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package scala.lms
package common
import simulacrum._
trait Ints2 extends Base {
@typeclass trait Num[A]{
@op("+") def plus(x:A, y: A): A
}
def __ifThenElse[A:Rep, B](cond: Boolean, thenp: => A, elsep: => B)(implicit pos: SourceContext, mB: Lift[B,A]): A
abstract class AbstractIfThenElse[T] extends Def[T] {
val cond: Exp[scala.Boolean]
val thenp: Block[T]
val elsep: Block[T]
}
case class IfThenElse[T:Typ](cond: Exp[Boolean], thenp: Block[T], elsep: Block[T]) extends AbstractIfThenElse[T]
scala> trait Exp[T] { def p = 1 }
defined trait Exp
scala> trait Expr { type E[T] <: Exp[T] }
warning: there was one feature warning; re-run with -feature for details
defined trait Expr
scala> object G extends Expr { type E[T] = Exp[T]; def e:E[Any] = new Exp[Any]{} }
defined object G
test("4") {
trait Linp4 extends Matrices with BoolAlgebra {
val det: Matrix[X] => X = toplevel("det", { (a: Matrix[X]) =>
requires(a.rows == a.cols)
a.reflectMutableInput
requires(a.rows == a.cols)
val order = a.rows
if (order == 1)
a(0, 0)
else {
package org.deeplearning4j.rl4j;
import org.deeplearning4j.rl4j.gym.space.Box;
import org.deeplearning4j.rl4j.learning.ILearning;
import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearningDiscreteDense;
import org.deeplearning4j.rl4j.mdp.gym.GymEnv;
import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdDense;
import org.deeplearning4j.rl4j.policy.DQNPolicy;
Xcursor.theme: Vanilla-DMZ-AA
Xcursor.size: 22
URxvt*termName: rxvt
URxvt.saveLines: 32767
URxvt*scrollstyle: plain
URxvt*scrollBar_right: true
package org.deeplearning4j.rl4j;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
package org.deeplearning4j.rl4j;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
package org.deeplearning4j.rl4j;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
package org.deeplearning4j.rl4j;
/**
* @author rubenfiszel ([email protected]) on 8/9/16.
*/
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;