Error occurs: Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)

[dorioliu]

I am using FALCONN in my dataset which based on the example "glove.py" ,my dataset has 10000 points and each 1024 dimension, when i used 8000 points of the dataset for query, It was OK, but when i seted it to 9000 or more points , error occurs " Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)"|, my computer has a RAM of 16GB, memory usage only 17%., so why? Thanks.

`import numpy as np
import falconn
import timeit
import math
import pandas as pd
import sys
import psutil
import os
if name == 'main':
dataset_file = pd.read_csv('./feature_day_lr.csv', dtype=np.float32)
# number_of_queries = 10000
number_of_tables = 50

dataset = np.array(dataset_file.iloc[:, 1:])   # dataset with 20000 rows and 1024 columns

assert dataset.dtype == np.float32

# Normalize all the lenghts, since we care about the cosine similarity.
print('Normalizing the dataset')
dataset /= np.linalg.norm(dataset, axis=1).reshape(-1, 1)
print('Done')

# Choose random data points to be queries.
print('Generating queries')
np.random.seed(4057218)
np.random.shuffle(dataset)
# queries = dataset[:len(dataset) - number_of_queries]
queries = dataset[0:100]
dataset = dataset[10000:16000]
print('Done')

# Center the dataset and the queries: this improves the performance of LSH quite a bit.
print('Centering the dataset and queries')
center = np.mean(dataset, axis=0)
dataset -= center
queries -= center
print('Done')
#
params_cp = falconn.LSHConstructionParameters()
params_cp.dimension = len(dataset[0])
params_cp.lsh_family = falconn.LSHFamily.CrossPolytope
params_cp.distance_function = falconn.DistanceFunction.EuclideanSquared
params_cp.l = number_of_tables
# we set one rotation, since the data is dense enough,
# for sparse data set it to 2
params_cp.num_rotations = 1
params_cp.seed = 5721840
# we want to use all the available threads to set up
params_cp.num_setup_threads = 0  # 0 o use all the available threads to set up
params_cp.storage_hash_table = falconn.StorageHashTable.BitPackedFlatHashTable
# params_cp.storage_hash_table = falconn.StorageHashTable.LinearProbingHashTable
# we build 18-bit hashes so that each table has
# 2^18 bins; this is a good choise since 2^18 is of the same
# order of magnitude as the number of data points
falconn.compute_number_of_hash_functions(15, params_cp)
#
print('Constructing the LSH table')
t1 = timeit.default_timer()
table = falconn.LSHIndex(params_cp)
table.setup(dataset)
t2 = timeit.default_timer()
print('Done')
print('Construction time: {}'.format(t2 - t1))

# report the memory usage
info = psutil.virtual_memory()
print info.percent
# print sys.getsizeof(table)

# initialize query_object
query_object = table.construct_query_object()

# using the binary search
print('Choosing number of probes')
number_of_probes = number_of_tables
query_object.set_num_probes(number_of_probes)

# final evaluation
t1 = timeit.default_timer()
result = []
for query in queries:
    result.append(query_object.find_k_nearest_neighbors(query, 5))
t2 = timeit.default_timer()
print('Query time: {}'.format((t2 - t1) / len(queries)))`