log compute - taylor series #957
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| # Taylor series | ||
| assert (log(0.99999)).ae(-1.0000050000287824e-5) | ||
| assert (log(1.00001)).ae(9.9999500003988414e-6) |
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I don't see changes.
Did you realize what's doing your code?
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With default precision of 53, I tested locally for log(0.99999) and log(1.00001) and it ran through to do taylor series compute. The code checks for tfixed (distance from 1) and compares to a threshold scaled to a wp that is 0.001 in floating point?
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With default precision of 53, I tested locally for log(0.99999) and log(1.00001) and it ran through to do taylor series compute.
Great, you have found values, that trigger you code path.
But results are same wrt the master (or not?). Could you argue why we need this?
The code checks for tfixed (distance from 1) and compares to a threshold scaled to a wp that is 0.001 in floating point?
Did you ask me?
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| # Taylor series | |
| assert (log(0.99999)).ae(-1.0000050000287824e-5) | |
| assert (log(1.00001)).ae(9.9999500003988414e-6) | |
| # Taylor series | |
| assert log(0.99999).ae(-1.0000050000287824e-5) | |
| assert log(1.00001).ae(9.9999500003988414e-6) |
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We may not need it I guess. We wouldn't need to compute as many taylor series terms as the general case of code provided later.
Depending on the threshold, I wasn't sure what was close to 1 enough. I was looking for feedback.
| if (tfixed <= threshold): | ||
| s = log_taylor(to_fixed(x, wp), wp) | ||
| sb = s.bit_length() | ||
| rman = s << (sb) |
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What's happens here, for example? Why mantissa corrected this way?
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Constructing the mantissa from the fixed point need to be normalize it with MSB. Which is used using bit length and shifted left by this amount to get no leading zeros. Also, then constructing the exponent to input into creating a mpf. rman is constructed using sb, and to get floating point representation, we also need the working precision wp, and by exponent laws of base 2 its wp+sb.
Still learning, this is new :)
| # Taylor series | ||
| assert (log(0.99999)).ae(-1.0000050000287824e-5) | ||
| assert (log(1.00001)).ae(9.9999500003988414e-6) |
There was a problem hiding this comment.
With default precision of 53, I tested locally for log(0.99999) and log(1.00001) and it ran through to do taylor series compute.
Great, you have found values, that trigger you code path.
But results are same wrt the master (or not?). Could you argue why we need this?
The code checks for tfixed (distance from 1) and compares to a threshold scaled to a wp that is 0.001 in floating point?
Did you ask me?
| # Taylor series | ||
| assert (log(0.99999)).ae(-1.0000050000287824e-5) | ||
| assert (log(1.00001)).ae(9.9999500003988414e-6) |
There was a problem hiding this comment.
| # Taylor series | |
| assert (log(0.99999)).ae(-1.0000050000287824e-5) | |
| assert (log(1.00001)).ae(9.9999500003988414e-6) | |
| # Taylor series | |
| assert log(0.99999).ae(-1.0000050000287824e-5) | |
| assert log(1.00001).ae(9.9999500003988414e-6) |
| # if close enough to 1, use Taylor series | ||
| # since Taylor series converges rapidly |
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You removed AGM mention. Why? Are you know what's it about?
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I know it's used for high precision due to its convergence properties which contrasts with taylor series depending on x.
Don't know how it would be applied to current code.
| else: | ||
| tfixed = to_fixed(x, wp) - (MPZ_ONE << wp) | ||
| threshold = (1 << wp) // 1000 | ||
| if (tfixed <= threshold): |
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| if (tfixed <= threshold): | |
| if tfixed <= threshold: |
| tfixed = (MPZ_ONE << wp) - to_fixed(x,wp) | ||
| else: | ||
| tfixed = to_fixed(x, 8000 wp) - (MPZ_ONE << wp) | ||
| threshold = (1 << wp) // 1000 |
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Not sure here, looking for feedback.
|
Hi, it is my code. If your referring to borjha19, thats an old Github account of mine that I don't use and forgot about. Not sure why its showing up here. If your not referring to that, I don't know what you mean. I wrote this code and it wasn't behalf on anyone. |
Comments in libelefun.py, new tests.
-> Old PR accidently removed ctx_mp file, different refactor changes.