def __init__(self,numin,numclasses): self.numin = numin self.numclasses = numclasses self.params = 0.01 * randn(self.numin*self.numclasses+self.numclasses) self.scorefunc = logreg_score(self.numin,self.numclasses,self.params) self.scorefuncs = [scorefunc] Contrastive.__init__(self,normalizeacrosscliques=False)
def __init__(self,numin,numout): self.numin = numin self.numout = numout self.params = 0.01 * randn(numin*numout+numout) self.scorefunc = scorefunc.LinearRegressionScore(numin,numout,self.params) self.scorefuncs = [self.scorefunc] Contrastive.__init__(self,normalizeacrosscliques=False)
def __init__(self, numin, numclasses): self.numin = numin self.numclasses = numclasses self.params = 0.01 * randn(self.numin * self.numclasses + self.numclasses) self.scorefunc = logreg_score(self.numin, self.numclasses, self.params) self.scorefuncs = [scorefunc] Contrastive.__init__(self, normalizeacrosscliques=False)
def __init__(self, numin, numout): self.numin = numin self.numout = numout self.params = 0.01 * randn(numin * numout + numout) self.scorefunc = scorefunc.LinearRegressionScore( numin, numout, self.params) self.scorefuncs = [self.scorefunc] Contrastive.__init__(self, normalizeacrosscliques=False)
def __init__(self,numin,numhid1,numhid2,numhid3,numout): self.numin = numin self.numhid1 = numhid1 self.numhid2 = numhid2 self.numhid3 = numhid3 self.numout = numout self.params = 0.1 * randn(scorefunc.Islsl.numparams(\ numin,numhid1,numhid2,numhid3,numout)) self.scorefuncs = [scorefunc.Islsl(\ numin,numhid1,numhid2,numhid3,numout,self.params)] Contrastive.__init__(self,normalizeacrosscliques=False)
def __init__(self,numin,numhid,numout,params=None): self.numin = numin self.numhid = numhid self.numout = numout self.params = params if self.params == None: self.params = 0.01 * randn(self.numin*self.numhid+self.numhid+\ self.numhid*self.numout+self.numout) self.scorefuncs = [scorefunc.SigmoidhiddenLinearoutputScore\ (numin,numhid,numout,self.params)] Contrastive.__init__(self,normalizeacrosscliques=False)
def __init__(self, numin, numhid, numout, params=None): self.numin = numin self.numhid = numhid self.numout = numout self.params = params if self.params == None: self.params = 0.01 * randn(self.numin*self.numhid+self.numhid+\ self.numhid*self.numout+self.numout) self.scorefuncs = [scorefunc.SigmoidhiddenLinearoutputScore\ (numin,numhid,numout,self.params)] Contrastive.__init__(self, normalizeacrosscliques=False)
def __init__(self, n_nodes): self.n_nodes = zip(n_nodes[:-1], n_nodes[1:]) self.params = 0.01 * randn(sum(map(lambda (a,b): (a+1)*b + (b+1)*a, self.n_nodes))) self.layers = [] start, stop = 0, self.params.shape[0] for (numin, numhid) in self.n_nodes: _in = (numin+1)*numhid _out = (numhid+1)*numin assert start+_in <= stop-_out, (start, _in, stop, _out) idx = range(start, start+_in) + range(stop-_out, stop) start += _in stop -= _out self.layers += [Isl(numin, numhid, numin, self.params[idx])] Contrastive.__init__(self, normalizeacrosscliques=False)
def __init__(self,numvis,numhid,sparsitygain=0.0,targethidprobs=0.2,\ cditerations=1,normalizeacrosscliques=True,\ meanfield_output=True,verbose=False): self.targethidprobs = targethidprobs self.numvis = numvis self.numhid = numhid self.cditerations = cditerations self.sparsitygain = sparsitygain self.meanfield_output = meanfield_output self.params = 0.01*randn(numvis*numhid+numvis+numhid) self.wyh = asmatrix(reshape(self.params[:numvis*numhid],(numvis,numhid))) self.wy = asmatrix(self.params[numvis*numhid:numvis*numhid+numvis]).T self.wh = asmatrix(self.params[numvis*numhid+numvis:]).T self.verbose = verbose Contrastive.__init__(self,normalizeacrosscliques)
def __init__(self, n_nodes): self.n_nodes = zip(n_nodes[:-1], n_nodes[1:]) self.params = 0.01 * randn( sum(map(lambda (a, b): (a + 1) * b + (b + 1) * a, self.n_nodes))) self.layers = [] start, stop = 0, self.params.shape[0] for (numin, numhid) in self.n_nodes: _in = (numin + 1) * numhid _out = (numhid + 1) * numin assert start + _in <= stop - _out, (start, _in, stop, _out) idx = range(start, start + _in) + range(stop - _out, stop) start += _in stop -= _out self.layers += [Isl(numin, numhid, numin, self.params[idx])] Contrastive.__init__(self, normalizeacrosscliques=False)
def __init__(self,normalizeacrosscliques=False): Contrastive.__init__(self,normalizeacrosscliques)