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Refactoring package names
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‎README.md

+2-2
Original file line numberDiff line numberDiff line change
@@ -70,8 +70,8 @@ image classification task.
7070
```scala
7171
{
7272
import _root_.ammonite.ops._
73-
import _root_.io.github.mandar2812.dynaml.pipes.DataPipe
74-
import _root_.io.github.mandar2812.dynaml.tensorflow.{
73+
import _root_.io.github.tailhq.dynaml.pipes.DataPipe
74+
import _root_.io.github.tailhq.dynaml.tensorflow.{
7575
dtflearn,
7676
dtfutils,
7777
dtfdata,

‎build.sbt

+12-12
Original file line numberDiff line numberDiff line change
@@ -5,7 +5,7 @@ import sbtbuildinfo.BuildInfoPlugin.autoImport._
55
import org.scoverage.coveralls.Imports.CoverallsKeys._
66

77
val mainVersion = "v2.0-SNAPSHOT"
8-
maintainer := "Mandar Chandorkar <mandar2812@gmail.com>"
8+
maintainer := "Mandar Chandorkar <tailhq@gmail.com>"
99
packageSummary := "Scala Library/REPL for Machine Learning Research"
1010
packageDescription := "DynaML is a Scala & JVM Machine Learning toolbox for research, education & industry."
1111

@@ -49,7 +49,7 @@ lazy val pipes = (project in file("dynaml-pipes"))
4949
.settings(
5050
name := "dynaml-pipes",
5151
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
52-
buildInfoPackage := "io.github.mandar2812.dynaml.pipes",
52+
buildInfoPackage := "io.github.tailhq.dynaml.pipes",
5353
buildInfoUsePackageAsPath := true,
5454
version := mainVersion
5555
)
@@ -62,7 +62,7 @@ lazy val core = (project in file("dynaml-core"))
6262
.settings(
6363
name := "dynaml-core",
6464
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
65-
buildInfoPackage := "io.github.mandar2812.dynaml",
65+
buildInfoPackage := "io.github.tailhq.dynaml",
6666
buildInfoUsePackageAsPath := true,
6767
version := mainVersion
6868
)
@@ -74,7 +74,7 @@ lazy val examples = (project in file("dynaml-examples"))
7474
.settings(
7575
name := "dynaml-examples",
7676
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
77-
buildInfoPackage := "io.github.mandar2812.dynaml.examples",
77+
buildInfoPackage := "io.github.tailhq.dynaml.examples",
7878
buildInfoUsePackageAsPath := true,
7979
version := mainVersion
8080
)
@@ -86,7 +86,7 @@ lazy val repl = (project in file("dynaml-repl"))
8686
name := "dynaml-repl",
8787
version := mainVersion,
8888
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
89-
buildInfoPackage := "io.github.mandar2812.dynaml.repl",
89+
buildInfoPackage := "io.github.tailhq.dynaml.repl",
9090
buildInfoUsePackageAsPath := true,
9191
libraryDependencies ++= replDependencies
9292
)
@@ -99,7 +99,7 @@ lazy val tensorflow = (project in file("dynaml-tensorflow"))
9999
name := "dynaml-tensorflow",
100100
version := mainVersion,
101101
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
102-
buildInfoPackage := "io.github.mandar2812.dynaml.tensorflow",
102+
buildInfoPackage := "io.github.tailhq.dynaml.tensorflow",
103103
buildInfoUsePackageAsPath := true,
104104
libraryDependencies ++= tensorflowDependency
105105
.map(_.excludeAll(excludeSlf4jBindings: _*))
@@ -113,7 +113,7 @@ lazy val notebook = (project in file("dynaml-notebook"))
113113
name := "dynaml-notebook",
114114
version := mainVersion,
115115
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
116-
buildInfoPackage := "io.github.mandar2812.dynaml.jupyter",
116+
buildInfoPackage := "io.github.tailhq.dynaml.jupyter",
117117
buildInfoUsePackageAsPath := true,
118118
libraryDependencies ++= notebookDepencencies
119119
.map(_.excludeAll(excludeSlf4jBindings: _*))
@@ -136,9 +136,9 @@ lazy val DynaML = (project in file("."))
136136
version := mainVersion,
137137
fork in run := true,
138138
fork in test := true,
139-
mainClass in Compile := Some("io.github.mandar2812.dynaml.DynaML"),
139+
mainClass in Compile := Some("io.github.tailhq.dynaml.DynaML"),
140140
buildInfoKeys := Seq[BuildInfoKey](name, version, scalaVersion, sbtVersion),
141-
buildInfoPackage := "io.github.mandar2812",
141+
buildInfoPackage := "io.github.tailhq",
142142
buildInfoUsePackageAsPath := true,
143143
dataDirectory := new File("data"),
144144
mappings in Universal ++= dataDirectory.value
@@ -171,7 +171,7 @@ lazy val DynaML = (project in file("."))
171171
"-J-XX:HeapBaseMinAddress=32G"
172172
),
173173
scalacOptions in Universal ++= Seq("-Xlog-implicits"),
174-
initialCommands in console := """io.github.mandar2812.dynaml.DynaML.main(Array())""",
174+
initialCommands in console := """io.github.tailhq.dynaml.DynaML.main(Array())""",
175175
dockerfile in docker := {
176176
val appDir: File = stage.value
177177
val targetDir = "/app"
@@ -184,10 +184,10 @@ lazy val DynaML = (project in file("."))
