Exemplo n.º 1
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def test_logsumexp():
    x = np.abs(np.random.normal(1e-42, size=100000))
    logx = np.log(x)
    assert np.allclose(np.exp(logsumexp(logx)), x.sum())

    X = np.vstack([x, x])
    logX = np.vstack([logx, logx])
    assert np.allclose(np.exp(logsumexp(logX, axis=0)), X.sum(axis=0))
    assert np.allclose(np.exp(logsumexp(logX, axis=1)), X.sum(axis=1))
Exemplo n.º 2
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def test_logsumexp():
    x = np.abs(np.random.normal(1e-42, size=100000))
    logx = np.log(x)
    assert np.allclose(np.exp(logsumexp(logx)), x.sum())

    X = np.vstack([x, x])
    logX = np.vstack([logx, logx])
    assert np.allclose(np.exp(logsumexp(logX, axis=0)), X.sum(axis=0))
    assert np.allclose(np.exp(logsumexp(logX, axis=1)), X.sum(axis=1))
Exemplo n.º 3
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    def test_decode(self):
        _logprob, state_sequence = self.hmm.decode(self.framelogprob)

        n_samples, n_components = self.framelogprob.shape
        norm = logsumexp(self.framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(self.framelogprob -
                               np.tile(norm, (1, n_components)))
        gmmstate_sequence = gmmposteriors.argmax(axis=1)
        assert np.allclose(state_sequence, gmmstate_sequence)
Exemplo n.º 4
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    def test_decode(self):
        _logprob, state_sequence = self.hmm.decode(self.framelogprob)

        n_samples, n_components = self.framelogprob.shape
        norm = logsumexp(self.framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(self.framelogprob -
                               np.tile(norm, (1, n_components)))
        gmmstate_sequence = gmmposteriors.argmax(axis=1)
        assert np.allclose(state_sequence, gmmstate_sequence)
Exemplo n.º 5
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    def test_score_samples(self):
        logprob, hmmposteriors = self.hmm.score_samples(self.framelogprob)

        n_samples, n_components = self.framelogprob.shape
        assert np.allclose(hmmposteriors.sum(axis=1), np.ones(n_samples))

        norm = logsumexp(self.framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(self.framelogprob
                               - np.tile(norm, (1, n_components)))
        assert np.allclose(hmmposteriors, gmmposteriors)
Exemplo n.º 6
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    def test_score_samples(self):
        logprob, hmmposteriors = self.hmm.score_samples(self.framelogprob)

        n_samples, n_components = self.framelogprob.shape
        assert np.allclose(hmmposteriors.sum(axis=1), np.ones(n_samples))

        norm = logsumexp(self.framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(self.framelogprob
                               - np.tile(norm, (1, n_components)))
        assert np.allclose(hmmposteriors, gmmposteriors)
Exemplo n.º 7
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    def test_hmm_decode_consistent_with_gmm(self):
        n_components = 8
        nobs = 10
        h = StubHMM(n_components)

        # Add dummy observations to stub.
        framelogprob = np.log(self.prng.rand(nobs, n_components))
        h.framelogprob = framelogprob

        # If startprob and transmat are uniform across all states (the
        # default), the transitions are uninformative - the model
        # reduces to a GMM with uniform mixing weights (in terms of
        # posteriors, not likelihoods).
        h.startprob_ = np.ones(n_components) / n_components
        h.transmat_ = np.ones((n_components, n_components)) / n_components
        viterbi_ll, state_sequence = h.decode(framelogprob)

        norm = logsumexp(framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components)))
        gmmstate_sequence = gmmposteriors.argmax(axis=1)
        assert_array_equal(state_sequence, gmmstate_sequence)
Exemplo n.º 8
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    def test_hmm_decode_consistent_with_gmm(self):
        n_components = 8
        nobs = 10
        h = StubHMM(n_components)

        # Add dummy observations to stub.
        framelogprob = np.log(self.prng.rand(nobs, n_components))
        h.framelogprob = framelogprob

        # If startprob and transmat are uniform across all states (the
        # default), the transitions are uninformative - the model
        # reduces to a GMM with uniform mixing weights (in terms of
        # posteriors, not likelihoods).
        h.startprob_ = np.ones(n_components) / n_components
        h.transmat_ = np.ones((n_components, n_components)) / n_components
        viterbi_ll, state_sequence = h.decode(framelogprob)

        norm = logsumexp(framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components)))
        gmmstate_sequence = gmmposteriors.argmax(axis=1)
        assert np.allclose(state_sequence, gmmstate_sequence)
Exemplo n.º 9
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    def test_hmm_score_samples_consistent_with_gmm(self):
        n_components = 8
        nobs = 10
        h = StubHMM(n_components)

        # Add dummy observations to stub.
        framelogprob = np.log(self.prng.rand(nobs, n_components))
        h.framelogprob = framelogprob

        # If startprob and transmat are uniform across all states (the
        # default), the transitions are uninformative - the model
        # reduces to a GMM with uniform mixing weights (in terms of
        # posteriors, not likelihoods).
        h.startprob_ = np.ones(n_components) / n_components
        h.transmat_ = np.ones((n_components, n_components)) / n_components
        logprob, hmmposteriors = h.score_samples(framelogprob)

        assert_array_almost_equal(hmmposteriors.sum(axis=1), np.ones(nobs))

        norm = logsumexp(framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components)))
        assert_array_almost_equal(hmmposteriors, gmmposteriors)
Exemplo n.º 10
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    def test_hmm_score_samples_consistent_with_gmm(self):
        n_components = 8
        nobs = 10
        h = StubHMM(n_components)

        # Add dummy observations to stub.
        framelogprob = np.log(self.prng.rand(nobs, n_components))
        h.framelogprob = framelogprob

        # If startprob and transmat are uniform across all states (the
        # default), the transitions are uninformative - the model
        # reduces to a GMM with uniform mixing weights (in terms of
        # posteriors, not likelihoods).
        h.startprob_ = np.ones(n_components) / n_components
        h.transmat_ = np.ones((n_components, n_components)) / n_components
        logprob, hmmposteriors = h.score_samples(framelogprob)

        assert_array_almost_equal(hmmposteriors.sum(axis=1), np.ones(nobs))

        norm = logsumexp(framelogprob, axis=1)[:, np.newaxis]
        gmmposteriors = np.exp(framelogprob - np.tile(norm, (1, n_components)))
        assert_array_almost_equal(hmmposteriors, gmmposteriors)