Machine Learning-Python

Testing your Homework 2 Solutions...

In [1]: import numpy as np

In [2]: import homework2solution as hw

In [3]: #Testing the find_closest_example function

In [4]: #We'll place a datapoint in each quadrant (in 2D plane example)

In [5]: tdata = np.array([ [-1,1], [1,1], [1, -1], [-1, -1] ])

In [6]: hw.find_closest_example(tdata, np.array([-6,9]) )

Out[6]: 0

In [7]: hw.find_closest_example(tdata, np.array([4,9]) )

   ...:

Out[7]: 1

In [8]: hw.find_closest_example(tdata, np.array([-3,-4]) )

Out[8]: 3

In [9]: hw.find_closest_example(tdata, np.array([3,-4]) )

Out[9]: 2

In [10]: #Note that 0 means the first example, or the example in the first row in the training data

In [11]: #Now testing three dimensional problems

In [12]: tdata = np.array([ [1, -5, 1], [3, 5, 0], [-5, -6, -7] ])

In [13]: hw.find_closest_example(tdata, np.array([-2, -4,-8]) )

Out[13]: 2

In [14]: # Now testing the calculate_centroid_pos function

In [15]: mydata = np.array( [ [1, 2], [3, 4], [5, 6] ])

In [16]: hw.calculate_centroid_pos(mydata)

Out[16]: array([3., 4.])

In [17]: # Now testing higher dimensions

In [18]: mydata = np.array([ [ 1, 2, 3], [4, 5, 6], [7, 8, 9] ])

In [19]: hw.calculate_centroid_pos(mydata)

Out[19]: array([4., 5., 6.])

In [20]: # Now simulating perceptron classification (using examples from our lecture slides)

In [21]: w = np.array([-5, 1, 1])

In [22]: x = np.array([ [ 1, 5] ])

In [23]: hw.classify_examples(w, x)

Out[23]: array([1.])

In [24]: x = np.array([ [1, 3] ])

In [25]: hw.classify_examples(w, x)

Out[25]: array([-1.])

In [26]: x = np.array([ [1, 1] ])

In [27]: hw.classify_examples(w, x)

Out[27]: array([-1.])

In [28]: x = np.array([ [4, 3] ])

In [29]: hw.classify_examples(w, x)

Out[29]: array([1.])

In [30]: # Now we pass them as a batch 2D numpy array

In [31]: x = np.array([ [1,5], [1, 3], [1, 1], [4, 3] ])

In [32]: hw.classify_examples(w, x)

Out[32]: array([ 1., -1., -1., 1.])

In [33]: # Good luck! :)