184184
},
185185
imageNames in docker := Seq(
186186
// Sets the latest tag
187-
ImageName(s"mandar2812/${name.value.toLowerCase}:latest"),
187+
ImageName(s"tailhq/${name.value.toLowerCase}:latest"),
188188
// Sets a name with a tag that contains the project version
189189
ImageName(
190-
namespace = Some("mandar2812"),
190+
namespace = Some("tailhq"),
191191
repository = name.value.toLowerCase,
192192
tag = Some(version.value)
193193
)

‎conf/DynaMLInit.scala

+23-23
Original file line numberDiff line numberDiff line change
@@ -11,49 +11,49 @@ import org.apache.spark.SparkContext
1111
import org.apache.spark.SparkConf
1212
import org.apache.spark.rdd.RDD
1313
//Load Wisp-Highcharts for plotting
14-
import io.github.mandar2812.dynaml.graphics.charts.Highcharts._
14+
import io.github.tailhq.dynaml.graphics.charts.Highcharts._
1515
//Import spire implicits for definition of
1616
//fields, algebraic structures on primitive types
1717
import spire.implicits._
1818
/*
1919
* DynaML imports
2020
* */
21-
import io.github.mandar2812.dynaml.analysis.VectorField
22-
import io.github.mandar2812.dynaml.analysis.implicits._
23-
import io.github.mandar2812.dynaml.algebra._
21+
import io.github.tailhq.dynaml.analysis.VectorField
22+
import io.github.tailhq.dynaml.analysis.implicits._
23+
import io.github.tailhq.dynaml.algebra._
2424
//Load 3d graphics capabilities
25-
import io.github.mandar2812.dynaml.graphics.plot3d
25+
import io.github.tailhq.dynaml.graphics.plot3d
2626
//The pipes API
27-
import io.github.mandar2812.dynaml.pipes._
28-
import io.github.mandar2812.dynaml.DynaMLPipe
29-
import io.github.mandar2812.dynaml.DynaMLPipe._
27+
import io.github.tailhq.dynaml.pipes._
28+
import io.github.tailhq.dynaml.DynaMLPipe
29+
import io.github.tailhq.dynaml.DynaMLPipe._
3030
//Load the DynaML model api members
31-
import io.github.mandar2812.dynaml.models._
32-
import io.github.mandar2812.dynaml.models.neuralnets._
33-
import io.github.mandar2812.dynaml.models.svm._
34-
import io.github.mandar2812.dynaml.models.lm._
31+
import io.github.tailhq.dynaml.models._
32+
import io.github.tailhq.dynaml.models.neuralnets._
33+
import io.github.tailhq.dynaml.models.svm._
34+
import io.github.tailhq.dynaml.models.lm._
3535
//Utility functions
36-
import io.github.mandar2812.dynaml.utils
36+
import io.github.tailhq.dynaml.utils
3737
//Kernels for GP,SVM models
38-
import io.github.mandar2812.dynaml.kernels._
38+
import io.github.tailhq.dynaml.kernels._
3939
//Shell examples
40-
import io.github.mandar2812.dynaml.examples._
40+
import io.github.tailhq.dynaml.examples._
4141
//Load neural net primitives
42-
import io.github.mandar2812.dynaml.models.neuralnets.TransferFunctions._
42+
import io.github.tailhq.dynaml.models.neuralnets.TransferFunctions._
4343
//The probability API
44-
import io.github.mandar2812.dynaml.probability._
45-
import io.github.mandar2812.dynaml.probability.distributions._
44+
import io.github.tailhq.dynaml.probability._
45+
import io.github.tailhq.dynaml.probability.distributions._
4646
//Wavelet API
47-
import io.github.mandar2812.dynaml.wavelets._
47+
import io.github.tailhq.dynaml.wavelets._
4848
//OpenML support
49-
import io.github.mandar2812.dynaml.openml.OpenML
49+
import io.github.tailhq.dynaml.openml.OpenML
5050
//Spark support
51-
import io.github.mandar2812.dynaml.DynaMLSpark._
51+
import io.github.tailhq.dynaml.DynaMLSpark._
5252
//Renjin imports
5353
import javax.script._
5454
//TensorFlow imports
55-
import _root_.io.github.mandar2812.dynaml.tensorflow._
56-
import _root_.io.github.mandar2812.dynaml.tensorflow.implicits._
55+
import _root_.io.github.tailhq.dynaml.tensorflow._
56+
import _root_.io.github.tailhq.dynaml.tensorflow.implicits._
5757
import org.renjin.script._
5858
import org.renjin.sexp._
5959
val r_engine_factory = new RenjinScriptEngineFactory()

‎docs-old/core/core_ann.md

+3-4
Original file line numberDiff line numberDiff line change
@@ -70,11 +70,10 @@ val prediction = model.predict(pattern)
7070

7171
Autoencoders can be created using the [```AutoEncoder```](https://transcendent-ai-labs.github.io/api_docs/DynaML/recent/dynaml-core/index.html#io.github.mandar2812.dynaml.models.neuralnets.AutoEncoder) class. Its constructor has the following arguments.
7272

73-
7473
```scala
75-
import io.github.mandar2812.dynaml.models.neuralnets._
76-
import io.github.mandar2812.dynaml.models.neuralnets.TransferFunctions._
77-
import io.github.mandar2812.dynaml.optimization.BackPropagation
74+
import io.github.tailhq.dynaml.models.neuralnets._
75+
import io.github.tailhq.dynaml.models.neuralnets.TransferFunctions._
76+
import io.github.tailhq.dynaml.optimization.BackPropagation
7877

