[WIP] nd4s - data types (#51)

* Fixed tests

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Added conversions for Long

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Added conversions for Long

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Added data types

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Failing test

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Types in conversions

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Added mixins for integer types

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Conversion of different types to scalar

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Tests added

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Tests for arrays construction

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Construction tests

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Fixed slicing

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Add own Executioner implementation to nd4s

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Filter operation activated

* Collection tests activated

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Types in operations

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Commented unused code

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* Types in operations

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* String implicit conversion added

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* String implicit conversion added

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

nd4s/src/main/scala/org/nd4s/CollectionLikeNDArray.scala

@@ -19,7 +19,7 @@ import org.nd4s.Implicits._
 import org.nd4j.linalg.api.ndarray.INDArray
 import org.nd4j.linalg.api.ops.Op
 import org.nd4j.linalg.factory.Nd4j
-import org.nd4s.ops.{ BitFilterOps, FilterOps, MapOps }
+import org.nd4s.ops.{ BitFilterOps, FilterOps, FunctionalOpExecutioner, MapOps }
 
 import scalaxy.loops._
 import scala.language.postfixOps
@@ -33,7 +33,7 @@ trait CollectionLikeNDArray[A <: INDArray] {
 
   def filter(f: Double => Boolean)(implicit ev: NDArrayEvidence[A, _]): A = notCleanedUp { _ =>
     val shape = underlying.shape()
-    ev.reshape(Nd4j.getExecutioner
+    ev.reshape(FunctionalOpExecutioner.apply
                  .exec(FilterOps(ev.linearView(underlying), f): Op)
                  .asInstanceOf[A],
                shape.map(_.toInt): _*)
@@ -41,7 +41,7 @@ trait CollectionLikeNDArray[A <: INDArray] {
 
   def filterBit(f: Double => Boolean)(implicit ev: NDArrayEvidence[A, _]): A = notCleanedUp { _ =>
     val shape = underlying.shape()
-    ev.reshape(Nd4j.getExecutioner
+    ev.reshape(FunctionalOpExecutioner.apply
                  .exec(BitFilterOps(ev.linearView(underlying), f): Op)
                  .asInstanceOf[A],
                shape.map(_.toInt): _*)
@@ -49,7 +49,7 @@ trait CollectionLikeNDArray[A <: INDArray] {
 
   def map(f: Double => Double)(implicit ev: NDArrayEvidence[A, _]): A = notCleanedUp { _ =>
     val shape = underlying.shape()
-    ev.reshape(Nd4j.getExecutioner
+    ev.reshape(FunctionalOpExecutioner.apply
                  .exec(MapOps(ev.linearView(underlying), f): Op)
                  .asInstanceOf[A],
                shape.map(_.toInt): _*)

nd4s/src/main/scala/org/nd4s/Implicits.scala

@@ -12,12 +12,14 @@
   * under the License.
   *
   * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
+    ******************************************************************************/
 package org.nd4s
 
+import org.nd4j.linalg.api.buffer.DataType
 import org.nd4j.linalg.api.ndarray.INDArray
 import org.nd4j.linalg.factory.Nd4j
 import org.nd4j.linalg.indexing.{ INDArrayIndex, NDArrayIndex }
+
 import scala.collection.breakOut
 object Implicits {
 
@@ -34,7 +36,7 @@ object Implicits {
   implicit def sliceProjection2NDArray(sliced: SliceProjectedNDArray): INDArray = sliced.array
 
   /*
-   Avoid using Numeric[T].toDouble(t:T) for sequence transformation in XXColl2INDArray to minimize memory consumption.
+     Avoid using Numeric[T].toDouble(t:T) for sequence transformation in XXColl2INDArray to minimize memory consumption.
    */
 
   implicit def floatArray2INDArray(s: Array[Float]): FloatArray2INDArray =
@@ -48,7 +50,7 @@ object Implicits {
       Nd4j.create(underlying, shape, ord.value, offset)
 
     def asNDArray(shape: Int*): INDArray =
-      Nd4j.create(underlying, shape.toArray: _*)
+      Nd4j.create(underlying.toArray, shape.toArray: _*)
 
     def toNDArray: INDArray = Nd4j.create(underlying)
   }
@@ -64,7 +66,7 @@ object Implicits {
       Nd4j.create(underlying, shape, offset, ord.value)
 
     def asNDArray(shape: Int*): INDArray =
-      Nd4j.create(underlying, shape.toArray: _*)
+      Nd4j.create(underlying.toArray, shape.toArray: _*)
 
     def toNDArray: INDArray = Nd4j.create(underlying)
   }
@@ -76,54 +78,168 @@ object Implicits {
   implicit def jintColl2INDArray(s: Seq[java.lang.Integer]): IntArray2INDArray =
     new IntArray2INDArray(s.map(x => x: Int)(breakOut))
   class IntArray2INDArray(val underlying: Array[Int]) extends AnyVal {
-    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray =
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = {
-      Nd4j.create(underlying.map(_.toFloat), shape, ord.value, offset)
+      val strides = Nd4j.getStrides(shape, ord.value)
+      Nd4j.create(underlying, shape.map(_.toLong), strides.map(_.toLong), ord.value, DataType.INT)
+    }
 
-    def asNDArray(shape: Int*): INDArray =
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying: _*)
-      Nd4j.create(underlying.map(_.toFloat), shape.toArray: _*)
-
-    def toNDArray: INDArray = Nd4j.create(underlying.map(_.toFloat).toArray)
   }
 
