NextCloud/dlib
1
0
Fork 0
mirror of https://github.com/davisking/dlib.git synced 2024-11-01 10:14:53 +08:00
Code Issues Packages Projects Releases Wiki Activity

improved example

Browse Source
This commit is contained in:
Davis King 2013-06-06 20:38:52 -04:00
parent c1c6fb505a
commit c588d9409c
1 changed files with 11 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
python_examples/sequence_segmenter.py
View File

@ -46,12 +46,12 @@ def sentence_to_vectors(sentence):
# Dlib also supports the use of a sparse vector representation. This is more efficient
# than the above form when you have very high dimensional vectors that are mostly full of
# zeros. In dlib, each sparse vector is represented as an array of pair objects. Each
# pair contains an index and value pair. Any index in the vector not listed is implicitly
# zero.
# pair contains an index and value. Any index not listed in the vector is implicitly
# associated with a value of zero.
def sentence_to_sparse_vectors(sentence):
vects = dlib.sparse_vectors()
vects = dlib.sparse_vectors()
has_cap = dlib.sparse_vector()
no_cap = dlib.sparse_vector()
no_cap = dlib.sparse_vector()
# make has_cap equivalent to dlib.vector([1])
has_cap.append(dlib.pair(0,1))
# Since we didn't add anything to no_cap it is equivalent to dlib.vector([0])
@ -142,6 +142,9 @@ params = dlib.segmenter_params()
params.window_size = 3
params.use_high_order_features = True
params.use_BIO_model = True
# This is the common SVM C parameter. Larger values encourage the trainer to attempt to
# fit the data exactly but might overfit. In general, you determine this parameter by
# cross-validation.
params.C = 10
# Train a model. The model object is responsible for predicting the locations of names in
@ -155,6 +158,10 @@ model = dlib.train_sequence_segmenter(training_sequences, segments, params)
for i in range(len(sentences)):
print_segment(sentences[i], model.segment_sequence(training_sequences[i]))
# Lets also try segmenting a new sentence. This will print out "Bob Bucket"
test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
print_segment(test_sentence, model.segment_sequence(sentence_to_vectors(test_sentence)))
# We can also measure the accuracy of a model relative to some labeled data. This
# statement prints the precision, recall, and F1-score of the model relative to the data in
# training_sequences/segments.