summaryrefslogtreecommitdiff
path: root/training
diff options
context:
space:
mode:
authorPatrick Simianer <p@simianer.de>2015-02-01 19:56:17 +0100
committerPatrick Simianer <p@simianer.de>2015-02-01 19:56:17 +0100
commitb31708e1066ff699c7c06d1edb7c8223b84d62cd (patch)
tree1aeab1bb47cf0c911ed4008f57ee4842969c6b20 /training
parent0f5a50a50c0a1984354ffbf131016b912f7be633 (diff)
dtrain: rm fix_features
Diffstat (limited to 'training')
-rw-r--r--training/dtrain/dtrain.cc21
1 files changed, 3 insertions, 18 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 4c5972a1..64fbf80d 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -43,7 +43,6 @@ dtrain_init(int argc, char** argv, po::variables_map* conf)
("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times")
("check", po::value<bool>()->zero_tokens(), "produce list of loss differentials")
("output_ranking", po::value<string>()->default_value(""), "Output kbests with model scores and metric per iteration to this folder.")
- ("fix_features", po::value<bool>()->zero_tokens(), "Ignore all features that are not in input_weights.")
("noup", po::value<bool>()->zero_tokens(), "do not update weights");
po::options_description cl("Command Line Options");
cl.add_options()
@@ -112,12 +111,10 @@ main(int argc, char** argv)
if (conf.count("verbose")) verbose = true;
bool noup = false;
if (conf.count("noup")) noup = true;
- bool rescale = false;
- if (conf.count("rescale")) rescale = true;
bool keep = false;
if (conf.count("keep")) keep = true;
- bool fix_features = false;
- if (conf.count("fix_features")) fix_features = true;
+ bool rescale = false;
+ if (conf.count("rescale")) rescale = true;
const unsigned k = conf["k"].as<unsigned>();
const unsigned N = conf["N"].as<unsigned>();
@@ -196,16 +193,8 @@ main(int argc, char** argv)
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
SparseVector<weight_t> lambdas, cumulative_penalties, w_average, fixed;
- if (conf.count("input_weights")) {
+ if (conf.count("input_weights"))
Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
- if (fix_features) {
- Weights::InitSparseVector(decoder_weights, &fixed);
- SparseVector<weight_t>::iterator it = fixed.begin();
- for (; it != fixed.end(); ++it) {
- it->second = 1.0;
- }
- }
- }
Weights::InitSparseVector(decoder_weights, &lambdas);
// meta params for perceptron, SVM
@@ -344,8 +333,6 @@ main(int argc, char** argv)
if (next || stop) break;
// weights
- if (fix_features)
- lambdas.cw_mult(fixed);
lambdas.init_vector(&decoder_weights);
// getting input
@@ -657,8 +644,6 @@ main(int argc, char** argv)
// write weights to file
if (select_weights == "best" || keep) {
- if (fix_features)
- lambdas.cw_mult(fixed);
lambdas.init_vector(&decoder_weights);
string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
Weights::WriteToFile(w_fn, decoder_weights, true);