-  implicit class FloatMtrix2INDArray(val underlying: Seq[Seq[Float]]) extends AnyVal {
+  implicit def longColl2INDArray(s: Seq[Long]): LongArray2INDArray =
+    new LongArray2INDArray(s.toArray)
+  implicit def longArray2INDArray(s: Array[Long]): LongArray2INDArray =
+    new LongArray2INDArray(s)
+  implicit def jlongColl2INDArray(s: Seq[java.lang.Long]): LongArray2INDArray =
+    new LongArray2INDArray(s.map(x => x: Long)(breakOut))
+  class LongArray2INDArray(val underlying: Array[Long]) extends AnyVal {
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = {
+      val strides = Nd4j.getStrides(shape, ord.value)
+      Nd4j.create(underlying, shape.map(_.toLong), strides.map(_.toLong), ord.value, DataType.LONG)
+    }
+
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying: _*)
+  }
+
+  implicit def shortColl2INDArray(s: Seq[Short]): ShortArray2INDArray =
+    new ShortArray2INDArray(s.toArray)
+  implicit def shortArray2INDArray(s: Array[Short]): ShortArray2INDArray =
+    new ShortArray2INDArray(s)
+  implicit def jshortColl2INDArray(s: Seq[java.lang.Short]): ShortArray2INDArray =
+    new ShortArray2INDArray(s.map(x => x: Short)(breakOut))
+  class ShortArray2INDArray(val underlying: Array[Short]) extends AnyVal {
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = {
+      val strides = Nd4j.getStrides(shape, ord.value)
+      Nd4j.create(underlying, shape.map(_.toLong), strides.map(_.toLong), ord.value, DataType.SHORT)
+    }
+
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying: _*)
+  }
+
+  implicit def byteColl2INDArray(s: Seq[Byte]): ByteArray2INDArray =
+    new ByteArray2INDArray(s.toArray)
+  implicit def byteArray2INDArray(s: Array[Byte]): ByteArray2INDArray =
+    new ByteArray2INDArray(s)
+  implicit def jbyteColl2INDArray(s: Seq[java.lang.Byte]): ByteArray2INDArray =
+    new ByteArray2INDArray(s.map(x => x: Byte)(breakOut))
+  class ByteArray2INDArray(val underlying: Array[Byte]) extends AnyVal {
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = {
+      val strides = Nd4j.getStrides(shape, ord.value)
+      Nd4j.create(underlying, shape.map(_.toLong), strides.map(_.toLong), ord.value, DataType.BYTE)
+    }
+
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying: _*)
+  }
+
+  implicit def booleanColl2INDArray(s: Seq[Boolean]): BooleanArray2INDArray =
+    new BooleanArray2INDArray(s.toArray)
+  implicit def booleanArray2INDArray(s: Array[Boolean]): BooleanArray2INDArray =
+    new BooleanArray2INDArray(s)
+  implicit def jbooleanColl2INDArray(s: Seq[java.lang.Boolean]): BooleanArray2INDArray =
+    new BooleanArray2INDArray(s.map(x => x: Boolean)(breakOut))
+  class BooleanArray2INDArray(val underlying: Array[Boolean]) extends AnyVal {
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = {
+      val strides = Nd4j.getStrides(shape, ord.value)
+      Nd4j.create(underlying, shape.map(_.toLong), strides.map(_.toLong), ord.value, DataType.BOOL)
+    }
+
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying: _*)
+  }
+
+  implicit def stringArray2INDArray(s: Array[String]): StringArray2INDArray =
+    new StringArray2INDArray(s)
+  implicit def stringArray2CollArray(s: Seq[String]): StringArray2INDArray =
+    new StringArray2INDArray(s.toArray)
+  implicit def jstringColl2INDArray(s: Seq[java.lang.String]): StringArray2INDArray =
+    new StringArray2INDArray(s.map(x => x: String)(breakOut))
+  class StringArray2INDArray(val underlying: Array[String]) extends AnyVal {
+    def mkNDArray(shape: Array[Int], ord: NDOrdering = NDOrdering(Nd4j.order()), offset: Int = 0): INDArray = ???
+
+    def asNDArray(shape: Int*): INDArray = ???
+
+    def toNDArray: INDArray = Nd4j.create(underlying: _*)
+  }
+
+  implicit class FloatMatrix2INDArray(val underlying: Seq[Seq[Float]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
       Nd4j.create(underlying.map(_.toArray).toArray, ord.value)
     def toNDArray: INDArray = Nd4j.create(underlying.map(_.toArray).toArray)
   }
 
-  implicit class FloatArrayMtrix2INDArray(val underlying: Array[Array[Float]]) extends AnyVal {
+  implicit class FloatArrayMatrix2INDArray(val underlying: Array[Array[Float]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
       Nd4j.create(underlying, ord.value)
     def toNDArray: INDArray = Nd4j.create(underlying)
   }
 
-  implicit class DoubleMtrix2INDArray(val underlying: Seq[Seq[Double]]) extends AnyVal {
+  implicit class DoubleMatrix2INDArray(val underlying: Seq[Seq[Double]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
       Nd4j.create(underlying.map(_.toArray).toArray, ord.value)
     def toNDArray: INDArray = Nd4j.create(underlying.map(_.toArray).toArray)
   }
 
-  implicit class DoubleArrayMtrix2INDArray(val underlying: Array[Array[Double]]) extends AnyVal {
+  implicit class DoubleArrayMatrix2INDArray(val underlying: Array[Array[Double]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
       Nd4j.create(underlying, ord.value)
     def toNDArray: INDArray = Nd4j.create(underlying)
   }
 
-  implicit class IntMtrix2INDArray(val underlying: Seq[Seq[Int]]) extends AnyVal {
+  implicit class IntMatrix2INDArray(val underlying: Seq[Seq[Int]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
-      Nd4j.create(underlying.map(_.map(_.toFloat).toArray).toArray, ord.value)
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
     def toNDArray: INDArray =
-      Nd4j.create(underlying.map(_.map(_.toFloat).toArray).toArray)
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
   }
 
-  implicit class IntArrayMtrix2INDArray(val underlying: Array[Array[Int]]) extends AnyVal {
+  implicit class IntArrayMatrix2INDArray(val underlying: Array[Array[Int]]) extends AnyVal {
     def mkNDArray(ord: NDOrdering): INDArray =
-      Nd4j.create(underlying.map(_.map(_.toFloat)), ord.value)
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
-    def toNDArray: INDArray = Nd4j.create(underlying.map(_.map(_.toFloat)))
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying.map(_.toArray).toArray)
   }
 
-  implicit class Num2Scalar[T](val underlying: T)(implicit ev: Numeric[T]) {
+  implicit class LongMatrix2INDArray(val underlying: Seq[Seq[Long]]) extends AnyVal {
+    def mkNDArray(ord: NDOrdering): INDArray =
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
+    def toNDArray: INDArray =
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
+  }
+
+  implicit class LongArrayMatrix2INDArray(val underlying: Array[Array[Long]]) extends AnyVal {
+    def mkNDArray(ord: NDOrdering): INDArray =
+      Nd4j.createFromArray(underlying.map(_.toArray).toArray)
+    def toNDArray: INDArray = Nd4j.createFromArray(underlying.map(_.toArray).toArray)
+  }
+
+  /*implicit class Num2Scalar[T](val underlying: T)(implicit ev: Numeric[T]) {
     def toScalar: INDArray = Nd4j.scalar(ev.toDouble(underlying))
+  }*/
+
+  implicit class Float2Scalar(val underlying: Float) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class Double2Scalar(val underlying: Double) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class Long2Scalar(val underlying: Long) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class Int2Scalar(val underlying: Int) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class Byte2Scalar(val underlying: Byte) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class Boolean2Scalar(val underlying: Boolean) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
+  }
+
+  implicit class String2Scalar(val underlying: String) {
+    def toScalar: INDArray = Nd4j.scalar(underlying)
   }
 
