from scipy.spatial import distance

def euc(a,b):
    return distance.euclidean(a,b)

class ScrappyKNN():
    def fit( self, X_train, y_train ):
        self.X_train = X_train
        self.y_train = y_train

    def predict( self, X_test ):
        predictions=[]

        for row in X_test:
            label = self.closest(row)
            predictions.append( label )
        return predictions

    def closest( self, row ):
        best_dist = euc( row, self.X_train[0] )
        best_index = 0
        for i in range( 1, len( self.X_train)):
            dist = euc( row, self.X_train[i] )
            if dist < best_dist:
                best_dist = dist
                best_index = i

        return self.y_train[ best_index ]

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=.5 )

my_classifier = ScrappyKNN()

my_classifier.fit( X_train, y_train )

predictions = my_classifier.predict( X_test )

from sklearn.metrics import accuracy_score
print accuracy_score( y_test, predictions )
