Move HTTP links to HTTPS (when the content matches)

Author: justinormont

README.md

@@ -56,7 +56,7 @@ We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md
 
 Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/mlnet](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/mlnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
-This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
+This project has adopted the code of conduct defined by the [Contributor Covenant](https://contributor-covenant.org/) to clarify expected behavior in our community.
 For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).
 
 ## Examples
@@ -94,7 +94,7 @@ ML.NET is licensed under the [MIT license](LICENSE).
 
 ## .NET Foundation
 
-ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.
+ML.NET is a [.NET Foundation](https://www.dotnetfoundation.org/projects) project.
 
 There are many .NET related projects on GitHub.
 

build.proj

@@ -81,7 +81,7 @@
       DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />
 
     <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
-      Url="http://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
+      Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
       DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
   </ItemGroup>
 

docs/building/unix-instructions.md

@@ -45,7 +45,7 @@ On macOS a few components are needed which are not provided by a default develop
 * gcc
 * All the requirements necessary to run .NET Core 2.0 applications. To view macOS prerequisites click [here](https://docs.microsoft.com/en-us/dotnet/core/macos-prerequisites?tabs=netcore2x).
 
-One way of obtaining CMake and gcc is via [Homebrew](http://brew.sh):
+One way of obtaining CMake and gcc is via [Homebrew](https://brew.sh):
 ```sh
 $ brew install cmake
 $ brew install gcc

docs/code/IdvFileFormat.md

@@ -116,8 +116,8 @@ The enum for compression kind is one byte, and follows this scheme:
 Compression Kind                                               | Code
 ---------------------------------------------------------------|-----
 None                                                           | 0
-DEFLATE (i.e., [RFC1951](http://www.ietf.org/rfc/rfc1951.txt)) | 1
-zlib (i.e., [RFC1950](http://www.ietf.org/rfc/rfc1950.txt))    | 2
+DEFLATE (i.e., [RFC1951](https://www.ietf.org/rfc/rfc1951.txt)) | 1
+zlib (i.e., [RFC1950](https://www.ietf.org/rfc/rfc1950.txt))    | 2
 
 None means no compression. DEFLATE is the default scheme. There is a tendency
 to conflate zlib and DEFLATE, so to be clear: zlib can be (somewhat inexactly)

docs/release-notes/0.2/release-0.2.md

@@ -39,7 +39,7 @@ Below are some of the highlights from this release.
       their taste in movies. 
 
     * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
-      clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
+      clustering](https://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
       with [Yinyang K-means
       acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
       [This

docs/release-notes/0.3/release-0.3.md

@@ -39,15 +39,15 @@ Below are some of the highlights from this release.
     * FFM is a streaming learner so it does not require the entire dataset to
       fit in memory.
     * You can learn more about FFM
-      [here](http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) and some of the
+      [here](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) and some of the
       speedup approaches that are used in ML.NET
       [here](https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf).
 
 * Added [LightGBM](https://github.com/Microsoft/LightGBM) as a learner for
   binary classification, multiclass classification, and regression (#392)
 
     * LightGBM is a tree based gradient boosting machine. It is under the
-      umbrella of the [DMTK](http://github.com/microsoft/dmtk) project at
+      umbrella of the [DMTK](https://github.com/microsoft/dmtk) project at
       Microsoft.
     * The LightGBM repository shows various [comparison
       experiments](https://github.com/Microsoft/LightGBM/blob/6488f319f243f7ff679a8e388a33e758c5802303/docs/Experiments.rst#comparison-experiment)

docs/release-notes/0.4/release-0.4.md

@@ -64,7 +64,7 @@ Below are some of the highlights from this release.
     * Several options for pretrained embeddings are available:
       [GloVe](https://nlp.stanford.edu/projects/glove/),
       [fastText](https://en.wikipedia.org/wiki/FastText), and
-      [SSWE](http://anthology.aclweb.org/P/P14/P14-1146.pdf). The pretrained model is downloaded automatically on first use.
+      [SSWE](https://anthology.aclweb.org/P/P14/P14-1146.pdf). The pretrained model is downloaded automatically on first use.
     * Documentation can be found
       [here](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.transforms.wordembeddings?view=ml-dotnet).
 
@@ -85,4 +85,4 @@ Shoutout to [dsyme](https://github.com/dsyme),
 [jwood803](https://github.com/jwood803),
 [sharwell](https://github.com/sharwell),
 [JoshuaLight](https://github.com/JoshuaLight), and the ML.NET team for their
-contributions as part of this release! 
+contributions as part of this release! 

src/Microsoft.ML.Core/Utilities/DoubleParser.cs

@@ -345,7 +345,7 @@ public static bool TryParse(out Double value, string s, int ichMin, int ichLim,
             // Taking the high 64 bits of the 128 bit result should give us enough bits to get the
             // right answer most of the time. Note, that it's not guaranteed that we always get the
             // right answer. Guaranteeing that takes much more work.... See the paper by David Gay at
-            // http://www.ampl.com/REFS/rounding.pdf.
+            // https://www.ampl.com/REFS/rounding.pdf.
             Contracts.Assert((num & TopTwoBits) != 0);
             Contracts.Assert((mul & TopBit) != 0);
 

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
     /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>.
     /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/>
     /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
-    /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
+    /// Sampling is done according to the algorithm in this paper: <a href="https://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">https://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
     /// </summary>
     public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
     {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
     /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>.
     /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/>
     /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
-    /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
+    /// Sampling is done according to the algorithm in this paper: <a href="https://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">https://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
     /// </summary>
     public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
     {

src/Microsoft.ML.Core/Utilities/Stats.cs

@@ -199,7 +199,7 @@ public static int SampleFromPoisson(IRandom rand, double lambda)
         }
 
         // Mean refers to the mu parameter. Scale refers to the b parameter.
-        // http://en.wikipedia.org/wiki/Laplace_distribution
+        // https://en.wikipedia.org/wiki/Laplace_distribution
         public static Float SampleFromLaplacian(IRandom rand, Float mean, Float scale)
         {
             Float u = rand.NextSingle();
@@ -215,7 +215,7 @@ public static Float SampleFromLaplacian(IRandom rand, Float mean, Float scale)
 
         /// <summary>
         /// Sample from a standard Cauchy distribution:
-        /// http://en.wikipedia.org/wiki/Lorentzian_function
+        /// https://en.wikipedia.org/wiki/Lorentzian_function
         /// </summary>
         /// <param name="rand"></param>
         /// <returns></returns>