   implicit def intArray2IndexRangeArray(arr: Array[Int]): Array[IndexRange] =

nd4s/src/main/scala/org/nd4s/NDArrayEvidence.scala

@@ -22,6 +22,9 @@ import org.nd4s.Implicits._
 object Evidences {
   implicit val double = DoubleNDArrayEvidence
   implicit val float = FloatNDArrayEvidence
+  implicit val int = IntNDArrayEvidence
+  implicit val long = LongNDArrayEvidence
+  implicit val byte = ByteNDArrayEvidence
 }
 
 object NDArrayEvidence {
@@ -342,3 +345,201 @@ case object FloatNDArrayEvidence extends RealNDArrayEvidence[Float] {
 
   override def lessThan(left: Float, right: Float): Boolean = left < right
 }
+
+trait IntegerNDArrayEvidence[Value] extends NDArrayEvidence[INDArray, Value] {
+  def remainder(a: INDArray, that: INDArray): INDArray = a.remainder(that)
+
+  def add(a: INDArray, that: INDArray): INDArray = a.add(that)
+
+  def sub(a: INDArray, that: INDArray): INDArray = a.sub(that)
+
+  def mul(a: INDArray, that: INDArray): INDArray = a.mul(that)
+
+  def mmul(a: INDArray, that: INDArray): INDArray = a.mmul(that)
+
+  def div(a: INDArray, that: INDArray): INDArray = a.div(that)
+
+  def rdiv(a: INDArray, that: INDArray): INDArray = a.rdiv(that)
+
+  def addi(a: INDArray, that: INDArray): INDArray = a.addi(that)
+
+  def subi(a: INDArray, that: INDArray): INDArray = a.subi(that)
+
+  def muli(a: INDArray, that: INDArray): INDArray = a.muli(that)
+
+  def mmuli(a: INDArray, that: INDArray): INDArray = a.mmuli(that)
+
+  def remainderi(a: INDArray, that: INDArray): INDArray = a.remainder(that)
+
+  def remainderi(a: INDArray, that: Number): INDArray = a.remainderi(that)
+
+  def divi(a: INDArray, that: INDArray): INDArray = a.divi(that)
+
+  def rdivi(a: INDArray, that: INDArray): INDArray = a.rdivi(that)
+
+  def remainder(a: INDArray, that: Number): INDArray = a.remainder(that)
+
+  def add(a: INDArray, that: Number): INDArray = a.add(that)
+
+  def sub(a: INDArray, that: Number): INDArray = a.sub(that)
+
+  def mul(a: INDArray, that: Number): INDArray = a.mul(that)
+
+  def div(a: INDArray, that: Number): INDArray = a.div(that)
+
+  def rdiv(a: INDArray, that: Number): INDArray = a.rdiv(that)
+
+  def addi(a: INDArray, that: Number): INDArray = a.addi(that)
+
+  def subi(a: INDArray, that: Number): INDArray = a.subi(that)
+
+  def muli(a: INDArray, that: Number): INDArray = a.muli(that)
+
+  def divi(a: INDArray, that: Number): INDArray = a.divi(that)
+
+  def rdivi(a: INDArray, that: Number): INDArray = a.rdivi(that)
+
+  def put(a: INDArray, i: Int, element: INDArray): INDArray = a.put(i, element)
+
+  def put(a: INDArray, i: Array[Int], element: INDArray): INDArray = a.put(i, element)
+
+  def get(a: INDArray, i: INDArrayIndex*): INDArray = a.get(i: _*)
+
+  def reshape(a: INDArray, i: Int*): INDArray = a.reshape(i.map(_.toLong): _*)
+
+  def linearView(a: INDArray): INDArray = a.reshape(-1)
+
+  def dup(a: INDArray): INDArray = a.dup()
+
+  def update(a: INDArray, indices: Array[IndexRange], i: Int): INDArray =
+    a.update(indices, i)
+
+  def update(a: INDArray, indices: Array[IndexRange], i: INDArray): INDArray =
+    a.update(indices, i)
+}
+
+case object IntNDArrayEvidence extends IntegerNDArrayEvidence[Int] {
+
+  def sum(ndarray: INDArray): Int = ndarray.sumNumber().intValue()
+
+  def mean(ndarray: INDArray): Int = ndarray.meanNumber().intValue()
+
+  def normMax(ndarray: INDArray): Int = ndarray.normmaxNumber().intValue()
+
+  def norm1(ndarray: INDArray): Int = ndarray.norm1Number().intValue()
+
+  def norm2(ndarray: INDArray): Int = ndarray.norm2Number().intValue()
+
+  def max(ndarray: INDArray): Int = ndarray.maxNumber().intValue()
+
+  def min(ndarray: INDArray): Int = ndarray.minNumber().intValue()
+
+  def standardDeviation(ndarray: INDArray): Int = ndarray.stdNumber().intValue()
+
+  def product(ndarray: INDArray): Int = ndarray.prodNumber().intValue()
+
+  def variance(ndarray: INDArray): Int = ndarray.varNumber().intValue()
+
+  def get(a: INDArray, i: Int): Int = a.getInt(i)
+
+  def get(a: INDArray, i: Int, j: Int): Int = a.getInt(i, j)
+
+  def get(a: INDArray, i: Int*): Int = a.getInt(i: _*)
+
+  def create(arr: Array[Int]): INDArray = arr.toNDArray
+
+  def create(arr: Array[Int], shape: Int*): INDArray = arr.toNDArray
+
+  def create(arr: Array[Int], shape: Array[Int], ordering: NDOrdering, offset: Int): INDArray =
+    arr.mkNDArray(shape, ordering, offset)
+
+  def greaterThan(left: Int, right: Int): Boolean = left > right
+
+  def lessThan(left: Int, right: Int): Boolean = left < right
+}
+
+case object LongNDArrayEvidence extends IntegerNDArrayEvidence[Long] {
+
+  def sum(ndarray: INDArray): Long = ndarray.sumNumber().longValue()
+
+  def mean(ndarray: INDArray): Long = ndarray.meanNumber().longValue()
+
+  def normMax(ndarray: INDArray): Long = ndarray.normmaxNumber().longValue()
+
+  def norm1(ndarray: INDArray): Long = ndarray.norm1Number().longValue()
+
+  def norm2(ndarray: INDArray): Long = ndarray.norm2Number().longValue()
+
+  def max(ndarray: INDArray): Long = ndarray.maxNumber().longValue()
+
+  def min(ndarray: INDArray): Long = ndarray.minNumber().longValue()
+
+  def standardDeviation(ndarray: INDArray): Long = ndarray.stdNumber().longValue()
+
+  def product(ndarray: INDArray): Long = ndarray.prodNumber().longValue()
+
+  def variance(ndarray: INDArray): Long = ndarray.varNumber().longValue()
+
+  def get(a: INDArray, i: Int): Long = a.getLong(i)
+
+  def get(a: INDArray, i: Int, j: Int): Long = a.getLong(i, j)
+
+  def get(a: INDArray, i: Int*): Long = a.getLong(i.map(_.toLong): _*)
+
+  def create(arr: Array[Long]): INDArray = arr.toNDArray
+
+  def create(arr: Array[Long], shape: Int*): INDArray = arr.toNDArray
+
+  def create(arr: Array[Long], shape: Array[Int], ordering: NDOrdering, offset: Int): INDArray =
+    arr.mkNDArray(shape, ordering, offset)
+
+  def greaterThan(left: Long, right: Long): Boolean = left > right
+
+  def lessThan(left: Long, right: Long): Boolean = left < right
+
+  def update(a: INDArray, indices: Array[IndexRange], i: Long): INDArray =
+    a.update(indices, i)
+}
+
+case object ByteNDArrayEvidence extends IntegerNDArrayEvidence[Byte] {
+
+  def sum(ndarray: INDArray): Byte = ndarray.sumNumber().byteValue()
+
+  def mean(ndarray: INDArray): Byte = ndarray.meanNumber().byteValue()
+
+  def normMax(ndarray: INDArray): Byte = ndarray.normmaxNumber().byteValue()
+
+  def norm1(ndarray: INDArray): Byte = ndarray.norm1Number().byteValue()
+
+  def norm2(ndarray: INDArray): Byte = ndarray.norm2Number().byteValue()
+
+  def max(ndarray: INDArray): Byte = ndarray.maxNumber().byteValue()
+
+  def min(ndarray: INDArray): Byte = ndarray.minNumber().byteValue()
+
+  def standardDeviation(ndarray: INDArray): Byte = ndarray.stdNumber().byteValue()
+
+  def product(ndarray: INDArray): Byte = ndarray.prodNumber().byteValue()
+
+  def variance(ndarray: INDArray): Byte = ndarray.varNumber().byteValue()
+
+  def get(a: INDArray, i: Int): Byte = a.getInt(i).toByte
+
+  def get(a: INDArray, i: Int, j: Int): Byte = a.getInt(i, j).toByte
+
+  def get(a: INDArray, i: Int*): Byte = a.getInt(i.map(_.toInt): _*).toByte
+
+  def create(arr: Array[Byte]): INDArray = arr.toNDArray
+
+  def create(arr: Array[Byte], shape: Int*): INDArray = arr.toNDArray
+
+  def create(arr: Array[Byte], shape: Array[Int], ordering: NDOrdering, offset: Int): INDArray =
+    arr.mkNDArray(shape, ordering, offset)
+
+  def greaterThan(left: Byte, right: Byte): Boolean = left > right
+
+  def lessThan(left: Byte, right: Byte): Boolean = left < right
+
+  def update(a: INDArray, indices: Array[IndexRange], i: Byte): INDArray =
+    a.update(indices, i)
+}

