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import numpy as np
from jax import random
from neural_tangents import stax

random_key = random.PRNGKey(42)
SAMPLE_SIZE = 100
BATCH_SIZE = 25


def get_mlp_kernel_fn():

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import numpy as np 
from sklearn.neighbors import NearestNeighbors
from scipy import spatial
import math
from multiprocessing.pool import Pool
import multiprocessing

def main(X, Y, k, alpha):
    nbrs = NearestNeighbors(n_neighbors=k, algorithm='kd_tree').fit(X)
    tree = spatial.KDTree(Y)

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from collections import OrderedDict
from tqdm import tqdm
import torch.nn.functional as F
import torch.utils.data as td
from torch import nn
import torch
import numpy as np
from deepctr_torch.inputs import SparseFeat, DenseFeat
import os
import sys

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from deepctr_torch.inputs import SparseFeat, DenseFeat
import numpy as np
import torch
from torch import nn
import torch.utils.data as td
import torch.nn.functional as F
from tqdm import tqdm

import sys
MAX = sys.maxsize

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from deepctr_torch.inputs import SparseFeat, DenseFeat
import numpy as np
import torch
from torch import nn
import torch.utils.data as td
import torch.nn.functional as F
from tqdm import tqdm

import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

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[ 50%] Building CXX object CMakeFiles/recsys.dir/main.cpp.o
In file included from /usr/include/arrow/util/variant.h:20:0,
                 from /usr/include/arrow/type.h:34,
                 from /usr/include/parquet/properties.h:27,
                 from /usr/include/parquet/metadata.h:29,
                 from /usr/include/parquet/file_reader.h:26,
                 from /home/xxx/main.cpp:10:
/usr/include/arrow/vendored/variant.hpp:911:41: error: ‘lib’ has not been declared
   struct variant_size<variant<Ts...>> : lib::size_constant<sizeof...(Ts)> {};
                                         ^~~

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cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(recsys)

find_package(Torch REQUIRED)
find_library(ROCKSDB_LIB rocksdb)
find_package(Boost 1.67.0 REQUIRED COMPONENTS system filesystem)

add_executable(recsys main.cpp)
target_link_libraries(recsys "${Boost_LIBRARIES}" "${ROCKSDB_LIB}") #It works fine

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import numpy as np
import math
from scipy.spatial import distance

n = 2000
p = 800
r = 1000
print("r/n:", r/n)
print("p/n:", p/n)
print("theory result:", math.log(1+(n-p)/(r-p)))

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void PerfSampleWrap::signal_handler(int sig_num, siginfo_t *sig_info, void *context)
{
    perf_event_mmap_page *mmap_buffer = (perf_event_mmap_page *) data->buffers[index];
    while (__mmap_get_remain_size(mmap_buffer) > 0)
    {
        uint64_t remain = __mmap_get_remain_size(mmap_buffer);
        if (remain < sizeof(perf_event_header))
        {
            __mmap_move_index(mmap_buffer, remain);
            break;

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0x7f0f2c33b740	__pyx_f_6mtrand_cont0_array	/home/ychen/.local/lib/python3.6/site-packages/numpy/random/mtrand.cpython-36m-x86_64-linux-gnu.so
0x7f0f2c33d270	__pyx_pw_6mtrand_11RandomState_17random_sample	/home/ychen/.local/lib/python3.6/site-packages/numpy/random/mtrand.cpython-36m-x86_64-linux-gnu.so
0x562330	PyCFunction_Call	python3
0x7f0f2c31f360	__pyx_pw_6mtrand_11RandomState_29rand	/home/ychen/.local/lib/python3.6/site-packages/numpy/random/mtrand.cpython-36m-x86_64-linux-gnu.so
0x4f83a0	PyEval_CallObjectWithKeywords	python3
0x4f9460	arr1 = np.random.rand(size, size)	../python/num.py:8
0x4f5e40	PyEval_CallObjectWithKeywords	python3
0x4f9000	PyEval_EvalCode	python3
0x641580	PyParser_ASTFromFileObject	python3
0x6415d0	PyRun_FileExFlags	python3