Learning Machines

Taught by Patrick Hebron at ITP, Fall 2015

Linear Algebra in Python and Numpy (Part 2):

Continued from Linear Algebra in Python and Numpy (Part 1)

Documentation:

Importing Numpy library:

``import numpy as np``

Matrix Transposition:

``````>>> a = np.array( [ [ 1.0, 2.0, 3.0 ], [ 4.0, 5.0, 6.0 ] ] )
>>> a.T
array([[ 1.,  4.],
[ 2.,  5.],
[ 3.,  6.]])``````

``````>>> a = np.array( [ [  1.0,  2.0,  3.0 ], [  4.0,  5.0,  6.0 ] ] )
>>> b = np.array( [ [ 10.0, 20.0, 30.0 ], [ 40.0, 50.0, 60.0 ] ] )
>>> a + b
array([[ 11.,  22.,  33.],
[ 44.,  55.,  66.]])``````

Matrix Subtraction:

``````>>> a = np.array( [ [  1.0,  2.0,  3.0 ], [  4.0,  5.0,  6.0 ] ] )
>>> b = np.array( [ [ 10.0, 20.0, 30.0 ], [ 40.0, 50.0, 60.0 ] ] )
>>> a - b
array([[ -9., -18., -27.],
[-36., -45., -54.]])``````

``````>>> a = np.array( [ [  1.0,  2.0,  3.0 ], [  4.0,  5.0,  6.0 ] ] )
>>> b = np.array( [ [ 10.0, 20.0, 30.0 ], [ 40.0, 50.0, 60.0 ] ] )
>>> a * b
array([[  10.,   40.,   90.],
[ 160.,  250.,  360.]])``````

Matrix Multiplication:

``````>>> a = np.array( [ [ 2, -4, 6 ], [ 5, 7, -3 ] ] )
>>> b = np.array( [ [ 8, -5 ], [ 9, 3 ], [ -1, 4 ] ] )
>>> np.dot( a, b )
array([[-26,   2],
[106, -16]])``````

``````>>> a = np.array( [ [ 1.0, 2.0, 3.0 ], [ 4.0, 5.0, 6.0 ] ] )
>>> a + 3.14
array([[ 4.14,  5.14,  6.14],
[ 7.14,  8.14,  9.14]])``````

Matrix-Scalar Subtraction:

``````>>> a = np.array( [ [ 1.0, 2.0, 3.0 ], [ 4.0, 5.0, 6.0 ] ] )
>>> a - 3.14
array([[-2.14, -1.14, -0.14],
[ 0.86,  1.86,  2.86]])``````

Matrix-Scalar Multiplication:

``````>>> a = np.array( [ [ 1.0, 2.0, 3.0 ], [ 4.0, 5.0, 6.0 ] ] )
>>> a * 3.14
array([[  3.14,   6.28,   9.42],
[ 12.56,  15.7 ,  18.84]])``````

Matrix-Scalar Division:

``````>>> a = np.array( [ [ 1.0, 2.0, 3.0 ], [ 4.0, 5.0, 6.0 ] ] )
>>> a / 3.14
array([[ 0.31847134,  0.63694268,  0.95541401],
[ 1.27388535,  1.59235669,  1.91082803]])``````