# -*- coding: iso-8859-1 -*-## This file is a part of the Bayes Blocks library## Copyright (C) 2001-2006 Markus Harva, Antti Honkela, Alexander# Ilin, Tapani Raiko, Harri Valpola and Tomas 謘tman.## This program is free software; you can redistribute it and/or modify# it under the terms of the GNU General Public License as published by# the Free Software Foundation; either version 2, or (at your option)# any later version.## This program is distributed in the hope that it will be useful,# but WITHOUT ANY WARRANTY; without even the implied warranty of# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the# GNU General Public License (included in file License.txt in the# program package) for more details.## $Id: Helpers.py 7 2006-10-26 10:26:41Z ah $#from Label import *import Nettry:    import numpy.oldnumeric as Numeric    import numpy as MLab    import numpy.random as RandomArray    from numpy.linalg import inv, eighexcept:    import Numeric    from LinearAlgebra import inverse as inv    from LinearAlgebra import Heigenvectors as eigh    import RandomArray    import MLabimport stringimport fileinputimport redef LoadMatlabArray(file, array):    try:        mat = Net.MatArray()    except:        raise RuntimeError("Matlab arrays are not supported in this library version")    mat.LoadFromFile(file, array)    res = []    for i in range(mat.Size(2)):        res.append(mat.ExtractDV(i))    return resdef CostDifference(node):    grad = Net.DSSet()    val = Net.DSSet()    node.ChildGradReal(grad)    if (grad.ex):        node.GetReal(val, Net.DFlags(1,1,1))    else:        node.GetReal(val, Net.DFlags(1,1))        val.ex = 1    cdiff = node.Cost() + grad.var * (val.var - val.mean * val.mean) +\            grad.mean * val.mean + grad.ex * (val.ex - 1)    return -cdiffdef CostDifferenceV(node):    grad = Net.DVSet()    val = Net.DVH()    node.ChildGradRealV(grad)    if (len(grad.ex)):        node.GetRealV(val, Net.DFlags(1,1,1))    else:        node.GetRealV(val, Net.DFlags(1,1))    cdiff = node.Cost()    for i in range(len(grad.mean)):        cdiff += grad.mean[i] * val.Mean(i)    for i in range(len(grad.var)):        cdiff += grad.var[i] * (val.Var(i) - val.Mean(i) * val.Mean(i))    for i in range(len(grad.ex)):      cdiff += grad.ex[i] * (val.Exp(i) - 1)    return -cdiffdef LoadAsciiData(file):    res = []    for line in fileinput.input(file):        s = map(float, string.split(line))        tmp = Net.DV(len(s))        for i in range(len(s)):            tmp[i] = s[i]        res.append(tmp)    return resdef ParentList(node):    res = []    i = 0    newpar = node.GetParent(i)    while newpar:        res.append(newpar)        i += 1        newpar = node.GetParent(i)    return resdef VariableParents(node):    nodes=[node]    variables=[]    while(len(nodes)):        for n in ParentList(nodes.pop()):            if isinstance(n, PyNet.Variable):                variables.append(n)            else:                nodes.append(n)    return variablesdef ChildList(node):    res = []    i = 0    newpar = node.GetChild(i)    while newpar:        res.append(newpar)        i += 1        newpar = node.GetChild(i)    return resdef FindLeaf(rootnode, parind):    parent = rootnode.GetParent(parind)    if not re.match('Sum2', parent.GetType()):        return [rootnode, parent]    roots = [parent]    while 1:        newroots = []        for root in roots:            for par in ParentList(root):                if re.match('Sum2', par.GetType()):                    newroots.append(par)                else:                    return [root, par]        roots = newrootsdef AddSum2(net, rootnode, parind, newpar, label):    leaf = FindLeaf(rootnode, parind)    tmp = Net.Sum2(net, label, leaf[1], newpar)    