Esempio n. 1
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def test_sparse_vectorss():
    svss = sparse_vectorss()
    assert len(svss) == 0

    svss.resize(5)
    for svs in svss:
        assert len(svs) == 0

    svss.clear()
    assert len(svss) == 0

    svss.extend([
        sparse_vectors([
            sparse_vector([pair(1, 2), pair(3, 4)]),
            sparse_vector([pair(5, 6), pair(7, 8)])
        ])
    ])

    assert len(svss) == 1
    assert svss[0][0][0].first == 1
    assert svss[0][0][0].second == 2
    assert svss[0][0][1].first == 3
    assert svss[0][0][1].second == 4
    assert svss[0][1][0].first == 5
    assert svss[0][1][0].second == 6
    assert svss[0][1][1].first == 7
    assert svss[0][1][1].second == 8

    deser = pickle.loads(pickle.dumps(svss, 2))
    assert deser == svss
def sentence_to_sparse_vectors(sentence):
    vects   = dlib.sparse_vectors()
    has_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])

    for word in sentence.split():
        if (word[0].isupper()):
            vects.append(has_cap)
        else:
            vects.append(no_cap)
    return vects
Esempio n. 3
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def sentence_to_sparse_vectors(sentence):
    vects = dlib.sparse_vectors()
    has_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])

    for word in sentence.split():
        if (word[0].isupper()):
            vects.append(has_cap)
        else:
            vects.append(no_cap)
    return vects
Esempio n. 4
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def training_data():
    r = Random(0)
    predictors = vectors()
    sparse_predictors = sparse_vectors()
    response = array()
    for i in range(30):
        for c in [-1, 1]:
            response.append(c)
            values = [r.random() + c * 0.5 for _ in range(3)]
            predictors.append(vector(values))
            sp = sparse_vector()
            for i, v in enumerate(values):
                sp.append(pair(i, v))
            sparse_predictors.append(sp)
    return predictors, sparse_predictors, response
Esempio n. 5
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def training_data():
    r = Random(0)
    predictors = vectors()
    sparse_predictors = sparse_vectors()
    response = array()
    for i in range(30):
        for c in [-1, 1]:
            response.append(c)
            values = [r.random() + c * 0.5 for _ in range(3)]
            predictors.append(vector(values))
            sp = sparse_vector()
            for i, v in enumerate(values):
                sp.append(pair(i, v))
            sparse_predictors.append(sp)
    return predictors, sparse_predictors, response