You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
91 lines
3.0 KiB
91 lines
3.0 KiB
/* |
|
* Copyright (C) 2017 The Android Open Source Project |
|
* |
|
* Licensed under the Apache License, Version 2.0 (the "License"); |
|
* you may not use this file except in compliance with the License. |
|
* You may obtain a copy of the License at |
|
* |
|
* http://www.apache.org/licenses/LICENSE-2.0 |
|
* |
|
* Unless required by applicable law or agreed to in writing, software |
|
* distributed under the License is distributed on an "AS IS" BASIS, |
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
* See the License for the specific language governing permissions and |
|
* limitations under the License. |
|
*/ |
|
|
|
#include "lang_id/relevant-script-feature.h" |
|
|
|
#include <string> |
|
|
|
#include "common/feature-extractor.h" |
|
#include "common/feature-types.h" |
|
#include "common/task-context.h" |
|
#include "common/workspace.h" |
|
#include "lang_id/script-detector.h" |
|
#include "util/base/logging.h" |
|
#include "util/strings/utf8.h" |
|
|
|
namespace libtextclassifier { |
|
namespace nlp_core { |
|
namespace lang_id { |
|
|
|
bool RelevantScriptFeature::Setup(TaskContext *context) { return true; } |
|
|
|
bool RelevantScriptFeature::Init(TaskContext *context) { |
|
set_feature_type(new NumericFeatureType(name(), kNumRelevantScripts)); |
|
return true; |
|
} |
|
|
|
void RelevantScriptFeature::Evaluate(const WorkspaceSet &workspaces, |
|
const LightSentence &sentence, |
|
FeatureVector *result) const { |
|
// We expect kNumRelevantScripts to be small, so we stack-allocate the array |
|
// of counts. Still, if that changes, we want to find out. |
|
static_assert( |
|
kNumRelevantScripts < 25, |
|
"switch counts to vector<int>: too big for stack-allocated int[]"); |
|
|
|
// counts[s] is the number of characters with script s. |
|
// Note: {} "value-initializes" the array to zero. |
|
int counts[kNumRelevantScripts]{}; |
|
int total_count = 0; |
|
for (int i = 0; i < sentence.num_words(); ++i) { |
|
const std::string &word = sentence.word(i); |
|
const char *const word_end = word.data() + word.size(); |
|
const char *curr = word.data(); |
|
|
|
// Skip over token start '^'. |
|
TC_DCHECK_EQ(*curr, '^'); |
|
curr += GetNumBytesForNonZeroUTF8Char(curr); |
|
while (true) { |
|
const int num_bytes = GetNumBytesForNonZeroUTF8Char(curr); |
|
Script script = GetScript(curr, num_bytes); |
|
|
|
// We do this update and the if (...) break below *before* incrementing |
|
// counts[script] in order to skip the token end '$'. |
|
curr += num_bytes; |
|
if (curr >= word_end) { |
|
TC_DCHECK_EQ(*(curr - num_bytes), '$'); |
|
break; |
|
} |
|
TC_DCHECK_GE(script, 0); |
|
TC_DCHECK_LT(script, kNumRelevantScripts); |
|
counts[script]++; |
|
total_count++; |
|
} |
|
} |
|
|
|
for (int script_id = 0; script_id < kNumRelevantScripts; ++script_id) { |
|
int count = counts[script_id]; |
|
if (count > 0) { |
|
const float weight = static_cast<float>(count) / total_count; |
|
FloatFeatureValue value(script_id, weight); |
|
result->add(feature_type(), value.discrete_value); |
|
} |
|
} |
|
} |
|
|
|
} // namespace lang_id |
|
} // namespace nlp_core |
|
} // namespace libtextclassifier
|
|
|