k_nearest_neighbours.py

import numpy as np
import pandas as pd


class KNN:
    def __init__(self, k, x_train, y_train):
        self.k = k
        self.x_train = x_train
        self.y_train = y_train

    def predict(self, new_data_point):
        self.new_data_point = new_data_point
        self.distances = np.sqrt(np.sum((self.x_train-self.new_data_point)**2, axis=1))
        self.ordered_indices = np.argsort(self.distances)
        self.prediction = np.argmax(np.bincount(self.y_train[self.ordered_indices][:self.k]))
        return self.prediction

if __name__ == "__main__":
    df = pd.read_csv("iris.csv")
    df = df.drop("Id", axis=1)
    y_train = np.array([], dtype=int)

    for i in df.index:
        match df.loc[i, "Species"]:
            case "Iris-setosa":
                y_train = np.append(y_train,[0])
            case "Iris-versicolor":
                y_train = np.append(y_train,[1])
            case "Iris-virginica":
                y_train = np.append(y_train,[2])

    df = df.drop("Species", axis=1)
    x_train = df.to_numpy()

    iris_classifier = KNN(int(input("What value should k be?\n")),x_train,y_train)

    new_data_point = np.array([float(input("What is the sepal length (cm)?\n")),float(input("What is the sepal width (cm)\n")),float(input("What is the petal length (cm)?\n")),float(input("What is the petal width (cm)?\n"))])

    print(iris_classifier.predict(new_data_point))