[razvan@ohio ~]$ python3
Python 3.6.5 |Anaconda, Inc.| (default, Apr 26 2018, 08:42:37) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> b = np.arange(5)
>>> b
array([0, 1, 2, 3, 4])
>>> b + 1
array([1, 2, 3, 4, 5])
>>> a.shape
(4,)
>>> np.arange(12)
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a = np.arange(12)
>>> a.reshape(3, 4)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> b = a.reshape(3, 4)
>>> b.shape
(3, 4)
>>> a
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a.shape
(12,)
>>> a
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a.shape = 3, 4
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a.ravel()
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a.shape = 12,
>>> a
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a.reshape(-1, 4)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> a.shape = 3, 4
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> b
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> b = np.array(1, 1, 1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: only 2 non-keyword arguments accepted
>>> b = np.array([1, 1, 1])
>>> b
array([1, 1, 1])
>>> b = np.ones(3)
>>> b
array([1., 1., 1.])
>>> b = np.ones(3, dtype = np.int8)
>>> b
array([1, 1, 1], dtype=int8)
>>> b = np.ones(3)
>>> b
array([1., 1., 1.])
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a + b
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: operands could not be broadcast together with shapes (3,4) (3,) 
>>> a.shape
(3, 4)
>>> b.shape
(3,)
>>> a.T
array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])
>>> a.T + b
array([[ 1.,  5.,  9.],
       [ 2.,  6., 10.],
       [ 3.,  7., 11.],
       [ 4.,  8., 12.]])
>>> l = [1, 2, 3)
  File "<stdin>", line 1
    l = [1, 2, 3)
                ^
SyntaxError: invalid syntax
>>> l = [1, 2, 3]
>>> t = (1, 2, 3)
>>> l[0] = -1
>>> l
[-1, 2, 3]
>>> t[0]
1
>>> t[0] = -1
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>> 3 * t
(1, 2, 3, 1, 2, 3, 1, 2, 3)
>>> 3 * (1)
3
>>> 3 * (1,)
(1, 1, 1)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a.T
array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])
>>> a.T.shape
(4, 3)
>>> b
array([1., 1., 1.])
>>> b.shape
(3,)
>>> a.T + b
array([[ 1.,  5.,  9.],
       [ 2.,  6., 10.],
       [ 3.,  7., 11.],
       [ 4.,  8., 12.]])
>>> X = a.T
>>> X
array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])
>>> X.min()
0
>>> X.min(axis = 0)
array([0, 4, 8])
>>> min
<built-in function min>
>>> min = X.min(axis = 0)
>>> min
array([0, 4, 8])
>>> max = X.max(axis = 0)
>>> max
array([ 3,  7, 11])
>>> np.random.shuffle(X)
>>> X
array([[ 3,  7, 11],
       [ 1,  5,  9],
       [ 0,  4,  8],
       [ 2,  6, 10]])
>>> min = X.min(axis = 0)
>>> max = X.max(axis = 0)
>>> min
array([0, 4, 8])
>>> max
array([ 3,  7, 11])
>>> min.shape
(3,)
>>> X - min
array([[3, 3, 3],
       [1, 1, 1],
       [0, 0, 0],
       [2, 2, 2]])
>>> max - min
array([3, 3, 3])
>>> (X - min) / (max - min)
array([[1.        , 1.        , 1.        ],
       [0.33333333, 0.33333333, 0.33333333],
       [0.        , 0.        , 0.        ],
       [0.66666667, 0.66666667, 0.66666667]])
>>> X
array([[ 3,  7, 11],
       [ 1,  5,  9],
       [ 0,  4,  8],
       [ 2,  6, 10]])
>>> X.min(axis=0)
array([0, 4, 8])
>>> X.argmin(axis=0)
array([2, 2, 2])
>>> X = np.random.random(12)
>>> X
array([0.71837621, 0.90443592, 0.92950349, 0.99297252, 0.95578164,
       0.76235967, 0.41015834, 0.68023226, 0.6819877 , 0.89050657,
