Olexandr Isayev isayev

## pca_svds.py
def pca(X, npc):
    n_samples, n_features = X.shape
    Xmean = np.mean(X, axis=0)
    U, s, Vt = scipy.sparse.linalg.svds(X - Xmean, k=npc)
    order = np.argsort(-s) # sort s in descending order

    # svds returns U, s, Vt sorder in ascending order. We want descending
    s = s[order]
    W = Vt[order,:]
	def pca(X, npc):
	n_samples, n_features = X.shape
	Xmean = np.mean(X, axis=0)
	U, s, Vt = scipy.sparse.linalg.svds(X - Xmean, k=npc)
	order = np.argsort(-s) # sort s in descending order

	# svds returns U, s, Vt sorder in ascending order. We want descending
	s = s[order]
	W = Vt[order,:]