rednaxelafx

package fx.jvm.hotspot.tools;

import java.util.List;

import sun.jvm.hotspot.tools.Tool;

public class PrintThreadIds extends Tool {
    public static void main(String[] args) {
        PrintThreadIds tool = new PrintThreadIds();
        tool.start(args);

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

sadache

Asynchronicity is the price to pay, you better know what you're paying for...

Let's share some vocabulary first:

Thread: The primitive responsible of executing code on the processor, you can give an existing (or a new) Thread some code, and it will execute it. Normally you can have a few hundreds on a JVM, arguments that you can tweak your way out to thousands. Worth noting that multitasking is achieved when using multiple Threads. Multiple Threads can exist for a single processor in which case multitasking happens when this processor switches between threads, called context switching, which will give the impression of things happenning in parallel. An example of a direct, and probably naive, use of a new Thread in Java:

public class MyRunnable implements Runnable {
  public void run(){
 System.out.println("MyRunnable running");

kiennt

package patmat

import common._

/**
 * Assignment 4: Huffman coding
 *
 */
object Huffman {

StevenMaude

#!/usr/bin/env python
# encoding: utf-8
import codecs
import os
import sys

import numpy as np

from sklearn.feature_extraction.text import TfidfVectorizer

svpino

// Programming challenge: rotating a matrix 90 degrees in place
// Original post: https://blog.svpino.com/2015/05/10/programming-challenge-rotating-a-matrix-90-degrees-in-place

public class RotatingMatrix90DegreesInPlace {

    private static int[][] matrix = { 
        { 1, 2, 3, 4 }, 
        { 5, 6, 7, 8 }, 
        { 9, 10, 11, 12 }, 
        { 13, 14, 15, 16 } 

alvations

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

psyyz10

This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification

• CNN:

  1. Related Paper: Convolutional Neural Networks for Sentence Classification. EMNLP 2014

     A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

     Code:

shibuiwilliam

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.layers import Dense
from keras.models import Model


# create training data
def binary_encode(i, num_digits):