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>

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

@@ -551,7 +551,7 @@ public static long WriteStringStream(this BinaryWriter writer, IEnumerable<strin
         /// <summary>
         /// Writes what Microsoft calls a UTF-7 encoded number in the binary reader and
         /// writer string methods. For non-negative integers this is equivalent to LEB128
-        /// (see http://en.wikipedia.org/wiki/LEB128).
+        /// (see https://en.wikipedia.org/wiki/LEB128).
         /// </summary>
         public static void WriteLeb128Int(this BinaryWriter writer, ulong value)
         {
@@ -1136,4 +1136,4 @@ public static void CheckOptionalUserDirectory(string file, string userArgument)
         }
 #pragma warning restore MSML_ContractsNameUsesNameof
     }
-}
+}

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

@@ -93,7 +93,7 @@ public override string ToString()
         /// Accumulates one more value, optionally weighted.
         /// This accumulation procedure is based on the following,
         /// with adjustments as appropriate for weighted instances:
-        /// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+        /// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
         /// </summary>
         /// <param name="v">The value</param>
         /// <param name="w">The weight given to this value</param>
@@ -174,7 +174,7 @@ public sealed class SummaryStatisticsUpToSecondOrderMoments : SummaryStatisticsB
     /// A class for one-pass accumulation of weighted summary statistics, up
     /// to the fourth moment. The accumulative algorithms used here may be
     /// reviewed at
-    /// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+    /// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
     /// All quantities are weighted, except for <c>RawCount</c>.
     /// </summary>
     public sealed class SummaryStatistics : SummaryStatisticsBase
@@ -311,7 +311,7 @@ public override string ToString()
         /// Accumulates one more value, optionally weighted.
         /// This accumulation procedure is based on the following,
         /// with adjustments as appropriate for weighted instances:
-        /// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+        /// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
         /// </summary>
         /// <param name="v">The value</param>
         /// <param name="w">The weight given to this value</param>

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

@@ -15,7 +15,7 @@ namespace Microsoft.ML.Runtime.Internal.Utilities
     /// the target function "minimum description length".
     /// The algorithm is outlineed in an article
     /// "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning"
-    /// [Fayyad, Usama M.; Irani, Keki B. (1993)] http://ijcai.org/Past%20Proceedings/IJCAI-93-VOL2/PDF/022.pdf
+    /// [Fayyad, Usama M.; Irani, Keki B. (1993)] https://ijcai.org/Past%20Proceedings/IJCAI-93-VOL2/PDF/022.pdf
     ///
     /// The class can be used several times sequentially, it is stateful and not thread-safe.
     /// Both Single and Double precision processing is implemented, and is identical.

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
             /// <summary>
             /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
-            /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
+            /// <a href="https://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">https://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
             /// </summary>
             protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
             {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
             /// <summary>
             /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
-            /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
+            /// <a href="https://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">https://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
             /// </summary>
             protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
             {

src/Microsoft.ML.Data/Model/Pfa/ICanSavePfa.cs

@@ -35,7 +35,7 @@ public interface ISaveAsPfa : ICanSavePfa
     }
 
     /// <summary>
-    /// This data model component is savable as PFA. See http://dmg.org/pfa/ .
+    /// This data model component is savable as PFA. See https://dmg.org/pfa/ .
     /// </summary>
     public interface ITransformCanSavePfa : ISaveAsPfa, IDataTransform
     {
@@ -111,4 +111,4 @@ public interface IDistCanSavePfa : ISingleCanSavePfa, IValueMapperDist
         void SaveAsPfa(BoundPfaContext ctx, JToken input,
             string score, out JToken scoreToken, string prob, out JToken probToken);
     }
-}
+}

src/Microsoft.ML.FastTree/QuantileStatistics.cs

@@ -67,7 +67,7 @@ public QuantileStatistics(Float[] data, Float[] weights = null, bool isSorted =
 
         /// <summary>
         /// There are many ways to estimate quantile. This implementations is based on R-8, SciPy-(1/3,1/3)
-        /// http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population
+        /// https://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population
         /// </summary>
         public Float GetQuantile(Float p)
         {
@@ -131,7 +131,7 @@ private Float GetRank(Float p)
             }
 
