本文整理匯總了Python中numpy.logaddexp2方法的典型用法代碼示例。如果您正苦於以下問題：Python numpy.logaddexp2方法的具體用法？Python numpy.logaddexp2怎麽用？Python numpy.logaddexp2使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy的用法示例。
在下文中一共展示了numpy.logaddexp2方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_NotImplemented_not_returned
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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,
np.greater, np.greater_equal, np.less, np.less_equal,
np.equal, np.not_equal]
a = np.array('1')
b = 1
c = np.array([1., 2.])
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
assert_raises(TypeError, f, c, a) 示例2: test_NotImplemented_not_returned
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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) 示例3: _baum_welch_step
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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 示例4: test_logaddexp2_values
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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_) 示例5: test_logaddexp2_range
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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) 示例6: test_inf
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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) 示例7: test_nan
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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))) 示例8: test_logaddexp2_values
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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) 示例9: test_logaddexp2_range
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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) 示例10: test_inf
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import logaddexp2 [as 別名]
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='ignore'):
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)