from __future__ import print_function
import torch
import numpy as np

# pyTorch tensoor

x = torch.Tensor(5, 3)
print('matrix x:\n', x)
print('matrix x size:\n', x.size())

y = torch.rand(5, 3)
print('matrix x + y:\n', x + y)

print('matrix x + y again:\n', torch.add(x, y))

result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
print('matrix x + y result:\n', result)

y.add_(x)
print('matrix x add to y:\n', y)

print('col 2 of matrix x:\n', x[:, 1])

# convert to numpy

a = torch.ones(5)
print('torch array full with number 1:\n', a)

b = a.numpy()
print('numpy array:\n', b)

a.add_(1)
print('torch array after change:\n', a)
print('numpy array after change:\n', b)

na = np.ones(5)
nb = torch.from_numpy(na)
np.add(na, 1, out=na)
print('another numpy array after change:\n', na)
print('another torch array after change:\n', nb)

# CUDA

if torch.cuda.is_available():
    x = x.cuda()
    y = y.cuda()
    print('result from GPU:\n', x + y)