from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import accuracy_score

from scipy.spatial import distance

def euc(a,b):

return distance.euclidean(a, b)

class ScrappyKNN():

def fit(self, features_train, labels_train):

self.features_train = features_train

self.labels_train = labels_train

def predict(self, features_test):

predictions = []

for item in features_test:

label = self.closest(item)

predictions.append(label)

return predictions

def closest(self, item):

best_dist = euc(item, self.features_train[0])

best_index = 0

for i in range(1,len(self.features_train)):

dist = euc(item, self.features_train[i])

if dist < best_dist:

best_dist = dist

best_index = i

return self.labels_train[best_index]

iris = datasets.load_iris()

print(iris)

features = iris.data

labels = iris.target

print(features)

print(labels)

features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=.5)

#print(len(features))

#print(len(features_train))

my_classifier = ScrappyKNN()

#my_classifier = KNeighborsClassifier()

my_classifier.fit(features_train, labels_train)

prediction = my_classifier.predict(features_test)

print(prediction)

print(accuracy_score(labels_test, prediction))

iris1 = [[7.1, 2.9, 5.3, 2.4]] #virginica

iris_prediction = my_classifier.predict(iris1)

if iris_prediction == 0:

print("Setosa")

if iris_prediction == 1:

print("Versicolor")

if iris_prediction == 2:

print("Virginica")