# https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
import numpy as np
from prettytable import PrettyTable
# load iris dataset
# -----------------------------------------
from sklearn import datasets
iris = datasets.load_iris()
# sample test data
# -----------------------------------------
S_test = np.array([
[1, 9, 0, 4, 5, 3, 2],
[9, 0, 4, 5, 3, 2, 1],
[0, 4, 5, 3, 2, 1, 9],
[4, 5, 3, 2, 1, 9, 0]]
)
S_test = S_test / np.array([2.3, 4, 1.5, 4]).reshape(-1, 1)
# S_test
S_test = S_test + np.array([4, 2, 1, 0]).reshape(-1, 1)
S_test = S_test.transpose()
# sklearn
# -----------------------------------------
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
# split training dataset
X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target, test_size=0.2)
# training and predictions
classifier = KNeighborsClassifier(n_neighbors=3, metric='euclidean')
classifier.fit(X_train, Y_train)
# evaluate
# Y_prediction = classifier.predict(X_test)
# print(confusion_matrix(Y_test, Y_prediction))
# print(classification_report(Y_test, Y_prediction))
# print(accuracy_score(Y_test, Y_prediction))
# sample classification
# -----------------------------------------
Y_prediction = classifier.predict(S_test)
# print(Y_prediction)
# prettytable
# -----------------------------------------------------------
pt = PrettyTable(('sample', 'class'))
for row in list(zip(np.round(S_test, 4), Y_prediction)): pt.add_row(row)
pt.align['sample'] = 'l'
pt.align['class'] = 'c'
print(pt)
# +-------------------------------+-------+
# | sample | class |
# +-------------------------------+-------+
# | [4.4348 4.25 1. 1. ] | 0 |
# | [7.913 2. 3.6667 1.25 ] | 1 |
# | [4. 3. 4.3333 0.75 ] | 1 |
# | [5.7391 3.25 3. 0.5 ] | 1 |
# | [6.1739 2.75 2.3333 0.25 ] | 0 |
# | [5.3043 2.5 1.6667 2.25 ] | 0 |
# | [4.8696 2.25 7. 0. ] | 2 |
# +-------------------------------+-------+