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Fixed bug in esvd for m>n

This commit is contained in:
Arnar Flatberg 2007-07-26 18:32:48 +00:00
parent 7fd4ac6225
commit 47b89cc411
1 changed files with 17 additions and 10 deletions
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25
fluents/lib/engines.py
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@ -8,6 +8,11 @@ from scipy.linalg import svd,inv
from scipy import dot,empty,eye,newaxis,zeros,sqrt,diag,\ from scipy import dot,empty,eye,newaxis,zeros,sqrt,diag,\
apply_along_axis,mean,ones,randn,empty_like,outer,c_,\ apply_along_axis,mean,ones,randn,empty_like,outer,c_,\
rand,sum,cumsum,matrix rand,sum,cumsum,matrix
has_sym=True
try:
import symmeig
except:
has_sym = False
def pca(a, aopt, scale='scores', mode='normal'): def pca(a, aopt, scale='scores', mode='normal'):
""" Principal Component Analysis model """ Principal Component Analysis model
@ -18,11 +23,11 @@ def pca(a, aopt, scale='scores', mode='normal'):
""" """
m, n = a.shape m, n = a.shape
#print "rows: %s cols: %s" %(m,n)
if m*3>n: if m>(n+100) or n>(m+100):
u, s, v = esvd(a) u, s, v = esvd(a)
else: else:
u, s, vt = svd(a, full_matrices=0) u, s, vt = svd(a, 0)
v = vt.T v = vt.T
eigvals = (1./m)*s eigvals = (1./m)*s
T = u*s T = u*s
@ -250,6 +255,7 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', sca
X, mnX = center(X, xctr) X, mnX = center(X, xctr)
Y, mnY = center(Y, xctr) Y, mnY = center(Y, xctr)
Z, mnZ = center(Z, zctr) Z, mnZ = center(Z, zctr)
print Z.mean(1)
varX = pow(X, 2).sum() varX = pow(X, 2).sum()
varY = pow(Y, 2).sum() varY = pow(Y, 2).sum()
@ -343,28 +349,29 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', sca
def m_shape(array): def m_shape(array):
return matrix(array).shape return matrix(array).shape
def esvd(data, economy=1): def esvd(data):
"""SVD with the option of economy sized calculation """SVD with the option of economy sized calculation
Calculate subspaces of X'X or XX' depending on the shape Calculate subspaces of X'X or XX' depending on the shape
of the matrix. of the matrix.
Good for extreme fat or thin matrices Good for extreme fat or thin matrices
:notes:
Numpy supports this by setting full_matrices=0 Numpy supports this by setting full_matrices=0
""" """
m, n = data.shape m, n = data.shape
if m>=n: if m>=n:
data = dot(data.T, data) kernel = dot(data.T, data)
u, s, vt = svd(data) u, s, vt = svd(kernel)
u = dot(data, vt.T) u = dot(data, vt.T)
v = vt.T v = vt.T
for i in xrange(n): for i in xrange(n):
s[i] = vnorm(u[:,i]) s[i] = vnorm(u[:,i])
u[:,i] = u[:,i]/s[i] u[:,i] = u[:,i]/s[i]
else: else:
data = dot(data, data.T) kernel = dot(data, data.T)
data = (data + data.T)/2.0 #data = (data + data.T)/2.0
u, s, vt = svd(data) u, s, vt = svd(kernel)
v = dot(u.T, data) v = dot(u.T, data)
for i in xrange(m): for i in xrange(m):
s[i] = vnorm(v[i,:]) s[i] = vnorm(v[i,:])