knn.py

from scipy.spatial import distance
from collections import Counter

class KNN():
    def fit(self, X_train, Y_train):
        self.X_train = X_train
        self.Y_train = Y_train

    def predict(self, X_test, k):
        predictions = []
        for row in X_test:
            label = self.closest(row,k)
            predictions.append(label)
        return predictions

    def closest(self, row, k):
        distances = []
        for i in range(len(self.X_train)):
            distances.append((i,distance.euclidean(row,self.X_train[i])))
        distances = sorted(distances, key=lambda x:x[1])[0:k]
        k_indeces = []
        for i in range(k):
            k_indeces.append(distances[i][0])
        k_labels = []
        for i in range(k):
            k_labels.append(self.Y_train[k_indeces[i]])
        c = Counter(k_labels)
        return c.most_common()[0][0]

k = input("Enter k: ")

from sklearn import datasets
iris = datasets.load_iris()

X = iris.data
Y = iris.target

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .75)

from sklearn.neighbors import KNeighborsClassifier
classifier = KNN()

print "Fitting classifier..."
classifier.fit(X_train, Y_train)
print "Successfully fitted classifier"
print "Making predictions"
predictions = classifier.predict(X_test, k)
print "Completed making predictions"
from sklearn.metrics import accuracy_score
print "Accuracy:", accuracy_score(Y_test, predictions)*100, "%"