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knn.py
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from sklearn.model_selection import train_test_split
import random
from math import sqrt
import pandas as pd
import numpy as np
random.seed(42)
def euclidean_distance(value1, value2):
distance = 0
for i in range(len(value1)):
distance += (value1[i] - value2[i])**2
return sqrt(distance)
def k_nearest_neighbors(data, label, test_instance, k):
distances = []
for i in range(len(data)):
tmp_dist = euclidean_distance(test_instance, data[i])
distances.append((data[i], tmp_dist, label[i]))
distances.sort(key=lambda tmp: tmp[1])
neighbors = distances[:k]
prediction = []
for tmp in range(len(neighbors)):
prediction.append(neighbors[tmp][-1])
return(max(prediction))
def test_iris():
# load the data
print('KNN with Iris Dataset')
dataset = pd.read_csv('data/iris.data', header=None)
dataset = np.array(dataset)
data = dataset[:, 0:4]
label = dataset[:, 4]
data_train, data_test, label_train, label_test = train_test_split(
data, label, test_size=0.2, random_state=12)
k = 6
print('Example Test with ' + str(k) + ' nearest neighbors :')
prediction = k_nearest_neighbors(data_train, label_train,
data_test[5], k)
print('True Label : ' + label_test[5], '\nPredicted Label : ' + prediction)
predictions = []
for i in range(len(data_test)):
tmp = k_nearest_neighbors(data_train, label_train,
data_test[i], k)
predictions.append(tmp)
acc = (label_test == predictions).sum()/len(predictions)
print('Accuracy : ', + acc)
df = pd.DataFrame(data_test)
df.insert(4, '4', label_test)
df.insert(5, '5', predictions)
df.to_csv('data/KNN_6_iris_test_true_predict.csv', index=False, header=False)
print('Task Complete')
def test_satellite():
# load the data
print('KNN with Satellite Dataset')
dataset = pd.read_csv('data/satellite/sat.trn', header=None)
dataset = np.array(dataset)
data_train = dataset[:, 0:36]
label_train = dataset[:, 36]
test_set = pd.read_csv('data/satellite/sat.tst', header=None)
test_set = np.array(test_set)
data_test = test_set[:, 0:36]
label_test = test_set[:, 36]
k = 2
print('Example Test with ' + str(k) + ' nearest neighbors :')
prediction = k_nearest_neighbors(data_train, label_train,
data_test[5], k)
print('True Label : ' + str(label_test[5]), '\nPredicted Label : ' +
str(prediction))
predictions = []
for i in range(len(data_test)):
tmp = k_nearest_neighbors(data_train, label_train,
data_test[i], k)
predictions.append(tmp)
acc = (label_test == predictions).sum()/len(predictions)
print('Accuracy : ', + acc)
df = pd.DataFrame(data_test)
df.insert(36, '36', label_test)
df.insert(37, '37', predictions)
df.to_csv('data/satellite/KNN_2_sat_test_true_predict.csv', index=False,
header=False)
print('Task Complete')
def test_shuttle():
# load the data
print('KNN with Shuttle Dataset')
dataset = pd.read_csv('data/shuttle/shuttle.trn', header=None)
dataset = np.array(dataset)
data_train = dataset[:, 0:9]
label_train = dataset[:, 9]
test_set = pd.read_csv('data/shuttle/shuttle.tst', header=None)
test_set = np.array(test_set)
data_test = test_set[:, 0:9]
label_test = test_set[:, 9]
k = 4
print('Example Test with ' + str(k) + ' nearest neighbors :')
prediction = k_nearest_neighbors(data_train, label_train,
data_test[5], k)
print('True Label : ' + str(label_test[5]), '\nPredicted Label : ' +
str(prediction))
predictions = []
for i in range(len(data_test)):
tmp = k_nearest_neighbors(data_train, label_train,
data_test[i], k)
predictions.append(tmp)
acc = (label_test == predictions).sum()/len(predictions)
print('Accuracy : ', + acc)
df = pd.DataFrame(data_test)
df.insert(9, '9', label_test)
df.insert(10, '10', predictions)
df.to_csv('data/shuttle/KNN_4_shut_test_true_predict.csv', index=False,
header=False)
print('Task Complete')
if __name__ == "__main__":
# uncomment whichever you want to run
# note: shuttle dataset takes too much time
test_iris()
# test_satellite()
# test_shuttle()