Comments (5)
That's a really weird error - if I understand it correctly, it's complaining about not having the random
headers for C++. What kind of compiler are you using?
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gcc --version
Configured with: --prefix=/Library/Developer/CommandLineTools/usr --with-gxx-include-dir=/usr/include/c++/4.2.1
Apple LLVM version 9.1.0 (clang-902.0.39.2)
Target: x86_64-apple-darwin17.4.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin
from contextualbandits.
yes, but when I test as follow, it just worked...I will do more test, thx
gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch x86_64 -arch x86_64 -c test.cpp -o test.o -O3 -march=native -std=c++11
test.cpp
#include <random>
#include <iostream>
int main()
{
std::random_device dev;
std::mt19937 rng(dev());
std::uniform_int_distributionstd::mt19937::result_type dist6(1,6); // distribution in range [1, 6]
std::cout << dist6(rng) << std::endl;
}
from contextualbandits.
No idea then, other than perhaps having to do with using GCC's headers with CLANG compiler. Perhaps your python setup is not using the compiler with those same arguments. Maybe you could try something like this:
export CC=clang
export CXX=clang++
pip install contextualbandits
(or replace clang->gcc, clang++->g++)
In order to make sure that the right compilation command is being issued, you could also download this repository and run:
python setup.py build_ext --inplace --force
Then you will see the full compilation commands starting with gcc/g++/clang/clang++ or whatever compiler is being used, and determine whether it has the same --with-gxx-include-dir
.
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I recall BTW I've gotten such an error in the past when I've tried to compile some c++ file with a c compiler.
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Related Issues (20)
- Different predictions of the same model.dill file in different CPUs for the LinUCB algorithm HOT 1
- Support for continuous rewards HOT 1
- ParametricTS fails with: '_OneVsRest' object has no attribute 'beta_counters' HOT 2
- XGBClassifier becomes un-serializable after being used as a base_model HOT 2
- Need more understanding for the method beta_prior HOT 1
- Any good strategies to run Simulation other then one in Online policy example HOT 1
- How to deal with this Scenario while applying CB techniques HOT 2
- Getting topN arm features in Offpolicy method HOT 4
- Type error if beta_prior == "auto" and nchoices is list HOT 1
- Possibly unexpected behaviour of decision function HOT 1
- _BasePolicy.add_arm(); NameError: name 'base_algorithm' is not defined HOT 1
- AssertionError: online_contextual_bandits.ipynb HOT 1
- TypeError: contextual bandits with custom 'choice_names' (online.py) HOT 3
- Getting topN arm features in onlinepolicy method HOT 9
- topN inputs clarification HOT 3
- warm_start for online policy HOT 1
- Doubt in DREstimator HOT 3
- New release HOT 1
- If r != 0 then could be working for negative reward as well? HOT 1
- Getting error while importing packages HOT 1
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