K-Nearest Neighbor classifier

Implementation of K-NN classifier with three distance metrics:

1. euclidean
2. manhattan
3. cosine

Example:

>>> from k_neighbors import K_NN
>>> clf = K_NN(K=3, metric = 'euclidean')
>>> X = [[3.4, 0.2], [3.7, 0.4], [3.6, 0.2], [3.3, 0.5], [3.4, 0.2],
...      [3.2, 1.8], [2.8, 1.3], [2.5, 1.5], [2.8, 1.2], [2.9, 1.3],
...      [3.2, 1.8], [2.8, 1.8], [3.0, 1.8], [2.8, 2.1], [3.0, 1.6]]
>>> y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
>>> clf.fit(X, y)
>>> clf.predict([3.3, 0.4])
[0]
>>> clf.predict([3.0, 1.4])
[1]
>>> clf.predict([3.2, 1.9])
[2]

>>> clf.fit(X,y)
>>> clf.predict([1,4])
[0]
>>> clf.predict([4,3])
[1]
>>> clf.predict([4,1])
[2]

k-NN for alphabetical data using feature extraction (one-hot representation):

>>> train = [['med', 'low', '3', '2', 'small', 'high'],
...        ['high', 'low', '5more', 'more', 'med', 'high'],
...        ['high', 'med', '4', '4', 'big', 'high'],
...        ['high', 'high', '4', 'more', 'big', 'high'],
...        ['med', 'high', '2', 'more', 'big', 'high'],
...        ['vhigh', 'high', '4', 'more', 'small', 'high'],
...        ['med', 'low', '5more', 'more', 'big', 'med'],
...        ['high', 'med', '3', '2', 'big', 'low'],
...        ['med', 'high', '4', 'more', 'big', 'low'],
...        ['high', 'vhigh', '3', '2', 'med', 'med']]
>>>
>>> target = ['unacc', 'acc', 'acc', 'acc', 'acc', 'unacc', 'good', 'unacc', 'unacc', 'unacc']
>>>
>>> from vectorizer import FeatExtraction
>>> vec = FeatExtraction()
>>> vec.fit(train)
>>>
>>> test = [['high', 'low', '3', 'more', 'small', 'high'],
...  ['high', 'high', '3', 'more', 'med', 'low'],
...  ['low', 'low', '2', '2', 'med', 'low'],
...  ['vhigh', 'vhigh', '2', '2', 'small', 'med'],
...  ['low', 'vhigh', '3', '4', 'small', 'high'],
...  ['vhigh', 'med', '2', 'more', 'big', 'high'],
...  ['vhigh', 'vhigh', '2', 'more', 'med', 'med'],
...  ['low', 'low', '2', '4', 'med', 'low'],
...  ['low', 'high', '2', '4', 'small', 'med'],
...  ['vhigh', 'med', '2', '2', 'med', 'low']]
>>>
>>> vec_train = vec.one_hot(train)
>>> vec_test = vec.one_hot(test)
>>>
>>> vec_train.astype(int)
array([[0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0],
       [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0],
       [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0],
       [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0],
       [0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0],
       [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0],
       [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
       [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1],
       [1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0]])
>>>
>>> vec_test.astype(int)
array([[1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0],
       [1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1],
       [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
       [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0],
       [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0],
       [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1],
       [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0],
       [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]])
>>>
>>> from k_neighbors import K_NN
>>> clf = K_NN(K=3,metric='cosine')
>>> clf.fit(vec_train, target)
>>> 
>>> clf.predict(vec_test)
['acc', 'acc', 'unacc', 'unacc', 'unacc', 'acc', 'acc', 'acc', 'acc', 'unacc']