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// Copyright 2020 Google Inc.
//
// 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.
package classifier
type frequencyTable struct {
counts map[tokenID]int // key: token ID, value: number of instances of that token
}
func newFrequencyTable() *frequencyTable {
return &frequencyTable{
counts: make(map[tokenID]int),
}
}
func (f *frequencyTable) update(d *indexedDocument) {
for _, tok := range d.Tokens {
f.counts[tok.ID]++
}
}
func (d *indexedDocument) generateFrequencies() {
d.f = newFrequencyTable()
d.f.update(d)
}
// TokenSimilarity returns a confidence score of how well d contains
// the tokens of o. This is used as a fast similarity metric to
// avoid running more expensive classifiers.
func (d *indexedDocument) tokenSimilarity(o *indexedDocument) float64 {
hits := 0
// For each token in the source document, see if the target has "enough" instances
// of that token to possibly be a match to the target.
// We count up all the matches, and divide by the total number of unique source
// tokens to get a similarity metric. 1.0 means that all the tokens in the target
// are present in the source in appropriate quantities. If the value here is lower
// than the desired matching threshold, the target can't possibly match the source.
// Profiling indicates a significant amount of time is spent here.
// Avoiding checking (or storing) "uninteresting" tokens (common English words)
// could help.
for t, c := range o.f.counts {
if d.f.counts[t] >= c {
hits++
}
}
return float64(hits) / float64(len(o.f.counts))
}
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