briverse17

I hereby claim:

To claim this, I am signing this object:

	DOWNLOAD_DIR="/tmp"
	DOWNLOAD_URL="https://code.visualstudio.com/sha/download?build=stable&os=linux-deb-x64"

	echo "Getting stable VSCode .deb distribution"
	wget -q --show-progress -nc -O "$DOWNLOAD_DIR/code_stable.deb" "$DOWNLOAD_URL"

	if [ -f "$DOWNLOAD_DIR/code_stable.deb" ]; then
	echo "Installing stable VSCode"
	apt install "$DOWNLOAD_DIR/code_stable.deb/"
	echo "Cleaning up..."

	new_medoids = []
	for i in range(0, self.k):
	new_medoid = self.medoids[i]
	old_medoids_cost = self.medoids_cost[i]
	for j in range(len(clusters[i])):

	#Cost of the current data points to be compared with the current optimal cost
	cur_medoids_cost = 0
	for dpoint_index in range(len(clusters[i])):
	cur_medoids_cost += euclideanDistance(clusters[i][j], clusters[i][dpoint_index])

	for i in range(self.max_iter):
	#Labels for this iteration
	cur_labels = []
	for medoid in range(0,self.k):
	#Dissimilarity cost of the current cluster
	self.medoids_cost[medoid] = 0
	for k in range(len(X)):
	#Distances from a data point to each of the medoids
	d_list = []
	for j in range(0,self.k):

	def initMedoids(self, X):
	'''
	Parameters
	----------
	X: input data.
	'''
	self.medoids = []

	#Starting medoids will be random members from data set X
	indexes = np.random.randint(0, len(X)-1,self.k)

	class k_medoids:
	def __init__(self, k = 2, max_iter = 300, has_converged = False):
	'''
	Class constructor
	Parameters
	----------
	- k: number of clusters.
	- max_iter: number of times centroids will move
	- has_converged: to check if the algorithm stop or not
	'''

	def isConverged(self, new_medoids):
	return set([tuple(x) for x in self.medoids]) == set([tuple(x) for x in new_medoids])