       0.37855087, 0.15034446])
>>> X.shape = 4, 3
>>> X
array([[0.71837621, 0.90443592, 0.92950349],
       [0.99297252, 0.95578164, 0.76235967],
       [0.41015834, 0.68023226, 0.6819877 ],
       [0.89050657, 0.37855087, 0.15034446]])
>>> X.argmin(axis=0)
array([2, 3, 3])
>>> W = np.random.random(30).rehsape(3, -1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'numpy.ndarray' object has no attribute 'rehsape'
>>> W = np.random.random(30).reshape(3, -1)
>>> W
array([[0.99475734, 0.55428205, 0.58548814, 0.80763834, 0.28555463,
        0.90708438, 0.14192279, 0.16564552, 0.19466479, 0.96587993],
       [0.97781482, 0.29174689, 0.70652168, 0.36166073, 0.85756781,
        0.06544065, 0.40766747, 0.37173832, 0.92486188, 0.96720715],
       [0.96922306, 0.2144513 , 0.29514287, 0.87003537, 0.91099423,
        0.34959078, 0.50505939, 0.94737582, 0.7710913 , 0.9439845 ]])
>>> logits = X @ W
>>> logits.shape
(4, 10)
>>> c = logits.max(axis = 1)
>>> c.shape
(4,)
>>> c
array([2.49987707, 2.66124073, 1.73414741, 1.40170791])
>>> a.T
array([[ 3,  7, 11],
       [ 1,  5,  9],
       [ 0,  4,  8],
       [ 2,  6, 10]])
>>> c.T
array([2.49987707, 2.66124073, 1.73414741, 1.40170791])
>>> c.shape
(4,)
>>> c.T.shape
(4,)
>>> c.shape = 4, 1
>>> c
array([[2.49987707],
       [2.66124073],
       [1.73414741],
       [1.40170791]])
>>> newl = logits - c
>>> newl
array([[ 0.        , -1.63849444, -1.16593641, -0.78388904, -0.67235399,
        -1.4641165 , -1.55975954, -1.16407865, -0.80682416, -0.05379814],
       [ 0.        , -1.66851855, -1.17958163, -0.85032949, -0.86354001,
        -1.43146998, -1.7456373 , -1.4192175 , -0.99612903, -0.05805396],
       [ 0.        , -1.16209521, -0.81212192, -0.5635211 , -0.41239265,
        -1.07916773, -1.05418373, -0.76723946, -0.49930835, -0.03627238],
       [ 0.        , -0.76543351, -0.56849939, -0.41478869, -0.68582367,
        -0.51661166, -1.04506897, -0.97104492, -0.76232106, -0.03352555]])
>>> exp
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'exp' is not defined
>>> exp = np.exp(newl)
>>> exp
array([[1.        , 0.19427231, 0.31163071, 0.45662671, 0.51050544,
        0.23128224, 0.21018661, 0.31221018, 0.44627311, 0.94762338],
       [1.        , 0.18852615, 0.30740732, 0.42727413, 0.42166673,
        0.2389574 , 0.17453372, 0.24190323, 0.36930625, 0.94359903],
       [1.        , 0.31283005, 0.44391511, 0.56920132, 0.66206426,
        0.33987828, 0.34847676, 0.464293  , 0.60695031, 0.96437758],
       [1.        , 0.46513225, 0.56637471, 0.66047983, 0.5036752 ,
        0.5965384 , 0.35166756, 0.37868713, 0.4665822 , 0.96703021]])
>>> sums = exp.sum(axis = 1)
>>> sums.shape
(4,)
>>> sums
array([4.62061069, 4.31317397, 5.71198667, 5.9561675 ])
>>> probs = exp / sums.reshape(4, 1)
>>> propbs
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'propbs' is not defined
>>> probs
array([[0.21642161, 0.04204473, 0.06744362, 0.09882389, 0.11048441,
        0.05005447, 0.04548892, 0.06756903, 0.09658314, 0.20508618],
       [0.23184782, 0.04370938, 0.07127172, 0.09906258, 0.09776251,
        0.05540175, 0.04046526, 0.05608474, 0.08562285, 0.21877138],
       [0.17507044, 0.05476729, 0.07771641, 0.09965032, 0.11590788,
        0.05950264, 0.06100798, 0.08128398, 0.10625906, 0.168834  ],
       [0.1678932 , 0.07809254, 0.09509046, 0.11089007, 0.08456364,
        0.10015474, 0.05904259, 0.06357899, 0.07833598, 0.16235779]])
>>> probs.sum(axis = 1)
array([1., 1., 1., 1.])
>>>