def predict_one(xi, m):
"""
Return the label and a :class:`c_double` array of decision values of
the test instance *xi* using :class:`LearnerModel` *m*.
*xi* can be a :class:`list` or a :class:`dict` as in LIBLINEAR python
interface. It can also be a LIBLINEAR feature_node array.
.. note::
This function is designed to analyze the result of one instance.
It has a severe efficiency issue and should be used only by
:func:`libshorttext.classifier.predict_single_text`. If many
instances need to be predicted, they should be stored in a file
and predicted by :func:`predict`.
.. warning::
The content of *xi* may be **changed** after the function call.
"""
if isinstance(xi, (list, dict)):
xi = liblinear.gen_feature_nodearray(xi)[0]
elif not isinstance(xi, POINTER(liblinear.feature_node)):
raise TypeError("xi should be a test instance")
print "pppppppppppppppp1"
learner_param = LearnerParameter(m.param_options[0], m.param_options[1])
if m.bias >= 0:
i = 0
while xi[i].index != -1: i += 1
# Already has bias, or bias reserved.
# Actually this statement should be true if
# the data is read by read_SVMProblem.
if i > 0 and xi[i-1].index == m.nr_feature + 1:
i -= 1
xi[i] = liblinear.feature_node(m.nr_feature + 1, m.bias)
xi[i+1] = liblinear.feature_node(-1, 0)
LearnerProblem.normalize_one(xi, learner_param, m.idf)
print "pppppppppppppppp2"
dec_values = (c_double * m.nr_class)()
label = liblinear.liblinear.predict_values(m, xi, dec_values)
return label, dec_values
def set_bias(self, bias):
if self.bias == bias:
return
node = liblinear.feature_node(self.n, bias)
if bias >= 0 and self.bias < 0:
self.n += 1
node = liblinear.feature_node(self.n, bias)
if bias < 0 and self.bias >= 0:
self.n -= 1
node = liblinear.feature_node(-1, bias)
for i in range(1,self.l):
self.x[i][-2] = node
self.x_space[self.n_x_space-2] = node
self.bias = bias
def set_bias(self, bias):
if self.bias == bias:
return
node = liblinear.feature_node(self.n, bias)
if bias >= 0 and self.bias < 0:
self.n += 1
node = liblinear.feature_node(self.n, bias)
if bias < 0 and self.bias >= 0:
self.n -= 1
node = liblinear.feature_node(-1, bias)
for i in range(1, self.l):
self.x[i][-2] = node
self.x_space[self.n_x_space - 2] = node
self.bias = bias
def predict_one(xi, m):
"""
Return the label and a :class:`c_double` array of decision values of
the test instance *xi* using :class:`LearnerModel` *m*.
*xi* can be a :class:`list` or a :class:`dict` as in LIBLINEAR python
interface. It can also be a LIBLINEAR feature_node array.
.. note::
This function is designed to analyze the result of one instance.
It has a severe efficiency issue and should be used only by
:func:`libshorttext.classifier.predict_single_text`. If many
instances need to be predicted, they should be stored in a file
and predicted by :func:`predict`.
.. warning::
The content of *xi* may be **changed** after the function call.
"""
if isinstance(xi, (list, dict)):
xi = liblinear.gen_feature_nodearray(xi)[0]
elif not isinstance(xi, POINTER(liblinear.feature_node)):
raise TypeError("xi should be a test instance")
learner_param = LearnerParameter(m.param_options[0], m.param_options[1])
if m.bias >= 0:
i = 0
while xi[i].index != -1:
i += 1
# Already has bias, or bias reserved.
# Actually this statement should be true if
# the data is read by read_SVMProblem.
if i > 0 and xi[i - 1].index == m.nr_feature + 1:
i -= 1
xi[i] = liblinear.feature_node(m.nr_feature + 1, m.bias)
xi[i + 1] = liblinear.feature_node(-1, 0)
LearnerProblem.normalize_one(xi, learner_param, m.idf)
dec_values = (c_double * m.nr_class)()
label = liblinear.liblinear.predict_values(m, xi, dec_values)
return label, dec_values