leaf[0].ReplacePtr(leaf[1], tmp)    tmp.AddChild(leaf[0])    leaf[1].NotifyDeath(leaf[0])def AddSum2V(net, rootnode, parind, newpar, label):    leaf = FindLeaf(rootnode, parind)    tmp = Net.Sum2V(net, label, leaf[1], newpar)    leaf[0].ReplacePtr(leaf[1], tmp)    tmp.AddChild(leaf[0])    leaf[1].NotifyDeath(leaf[0])def AddWeight(net, inode, onode, weightlabel, mnode, vnode,              addcost = 0, labelbase = ""):    weight = Net.Gaussian(net, weightlabel, mnode, vnode)    tmp = Net.ProdV(net, Label(labelbase + "_newprod"), weight, inode)    AddSum2V(net, onode, 0, tmp, Label(labelbase + "_newsum"))    weight.Update()    if not net.TryPruning(weight, 0, addcost):        return weightdef GetMean(node):    if IsSequence(node):        return Numeric.array([GetMean(n) for n in node], Numeric.Float)    else:        if node is None:            return 0.0        else:            tmp = Net.DSSet()            node.GetReal(tmp, Net.DFlags(1))            return tmp.meandef GetVar(node):    if IsSequence(node):        return Numeric.array([GetVar(n) for n in node], Numeric.Float)    else:        if node is None:            return -1        else:            tmp = Net.DSSet()            node.GetReal(tmp, Net.DFlags(0, 1))            return tmp.vardef GetExp(node):    if IsSequence(node):        return Numeric.array([GetExp(n) for n in node], Numeric.Float)    else:        if node is None:            return -1        else:            tmp = Net.DSSet()            node.GetReal(tmp, Net.DFlags(0, 0, 1))            return tmp.exdef GetMeanV(node):    if IsSequence(node):        return Numeric.array([GetMeanV(n) for n in node], Numeric.Float)    else:        tmp = Net.DVH()        if not node.GetRealV(tmp, Net.DFlags(1)):            return        if tmp.vec:            return Numeric.array(tmp.vec.mean, Numeric.Float)        else:            return tmp.scalar.meandef GetVarV(node):    if IsSequence(node):        return Numeric.array([GetVarV(n) for n in node], Numeric.Float)    else:        tmp = Net.DVH()        if not node.GetRealV(tmp, Net.DFlags(0, 1)):            return        if tmp.vec:            return Numeric.array(tmp.vec.var, Numeric.Float)        else:            return tmp.scalar.vardef GetExpV(node):    if IsSequence(node):        return Numeric.array([GetExpV(n) for n in node], Numeric.Float)    else:        tmp = Net.DVH()        if not node.GetRealV(tmp, Net.DFlags(0, 0, 1)):            return        if tmp.vec:            return Numeric.array(tmp.vec.ex, Numeric.Float)        else:            return tmp.scalar.exdef GetDiscrete(node):    if IsSequence(node):        return Numeric.array([GetDiscrete(n) for n in node], Numeric.Float)    else:        return Numeric.array(node.GetDiscrete(), Numeric.Float)def GetDiscreteV(node):    if IsSequence(node):        return Numeric.array([GetDiscreteV(n) for n in node], Numeric.Float)    else:        tmp = Net.VDDH()        node.GetDiscreteV(tmp)        tdim = tmp.vec.size()        kdim = tmp.vec.DDsize()        ary = Numeric.zeros((kdim, tdim), Numeric.Float)        for i in range(kdim):            for j in range(tdim):                val = tmp.vec.Get(j, i)                ary[i,j] = val        return arydef GetLabel(node):    if IsSequence(node):        return map(GetLabel, node)    else:        return node.GetLabel()def GetType(node):    if IsSequence(node):        return map(GetType, node)    else:        return node.GetType()def GetCost(variable):    if type(variable) == type([]):        return map(GetCost, variable)    else:        return variable.Cost()def DoPCA(x, dim, returnA=0, returnW=0, returnd=0, removemean=1):    xdim = x.shape    if removemean:        x0 = x - MLab.mean(x, 1)[:, Numeric.NewAxis]