knn_on_irisDataset.py

import knn_final
#do copy knn_final before running 
if __name__ == '__main__':
	from sklearn import datasets
	iris = datasets.load_iris()
	print(iris["data"])
	predictors = iris.data[:,0:2]
	outcomes = iris.target
	plt.plot(predictors[outcomes==0][:,0],predictors[outcomes==0][:,1],"ro")
	plt.plot(predictors[outcomes==1][:,0],predictors[outcomes==1][:,1],"go")
	plt.plot(predictors[outcomes==2][:,0],predictors[outcomes==2][:,1],"bo")
	plt.show()
	k = 5; limits = (4,8,1.5,4.5); h = 0.1
	(xx,yy,prediction_grid)=make_prediction_grid(predictors,outcomes,limits,h,k)
	plot_prediction_grid(xx,yy,prediction_grid)
	from sklearn.neighbors import KNeighborsClassifier
	knn = KNeighborsClassifier(n_neighbors=5)
	knn.fit(predictors,outcomes)
	sk_predictions = knn.predict(predictors)
	mypredictions = np.array([knn_predict(p,predictors,outcomes,5) for p in predictors])
	print("Mutual Accuracy ",100*np.mean(sk_predictions==mypredictions))
	print("SciKIt Accuracy",100*np.mean(sk_predictions==outcomes))
	print("My Model Accuracy",100*np.mean(mypredictions==outcomes))