http://www.jitbit.com/news/181-jitbits-sql-interview-questions/
employees
- employee_id
- department_id
- boss_id
- name
# Modify this file accordingly for your specific requirement. | |
# http://www.thegeekstuff.com | |
# 1. Delete all existing rules | |
iptables -F | |
# 2. Set default chain policies | |
iptables -P INPUT DROP | |
iptables -P FORWARD DROP | |
iptables -P OUTPUT DROP |
public class Index | |
{ | |
public static void BuildIndex() | |
{ | |
var directory = FSDirectory.Open(new DirectoryInfo("C:\\temp\\test\\")); | |
Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_30); | |
var indexWriter = new IndexWriter(directory, analyzer, true, IndexWriter.MaxFieldLength.UNLIMITED); |
http://www.jitbit.com/news/181-jitbits-sql-interview-questions/
employees
import logging | |
import multiprocessing | |
import time | |
import mplog | |
FORMAT = '%(asctime)s - %(processName)s - %(levelname)s - %(message)s' | |
logging.basicConfig(level=logging.DEBUG, format=FORMAT) | |
existing_logger = logging.getLogger('x') |
# (C) Mathieu Blondel, November 2013 | |
# License: BSD 3 clause | |
import numpy as np | |
def ranking_precision_score(y_true, y_score, k=10): | |
"""Precision at rank k | |
Parameters |
You can create a new empty branch like this: | |
$ git checkout --orphan NEWBRANCH | |
--orphan creates a new branch, but it starts without any commit. After running the above command you are on a new branch "NEWBRANCH", and the first commit you create from this state will start a new history without any ancestry. | |
The --orphan command keeps the index and the working tree files intact in order to make it convenient for creating a new history whose trees resemble the ones from the original branch. | |
Since you want to create a new empty branch that has nothing to do with the original branch, you can delete all files in the new working directory: |
from __future__ import absolute_import | |
from __future__ import print_function | |
from functools import reduce | |
import re | |
import tarfile | |
import numpy as np | |
np.random.seed(1337) # for reproducibility | |
bAs such, I agree strongly with you that this won't make a good test dataset for testing various RNN architectures.from keras.callbacks import EarlyStopping |
import scipy.io | |
import numpy as np | |
data = scipy.io.loadmat("subject.mat") | |
for i in data: | |
if '__' not in i and 'readme' not in i: | |
np.savetxt(("filesforyou/"+i+".csv"),data[i],delimiter=',') |