stuff

fluents/lib/blmfuncs.py

@@ -520,22 +520,22 @@ class PcaOptions(Options):
         opt['engine'] = pca
         opt['mode'] = 'normal' # how much info to calculate
         opt['amax'] = 10
-        opt['aopt'] = 100
+        opt['aopt'] = 5
         opt['auto_aopt'] = False
         opt['center'] = True
         opt['center_mth'] = mat_center
         opt['scale'] = 'scores'
 
         opt['calc_conf'] = False
-        opt['n_sets'] = 5
+        opt['n_sets'] = 7
         opt['strict'] = True
         opt['p_center'] = 'med'
-        opt['alpha'] = .8
+        opt['alpha'] = .2
         opt['cov_center'] = 'med'
         opt['crot'] = True
 
         opt['calc_cv'] = False
-        opt['calc_pert'] = True
+        opt['calc_pert'] = False
         opt['pert_val_method'] = 'random_diag'
         opt['cv_val_method'] = 'random'
         opt['cv_val_sets'] = 10
@@ -673,16 +673,16 @@ class LplsOptions(Options):
         opt['mode'] = 'normal' # how much info to calculate
         opt['amax'] = 10
         opt['aopt'] = 4
-        opt['xz_alpha'] = .6
+        opt['xz_alpha'] = .3
         opt['auto_aopt'] = False
         opt['center'] = True
         opt['center_mth'] = [2, 0, 1]
         opt['scale'] = 'scores'
-        opt['calc_conf'] = True
+        opt['calc_conf'] = False
         opt['n_sets'] = 75
         opt['strict'] = False
         opt['p_center'] = 'med'
-        opt['alpha'] = .4
+        opt['alpha'] = .2
         opt['cov_center'] = 'med'
         opt['crot'] = True
 
@@ -709,7 +709,7 @@ class LplsOptions(Options):
                             (blmplots.LplsZCorrelationPlot, 'Z corr.', True)
                             ]
         
-        opt['out_data'] = ['T','P', 'tsqx', 'tsqz', 'L','K']
+        opt['out_data'] = ['T','P','L','K']
         opt['out_plots'] = [blmplots.PlsScorePlot,
                             blmplots.LplsXLoadingPlot,
                             blmplots.LplsZLoadingPlot,

fluents/lib/engines.py

@@ -62,11 +62,12 @@ def pca(a, aopt,scale='scores',mode='normal',center_axis=0):
     array([0.,99.8561562,  100.])
 
     """
+    m, n = a.shape
+    assert(aopt<=min(m,n))
     if center_axis>=0:
         a = a - expand_dims(a.mean(center_axis), center_axis)
-    m, n = a.shape
     if m>(n+100) or n>(m+100):
-        u, e, v = esvd(a)
+        u, e, v = esvd(a, amax=None) # fixme:amax option need to work with expl.var
         s = sqrt(e)
     else:
         u, s, vt = svd(a, 0)
@@ -696,7 +697,7 @@ def esvd(data, amax=None):
         kernel = dot(data.T, data)
         if has_sym:
             if not amax:
-                amax = n
+                amax = n-1
             pcrange = [n-amax, n]
             s, v = symeig(kernel, range=pcrange, overwrite=True)
             s = s[::-1]
@@ -705,20 +706,20 @@ def esvd(data, amax=None):
             u, s, vt = svd(kernel)
             v = vt.T
         u = dot(data, v)
-        for i in xrange(n):
+        for i in xrange(amax):
             s[i] = vnorm(u[:,i])
             u[:,i] = u[:,i]/s[i]
     else:
         kernel = dot(data, data.T)
         if has_sym:
             if not amax:
-                amax = m
+                amax = m-1
             pcrange = [m-amax, m]
             s, u = symeig(kernel, range=pcrange, overwrite=True)
         else:
             u, s, vt = svd(kernel)
         v = dot(u.T, data)
-        for i in xrange(m):
+        for i in xrange(amax):
             s[i] = vnorm(v[i,:])
             v[i,:] = v[i,:]/s[i]