https://github.com/recski/HunTag
Tip revision: ac681ac97f8677f25c29060fc8a168d68aca22d9 authored by Gábor Recski on 18 January 2016, 08:52:12 UTC
Update README.md
Update README.md
Tip revision: ac681ac
liblinear.patch
*** liblinear.py Wed Apr 25 07:32:21 2012
--- liblinear.py Thu Aug 30 10:59:42 2012
***************
*** 42,55 ****
_types = [c_int, c_double]
_fields_ = genFields(_names, _types)
def gen_feature_nodearray(xi, feature_max=None, issparse=True):
if isinstance(xi, dict):
index_range = xi.keys()
elif isinstance(xi, (list, tuple)):
xi = [0] + xi # idx should start from 1
index_range = range(1, len(xi))
else:
! raise TypeError('xi should be a dictionary, list or tuple')
if feature_max:
assert(isinstance(feature_max, int))
--- 42,77 ----
_types = [c_int, c_double]
_fields_ = genFields(_names, _types)
+ def gen_feature_nodearray_from_array(xi, feature_max=None, issparse=True):
+ values = xi
+ if feature_max:
+ assert(isinstance(feature_max, int))
+ values = filter(lambda x: x[0] <= feature_max, values)
+ if issparse:
+ values = filter(lambda x: x[1] != 0, values)
+
+ values = sorted(values, key=lambda x: x[0])
+ ret = (feature_node * (len(values)+2))()
+ ret[-1].index = -1 # for bias term
+ ret[-2].index = -1
+ for idx, (feature, value) in enumerate(values):
+ ret[idx].index = feature
+ ret[idx].value = value
+ max_idx = 0
+ if values:
+ max_idx = values[-1][0]
+ return ret, max_idx
+
def gen_feature_nodearray(xi, feature_max=None, issparse=True):
if isinstance(xi, dict):
index_range = xi.keys()
elif isinstance(xi, (list, tuple)):
xi = [0] + xi # idx should start from 1
index_range = range(1, len(xi))
+ elif isinstance(xi, Array):
+ return gen_feature_nodearray_from_array(xi, feature_max, issparse)
else:
! raise TypeError('xi should be a dictionary, list, tuple or ctypes.Array')
if feature_max:
assert(isinstance(feature_max, int))