def _predict(self, x, confidence=False):
ps = np.array([predict(x, self.W[i], self.b[i]) for i in range(self.k)])
c = np.argmax(ps)
if confidence:
return c, ps[c]
else:
return c
def _predict(self, x, confidence=False):
ps = np.array([predict(x, self.W[i], self.b[i]) for i in range(self.k)])
c = np.argmax(ps)
if confidence:
return c,ps[c]
else:
return c
def probdist(self, x):
"""The probability distribution of class assignment.
Transforms the confidence scores into a probability via a logit function
:math:`\exp{\mathbf{w}^T \mathbf{x} + b} / Z`.
:return: a `k`-dimensional probability vector.
"""
ps = np.array([np.exp(predict(x, self.W[i], self.b[i]))
for i in range(self.k)])
Z = np.sum(ps)
return ps / Z
def probdist(self, x):
"""The probability distribution of class assignment.
Transforms the confidence scores into a probability via a logit function
:math:`\exp{\mathbf{w}^T \mathbf{x} + b} / Z`.
:return: a `k`-dimensional probability vector.
"""
ps = np.array([np.exp(predict(x, self.W[i], self.b[i]))
for i in range(self.k)])
Z = np.sum(ps)
return ps / Z
def __call__(self, x, confidence=False):
"""Predicts the target value for the given example.
:arg x: An instance in dense or sparse representation.
:arg confidence: whether to output confidence scores.
:returns: The class assignment and optionally a confidence score.
"""
if x.dtype != sparsedtype:
x = dense2sparse(x)
p = predict(x, self.w, self.bias)
return p
def __call__(self, x, confidence=False):
"""Predicts the target value for the given example.
:arg x: An instance in dense or sparse representation.
:arg confidence: whether to output confidence scores.
:returns: The class assignment and optionally a confidence score.
"""
if x.dtype != sparsedtype:
x = dense2sparse(x)
p = predict(x, self.w, self.bias)
return p
