CS代考程序代写 “””

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.

– 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, f”Input/output pairs must be 2D-arrays. X: {X.shape}, y: {y.shape}”
(N, D) = X.shape
assert N == y.shape[0], f”Number of samples must match for input/output pairs. X: {N}, y: {y.shape[0]}”
assert D == 2, f”Expected 2 features. Got: {D}”
assert y.shape[1] == 1, f”Y 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”), f”Invalid dataset: {dataset}”

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

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