def psi(x, y, sm, sparm):
"""Return a feature vector describing pattern x and label y.
This returns a sequence representing the feature vector describing
the relationship between a pattern x and label y. What psi is
depends on the problem. Its particulars are described in the
Tsochantaridis paper. The return value should be either a support
vector object of the type returned by svmlight.create_svector, or
a list of such objects."""
# In the case of binary classification, psi is just the class (+1
# or -1) times the feature vector for x, including that special
# constant bias feature we pretend that we have.
import svmlight
thePsi = [0.5*y*i for i in x]
thePsi.append(0.5*y) # Pretend as though x had an 1 at the end.
return svmlight.create_svector(thePsi)
def psi(x, y, sm, sparm):
"""Return a feature vector describing pattern x and label y.
This returns a sequence representing the feature vector describing
the relationship between a pattern x and label y. What psi is
depends on the problem. Its particulars are described in the
Tsochantaridis paper. The return value should be either a support
vector object of the type returned by svmlight.create_svector, or
a list of such objects."""
#we ignore the type of y in this function
y2,y_type = y;
qid, feature_id_list, feature_value_list = x
# Add in the weight for the bias term for each example.
thepsi = {}
for i,label in enumerate(y2):
for k,v in itertools.izip(feature_id_list[i],feature_value_list[i]):
thepsi[k] = thepsi.get(k,0.0) + label*v
intermediate_psi = sorted(thepsi.items())
return svmlight.create_svector(intermediate_psi)
def psi(x, y, sm, sparm):
# Just increment the feature index to the appropriate stack position.
return svmlight.create_svector([(f+(y-1)*sparm.num_features,v)
for f,v in x])