<s> import sys <EOL> import numpy as np <EOL> import lxmls . classifiers . linear_classifier as lc <EOL> from lxmls . util . my_math_utils import * <EOL> class Mira ( lc . LinearClassifier ) : <EOL> def __init__ ( self , nr_rounds = <NUM_LIT:10> , regularizer = <NUM_LIT:1.0> , averaged = True ) : <EOL> lc . LinearClassifier . __init__ ( self ) <EOL> self . trained = False <EOL> self . nr_rounds = nr_rounds <EOL> self . regularizer = regularizer <EOL> self . params_per_round = [ ] <EOL> self . averaged = averaged <EOL> def train ( self , x , y , seed = <NUM_LIT:1> ) : <EOL> self . params_per_round = [ ] <EOL> x_orig = x [ : , : ] <EOL> x = self . add_intercept_term ( x ) <EOL> nr_x , nr_f = x . shape <EOL> nr_c = np . unique ( y ) . shape [ <NUM_LIT:0> ] <EOL> w = np . zeros ( ( nr_f , nr_c ) ) <EOL> for round_nr in xrange ( self . nr_rounds ) : <EOL> for nr in xrange ( nr_x ) : <EOL> np . random . seed ( seed ) <EOL> perm = np . random . permutation ( nr_x ) <EOL> seed = seed + <NUM_LIT:1> <EOL> inst = perm [ nr ] <EOL> scores = self . get_scores ( x [ inst : inst + <NUM_LIT:1> , : ] , w ) <EOL> y_true = y [ inst : inst + <NUM_LIT:1> , <NUM_LIT:0> ] <EOL> y_hat = self . get_label ( x [ inst : inst + <NUM_LIT:1> , : ] , w ) <EOL> true_margin = scores [ : , y_true ] <EOL> predicted_margin = scores [ : , y_hat ] <EOL> dist = np . abs ( y_true - y_hat ) <EOL> loss = predicted_margin - true_margin + dist <EOL> if ( y_hat != y_true ) : <EOL> if ( predicted_margin == true_margin ) : <EOL> stepsize = <NUM_LIT:1> / self . regularizer <EOL> else : <EOL> stepsize = np . min ( [ <NUM_LIT:1> / self . regularizer , loss / l2norm_squared ( x [ inst : inst + <NUM_LIT:1> ] ) ] ) <EOL> w [ : , y_true ] += stepsize * x [ inst : inst + <NUM_LIT:1> , : ] . transpose ( ) <EOL> w [ : , y_hat ] -= stepsize * x [ inst : inst + <NUM_LIT:1> , : ] . transpose ( ) <EOL> self . params_per_round . append ( w . copy ( ) ) <EOL> self . trained = True <EOL> y_pred = self . test ( x_orig , w ) <EOL> acc = self . evaluate ( y , y_pred ) <EOL> self . trained = False <EOL> print "<STR_LIT>" % ( round_nr , acc ) <EOL> self . trained = True <EOL> if self . averaged == True : <EOL> new_w = <NUM_LIT:0> <EOL> for old_w in self . params_per_round : <EOL> new_w += old_w <EOL> new_w = new_w / len ( self . params_per_round ) <EOL> return new_w <EOL> return w </s>
