensor.tensor(B)
TestHooI(data)
def sub(X1, X2):
return tensor(X1.data - X2.data)
def teninner(X1,X2):
if(X1.shape != X2.shape):
raise ValueError("All the tensor's shape must be same!")
res = (X1.data) * (X2.data)
return tl.tensor(res,X1.shape)
def tenrands(shp):
data = np.random.random(shp)
return tl.tensor(data,shp)
def tensor_contraction(X1,X2):
return tl.tensor(np.tensordot(X1.data,X2.data,axes=2))
def tenzeros(shp):
data = np.ndarray(shp)
data.fill(0)
return tl.tensor(data,shp)
def add(X1, X2):
return tensor(X1.data + X2.data)
def tensor_times_vec(X,vec,mode):
ndim = X.ndims
return tl.tensor(T)
def fold(G, shape, mode):
return tl.tensor(X)
def tensor_sub(X1,X2):
return tl.tensor(X1.data-X2.data)
def tensor_add(X1,X2):
return tl.tensor(X1.data+X2.data)
def TestUnfoldAndFold():
ten1 = tensor(np.arange(24).reshape(3, 4, 2))
U = [base.unfold(ten1, i) for i in range(3)]
print([U[i].shape for i in range(3)])
ten = [base.fold(U[i], ten1.shape, i) for i in range(3)]
print([ten[i].shape for i in range(3)])
def tenones(shp):
data = np.ndarray(shp)
data.fill(1)
return tl.tensor(data,shp)
def kronecker(ten1,ten2):
res = np.kron(ten1.data,ten2.data)
return tl.tensor(res,res.shape)
def tenouter(X1,X2):
return tl.tensor(np.tensordot(X1.data, X2.data, axes=0))
