我们从Python开源项目中，提取了以下43个代码示例，用于说明如何使用numpy.logaddexp2()。
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)
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_)
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)
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)
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)))
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
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
