Searched refs:logx (Results 1 – 4 of 4) sorted by relevance
| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| D | FDistributionImpl.java | 92 final double logx = FastMath.log(x); in density() local
96 return FastMath.exp(nhalf*logn + nhalf*logx - logx + mhalf*logm - nhalf*lognxm - in density()
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| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| D | util_test.py | 811 def _reduce_weighted_logsumexp(self, logx, w, axis, keep_dims=False): argument
812 m = np.max(logx, axis=axis, keepdims=True)
813 sum_ = np.sum(w * np.exp(logx - m), axis=axis, keepdims=keep_dims)
825 logx = constant_op.constant(logx_)
826 expected = math_ops.reduce_logsumexp(logx, axis=-1)
827 grad_expected = gradients_impl.gradients(expected, logx)[0]
829 logx, axis=-1, return_sign=True)
830 grad_actual = gradients_impl.gradients(actual, logx)[0]
848 logx = constant_op.constant(logx_)
851 logx, w, axis=-1, return_sign=True)
[all …]
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| /external/arm-optimized-routines/math/ |
| D | powf.c | 195 double_t logx = log2_inline (ix); in powf() local
196 double_t ylogx = y * logx; /* Note: cannot overflow, y is single prec. */ in powf()
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | util.py | 1045 logx, argument
1108 with ops.name_scope(name, "reduce_weighted_logsumexp", [logx, w]):
1109 logx = ops.convert_to_tensor(logx, name="logx")
1111 lswe = math_ops.reduce_logsumexp(logx, axis=axis, keepdims=keep_dims)
1116 w = ops.convert_to_tensor(w, dtype=logx.dtype, name="w")
1117 log_absw_x = logx + math_ops.log(math_ops.abs(w))
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