             // This implementations is based on R-8, SciPy-(1/3,1/3)
-            // http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population
+            // https://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population
             var h = (_weights == null) ? (weightedLength + oneThird) * p + oneThird : weightedLength * p;
 
             if (_weights == null)

src/Microsoft.ML.FastTree/Training/EnsembleCompression/LassoBasedEnsembleCompressor.cs

@@ -12,7 +12,7 @@ namespace Microsoft.ML.Runtime.FastTree.Internal
     /// This implementation is based on:
     /// Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization
     /// Paths for Generalized Linear Models via Coordinate Descent.
-    /// http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
+    /// https://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
     /// </summary>
     /// <remarks>Author was Yasser Ganjisaffar during his internship.</remarks>
     public class LassoBasedEnsembleCompressor : IEnsembleCompressor<short>
@@ -556,4 +556,4 @@ public Ensemble GetCompressedEnsemble()
             return _compressedEnsemble;
         }
     }
-}
+}

src/Microsoft.ML.FastTree/doc.xml

@@ -31,7 +31,7 @@
         <para>For more information see:</para>
         <list type="bullet">
           <item><description><a href='https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting'>Wikipedia: Gradient boosting (Gradient tree boosting).</a></description></item>
-          <item><description><a href='http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451'>Greedy function approximation: A gradient boosting machine.</a></description></item>
+          <item><description><a href='https://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451'>Greedy function approximation: A gradient boosting machine.</a></description></item>
         </list>  
     </remarks>
     </member>
@@ -96,7 +96,7 @@
          Each tree in a decision forest outputs a Gaussian distribution.</para>
          <para>For more see: </para>
         <list  type='bullet'>
-          <item><description><a href='http://en.wikipedia.org/wiki/Random_forest'>Wikipedia: Random forest</a></description></item>
+          <item><description><a href='https://en.wikipedia.org/wiki/Random_forest'>Wikipedia: Random forest</a></description></item>
           <item><description><a href='http://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf'>Quantile regression forest</a></description></item>
           <item><description><a href='https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-stumps-to-trees-to-forests/'>From Stumps to Trees to Forests</a></description></item>
         </list>
@@ -138,7 +138,7 @@
         Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models.</a> from Yang, Quan, and Zou. 
         <para>For an introduction to Gradient Boosting, and more information, see:</para>
         <para><a href='https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting'>Wikipedia: Gradient boosting (Gradient tree boosting)</a></para>
-        <para><a href='http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451'>Greedy function approximation: A gradient boosting machine</a></para>
+        <para><a href='https://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451'>Greedy function approximation: A gradient boosting machine</a></para>
       </remarks>
     </member>
 
@@ -191,4 +191,4 @@
     </member>
 
   </members>
-</doc>
+</doc>

src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs

@@ -671,7 +671,7 @@ private static void ComputeAccelerationMemoryRequirement(long accelMemBudgetMb,
         }
 
         /// <summary>
-        /// KMeans|| Implementation, see http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
+        /// KMeans|| Implementation, see https://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
         /// This algorithm will require:
         /// - (k * overSampleFactor * rounds * diminsionality * 4) bytes for the final sampled clusters.
         /// - (k * overSampleFactor * numThreads * diminsionality * 4) bytes for the per-round sampling.
@@ -1357,7 +1357,7 @@ private static void Initialize(
             int neededPerThreadWorkStates = numThreads == 1 ? 0 : numThreads;
 
             // Accelerating KMeans requires the following data structures.
-            // The algorithm is based on the YinYang KMeans algorithm [ICML'15], http://research.microsoft.com/apps/pubs/default.aspx?id=252149
+            // The algorithm is based on the YinYang KMeans algorithm [ICML'15], https://research.microsoft.com/apps/pubs/default.aspx?id=252149
             // These data structures are allocated only as allowed by the _accelMemBudgetMb parameter
             // if _accelMemBudgetMb is zero, then the algorithm below reduces to the original KMeans++ implementation
             int bytesPerCluster =
@@ -1483,7 +1483,7 @@ public struct RowStats
         /// it expects to be able to sample numSamples * numThreads.
         ///
         /// This is based on the 'A-Res' algorithm in 'Weighted Random Sampling', 2005; Efraimidis, Spirakis:
-        /// http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
+        /// https://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf
         /// </summary>
         public static RowStats ParallelWeightedReservoirSample(
             IHost host, int numThreads,