nd4s/src/main/scala/org/nd4s/SliceableNDArray.scala

@@ -48,6 +48,7 @@ trait SliceableNDArray[A <: INDArray] {
       ev.create(filtered, targetShape, NDOrdering(underlying.ordering()), 0)
 
     } else {
+
       ev.get(underlying, getINDArrayIndexfrom(target: _*): _*)
     }
   }
@@ -121,10 +122,10 @@ trait SliceableNDArray[A <: INDArray] {
         underlying.shape().map(_.toInt).toList
 
     val originalTarget =
-      if (underlying.isRowVector && target.size == 1)
+      /*if (underlying.isRowVector && target.size == 1)
         IntRange(0) +: target
-      else
+      else*/
-        target
+      target
 
     @tailrec
     def modifyTargetIndices(input: List[IndexRange], i: Int, acc: List[INDArrayIndex]): List[INDArrayIndex] =

nd4s/src/main/scala/org/nd4s/ops/BitFilterOps.scala

@@ -55,4 +55,11 @@ class BitFilterOps(_x: INDArray, len: Int, f: Double => Boolean)
 
   override def op(origin: Float): Float = if (f(origin)) 1 else 0
 
+  override def op(origin: Short): Short = if (f(origin)) 1 else 0
+
+  override def op(origin: Int): Int = if (f(origin)) 1 else 0
+
+  override def op(origin: Long): Long = if (f(origin)) 1 else 0
+
+  override def op(origin: String): String = ???
 }

nd4s/src/main/scala/org/nd4s/ops/FilterOps.scala

@@ -54,4 +54,12 @@ class FilterOps(_x: INDArray, len: Int, f: Double => Boolean)
 
   override def op(origin: Float): Float = if (f(origin)) origin else 0
 
+  override def op(origin: Short): Short = if (f(origin)) origin else 0
+
+  override def op(origin: Int): Int = if (f(origin)) origin else 0
+
+  override def op(origin: Long): Long = if (f(origin)) origin else 0
+
+  override def op(origin: String): String = ???
+
 }

