From 7f1f639ee778eca96928312b0fc9b89f65637503 Mon Sep 17 00:00:00 2001
From: flatberg <flatberg@pvv.ntnu.no>
Date: Wed, 14 Mar 2007 16:31:25 +0000
Subject: [PATCH] Updates on metric, whitespace

---
 fluents/lib/cx_stats.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/fluents/lib/cx_stats.py b/fluents/lib/cx_stats.py
index 078df5f..2ff65fc 100644
--- a/fluents/lib/cx_stats.py
+++ b/fluents/lib/cx_stats.py
@@ -96,10 +96,11 @@ def procrustes(A, B, strict=True, center=False, verbose=False):
     else:
         return b_rot
 
-def expl_var_x(X, T):
-    """Returns explained variance of X."""
-    # centered X,Y
-    exp_var_x = diag(dot(T.T, T))*100/(sum(X**2))
+def expl_var_x(Xc, T):
+    """Returns explained variance of X.
+    T should carry variance in length, Xc has zero col-mean.
+    """
+    exp_var_x = diag(dot(T.T, T))*100/(sum(Xc**2))
     return exp_var_x
 
 def expl_var_y(Y, T, Q):
@@ -139,26 +140,28 @@ def pls_qvals(a, b, aopt=None, alpha=.3,
     n_false = zeros((n, n_iter), dtype='<f8')
 
     #full model
+    if metric!=None:
+        a = dot(a, metric)
     if algo=='bridge':
         dat = bridge(a, b, aopt, 'loads', 'fast')
     else:
         dat = pls(a, b, aopt, 'loads', 'fast')
-    Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo)
+    Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo, metric=metric)
     tsq_full = hotelling(Wcv, dat['W'], p_center=p_center,
                          alpha=alpha, crot=crot, strict=strict,
-                         cov_center=cov_center, metric=metric)
+                         cov_center=cov_center)
     t0 = time.time()
     Vs = shuffle_1d(b, n_iter)
-    for i,b_shuff in enumerate(Vs):
+    for i, b_shuff in enumerate(Vs):
         t1 = time.time()
         if algo=='bridge':
             dat = bridge(a, b_shuff, aopt, 'loads','fast')
         else:
             dat = pls(a, b, aopt, 'loads', 'fast')
-        Wcv = pls_jkW(a, b_shuff, aopt, n_blocks=None, algo=algo)
+        Wcv = pls_jkW(a, b_shuff, aopt, n_blocks=None, algo=algo, metric=metric)
         TSQ[:,i] = hotelling(Wcv, dat['W'], p_center=p_center,
                              alpha=alpha, crot=crot, strict=strict,
-                             cov_center=cov_center, metric=metric)
+                             cov_center=cov_center)
         print time.time() - t1
     sort_index = argsort(tsq_full)[::-1]
     back_sort_index = sort_index.argsort()
@@ -210,7 +213,7 @@ def leverage(aopt=1,*args):
         lev.append(lev_u)
     return lev
 
-def variances(a,t,p):
+def variances(a, t, p):
     """Returns explained variance and ind. var from blm-params.
     input:
           a -- full centered matrix