factored out packer

fluents/lib/blmfuncs.py

@@ -1,6 +1,7 @@
 """This module contains bilinear models(Functions)
 """
 import os
+import copy
 import pygtk
 import gtk
 import gtk.glade
@@ -9,13 +10,13 @@ from fluents.workflow import Function, OptionsDialog, Options
 from fluents.dataset import Dataset
 from fluents import plots, dataset, workflow, logger
 import scipy
-from engines import *
+from engines import pca, pls
 from cx_stats import leverage, variances, hotelling
 from cx_utils import mat_center
 from validation import *
+from packer import Packer
 import blmplots
-import engines
-import copy
+
 
 class Model(Function):
     """Base class of bilinear models.
@@ -194,7 +195,7 @@ class PLS(Model):
         self._options = PlsOptions()
         
     def validation(self, amax, n_sets, cv_val_method):
-        """Returns rmsec,rmsep for model.
+        """Returns rmsep for pls model.
         """
         m, n = self.model['E0'].shape
         if m>n:
@@ -229,8 +230,8 @@ class PLS(Model):
         else:
             self.model['w_tsq'] = None
 
-    def permutation_confidence(self, a, b, aopt, reg, n_iter, algo,
-                               sim_method):
+    def permutation_confidence(self, a, b, aopt, reg,
+                               n_iter, algo, sim_method):
         """Estimates cut off on significant vars by controlling fdr."""
 
         if self._options['calc_qvals']==True:
@@ -349,35 +350,6 @@ class PLS(Model):
             #run with current data and options
             return self.run_o(a, b)
 
-class Packer:
-    """A compression object used to speed up model calculations.
-
-    Often used in conjunction with crossvalidation and perturbations
-    analysis. 
-    """
-    def __init__(self,array):
-        self._shape = array.shape
-        self._array = array
-        self._packed_data = None
-        
-    def expand(self,a):
-        if self._inflater!=None:
-            return dot(self._inflater,a)
-        
-    def collapse(self,axis=None,mode='svd'):
-        if not axis:
-            axis = argmin(self._array.shape) # default is the smallest dim
-
-        if axis == 1:
-            self._array = self._array.T
-        u, s, vt = svd(self._array,full_matrices=0)
-        self._inflater = vt.T
-        self._packed_data = u*s
-        return self._packed_data
-
-    def get_packed_data(self):
-        return self._packed_data
-
 
 class PcaOptions(Options):
     """Options for Principal Component Analysis.
@@ -389,7 +361,7 @@ class PcaOptions(Options):
     def _set_default(self):
         opt = {}
         opt['algo'] = 'pca'
-        opt['engine'] = engines.pca
+        opt['engine'] = pca
         opt['mode'] = 'normal' # how much info to calculate
         opt['amax'] = 10
         opt['aopt'] = 100
@@ -460,7 +432,7 @@ class PlsOptions(Options):
     def _set_default(self):
         opt = {}
         opt['algo'] = 'pls'
-        opt['engine'] = engines.pls
+        opt['engine'] = pls
         opt['mode'] = 'normal' # how much info to calculate
         opt['amax'] = 10
         opt['aopt'] = 10
@@ -501,7 +473,7 @@ class PlsOptions(Options):
         
         opt['out_data'] = None
                             
-        opt['pack'] = False
+        opt['pack'] = True
         opt['calc_qvals'] = False
         opt['q_pert_method'] = 'shuffle_rows'
         opt['q_iter'] = 20