MikeDepies

<script>
	export let name;
	let input = {
		row : { start: 0, end: 0 },
		column : { start: 1, end: 100 }
	}
	let id = 0
	let columnCount = 480
	let classPeriods = []
	function addClassPeriod() {

MikeDepies

//..FileHandler Verticle
router.post("/fileUpload").handler(BodyHandler.create().setMergeFormAttributes(true)).handler {
    println(it.fileUploads().size)


    it.fileUploads().forEach({
        println(it.fileName())
    })

    it.response().end(

MikeDepies

MikeDepies

import java.util.Random;

import org.deeplearning4j.datasets.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.accum.distances.EuclideanDistance;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.factory.Nd4j;
import org.slf4j.Logger;

MikeDepies

import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseAccumulation;
import org.nd4j.linalg.api.ops.impl.accum.distances.EuclideanDistance;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.factory.Nd4j;

/**
 * Requires RC1<br/>
 * This is a sample implementation of K-Nearest Neighbor. KNN doesn't create a latent/hidden model, nor does it train. It simply uses it's dataset as a look up table. 
 * Then grabs the closest k data point/records. From there it is able to look at the labels for the K nearest neighbors and make a decision on what label to predict the incoming 

MikeDepies

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.mygroup</groupId>
  <artifactId>deeplearning-bt</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>deeplearning-bt</name>