nd4s/src/main/scala/org/nd4s/ops/FunctionalOpExecutioner.scala

@@ -0,0 +1,474 @@
+/*******************************************************************************
+  * Copyright (c) 2015-2018 Skymind, Inc.
+  *
+  * This program and the accompanying materials are made available under the
+  * terms of the Apache License, Version 2.0 which is available at
+  * https://www.apache.org/licenses/LICENSE-2.0.
+  *
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+  * License for the specific language governing permissions and limitations
+  * under the License.
+  *
+  * SPDX-License-Identifier: Apache-2.0
+  ******************************************************************************/
+package org.nd4s.ops
+
+import java.util.{ List, Map, Properties }
+
+import org.bytedeco.javacpp.Pointer
+import org.nd4j.linalg.api.buffer.{ DataBuffer, DataType, Utf8Buffer }
+import org.nd4j.linalg.api.ndarray.{ INDArray, INDArrayStatistics }
+import org.nd4j.linalg.api.ops.aggregates.{ Aggregate, Batch }
+import org.nd4j.linalg.api.ops._
+import org.nd4j.linalg.api.ops.executioner.OpExecutioner
+import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate
+import org.nd4j.linalg.api.ops.impl.summarystats.Variance
+import org.nd4j.linalg.api.rng.Random
+import org.nd4j.linalg.api.shape.{ LongShapeDescriptor, TadPack }
+import org.nd4j.linalg.cache.TADManager
+import org.nd4j.linalg.factory.Nd4j
+import org.nd4j.linalg.profiler.ProfilerConfig
+
+object FunctionalOpExecutioner {
+  def apply: FunctionalOpExecutioner = new FunctionalOpExecutioner()
+}
+class FunctionalOpExecutioner extends OpExecutioner {
+  def isVerbose: Boolean = ???
+
+  /**
+    * This method returns true if debug mode is enabled, false otherwise
+    *
+    * @return
+    */
+  def isDebug: Boolean = ???
+
+  /**
+    * This method returns type for this executioner instance
+    *
+    * @return
+    */
+  def `type`: OpExecutioner.ExecutionerType = ???
+
+  /**
+    * This method returns opName of the last invoked op
+    *
+    * @return
+    */
+  def getLastOp: String = ???
+
+  /**
+    * Execute the operation
+    *
+    * @param op the operation to execute
+    */
+  def exec(op: Op): INDArray =
+    op match {
+      case op: FilterOps    => exec(op.asInstanceOf[FilterOps])
+      case op: BitFilterOps => exec(op.asInstanceOf[BitFilterOps])
+      case op: MapOps       => exec(op.asInstanceOf[MapOps])
+      case _                => op.z()
+    }
+
+  def exec(op: FilterOps): INDArray = {
+    val retVal: INDArray = Nd4j.create(op.x.dataType(), op.x.shape().map(_.toLong): _*)
+    for (i <- 0 until op.x().length().toInt) {
+      val filtered = op.x.dataType() match {
+        case DataType.DOUBLE => op.op(op.x.getDouble(i.toLong))
+        case DataType.FLOAT  => op.op(op.x.getFloat(i.toLong))
+        case DataType.INT    => op.op(op.x.getInt(i))
+        case DataType.SHORT  => op.op(op.x.getInt(i))
+        case (DataType.LONG) => op.op(op.x.getLong(i.toLong))
+      }
+      retVal.putScalar(i, filtered)
+    }
+    retVal
+  }
+
+  def exec(op: BitFilterOps): INDArray = {
+    val retVal: INDArray = Nd4j.create(op.x.dataType(), op.x.shape().map(_.toLong): _*)
+    for (i <- 0 until op.x().length().toInt) {
+      val current = if (op.x.dataType() == DataType.DOUBLE) op.x().getDouble(i.toLong) else op.x().getInt(i)
+      val filtered = op.op(current)
+
+      retVal.putScalar(i, filtered)
+    }
+    retVal
+  }
+
+  def exec(op: MapOps): INDArray = {
+    val retVal: INDArray = Nd4j.create(op.x.dataType(), op.x.shape().map(_.toLong): _*)
+    for (i <- 0 until op.x().length().toInt) {
+      val current = if (op.x.dataType() == DataType.DOUBLE) op.x().getDouble(i.toLong) else op.x().getInt(i)
+      val filtered = op.op(current)
+
+      retVal.putScalar(i, filtered)
+    }
+    retVal
+  }
+
+  /** Execute a TransformOp and return the result
+    *
+    * @param op the operation to execute
+    */
+  def execAndReturn(op: TransformOp): TransformOp = ???
+
+  /**
+    * Execute and return the result from an accumulation
+    *
+    * @param op the operation to execute
+    * @return the accumulated result
+    */
+  def execAndReturn(op: ReduceOp): ReduceOp = ???
+
+  def execAndReturn(op: Variance): Variance = ???
+
+  /** Execute and return the result from an index accumulation
+    *
+    * @param op the index accumulation operation to execute
+    * @return the accumulated index
+    */
+  def execAndReturn(op: IndexAccumulation): IndexAccumulation = ???
+
+  /** Execute and return the result from a scalar op
+    *
+    * @param op the operation to execute
+    * @return the accumulated result
+    */
+  def execAndReturn(op: ScalarOp): ScalarOp = ???
+
+  /** Execute and return the result from a vector op
+    *
+    * @param op */
+  def execAndReturn(op: BroadcastOp): BroadcastOp = ???
+
+  /**
+    * Execute a reduceOp, possibly along one or more dimensions
+    *
+    * @param reduceOp the reduceOp
+    * @return the reduceOp op
+    */
+  def exec(reduceOp: ReduceOp): INDArray = ???
+
+  /**
+    * Execute a broadcast op, possibly along one or more dimensions
+    *
+    * @param broadcast the accumulation
+    * @return the broadcast op
+    */
+  def exec(broadcast: BroadcastOp): INDArray = ???
+
+  /**
+    * Execute ScalarOp
+    *
+    * @param broadcast
+    * @return
+    */
+  def exec(broadcast: ScalarOp): INDArray = ???
+
+  /**
+    * Execute an variance accumulation op, possibly along one or more dimensions
+    *
+    * @param accumulation the accumulation
+    * @return the accmulation op
+    */
+  def exec(accumulation: Variance): INDArray = ???
+
+  /** Execute an index accumulation along one or more dimensions
+    *
+    * @param indexAccum the index accumulation operation
+    * @return result
+    */
+  def exec(indexAccum: IndexAccumulation): INDArray = ???
+
+  /**
+    *
+    * Execute and return  a result
+    * ndarray from the given op
+    *
+    * @param op the operation to execute
+    * @return the result from the operation
+    */
+  def execAndReturn(op: Op): Op = ???
+
+  /**
+    * Execute MetaOp
+    *
+    * @param op
+    */
+  def exec(op: MetaOp): Unit = ???
+
+  /**
+    * Execute GridOp
+    *
+    * @param op
+    */
+  def exec(op: GridOp): Unit = ???
+
+  /**
+    *
+    * @param op
+    */
+  def exec(op: Aggregate): Unit = ???
+
+  /**
+    * This method executes previously built batch
+    *
+    * @param batch
+    */
+  def exec[T <: Aggregate](batch: Batch[T]): Unit = ???
+
+  /**
+    * This method takes arbitrary sized list of aggregates,
+    * and packs them into batches
+    *
+    * @param batch
+    */
+  def exec(batch: java.util.List[Aggregate]): Unit = ???
+
+  /**
+    * This method executes specified RandomOp using default RNG available via Nd4j.getRandom()
+    *
+    * @param op
+    */
