On 1 May 2010 16:36, Gökhan Sever <gokhansever@gmail.com> wrote:

Hello,

Is "b" an expected value? I am suspecting another floating point arithmetic issue.

I[1]: a = np.arange(1.6, 1.8, 0.1, dtype='float32')

I[2]: a O[2]: array([ 1.60000002,  1.70000005], dtype=float32)

I[3]: b = np.arange(1.7, 1.8, 0.1, dtype='float32')

I[4]: b O[4]: array([ 1.70000005,  1.79999995], dtype=float32)

A bit conflicting with the np.arange docstring:

"   Values are generated within the half-open interval ``[start, stop)``     (in other words, the interval including `start` but excluding `stop`). "

This is a floating-point issue; since 1.79999995 does not actually equal 1.8, it is included. This arises because 0.1, 1.7, and 1.8 cannot be exactly represented in floating-point. A good rule to avoid being annoyed by this is: only use arange for integers. Use linspace if you want floating-point. Anne

Thanks.

-- Gökhan

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Gökhan Sever wrote:

Hello,

Is "b" an expected value? I am suspecting another floating point arithmetic issue.

Exactly. You'll see the same type of problem with float64, too: In [17]: z = np.arange(1.7, 1.8, 0.1) In [18]: z Out[18]: array([ 1.7, 1.8]) In [19]: z[1] == 1.8 Out[19]: True In [20]: z[1] - 1 Out[20]: 0.80000000000000004 Fun stuff, eh? To avoid problems like this, I generally use linspace instead of arange. Warren

I[1]: a = np.arange(1.6, 1.8, 0.1, dtype='float32')

I[2]: a O[2]: array([ 1.60000002, 1.70000005], dtype=float32)

I[3]: b = np.arange(1.7, 1.8, 0.1, dtype='float32')

I[4]: b O[4]: array([ 1.70000005, 1.79999995], dtype=float32)

A bit conflicting with the np.arange docstring:

"/ Values are generated within the half-open interval ``[start, stop)`` (in other words, the interval including `start` but excluding `stop`). /"

Thanks.

-- Gökhan ------------------------------------------------------------------------

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On Sat, May 1, 2010 at 3:36 PM, Gökhan Sever <gokhansever@gmail.com> wrote:

Hello,

Is "b" an expected value? I am suspecting another floating point arithmetic issue.

I[1]: a = np.arange(1.6, 1.8, 0.1, dtype='float32')

I[2]: a O[2]: array([ 1.60000002, 1.70000005], dtype=float32)

I[3]: b = np.arange(1.7, 1.8, 0.1, dtype='float32')

I[4]: b O[4]: array([ 1.70000005, 1.79999995], dtype=float32)

A bit conflicting with the np.arange docstring:

"* Values are generated within the half-open interval ``[start, stop)`` (in other words, the interval including `start` but excluding `stop`). *"

Thanks.

-- Gökhan

Fair enough explanations to use np.linspace instead. What was confusing me above while a[1] and b[0] shows 1.70000005 only "b" steps up to 1.79999995 which "a" can't. The following is another surprise output: I[5]: c = np.arange(0.4, 0.5, 0.1, dtype='float32') I[6]: c O[6]: array([ 0.40000001], dtype=float32) Anyways, a Slashdotter might have seen me asking these questions. I will go read What Every Programmer Should Know About Floating-Point Arithmetic<http://developers.slashdot.org/story/10/05/02/1427214/What-Every-Programmer-Should-Know-About-Floating-Point-Arithmetic>article. -- Gökhan