Python numpy.logaddexp2() 使用实例

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Example 1

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 2

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 3

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 4

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 5

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 6

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 7

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 8

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 9

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 10

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 11

def sample_next(self, prev, incl_eos = True):
        """Samples a single word from context.

        Can be useful to debug the model, for example if you have a bigram model,
        and know the probability of X-Y should be really high, you can run
        sample_next([Y]) to see how often X get generated.

        incl_eos determines whether the space of words should include EOS or not.
        """
        wps = []
        tot = -np.inf # this is the log (total mass)
        for w in self.lm.vocab():
            if not incl_eos and w == "END_OF_SENTENCE":
                continue
            lp = self.lm.cond_logprob(w, prev)
            wps.append([w, lp/self.temp])
            tot = np.logaddexp2(lp/self.temp, tot)
        p = self.rnd.random()
        word = self.rnd.choice(wps)[0]
        s = -np.inf # running mass
        for w,lp in wps:
            s = np.logaddexp2(s, lp)
            if p < pow(2, s-tot):
                word = w
                break
        return word 

Example 12

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 13

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 14

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 15

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 16

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

Example 17

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 18

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 19

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 20

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 21

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 22

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 23

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 24

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 25

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 26

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 27

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 28

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 29

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 30

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 31

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 32

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 33

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 34

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 35

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 

Example 36

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 37

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 38

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 39

def _baum_welch_step(self, sequence, model, symbol_to_number):

        N = len(model._states)
        M = len(model._symbols)
        T = len(sequence)

        # compute forward and backward probabilities
        alpha = model._forward_probability(sequence)
        beta = model._backward_probability(sequence)

        # find the log probability of the sequence
        lpk = logsumexp2(alpha[T-1])

        A_numer = _ninf_array((N, N))
        B_numer = _ninf_array((N, M))
        A_denom = _ninf_array(N)
        B_denom = _ninf_array(N)

        transitions_logprob = model._transitions_matrix().T

        for t in range(T):
            symbol = sequence[t][_TEXT]  # not found? FIXME
            next_symbol = None
            if t < T - 1:
                next_symbol = sequence[t+1][_TEXT]  # not found? FIXME
            xi = symbol_to_number[symbol]

            next_outputs_logprob = model._outputs_vector(next_symbol)
            alpha_plus_beta = alpha[t] + beta[t]

            if t < T - 1:
                numer_add = transitions_logprob + next_outputs_logprob + \
                            beta[t+1] + alpha[t].reshape(N, 1)
                A_numer = np.logaddexp2(A_numer, numer_add)
                A_denom = np.logaddexp2(A_denom, alpha_plus_beta)
            else:
                B_denom = np.logaddexp2(A_denom, alpha_plus_beta)

            B_numer[:,xi] = np.logaddexp2(B_numer[:,xi], alpha_plus_beta)

        return lpk, A_numer, A_denom, B_numer, B_denom 

Example 40

def test_logaddexp2_values(self):
        x = [1, 2, 3, 4, 5]
        y = [5, 4, 3, 2, 1]
        z = [6, 6, 6, 6, 6]
        for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]):
            xf = np.log2(np.array(x, dtype=dt))
            yf = np.log2(np.array(y, dtype=dt))
            zf = np.log2(np.array(z, dtype=dt))
            assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) 

Example 41

def test_logaddexp2_range(self):
        x = [1000000, -1000000, 1000200, -1000200]
        y = [1000200, -1000200, 1000000, -1000000]
        z = [1000200, -1000000, 1000200, -1000000]
        for dt in ['f', 'd', 'g']:
            logxf = np.array(x, dtype=dt)
            logyf = np.array(y, dtype=dt)
            logzf = np.array(z, dtype=dt)
            assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 42

def test_inf(self):
        inf = np.inf
        x = [inf, -inf,  inf, -inf, inf, 1,  -inf,  1]
        y = [inf,  inf, -inf, -inf, 1,   inf, 1,   -inf]
        z = [inf,  inf,  inf, -inf, inf, inf, 1,    1]
        with np.errstate(invalid='raise'):
            for dt in ['f', 'd', 'g']:
                logxf = np.array(x, dtype=dt)
                logyf = np.array(y, dtype=dt)
                logzf = np.array(z, dtype=dt)
                assert_equal(np.logaddexp2(logxf, logyf), logzf) 

Example 43

def test_nan(self):
        assert_(np.isnan(np.logaddexp2(np.nan, np.inf)))
        assert_(np.isnan(np.logaddexp2(np.inf, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, 0)))
        assert_(np.isnan(np.logaddexp2(0, np.nan)))
        assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) 
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