+  def exec(op: RandomOp): INDArray = ???
+
+  /**
+    * This method executes specific RandomOp against specified RNG
+    *
+    * @param op
+    * @param rng
+    */
+  def exec(op: RandomOp, rng: Random): INDArray = ???
+
+  /**
+    * This method return set of key/value and
+    * key/key/value objects,
+    * describing current environment
+    *
+    * @return
+    */
+  def getEnvironmentInformation: Properties = ???
+
+  /**
+    * This method specifies desired profiling mode
+    *
+    * @param mode
+    */
+  @deprecated def setProfilingMode(mode: OpExecutioner.ProfilingMode): Unit = ???
+
+  /**
+    * This method stores specified configuration.
+    *
+    * @param config
+    */
+  def setProfilingConfig(config: ProfilerConfig): Unit = ???
+
+  /**
+    * Ths method returns current profiling
+    *
+    * @return
+    */
+  @deprecated def getProfilingMode: OpExecutioner.ProfilingMode = ???
+
+  /**
+    * This method returns TADManager instance used for this OpExecutioner
+    *
+    * @return
+    */
+  def getTADManager: TADManager = ???
+
+  /**
+    * This method prints out environmental information returned by getEnvironmentInformation() method
+    */
+  def printEnvironmentInformation(): Unit = ???
+
+  /**
+    * This method ensures all operations that supposed to be executed at this moment, are executed.
+    */
+  def push(): Unit = ???
+
+  /**
+    * This method ensures all operations that supposed to be executed at this moment, are executed and finished.
+    */
+  def commit(): Unit = ???
+
+  /**
+    * This method encodes array as thresholds, updating input array at the same time
+    *
+    * @param input
+    * @return encoded array is returned
+    */
+  def thresholdEncode(input: INDArray, threshold: Double): INDArray = ???
+
+  def thresholdEncode(input: INDArray, threshold: Double, boundary: Integer): INDArray = ???
+
+  /**
+    * This method decodes thresholds array, and puts it into target array
+    *
+    * @param encoded
+    * @param target
+    * @return target is returned
+    */
+  def thresholdDecode(encoded: INDArray, target: INDArray): INDArray = ???
+
+  /**
+    * This method returns number of elements affected by encoder
+    *
+    * @param indArray
+    * @param target
+    * @param threshold
+    * @return
+    */
+  def bitmapEncode(indArray: INDArray, target: INDArray, threshold: Double): Long = ???
+
+  /**
+    *
+    * @param indArray
+    * @param threshold
+    * @return
+    */
+  def bitmapEncode(indArray: INDArray, threshold: Double): INDArray = ???
+
+  /**
+    *
+    * @param encoded
+    * @param target
+    * @return
+    */
+  def bitmapDecode(encoded: INDArray, target: INDArray): INDArray = ???
+
+  /**
+    * This method returns names of all custom operations available in current backend, and their number of input/output arguments
+    *
+    * @return
+    */
+  def getCustomOperations: java.util.Map[String, CustomOpDescriptor] = ???
+
+  /**
+    * This method executes given CustomOp
+    *
+    * PLEASE NOTE: You're responsible for input/output validation
+    *
+    * @param op
+    */
+  def execAndReturn(op: CustomOp): CustomOp = ???
+
+  def exec(op: CustomOp): Array[INDArray] = ???
+
+  /**
+    * This method executes op with given context
+    *
+    * @param op
+    * @param context
+    * @return method returns output arrays defined within context
+    */
+  def exec(op: CustomOp, context: OpContext): Array[INDArray] = ???
+
+  def calculateOutputShape(op: CustomOp): java.util.List[LongShapeDescriptor] = ???
+
+  /**
+    * Equivalent to calli
+    */
+  def allocateOutputArrays(op: CustomOp): Array[INDArray] = ???
+
+  def enableDebugMode(reallyEnable: Boolean): Unit = ???
+
+  def enableVerboseMode(reallyEnable: Boolean): Unit = ???
+
+  def isExperimentalMode: Boolean = ???
+
+  def registerGraph(id: Long, graph: Pointer): Unit = ???
+
+  def executeGraph(id: Long,
+                   map: java.util.Map[String, INDArray],
+                   reverseMap: java.util.Map[String, Integer]): java.util.Map[String, INDArray] = ???
+
+  def forgetGraph(id: Long): Unit = ???
+
+  /**
+    * This method allows to set desired number of elements per thread, for performance optimization purposes.
+    * I.e. if array contains 2048 elements, and threshold is set to 1024, 2 threads will be used for given op execution.
+    *
+    * Default value: 1024
+    *
+    * @param threshold
+    */
+  def setElementsThreshold(threshold: Int): Unit = ???
+
+  /**
+    * This method allows to set desired number of sub-arrays per thread, for performance optimization purposes.
+    * I.e. if matrix has shape of 64 x 128, and threshold is set to 8, each thread will be processing 8 sub-arrays (sure, if you have 8 core cpu).
+    * If your cpu has, say, 4, cores, only 4 threads will be spawned, and each will process 16 sub-arrays
+    *
+    * Default value: 8
+    *
+    * @param threshold
+    */
+  def setTadThreshold(threshold: Int): Unit = ???
+
+  /**
+    * This method extracts String from Utf8Buffer
+    *
+    * @param buffer
+    * @param index
+    * @return
+    */
+  def getString(buffer: Utf8Buffer, index: Long): String = ???
+
+  /**
+    * This method returns OpContext which can be used (and reused) to execute custom ops
+    *
+    * @return
+    */
+  def buildContext: OpContext = ???
+
+  /**
+    *
+    * @param array
+    */
+  def inspectArray(array: INDArray): INDArrayStatistics = ???
+
+  /**
+    * This method returns shapeInfo DataBuffer
+    *
+    * @param shape
+    * @param stride
+    * @param elementWiseStride
+    * @param order
+    * @param dtype
+    * @return
+    */
+  def createShapeInfo(shape: Array[Long],
+                      stride: Array[Long],
+                      elementWiseStride: Long,
+                      order: Char,
+                      dtype: DataType,
+                      empty: Boolean): DataBuffer = ???
+
+  /**
+    * This method returns host/device tad buffers
+    */
+  def tadShapeInfoAndOffsets(array: INDArray, dimension: Array[Int]): TadPack = ???
+
+  /**
+    * This method returns constant buffer for the given jvm array
+    *
+    * @param values
+    * @return
+    */
+  def createConstantBuffer(values: Array[Long], desiredType: DataType): DataBuffer = ???
+
+  def createConstantBuffer(values: Array[Int], desiredType: DataType): DataBuffer = ???
+
+  def createConstantBuffer(values: Array[Float], desiredType: DataType): DataBuffer = ???
+
+  def createConstantBuffer(values: Array[Double], desiredType: DataType): DataBuffer = ???
+
+  @deprecated def scatterUpdate(op: ScatterUpdate.UpdateOp,
+                                array: INDArray,
+                                indices: INDArray,
+                                updates: INDArray,
+                                axis: Array[Int]): Unit = ???
+}