7978
//Cast the training data as a stream of (x,x),
8079
//where x are the DenseVector of features

‎docs-old/core/core_dtf.md

+7-7
Original file line numberDiff line numberDiff line change
@@ -5,7 +5,7 @@
55
To use DynaML's tensorflow API, import it in your code/script/DynaML shell session.
66

77
```scala
8-
import io.github.mandar2812.dynaml.tensorflow._
8+
import io.github.tailhq.dynaml.tensorflow._
99
import org.platanios.tensorflow.api._
1010
```
1111

@@ -20,7 +20,7 @@ There is more than one way to instantiate a tensor.
2020
### Enumeration of Values
2121

2222
```scala
23-
import io.github.mandar2812.dynaml.tensorflow._
23+
import io.github.tailhq.dynaml.tensorflow._
2424
import org.platanios.tensorflow.api._
2525

2626
//Create a float tensor
@@ -41,7 +41,7 @@ tensor_double.summarize()
4141
### From a Scala Sequence
4242

4343
```scala
44-
import io.github.mandar2812.dynaml.tensorflow._
44+
import io.github.tailhq.dynaml.tensorflow._
4545
import org.platanios.tensorflow.api._
4646

4747
val float_seq = Seq(1f, 2f, 3f, 4f)
@@ -64,7 +64,7 @@ When dealing with binary data formats, such as images and other binary numerical
6464
it is useful to be able to instantiate tensors from buffers of raw bytes.
6565

