firstClassifierKNeighbors.py

import random
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]
#import a dataset
from sklearn import datasets
iris = datasets.load_iris()

X = iris.data
Y = iris.target

#split into train and test
from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = .5)
#test_size = .5 means half data used for test

#from sklearn.neighbors import KNeighborsClassifier
my_classifier = ScrappyKNN()

#use train to train a classifier
my_classifier.fit(X_train, Y_train)

#use test to test accuracy
predictions = my_classifier.predict(X_test )

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