nd4s/src/main/scala/org/nd4s/ops/LeftAssociativeBinaryOp.scala

@@ -34,5 +34,13 @@ trait LeftAssociativeBinaryOp {
 
   def op(origin: Float): Float
 
+  def op(origin: Short): Short
+
+  def op(origin: Int): Int
+
+  def op(origin: Long): Long
+
+  def op(origin: String): String
+
 //  def op(origin: IComplexNumber): IComplexNumber
 }

nd4s/src/main/scala/org/nd4s/ops/MapOps.scala

@@ -49,4 +49,11 @@ class MapOps(_x: INDArray, f: Double => Double) extends BaseScalarOp(_x, null, _
 
   override def op(origin: Float): Float = f(origin).toFloat
 
+  override def op(origin: Short): Short = f(origin).toShort
+
+  override def op(origin: Int): Int = f(origin).toInt
+
+  override def op(origin: Long): Long = f(origin).toLong
+
+  override def op(origin: String): String = ???
 }

nd4s/src/test/scala/org/nd4s/DSLSpec.scala

@@ -29,7 +29,7 @@ class DSLSpec extends FlatSpec with Matchers {
 
     // This test just verifies that an INDArray gets wrapped with an implicit conversion
 
-    val nd = Nd4j.create(Array[Float](1, 2), Array(2, 1))
+    val nd = Nd4j.create(Array[Float](1, 2), Array(2, 1): _*)
     val nd1 = nd + 10L // + creates new array, += modifies in place
 
     nd.get(0) should equal(1)

nd4s/src/test/scala/org/nd4s/NDArrayCollectionAPITest.scala

@@ -19,7 +19,7 @@ import org.nd4s.Implicits._
 import org.scalatest.{ FlatSpec, Matchers }
 
 class NDArrayCollectionAPITest extends FlatSpec with Matchers {
-  "CollectionLikeNDArray" should "provides filter API" ignore {
+  "CollectionLikeNDArray" should "provides filter API" in {
     val ndArray =
       Array(
         Array(1, 2, 3),
@@ -38,7 +38,48 @@ class NDArrayCollectionAPITest extends FlatSpec with Matchers {
         ).toNDArray
     )
   }
-  it should "provides filter bitmask API" ignore {
+
+  "CollectionLikeNDArray from Floats" should "provides filter API" in {
+    val ndArray =
+      Array(
+        Array(1f, 2f, 3f),
+        Array(4f, 5f, 6f),
+        Array(7f, 8f, 9f)
+      ).toNDArray
+
+    val filtered = ndArray.filter(_ > 3)
+
+    assert(
+      filtered ==
+        Array(
+          Array(0f, 0f, 0f),
+          Array(4f, 5f, 6f),
+          Array(7f, 8f, 9f)
+        ).toNDArray
+    )
+  }
+
+  "CollectionLikeNDArray from Long " should "provides filter API" in {
+    val ndArray =
+      Array(
+        Array(1L, 2L, 3L),
+        Array(4L, 5L, 6L),
+        Array(7L, 8L, 9L)
+      ).toNDArray
+
+    val filtered = ndArray.filter(_ > 3)
+
+    assert(
+      filtered ==
+        Array(
+          Array(0L, 0L, 0L),
+          Array(4L, 5L, 6L),
+          Array(7L, 8L, 9L)
+        ).toNDArray
+    )
+  }
+
+  it should "provides filter bitmask API" in {
     val ndArray =
       Array(
         Array(1, 2, 3),
@@ -57,7 +98,7 @@ class NDArrayCollectionAPITest extends FlatSpec with Matchers {
         ).toNDArray
     )
   }
-  it should "provides map API" ignore {
+  it should "provides map API" in {
     val ndArray =
       Array(
         Array(1, 2, 3),

nd4s/src/test/scala/org/nd4s/NDArrayConstructionTest.scala

@@ -0,0 +1,121 @@
+/*******************************************************************************
+  * Copyright (c) 2015-2019 Skymind, Inc.
+  *
+  * This program and the accompanying materials are made available under the
+  * terms of the Apache License, Version 2.0 which is available at
+  * https://www.apache.org/licenses/LICENSE-2.0.
+  *
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+  * License for the specific language governing permissions and limitations
+  * under the License.
+  *
+  * SPDX-License-Identifier: Apache-2.0
+  ******************************************************************************/
+package org.nd4s
+
+import org.nd4j.linalg.api.buffer.DataType
+import org.nd4s.Implicits._
+import org.scalatest.FlatSpec
+
+class NDArrayConstructionTest extends FlatSpec with COrderingForTest {
+  self: OrderingForTest =>
+
+  it should "be able to create 2d matrix filled with integers" in {
+    val ndArray =
+      Array(
+        Array(1, 2),
+        Array(4, 5),
+        Array(7, 9)
+      ).mkNDArray(ordering)
+
+    assert(DataType.INT == ndArray.dataType())
+    assert(3 == ndArray.rows())
+    assert(2 == ndArray.columns())
+  }
+
+  it should "be able to create 2d matrix filled with long integers" in {
+    val ndArray =
+      Array(
+        Array(1L, 2L, 3L),
+        Array(4L, 5L, 6L),
+        Array(7L, 8L, 9L)
+      ).mkNDArray(ordering)
+
+    assert(DataType.LONG == ndArray.dataType())
+    assert(3 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create 2d matrix filled with float numbers" in {
+    val ndArray =
+      Array(
+        Array(1f, 2f, 3f),
+        Array(4f, 5f, 6f),
+        Array(7f, 8f, 9f)
+      ).mkNDArray(ordering)
+
+    assert(DataType.FLOAT == ndArray.dataType())
+    assert(3 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create 2d matrix filled with double numbers" in {
+    val ndArray =
+      Array(
+        Array(1d, 2d, 3d),
+        Array(4d, 5d, 6d),
+        Array(7d, 8d, 9d)
+      ).mkNDArray(ordering)
+
+    assert(DataType.DOUBLE == ndArray.dataType())
+    assert(3 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create vector filled with short integers" in {
+    val ndArray = Array[Short](1, 2, 3).toNDArray
+
+    assert(DataType.SHORT == ndArray.dataType())
+    assert(1 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create vector filled with byte values" in {
+    val ndArray = Array[Byte](1, 2, 3).toNDArray
+
+    assert(DataType.BYTE == ndArray.dataType())
+    assert(1 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create vector filled with boolean values" in {
+    val ndArray = Array(true, false, true).toNDArray
+
+    assert(DataType.BOOL == ndArray.dataType())
+    assert(1 == ndArray.rows())
+    assert(3 == ndArray.columns())
+  }
+
+  it should "be able to create vector from integer range" in {
+    val list = (0 to 9).toNDArray
+    assert(DataType.INT == list.dataType())
+
+    val stepped = list(1 -> 7 by 2)
+    assert(Array(1, 3, 5).toNDArray == stepped)
+    assert(DataType.INT == list.dataType())
+  }
+
+  it should "be able to create vector from strings" in {
+    val oneString = "testme".toScalar
+    assert("testme" == oneString.getString(0))
+    assert(DataType.UTF8 == oneString.dataType())
+
+    val someStrings = Array[String]("one", "two", "three").toNDArray
+    assert("one" == someStrings.getString(0))
+    assert("two" == someStrings.getString(1))
+    assert("three" == someStrings.getString(2))
+    assert(DataType.UTF8 == someStrings.dataType())
+  }
+}

nd4s/src/test/scala/org/nd4s/NDArrayExtractionTest.scala

@@ -48,20 +48,48 @@ trait NDArrayExtractionTestBase extends FlatSpec { self: OrderingForTest =>
     assert(extracted == expected)
   }
 