6666
```scala
67-
import io.github.mandar2812.dynaml.tensorflow._
67+
import io.github.tailhq.dynaml.tensorflow._
6868
import org.platanios.tensorflow.api._
6969

7070
val byte_buffer: Array[Byte] = _
@@ -77,7 +77,7 @@ val byte_tensor = dtf.tensor_from_buffer(INT32, shape)(byte_buffer)
7777
Apart from these functions, there are.
7878

7979
```scala
80-
import io.github.mandar2812.dynaml.tensorflow._
80+
import io.github.tailhq.dynaml.tensorflow._
8181
import org.platanios.tensorflow.api._
8282

8383
//Double tensor
@@ -99,8 +99,8 @@ probability API.
9999
```scala
100100
import breeze.stats.distributions._
101101

102-
import io.github.mandar2812.dynaml.probability._
103-
import io.github.mandar2812.dynaml.tensorflow._
102+
import io.github.tailhq.dynaml.probability._
103+
import io.github.tailhq.dynaml.tensorflow._
104104
import org.platanios.tensorflow.api._
105105

106106
val rv = RandomVariable(new LogNormal(0.0, 1.5))

‎docs-old/core/core_dtfdata.md

+30-30
Original file line numberDiff line numberDiff line change
@@ -15,9 +15,9 @@ access to the underlying collection.
1515
object gives the user easy access to the `DataSet` API.
1616

1717
```scala
18-
import _root_.io.github.mandar2812.dynaml.probability._
19-
import _root_.io.github.mandar2812.dynaml.pipes._
20-
import io.github.mandar2812.dynaml.tensorflow._
18+
import _root_.io.github.tailhq.dynaml.probability._
19+
import _root_.io.github.tailhq.dynaml.pipes._
20+
import io.github.tailhq.dynaml.tensorflow._
2121

2222

2323
val random_numbers = GaussianRV(0.0, 1.0) :* GaussianRV(1.0, 2.0)
@@ -38,9 +38,9 @@ DynaML data sets support several operations of the _map-reduce_ philosophy.
3838
Transform each element of type `X` into some other element of type `Y` (`Y` can possibly be the same as `X`).
3939

4040
```scala
41-
import _root_.io.github.mandar2812.dynaml.probability._
42-
import _root_.io.github.mandar2812.dynaml.pipes._
43-
import io.github.mandar2812.dynaml.tensorflow._
41+
import _root_.io.github.tailhq.dynaml.probability._
42+
import _root_.io.github.tailhq.dynaml.pipes._
43+
import io.github.tailhq.dynaml.tensorflow._
4444

4545

4646
val random_numbers = GaussianRV(0.0, 1.0)
@@ -67,10 +67,10 @@ Schematically, this process is
6767
`Iterable[X] -> Iterable[Iterable[Y]] -> Iterable[Y]`
6868

6969
```scala
70-
import _root_.io.github.mandar2812.dynaml.probability._
71-
import _root_.io.github.mandar2812.dynaml.pipes._
70+
import _root_.io.github.tailhq.dynaml.probability._
71+
import _root_.io.github.tailhq.dynaml.pipes._
7272
import scala.util.Random
73-
import io.github.mandar2812.dynaml.tensorflow._
73+
import io.github.tailhq.dynaml.tensorflow._
7474

7575
val random_gaussian_dataset = dtfdata.dataset(
7676
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -88,10 +88,10 @@ Collect only the elements which satisfy some predicate, i.e. a function which re
8888
elements to be selected (filtered) and `false` for the ones which should be discarded.
8989

9090
```scala
91-
import _root_.io.github.mandar2812.dynaml.probability._
92-
import _root_.io.github.mandar2812.dynaml.pipes._
91+
import _root_.io.github.tailhq.dynaml.probability._
92+
import _root_.io.github.tailhq.dynaml.pipes._
9393
import scala.util.Random
94-
import io.github.mandar2812.dynaml.tensorflow._
94+
import io.github.tailhq.dynaml.tensorflow._
9595

9696
val gaussian_dataset = dtfdata.dataset(
9797
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -122,10 +122,10 @@ x_t &= x_{t-1} + \epsilon \\
122122
$$
123123

124124
```scala
125-
import _root_.io.github.mandar2812.dynaml.probability._
126-
import _root_.io.github.mandar2812.dynaml.pipes._
125+
import _root_.io.github.tailhq.dynaml.probability._
126+
import _root_.io.github.tailhq.dynaml.pipes._
127127
import scala.util.Random
128-
import io.github.mandar2812.dynaml.tensorflow._
128+
import io.github.tailhq.dynaml.tensorflow._
129129

