Please in Python and only use Numpy package

Look at the sample, make sure the first classification works

yeah actually hw is the name of the file or the class, it is not mandatory, what matters is the function

def classify_examples (weights, test_examples): (5 pts) This function classifies examples using the perceptron algorithm. To do that, it receives one single dimensional numpy array weights, and 2D numpy array (test_examples) then returns a 1D numpy array with the classification results for each example (1 is positive, and -1 if it is a negative). Parameters: weights: 1-D numpy array with the perceptron weights and bias (bias is in position () test_examples: 2-D numpy array with the feature-values of the test examples. Note: the width (number of columns) of test_examples should be length of weights - 1. Note: assume weights and the features vectors follow the same order: e.g. We, wl, W2, W3... x1, x2, x3... Returns: 1D numpy array with the classification results of the test_examples: (each is either 1, or -1). In [25]: # Now simulating perceptron classification (using examples from our lecture slides) In [26]: W = np.array([-5, 1, 1]) In [27]: x = np.array( [1, 5] ) In [28]: hw.classify_examples(w, x) Out[28]: 1 In [29]: x = np.array([1, 3]) In [30]: hw.classify_examples(w, x) Out[30]: -1 In [31]: x = np.array([1, 1]) In [32]: hw.classify_examples(w, x) Out[32]: -1 In [33]: x = np.array([4, 3]) In [34] : hw.classify_examples(w, x) Out[34]: 1 In [35]: # Now we pass them as an 2D numpy array In [36]: x = np.array([ [1, 5], [1, 3], [1, 1], [4, 3] ]) In [37]: hw.classify_examples(w, x) Out[37]: array([ 1., -1., -1., 1.])