view ABSlambda.py @ 10:b48f725ceafa

Added new files split from pyCSalgos: ABSexact.py, ABSlambda.py, ABSmixed.py, AnalysisGenerate.py, algos.py

# -*- coding: utf-8 -*-
"""
Created on Fri Mar 09 14:06:13 2012

@author: ncleju
"""

import numpy
import pyCSalgos.BP.l1qc
import pyCSalgos.SL0.SL0_qc
import pyCSalgos.OMP.omp_QR
import pyCSalgos.TST.RecommendedTST

def sl0(y,M,Omega,epsilon,lbd,sigma_min, sigma_decrease_factor=0.5, mu_0=2, L=3, A_pinv=None, true_s=None):
  
  N,n = Omega.shape
  D = numpy.linalg.pinv(Omega)
  U,S,Vt = numpy.linalg.svd(D)
  aggDupper = numpy.dot(M,D)
  aggDlower = Vt[-(N-n):,:]
  aggD = numpy.concatenate((aggDupper, lbd * aggDlower))
  aggy = numpy.concatenate((y, numpy.zeros(N-n)))
  
  return pyCSalgos.SL0.SL0_approx.SL0_approx(aggD,aggy,epsilon,sigmamin,sigma_decrease_factor,mu_0,L,A_pinv,true_s)
  
def l1qc(y,M,Omega,epsilon,lbd, lbtol=1e-3, mu=10, cgtol=1e-8, cgmaxiter=200, verbose=False):
  
  N,n = Omega.shape
  D = numpy.linalg.pinv(Omega)
  U,S,Vt = numpy.linalg.svd(D)
  aggDupper = numpy.dot(M,D)
  aggDlower = Vt[-(N-n):,:]
  aggD = numpy.concatenate((aggDupper, lbd * aggDlower))
  aggy = numpy.concatenate((y, numpy.zeros(N-n)))

  return pyCSalgos.BP.l1qc.l1qc_logbarrier(x0,aggD,aggD.T,aggy,epsilon, lbtol, mu, cgtol, cgmaxiter, verbose)

def ompeps(y,M,Omega,epsilon,lbd):
  
  N,n = Omega.shape
  D = numpy.linalg.pinv(Omega)
  U,S,Vt = numpy.linalg.svd(D)
  aggDupper = numpy.dot(M,D)
  aggDlower = Vt[-(N-n):,:]
  aggD = numpy.concatenate((aggDupper, lbd * aggDlower))
  aggy = numpy.concatenate((y, numpy.zeros(N-n)))
  
  opts = dict()
  opts['stopCrit'] = 'mse'
  opts['stopTol'] = epsilon**2 / aggy.size
  return pyCSalgos.OMP.omp_QR.greed_omp_qr(aggy,aggD,aggD.shape[1],opts)[0]
  
def tst_recom(y,M,Omega,epsilon,lbd, nsweep=300, xinitial=None, ro=None):
  
  N,n = Omega.shape
  D = numpy.linalg.pinv(Omega)
  U,S,Vt = numpy.linalg.svd(D)
  aggDupper = numpy.dot(M,D)
  aggDlower = Vt[-(N-n):,:]
  aggD = numpy.concatenate((aggDupper, lbd * aggDlower))
  aggy = numpy.concatenate((y, numpy.zeros(N-n)))
  
  nsweep = 300
  tol = epsilon / numpy.linalg.norm(aggy)
  return pyCSalgos.RecomTST.RecommendedTST.RecommendedTST(aggD, aggy, nsweep, tol, xinitial, ro)