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seeditsolution merged 1 commit into from
Oct 3, 2020
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KNN_algorithm #348

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KNN_algorithm 
Attempted to write KNN algorithm using python and built a flower classifier model.
I have commented the lines involving classifier code from the in-built scikit-learn library.
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@MartianCoder-git
MartianCoder-git authored Oct 3, 2020
commit 5de973adb3ced00ba7979567546ddfb718f72b17
65 changes: 65 additions & 0 deletions KNN_algorithm
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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")