import numpy as np

# from scipy.spatial import distance

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn import metrics

class KNNClassifier(object):

def __init__(self):

self.X_train = None

self.y_train = None

def euc_distance(self, a, b):

return np.linalg.norm(a-b)

# return distance.euclidean(a, b)

def closest(self, row):

"""

Returns the label corresponding to the single closest training example.

This is a k=1 nearest neighbor(s) implementation.

:param row:

:return:

"""

dist = [self.euc_distance(row, trainer) for trainer in self.X_train]

best_index = dist.index(min(dist))

return self.y_train[best_index]

def fit(self, training_data, training_labels):

self.X_train = training_data

self.y_train = training_labels

def predict(self, to_classify):

predictions = []

for row in to_classify:

label = self.closest(row)

predictions.append(label)

return predictions

iris = datasets.load_iris()

X = iris.data

y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

classifier = KNeighborsClassifier() # k=5 by default

# classifier = KNNClassifier()

classifier.fit(X_train, y_train)

# print(y_train)

results = classifier.predict(X_test)

score = metrics.accuracy_score(y_test, results)

print("Accuracy: {0:f}".format(score))