gfrison

<?xml version="1.0" encoding="UTF-8"?>
<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd" xmlns="http://maven.apache.org/POM/4.0.0"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <modelVersion>4.0.0</modelVersion>
  <groupId>asgard</groupId>
  <artifactId>asgard</artifactId>
  <version>1.0.0-SNAPSHOT</version>
  <inceptionYear>2008</inceptionYear>
  <licenses>
    <license>

gfrison

import org.apache.commons.lang3.builder.ToStringBuilder;
import org.apache.commons.lang3.builder.ToStringStyle;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;

gfrison

import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;

gfrison

  /**
   * >   arr = {1, 2, 3}, size = 5
   * <   [1.0, 1.5, 2.0, 2.5, 3.0]
   * @param arr
   * @param newSize
   * @return
   */
  public static double[] growIntermediate(double[] arr, int newSize) {
    if (newSize < arr.length) {
      throw new IllegalArgumentException("newSize lower than array length");

gfrison

int __main__ (int var0)
  vars: int var1, int var2, int var3
  var2 = 2
  if (((var0 - 2) % 3) == 0)
    var1 = 1
  else
    var1 = 0
var3 = 1
for(; (var3<var0); var3 = (var3 + 1))
  if (var2<var0)

gfrison

static class CacheOp implements Op<Double> {
  private final AtomicDouble ref;

  public CacheOp(int i) {
    ref = new AtomicDouble(i);
  }

  public CacheOp() {
    this(0);
  }

gfrison

import static java.util.stream.IntStream.range;
import static java.util.stream.IntStream.rangeClosed;

private List<String> possibleNgrams(String[] words) {
    return rangeClosed(1, words.length)
        .mapToObj(
            window ->
                rangeClosed(0, words.length - window)
                    .mapToObj(
                        i ->

gfrison

Groovy also has a Time Category class which gives you DSL style syntax for manipulating dates. Here is an example:

import org.codehaus.groovy.runtime.TimeCategory
now = new Date()
println now
use(TimeCategory) {
    footballPractice = now + 1.week - 4.days + 2.hours - 3.seconds
}
println footballPractice
which will produce output like this:

gfrison

    public static Observable<Long> write(Path target, InputStream inputStream) {
        return Observable.create(subscriber -> {
            try {
                final AtomicLong offset = new AtomicLong(0);
                AsynchronousFileChannel afc = AsynchronousFileChannel.open(Utils.home.resolve(target), StandardOpenOption.WRITE,
                        StandardOpenOption.CREATE);
                afc.lock(new Object(), new CompletionHandler<FileLock, Object>() {
                    @Override
                    public void completed(final FileLock lock, final Object attachment) {
                        log.debug("write:{} acquired lock", target);

gfrison

var vertx = require("vertx.js")
var console = require('vertx/console');


console.log('module ' + JSON.stringify(module));

var dust;
var x = function(){
	var module = undefined;