[frames] | no frames]

source code

This module contains functions for linear gradients

 Functions
tuple
 ell2_ball_constraint_avg(w, w_hat, steps, x, y, mu, B, stepsize) Calculates and returns ell2 ball constraint avg source code
double
 hinge_loss(w, x, y) Calculates and returns hinge loss source code
double
 sparse_hinge_loss(w, indexes, vecs, y) Calculates hinge lss for sparse vectors and returns hinge loss value source code
double
 mse_loss(w, x, y) Calculates and returns mse loss source code
double
 logit_loss_ss(w, index, vecs, y) Calculates logit loss for sparse vectors and returns the value source code

 test() Tests ell2 ball constraint and hinge loss functions source code

 lazy_shrink(w, current_time, timestamps, indexes, u) Lazy shrink function source code

 incremental_average_sparse(w_hat, w, steps, indexes) Incremental average sparse function source code

 l1_prox_lazy(w, what, timestamps, current_time, steps, stepsize, mu, B, indexes, vecs, y) l1 prox lazy function source code

 svm_l1_prox_lazy(w, what, timestamps, current_time, steps, stepsize, mu, B, indexes, vecs, y) svm l1 prox lazy function source code

 svm_l2_prox_lazy(w, what, wscale, steps, stepsize, mu, B, indexes, vecs, y) svm l2 prox lazy function source code
 Function Details

### ell2_ball_constraint_avg(w, w_hat, steps, x, y, mu, B, stepsize)

source code

Calculates and returns ell2 ball constraint avg

Parameters:
• `w` (vector) - model vector
• `w_hat` (vector) - w_hat vector
• `steps` (number) - number of steps
• `x` (vector) - feature vector of the example
• `y` (number) - label of the example
• `mu` (double) - mu
• `B` (double) - B
• `stepsize` (double) - step size
Returns: tuple
returns w, w_hat, # of steps, mu and B

### hinge_loss(w, x, y)

source code

Calculates and returns hinge loss

Parameters:
• `w` (vector) - model vector
• `x` (vector) - feature vector of the example
• `y` (number) - label of the example
Returns: double
returns the hinge loss

### sparse_hinge_loss(w, indexes, vecs, y)

source code

Calculates hinge lss for sparse vectors and returns hinge loss value

Parameters:
• `w` (vector) - model vector
• `indexes` (vector) - indexes of the feature vector of the example
• `vecs` (vector) - vector values of the feature vector of the example
• `y` (number) - label of the example
Returns: double
returns the hinge loss

### mse_loss(w, x, y)

source code

Calculates and returns mse loss

Parameters:
• `w` (vector) - model vector
• `x` (vector) - feature vector of the example
• `y` (number) - label of the example
Returns: double
returns the mse loss

### logit_loss_ss(w, index, vecs, y)

source code

Calculates logit loss for sparse vectors and returns the value

Parameters:
• `w` (vector) - model vector
• `index` (vector) - indexes of the feature vector of the example
• `vecs` (vector) - vector values of the feature vector of the example
• `y` (number) - label of the example
Returns: double
returns the logit loss

### lazy_shrink(w, current_time, timestamps, indexes, u)

source code

Lazy shrink function

Parameters:
• `w` (vector) - model vector
• `current_time` (double) - current time
• `timestamps` (vector) - time stamps vector
• `indexes` (vector) - indexes of the example
• `u` (double) - u

### incremental_average_sparse(w_hat, w, steps, indexes)

source code

Incremental average sparse function

Parameters:
• `w_hat` (vector) - w hat vector
• `w` (vector) - model vector
• `steps` (double) - steps of the incremental stage
• `indexes` (vector) - indexes

### l1_prox_lazy(w, what, timestamps, current_time, steps, stepsize, mu, B, indexes, vecs, y)

source code

l1 prox lazy function

Parameters:
• `w` (vector) - model vector
• `what` (vector) - w hat
• `timestamps` (vector) - time stamps vector
• `current_time` (double) - current time
• `steps` (double) - steps of the incremental stage
• `stepsize` (number) - step size
• `mu` (double) - mu
• `B` (double) - B
• `indexes` (vector) - indexes vector of the example's feature vector
• `vecs` (vector) - vector values of the example's feature vector
• `y` (number) - label of the example

### svm_l1_prox_lazy(w, what, timestamps, current_time, steps, stepsize, mu, B, indexes, vecs, y)

source code

svm l1 prox lazy function

Parameters:
• `w` (vector) - model vector
• `what` (vector) - w hat
• `timestamps` (vector) - time stamps vector
• `current_time` (double) - current time
• `steps` (double) - steps of the incremental stage
• `stepsize` (number) - step size
• `mu` (double) - mu
• `B` (double) - B
• `indexes` (vector) - indexes vector of the example's feature vector
• `vecs` (vector) - vector values of the example's feature vector
• `y` (number) - label of the example

### svm_l2_prox_lazy(w, what, wscale, steps, stepsize, mu, B, indexes, vecs, y)

source code

svm l2 prox lazy function

Parameters:
• `w` (vector) - model vector
• `what` (vector) - w hat
• `wscale` (double) - used to scale model vector(w)
• `steps` (double) - steps of the incremental stage
• `stepsize` (number) - step size
• `mu` (double) - mu
• `B` (double) - B
• `indexes` (vector) - indexes vector of the example's feature vector
• `vecs` (vector) - vector values of the example's feature vector
• `y` (number) - label of the example

 Generated by Epydoc 3.0.1 on Mon Feb 28 00:56:44 2011 http://epydoc.sourceforge.net