I need help coding this in Python!!

**Note this is all one question and needs to be implemented using only packages imported below (CANNOT use any other packages such as seaborn or sklearn for this assignment**

->The description of what each method is in """quotations""" is the objective of the homework.

Thanks in advance!!!

from __future__ import absolute_import

from __future__ import print_function

from __future__ import division

%matplotlib inline

import sys

import matplotlib

import numpy as np

import matplotlib.pyplot as plt

import imageio

from tqdm import tqdm_notebook as tqdm

class KMeans(object):

def __init__(self):

pass

def _init_centers(self, points, K, **kwargs):

"""

Args:

points: NxD numpy array, where N is # points and D is the dimensionality

K: number of clusters

kwargs: any additional arguments you want

Return:

centers: ? x D numpy array, the centers. Note that it is possible that we just need fewer centers.

(e.g., 1 cluster is enough when all the points are the same).

"""

raise NotImplementedError

def _update_assignment(self, centers, points):

"""

Args:

centers: KxD numpy array, where K is the number of clusters, and D is the dimension

points: NxD numpy array, the observations

Return:

cluster_idx: numpy array of length N, the cluster assignment for each point

"""

raise NotImplementedError

def _update_centers(self, old_centers, cluster_idx, points):

"""

Args:

old_centers: old centers KxD numpy array, where K is the number of clusters, and D is the dimension

cluster_idx: numpy array of length N, the cluster assignment for each point

points: NxD numpy array, the observations

Return:

centers: new centers, ? x D numpy array, where K is the number of clusters, and D is the dimension.

Note that it is possible to have fewer centers after this step.

"""

raise NotImplementedError

def _get_loss(self, centers, cluster_idx, points):

"""

Args:

centers: KxD numpy array, where K is the number of clusters, and D is the dimension

cluster_idx: numpy array of length N, the cluster assignment for each point

points: NxD numpy array, the observations

Return:

loss: a single float number, which is the objective function of KMeans.

"""

raise NotImplementedError

def __call__(self, points, K, max_iters=100, abs_tol=1e-16, rel_tol=1e-16, **kwargs):

"""

Args:

points: NxD numpy array, where N is # points and D is the dimensionality

K: number of clusters

max_iters: maximum number of iterations

abs_tol: convergence criteria w.r.t absolute change of loss

rel_tol: convergence criteria w.r.t relative change of loss

kwargs: any additional arguments you want

Return:

cluster assignments: Nx1 int numpy array

cluster centers: ? x D numpy array, the centers

"""

centers = self._init_centers(points, K, **kwargs)

pbar = tqdm(range(max_iters))

for it in pbar:

cluster_idx = self._update_assignment(centers, points)

centers = self._update_centers(centers, cluster_idx, points)

loss = self._get_loss(centers, cluster_idx, points)

K = centers.shape[0]

if it:

diff = np.abs(prev_loss - loss)

if diff < abs_tol and diff / prev_loss < rel_tol:

break

prev_loss = loss

pbar.set_description('iter %d, loss: %.4f' % (it, loss))

return cluster_idx, centers