Assignment 1 of Pytorch: zero to GANs

import torch
import numpy as np

# .solve function
# example 1
df = torch.tensor([[6, 4.],
                   [4, 0]], dtype=torch.float32).t()
b = torch.tensor([[0, -2.]], dtype=torch.float32).t()
x, lu = torch.solve(b, df)
print(df, b, x, sep='\n')
# example 2
df = torch.tensor([[6, 4, 2],
                   [4, 0, 1],
                   [2, 5, 0.]], dtype=torch.float32).t()
b = torch.tensor([[0, -2., 4]], dtype=torch.float32).t()
x, lu = torch.solve(b, df)
print(df, b, x, sep='\n')
# example not working
df = torch.tensor([[6, 0, 4.],
                   [4, 1, 0]], dtype=torch.float32).t()
b = torch.tensor([[0, -2.]], dtype=torch.float32).t()
x, lu = torch.solve(b, df)
print(df, b, x, sep='\n')

tensor([[6., 4.],
        [4., 0.]])
tensor([[ 0.],
        [-2.]])
tensor([[-0.5000],
        [ 0.7500]])
tensor([[6., 4., 2.],
        [4., 0., 5.],
        [2., 1., 0.]])
tensor([[ 0.],
        [-2.],
        [ 4.]])
tensor([[ 4.2222],
        [-4.4444],
        [-3.7778]])

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-51-02657424936b> in <module>
     17                    [4, 1, 0]], dtype=torch.float32).t()
     18 b = torch.tensor([[0, -2.]], dtype=torch.float32).t()
---> 19 x, lu = torch.solve(b, df)
     20 print(df, b, x, sep='\n')

RuntimeError: A must be batches of square matrices, but they are 2 by 3 matrices

.solve applies the matrix equation AX = b and there should be the same number of rows and columns in matrix A, df in this case.

# .eig function
# example 1
H = torch.tensor([[0, 1, 0],
                  [1, 0, 0],
                  [0, 0, 6.]], dtype=torch.float32)
eigpairs = torch.eig(H, eigenvectors=True)
print(H, eigpairs, sep='\n')
# example 2
H = torch.tensor([[1, 0, 0],
                  [0, 1, 0],
                  [0, 0, 1.]], dtype=torch.float32)
eigpairs = torch.eig(H, eigenvectors=True)
print(H, eigpairs, sep='\n')
# example not working
H = torch.tensor([[0, 1, 0],
                  [1, 0, 0],
                  [0, 0, 6]], dtype=torch.int32)
eigpairs = torch.eig(H, eigenvectors=True)
print(H, eigpairs, sep='\n')

tensor([[0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 6.]])
torch.return_types.eig(
eigenvalues=tensor([[ 1.,  0.],
        [-1.,  0.],
        [ 6.,  0.]]),
eigenvectors=tensor([[ 0.7071, -0.7071,  0.0000],
        [ 0.7071,  0.7071,  0.0000],
        [ 0.0000,  0.0000,  1.0000]]))
tensor([[1., 0., 0.],
        [0., 1., 0.],
        [0., 0., 1.]])
torch.return_types.eig(
eigenvalues=tensor([[1., 0.],
        [1., 0.],
        [1., 0.]]),
eigenvectors=tensor([[1., 0., 0.],
        [0., 1., 0.],
        [0., 0., 1.]]))

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-52-54418a1b1eb1> in <module>
     16                   [1, 0, 0],
     17                   [0, 0, 6]], dtype=torch.int32)
---> 18 eigpairs = torch.eig(H, eigenvectors=True)
     19 print(H, eigpairs, sep='\n')

RuntimeError: _th_eig not supported on CPUType for Int