How to use

output

function

in

Array

trainingData[1].output.map((value) =>
       Float32Array.from([value])
      )

 ret = x.output;
} else if (x instanceof Input) {
 ret = x.size;

for (const i in trainingData) {
 const output = net.run(trainingData[i].input);
 const target = trainingData[i].output;
for (const i in not) {
 const output = net.run(not[i].input)[0];
 const target = not[i].output[0];
 expect(
  output < target + wiggle && output > target - wiggle
for (const i in xor) {
 const output = net.run(xor[i].input)[0];
 const target = xor[i].output[0];
 expect(
  output < target + wiggle && output > target - wiggle
for (const i in or) {
 const output = net.run(or[i].input)[0];
 const target = or[i].output[0];
 expect(
  output < target + wiggle && output > target - wiggle
for (const i in and) {
 const output = net.run(and[i].input)[0];
 const target = and[i].output[0];
 expect(
  output < target + wiggle && output > target - wiggle
for (const i in and) {

_.times(set.length, (index) => {
   let input = set[index].input;
   let target = set[index].output;
   let output = self.activate(input, { trace: false });
   error += cost(target, output);
  });

describe("Multi-Label BR Classifier online-trained on Single-class inputs", function() {
 var classifierOnline = new BinaryRelevanceWinnow();
 for (var i = 0; i <= retrain_count; ++i)
  for (var d = 0; d < dataset.length; ++d)
   classifierOnline.trainOnline(dataset[d].input, dataset[d].output);
 testMultiLabelClassifier(classifierOnline);

 // COMPARE TO BATCH CLASSIFIER:
 // Strangely, there is a slight difference, so this test is disabled:
 it.skip("is identical to batch classifier", function() {
  var classifierBatch = new BinaryRelevanceWinnow();
  classifierBatch.trainBatch(dataset);
  //console.log("batch: "); 	console.dir(classifierBatch.mapClassnameToClassifier);
  //console.log("online: "); 	console.dir(classifierOnline.mapClassnameToClassifier);
  classifierOnline.should.eql(classifierBatch);
 });
});

  warnings: true,
 },
 publicPath: webpackConfig.output.publicPath,
}));

for ( let j = 0; j < uiInstance.listof_item[i].output.length; ++j )
  if ( uiInstance.listof_item[i].output[j].idx > retval )
    retval = uiInstance.listof_item[i].output[j].idx + 1;

for ( let w = 0; w < uiInstance.listof_item[i].output.length; ++w )
  setof_terminal.push ( uiInstance.listof_item[i].output[w] );

describe("Meta-Labeler online-trained on Single-class inputs", function() {
 var classifierOnline = new MetaLabelerWinnow();
 for (var i = 0; i <= retrain_count; ++i)
  for (var d = 0; d < dataset.length; ++d)
   classifierOnline.trainOnline(dataset[d].input, dataset[d].output);

 var classifier = classifierOnline;
 it("classifies 1-class samples", function() {
  classifier.classify({ I: 1, want: 1, aa: 1 }).should.eql(["A"]);
  classifier.classify({ I: 1, want: 1, bb: 1 }).should.eql(["B"]);
  classifier.classify({ I: 1, want: 1, cc: 1 }).should.eql(['{"C":"c"}']);
 });

 it("knows its classes", function() {
  classifier.getAllClasses().should.eql(["A", "B", '{"C":"c"}']);
 });

 it("explains its decisions", function() {
  // classifier.classify({I:1 , want:1 , aa:1 , and:1 , bb:1 }, /*explain=*/1).should.have.property('explanation').with.property('ranking').with.lengthOf(3);
  classifier
   .classify({ I: 1, want: 1, aa: 1, and: 1, bb: 1 }, /*explain=*/ 1)
   .should.have.property("explanation")
   .with.property("ranking");
  // classifier.classify({I:1 , want:1 , aa:1 , and:1 , bb:1 }, /*explain=*/3).should.have.property('explanation').with.property('counting').with.lengthOf(1);
  classifier
   .classify({ I: 1, want: 1, aa: 1, and: 1, bb: 1 }, /*explain=*/ 3)
   .should.have.property("explanation")
   .with.property("counting");
 });
});

for (var j = t; j <= t + n; j++){
  set[j].input[1] = n / 20;
  set[j].output[0] = 0.5;

trainingData[1].output.map((value) =>
       Float32Array.from(value)
      )

trainingData[0].output.map((value) =>
       Float32Array.from(value)
      )

trainingData[0].output.map((value) =>
       Float32Array.from([value])
      )

for ( let j = 0; j < uiInstance.listof_item[i].output.length; ++j )
  if ( uiInstance.listof_item[i].output[j].idx > retval )
    retval = uiInstance.listof_item[i].output[j].idx + 1;

for ( let w = 0; w < uiInstance.listof_item[i].output.length; ++w )
  setof_terminal.push ( uiInstance.listof_item[i].output[w] );