Question about squared_distance fucntion

[zhangfanmark]

Hi!
First of all, thanks for providing this nice work!

While I am looking into the code, I found the squared_distance function is a little bit confusing. If Y is not provided (so Y = X), this function will do an option of X - X and then take the sum. So, isn't the return value zero?

SpectralNet/src/core/costs.py

Lines 11 to 33 in 43b0fca

	def squared_distance(X, Y=None, W=None):
	'''
	Calculates the pairwise distance between points in X and Y
	X: n x d matrix
	Y: m x d matrix
	W: affinity -- if provided, we normalize the distance
	returns: n x m matrix of all pairwise squared Euclidean distances
	'''
	if Y is None:
	Y = X
	# distance = squaredDistance(X, Y)
	sum_dimensions = list(range(2, K.ndim(X) + 1))
	X = K.expand_dims(X, axis=1)
	if W is not None:
	# if W provided, we normalize X and Y by W
	D_diag = K.expand_dims(K.sqrt(K.sum(W, axis=1)), axis=1)
	X /= D_diag
	Y /= D_diag
	squared_difference = K.square(X - Y)
	distance = K.sum(squared_difference, axis=sum_dimensions)
	return distance

Another question about the number of clusters K, can I use a relatively larger number when my dataset contains about 1 million samples? For example, over 1000?

Thanks!
Fan

[joshxin]

I guess you neglected line 25 : )