ML - Lab 5 - KNN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

# Load data
knndata = pd.read_csv("./knndata.txt")
print("knndata = ")
print(knndata)
print()
X = knndata.loc[:, ["X1", "X2"]]
y = knndata.loc[:, "Y"]

# Show data
plt.scatter(X[y == 1].X1, X[y == 1].X2, c="blue", marker="o", label="1")
plt.scatter(X[y == 2].X1, X[y == 2].X2, c="red", marker="+", label="2")
plt.title("Points")
plt.xlabel("X1")
plt.ylabel("X2")
plt.legend()
plt.show()

# Classifier kNN
from sklearn.neighbors import KNeighborsClassifier
k = 1
clf = KNeighborsClassifier(n_neighbors=k)
clf = clf.fit(X, y)
# Predict
new_X1 = 0.7
new_X2 = 0.4
new_X = [new_X1, new_X2]
print("k = " + str(k))
print(clf.predict([new_X]))                 # [new], where new = [..., ...]
print(clf.predict_proba([new_X]))           # [new], where new = [..., ...]
print()

k = 5
clf = KNeighborsClassifier(n_neighbors=k)
clf = clf.fit(X, y)
# Predict
new_X1 = 0.7
new_X2 = 0.4
new_X = [new_X1, new_X2]
print("k = " + str(k))
print(clf.predict([new_X]))                 # [new], where new = [..., ...]
print(clf.predict_proba([new_X]))           # [new], where new = [..., ...]
print()