[SOLVED] CS

CSCC11 Introduction to Machine Learning, Winter 2021, Assignment 3
B. Chan, Z. Zhang, D. Fleet

import matplotlib.pyplot as plt
import numpy as np

from utils import load_pickle_dataset

def visualize_2d_data(X, y):
This function generates a 2D scatter plot given the inputs and their corresponding labels.
Inputs with different classes are represented with different colours.

Args:
X (ndarray (shape: (N, D))): A NxD matrix consisting N D-dimensional inputs.
y (ndarray (shape: (N, 1))): A N-column vector consisting N scalar outputs (labels).

assert len(X.shape) == len(y.shape) == 2, fInput/output pairs must be 2D-arrays. X: {X.shape}, y: {y.shape}
(N, D) = X.shape
assert N == y.shape[0], fNumber of samples must match for input/output pairs. X: {N}, y: {y.shape[0]}
assert D == 2, fExpected 2 features. Got: {D}
assert y.shape[1] == 1, fY must be a column vector. Got: {y.shape}

# ====================================================
# TODO: Implement your solution within the box

# ====================================================

if __name__ == __main__:
# Support generic_1, generic_2, generic_3
dataset = generic_3

assert dataset in (generic_1, generic_2, generic_3, wine), fInvalid dataset: {dataset}

dataset_path = f./datasets/{dataset}.pkl
data = load_pickle_dataset(dataset_path)
visualize_2d_data(data[train_X], data[train_y])