130130
val gaussian_increments = dtfdata.dataset(
131131
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -146,10 +146,10 @@ The `reduce()` and `reduceLeft()` methods help in computing summary values from
146146
collection.
147147

148148
```scala
149-
import _root_.io.github.mandar2812.dynaml.probability._
150-
import _root_.io.github.mandar2812.dynaml.pipes._
149+
import _root_.io.github.tailhq.dynaml.probability._
150+
import _root_.io.github.tailhq.dynaml.pipes._
151151
import scala.util.Random
152-
import io.github.mandar2812.dynaml.tensorflow._
152+
import io.github.tailhq.dynaml.tensorflow._
153153

154154
val gaussian_increments = dtfdata.dataset(
155155
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -170,10 +170,10 @@ Some times transformations on data sets cannot be applied on each element indivi
170170
entire data collection is required for such a transformation.
171171

172172
```scala
173-
import _root_.io.github.mandar2812.dynaml.probability._
174-
import _root_.io.github.mandar2812.dynaml.pipes._
173+
import _root_.io.github.tailhq.dynaml.probability._
174+
import _root_.io.github.tailhq.dynaml.pipes._
175175
import scala.util.Random
176-
import io.github.mandar2812.dynaml.tensorflow._
176+
import io.github.tailhq.dynaml.tensorflow._
177177

178178
val gaussian_data = dtfdata.dataset(
179179
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -194,9 +194,9 @@ val resampled_data = gaussian_data.transform(resample)
194194
instance, it is possible to obtain a TensorFlow `Dataset`.
195195

196196
```scala
197-
import _root_.io.github.mandar2812.dynaml.probability._
198-
import _root_.io.github.mandar2812.dynaml.pipes._
199-
import io.github.mandar2812.dynaml.tensorflow._
197+
import _root_.io.github.tailhq.dynaml.probability._
198+
import _root_.io.github.tailhq.dynaml.pipes._
199+
import io.github.tailhq.dynaml.tensorflow._
200200
import org.platanios.tensorflow.api._
201201
import org.platanios.tensorflow.api.types._
202202
@@ -225,11 +225,11 @@ The classes `ZipDataSet[X, Y]` and `SupervisedDataSet[X, Y]` both represent data
225225
The `zip()` method can be used to create data sets consisting of tuples.
226226

227227
```scala
228-
import _root_.io.github.mandar2812.dynaml.probability._
229-
import _root_.io.github.mandar2812.dynaml.pipes._
228+
import _root_.io.github.tailhq.dynaml.probability._
229+
import _root_.io.github.tailhq.dynaml.pipes._
230230
import scala.util.Random
231231
import _root_.breeze.stats.distributions._
232-
import io.github.mandar2812.dynaml.tensorflow._
232+
import io.github.tailhq.dynaml.tensorflow._
233233

234234
val gaussian_data = dtfdata.dataset(
235235
GaussianRV(0.0, 1.0).iid(10000).draw
@@ -258,11 +258,11 @@ in the following ways.
258258

259259
```scala
260260

261-
import _root_.io.github.mandar2812.dynaml.probability._
262-
import _root_.io.github.mandar2812.dynaml.pipes._
261+
import _root_.io.github.tailhq.dynaml.probability._
262+
import _root_.io.github.tailhq.dynaml.pipes._
263263
import scala.util.Random
264264
import _root_.breeze.stats.distributions._
265-
import io.github.mandar2812.dynaml.tensorflow._
265+
import io.github.tailhq.dynaml.tensorflow._
266266

267267
val gaussian_data = dtfdata.dataset(
268268
GaussianRV(0.0, 1.0).iid(10000).draw

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