Updates

scripts/lpls/lpls.py

@@ -15,6 +15,7 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
         X : data matrix (m, n)
         Y : data matrix (m, l)
         Z : data matrix (n, o)
+        alpha : how much z influence (1=max, 0=none)
 
     :output:
       T : X-scores
@@ -36,7 +37,7 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
     if mean_ctr:
         xctr, yctr, zctr = mean_ctr
         X, mnX = center(X, xctr)
-        Y, mnY = center(Y, xctr)
+        Y, mnY = center(Y, yctr)
         Z, mnZ = center(Z, zctr)
 
     varX = pow(X, 2).sum()
@@ -116,7 +117,7 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
         T = T/tnorm
         Q = Q*tnorm
         W = W*tnorm
-    return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
+    return T, W, P, Q, U, L, K, B, b0, evx, evy, evz, mnX, mnY, mnZ
 
 def svd_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
     """
@@ -306,8 +307,14 @@ def bifpls(X, Y, Z, a_max, alpha):
     evz = 100.0*(1 - var_z/varZ)
 
 def center(a, axis):
-     # 0 = col center, 1 = row center, 2 = double center
-     # -1 = nothing
+    # 0 = col center, 1 = row center, 2 = double center
+    # -1 = nothing
+    if len(a.shape)==1:
+        mn = a.mean()
+        return a - mn, mn
+    if a.shape[0]==1 or a.shape[1]==1:
+        mn = a.mean()
+        return a - mn, mn
     if axis==-1:
         mn = zeros((a.shape[1],))
         return a - mn, mn
@@ -318,7 +325,7 @@ def center(a, axis):
         mn = a.mean(1)[:,newaxis]
         return a - mn , mn
     elif axis==2:
-        mn = a.mean(0) + a.mean(1)[:,newaxis] - a.mean()
+        mn = a.mean(1)[:,newaxis] + a.mean(0) - a.mean()
         return a - mn, mn
     else:
         raise IOError("input error: axis must be in [-1,0,1,2]")
@@ -367,27 +374,47 @@ def correlation_loadings(D, T, P, test=True):
 
 def cv_lpls(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,1]):
     """Performs crossvalidation to get generalisation error in lpls"""
+
+    # if double centering of x or y:
+    #     row-center prior to cross validation (as this is independent of subsets)
+    if mean_ctr[0]==2:
+        mnx_row = X.mean(1)[:,newaxis]
+        X = X - mnx_row
+        mean_ctr[0] = 0
+    else:
+        mnx_row = 0
+    if mean_ctr[1]==2:
+        if Y.shape[1]!=1:
+            mny_row = Y.mean(1)[:,newaxis]
+            Y = Y - mny_row
+    else:
+        mny_row = 0
+    
     cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=True)
     k, l = Y.shape
     Yhat = empty((a_max,k,l), 'd')
     for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
-        T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
-                                                                a_max=a_max,
-                                                                alpha=alpha,
-                                                                mean_ctr=mean_ctr,
-                                                                verbose=False)
+        T, W, P, Q, U, L, K, B, b0, evx, evy, evz, mnx, mny, mnz = nipals_lpls(xcal,ycal,Z,
+                                                                               a_max=a_max,
+                                                                               alpha=alpha,
+                                                                               mean_ctr=mean_ctr,
+                                                                               verbose=False)
+        
         for a in range(a_max):
-            Yhat[a,ind,:] = b0[a][0][0] + dot(xi, B[a])
+            xc = xi - mnx
+            Yhat[a,ind,:] = mny + dot(xc, B[a])
+        
     Yhat_class = zeros_like(Yhat)
     for a in range(a_max):
         for i in range(k):
-            Yhat_class[a,i,argmax(Yhat[a,i,:])]=1.0
+            Yhat_class[a,i,argmax(Yhat[a,i,:])] = 1.0
     class_err = 100*((Yhat_class+Y)==2).sum(1)/Y.sum(0).astype('d')
+    
     sep = (Y - Yhat)**2
     rmsep = sqrt(sep.mean(1))
     return rmsep, Yhat, class_err
 
-def jk_lpls(X, Y, Z, a_max, nsets=None, alpha=.5, mean_ctr=[2,0,1]):
+def jk_lpls(X, Y, Z, a_max, nsets=None, xz_alpha=.5, mean_ctr=[2,0,1]):
     cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
     m, n = X.shape
     k, l = Y.shape
@@ -398,12 +425,12 @@ def jk_lpls(X, Y, Z, a_max, nsets=None, alpha=.5, mean_ctr=[2,0,1]):
     WWz = empty((nsets, o, a_max), 'd')
     WWy = empty((nsets, l, a_max), 'd')
     for i, (xcal,xi,ycal,yi) in enumerate(cv_iter):
-        T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
-                                                                a_max=a_max,
-                                                                alpha=alpha,
-                                                                mean_ctr=mean_ctr,
-                                                                scale='loads',
-                                                                verbose=False)
+        T, W, P, Q, U, L, K, B, b0, evx, evy, evz,mnx,mny,mnz = nipals_lpls(xcal,ycal,Z,
+                                                                            a_max=a_max,
+                                                                            alpha=xz_alpha,
+                                                                            mean_ctr=mean_ctr,
+                                                                            scale='loads',
+                                                                            verbose=False)
         WWx[i,:,:] = W
         WWz[i,:,:] = L
         WWy[i,:,:] = Q