copt.utils.LogLoss¶

class
copt.utils.
LogLoss
(A, b, alpha=0.0)[source]¶ Logistic loss function.
The logistic loss function is defined as
\[\frac{1}{n}\sum_{i=1}^n b_i \log(\sigma(\bs{a}_i^T \bs{x})) + (1  b_i) \log(1  \sigma(\bs{a}_i^T \bs{x}))\]where \(\sigma\) is the sigmoid function \(\sigma(t) = 1/(1 + e^{t})\).
The input vector b verifies \(0 \leq b_i \leq 1\). When it comes from class labels, it should have the values 0 or 1.
References
http://fa.bianp.net/drafts/derivatives_logistic.html
Methods
Hessian
(x)Return a callable that performs dot products with the Hessian.
__init__
(A, b[, alpha])Initialize self.
f_grad
(x[, return_gradient])Attributes
lipschitz
max_lipschitz
partial_deriv