# Don't forget to import the package

# Using numpy, contruct the following matrices:

# a)
# A =   [[   0.,    0.,    0.],
#       [   0.,    0.,    0.],
#       [   0.,    0., 1000.]]


# b)
# B =   [[1., 0., 0.],
#       [0., 1., 0.],
#       [0., 0., 1.],
#       [1., 0., 0.],
#       [0., 1., 0.],
#       [0., 0., 1.]]


# c)
# C =   [[ 1.,  0.,  0.],
#        [ 0.,  1.,  0.],
#        [-1.,  5.,  0.],
#        [-1.,  0.,  5.]]


# d) Reshape C to get the following:
# D =   [[ 1.,  0.,  0.,  0.],
#       [ 1.,  0., -1.,  5.],
#       [ 0., -1.,  0.,  5.]]


# e) ... and the following:
# E =   [[ 1.,  0., -1., -1.],
#       [ 0.,  1.,  5.,  0.],
#       [ 0.,  0.,  0.,  5.]]


# f) In matrix E, increment all elements (add 1)
#    that are larger than 0.


# g) Set all elements of the first row in E to 0.


# h) Set all elements of the right half of E to 1.


# i) Get rid of the floating point part in the elements of E (round them to integers)


# j) Can you get a matrix product np.dot(D,E)?
#    If not, what simplest transformation you need to
#    apply to one of the matrices to make the product
#    possible? Provide results of applying that transform
#    to either of the matrices.