-  it should "be able to extract a part of 2d matrix with offset" ignore { //Ignored AB 2019/05/21 - https://github.com/deeplearning4j/deeplearning4j/issues/7657
+  it should "be able to extract a part of 2d matrix with double data and offset" in {
-    val ndArray = (1 to 9).mkNDArray(Array(2, 2), NDOrdering.C, offset = 4)
+    val ndArray = (1 to 9).map(_.toDouble).mkNDArray(Array(2, 2), NDOrdering.C, offset = 4)
 
     val expectedArray = Array(
-      Array(5, 6),
+      Array(5d, 6d),
-      Array(7, 8)
+      Array(7d, 8d)
     ).mkNDArray(ordering)
     assert(ndArray == expectedArray)
 
     val expectedSlice = Array(
-      Array(5),
+      Array(5d),
-      Array(7)
+      Array(7d)
     ).toNDArray
-    assert(ndArray(->, 0) == expectedSlice)
+    assert(expectedArray(->, 0 -> 1) == expectedSlice)
+  }
+
+  it should "be able to extract a part of 2d matrix with integer data" in {
+    val ndArray = (1 to 9).mkNDArray(Array(2, 2))
+
+    val expectedArray = Array(
+      Array(1, 2),
+      Array(3, 4)
+    ).mkNDArray(ordering)
+    assert(ndArray == expectedArray)
+
+    val expectedSlice = Array(
+      Array(1),
+      Array(3)
+    ).toNDArray
+    val actualSlice = expectedArray(->, 0 -> 1)
+    assert(actualSlice == expectedSlice)
+  }
+
+  it should " provide overloaded -> operator providing matrix slices as nd4j" in {
+
+    val expectedArray = (1 to 9).mkNDArray(Array(2, 2))
+    val expectedSlice = expectedArray.slice(0)
+    val actualSlice = expectedArray(0, ->)
+
+    Console.println(expectedSlice)
+
+    assert(actualSlice == expectedSlice)
   }
 
   it should "be able to extract a part of vertically long matrix in" in {
@@ -163,7 +191,7 @@ trait NDArrayExtractionTestBase extends FlatSpec { self: OrderingForTest =>
 
   // TODO: fix me. This is about INDArray having to be sliced by LONG indices
   // can't find the correct way to fix implicits without breaking other stuff.
-  it should "be able to extract sub-matrix with index range by step" ignore {
+  it should "be able to extract sub-matrix with index range by step" in {
     val ndArray =
       Array(
         Array(1, 2, 3),
@@ -249,9 +277,10 @@ trait NDArrayExtractionTestBase extends FlatSpec { self: OrderingForTest =>
     )
   }
 
-  "num2Scalar" should "convert number to Scalar INDArray" ignore { //Ignored AB 2019/05/21 - https://github.com/deeplearning4j/deeplearning4j/issues/7657
+  "num2Scalar" should "convert number to Scalar INDArray" in {
-    assert(1.toScalar == List(1).toNDArray)
+
-    assert(2f.toScalar == List(2).toNDArray)
+    assert(1.toScalar.data() == List(1).toNDArray.data())
-    assert(3d.toScalar == List(3).toNDArray)
+    assert(2f.toScalar.data() == List(2).toNDArray.data())
+    assert(3d.toScalar.data() == List(3).toNDArray.data())
   }
 }

nd4s/src/test/scala/org/nd4s/OperatableNDArrayTest.scala

@@ -26,27 +26,27 @@ import org.scalatest.{ FlatSpec, Matchers }
 class OperatableNDArrayTest extends FlatSpec with Matchers {
   "RichNDArray" should "use the apply method to access values" in {
     // -- 2D array
-    val nd2 = Nd4j.create(Array[Double](1, 2, 3, 4), Array(4, 1))
+    val nd2 = Nd4j.create(Array[Double](1, 2, 3, 4), Array[Int](1, 4): _*)
 
     nd2.get(0) should be(1)
-    nd2.get(3, 0) should be(4)
+    nd2.get(0, 3) should be(4)
 
     // -- 3D array
-    val nd3 = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2))
+    val nd3 = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array[Int](2, 2, 2): _*)
     nd3.get(0, 0, 0) should be(1)
     nd3.get(1, 1, 1) should be(8)
 
   }
 
   it should "use transpose abbreviation" in {
-    val nd1 = Nd4j.create(Array[Double](1, 2, 3), Array(3, 1))
+    val nd1 = Nd4j.create(Array[Double](1, 2, 3), Array(3, 1): _*)
     nd1.shape should equal(Array(3, 1))
     val nd1t = nd1.T
     nd1t.shape should equal(Array(1, 3))
   }
 
   it should "add correctly" in {
-    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2))
+    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2): _*)
     val b = a + 100
     a.get(0, 0, 0) should be(1)
     b.get(0, 0, 0) should be(101)
@@ -55,7 +55,7 @@ class OperatableNDArrayTest extends FlatSpec with Matchers {
   }
 
   it should "subtract correctly" in {
-    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2))
+    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2): _*)
     val b = a - 100
     a.get(0, 0, 0) should be(1)
     b.get(0, 0, 0) should be(-99)
@@ -69,7 +69,7 @@ class OperatableNDArrayTest extends FlatSpec with Matchers {
   }
 
   it should "divide correctly" in {
-    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2))
+    val a = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2): _*)
     val b = a / a
     a.get(1, 1, 1) should be(8)
     b.get(1, 1, 1) should be(1)
@@ -78,7 +78,7 @@ class OperatableNDArrayTest extends FlatSpec with Matchers {
   }
 
   it should "element-by-element multiply correctly" in {
-    val a = Nd4j.create(Array[Double](1, 2, 3, 4), Array(4, 1))
+    val a = Nd4j.create(Array[Double](1, 2, 3, 4), Array(4, 1): _*)
     val b = a * a
     a.get(3) should be(4) // [1.0, 2.0, 3.0, 4.0
     b.get(3) should be(16) // [1.0 ,4.0 ,9.0 ,16.0]
@@ -87,7 +87,7 @@ class OperatableNDArrayTest extends FlatSpec with Matchers {
   }
 
   it should "use the update method to mutate values" in {
-    val nd3 = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2))
+    val nd3 = Nd4j.create(Array[Double](1, 2, 3, 4, 5, 6, 7, 8), Array(2, 2, 2): _*)
     nd3(0) = 11
     nd3.get(0) should be(11)
 
@@ -125,7 +125,7 @@ class OperatableNDArrayTest extends FlatSpec with Matchers {
     val s: String = Nd4j.create(2, 2) + ""
   }
 
-  "Sum function" should "choose return value depending on INDArray type" ignore {
+  "Sum function" should "choose return value depending on INDArray type" in {
     val ndArray =
       Array(
         Array(1, 2),