ValueError by passing an int instead of a float

[mdelozzo]

Hi,

I got a ValueError when evaluating a SurrogateModel with an integer (long) instead of a float (double):

ValueError: Buffer dtype mismatch, expected 'double' but got 'long'

Here is the reproducer:

from numpy import array
from numpy import linspace
from smt.surrogate_models import RBF

input_data = linspace(-1, 1, 10)
output_data = input_data**2

smt_model = RBF()
smt_model.set_training_values(input_data, output_data)
smt_model.train()

smt_model.predict_values(array([1])) # KO
# smt_model.predict_values(array([1.0]))  # OK

Is this normal or a bug? If it's normal, what's the explanation for this?

Thank you in advance for your reply,

Best,

Matthias

[relf]

Hi Matthias. I would say it is normal. Historically, SMT only handles numpy arrays of floats.

[mdelozzo]

Hi Rémi,
Thanks for your quick reply.
The error message surprised me a little and the notion of buffer was not obvious (I was expecting to read input value or something like that). I'm creating a wrapper of SurrogateModel and I think I will force the conversion to float to avoid any problems.
As it is the expected SMT behavior, I close this issue.

[relf]

FYI, actually RBF is written in C++ and cythonized which explains the "weird" error message.