• Short answer:
It's because
>>> f64_info.max - f64_info.min
inf
• Long answer:
linspace(a,b,n) tries to calculate the step by (b-a)/n and fails at (b-a).
You need to either
– split your range into two parts and then glue them back:
np.r_[np.linspace(f64_info.min, 0, 5), np.linspace(0, f64_info.max, 5)[1:]]
– or select a range that fits into float64:
np.linspace(f64_info.min/2, f64_info.max/2, 10)
– or select np.float128 as a dtype for linspace (linux/macos only):
np.linspace(np.float128(f64_info.min), np.float128(f64_info.max), 10)
Best regards,
Lev
On Wed, Dec 29, 2021 at 8:01 PM Sebastian Gurovich <seb...@gmail.com
<mailto:seb...@gmail.com>> wrote:
Could it be you need to get a handle on the "epsilon machine"?
On Wed, 29 Dec 2021, 9:21 am , <alejandro.giacome...@gmail.com
<mailto:alejandro.giacome...@gmail.com>> wrote:
I am getting an interesting result, and I'm wondering if anyone
would care to give me some intuition of why.
The example is simple enough, I want to get a range of values
that are representable by a type:
```python
f64_info = np.finfo(np.float64)
valid_range = np.linspace(
start=f64_info.min, stop=f64_info.max, num=10
)
valid_range => array([ nan, inf,
inf, inf,
inf, inf, inf,
inf,
inf, 1.79769313e+308])
```
The minimum value is representable by the type, I can see it:
```python
f64_info.min => -1.7976931348623157e+308
```
I thought that maybe the valid range cannot start with the
minimun value, so I've tried a few alternatives:
```python
valid_range = np.linspace(
start=f64_info.min + f64_info.eps, stop=f64_info.max, num=10
)
valid_range => array([ nan, inf,
inf, inf,
inf, inf, inf,
inf,
inf, 1.79769313e+308])
valid_range = np.linspace(
start=f64_info.min + f64_info.tiny, stop=f64_info.max, num=10
)
valid_range => array([ nan, inf,
inf, inf,
inf, inf, inf,
inf,
inf, 1.79769313e+308])
```
I thought maybe the range is too wide, but I can do this:
```python
valid_range = np.linspace(
start=0, stop=f64_info.max, num=10
)
valid_range => array([0.00000000e+000, 1.99743682e+307,
3.99487363e+307, 5.99231045e+307,
7.98974727e+307, 9.98718408e+307,
1.19846209e+308, 1.39820577e+308,
1.59794945e+308, 1.79769313e+308])
...
valid_range = np.linspace(
start=f64_info.tiny, stop=f64_info.max, num=10
)
valid_range => array([2.22507386e-308, 1.99743682e+307,
3.99487363e+307, 5.99231045e+307,
7.98974727e+307, 9.98718408e+307,
1.19846209e+308, 1.39820577e+308,
1.59794945e+308, 1.79769313e+308])
...
f32_info = np.finfo(np.float32)
valid_range = np.linspace(
start=f32_info.tiny, stop=f32_info.max, num=10,
dtype=np.float32,
)
valid_range => array([1.1754944e-38, 3.7809150e+37,
7.5618299e+37, 1.1342745e+38,
1.5123660e+38, 1.8904575e+38, 2.2685490e+38,
2.6466405e+38,
3.0247320e+38, 3.4028235e+38], dtype=float32)
```
I know that linear space is arbitrary, and perhaps not that
useful. In fact this is valid:
```python
valid_range = np.logspace(
start=f64_info.minexp, stop=f64_info.maxexp, num=10,
base=2, endpoint=False
)
valid_range => array([2.22507386e-308, 8.67124674e-247,
3.37923704e-185, 1.31690901e-123,
5.13207368e-062, 2.00000000e+000, 7.79412037e+061,
3.03741562e+123,
1.18369915e+185, 4.61294681e+246])
```
But I'm still confused on why linear space is invalid
Thanks!
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