diff --git a/docs/code/EntryPoints.md b/docs/code/EntryPoints.md index 49d6b2999f..9fb11a6e99 100644 --- a/docs/code/EntryPoints.md +++ b/docs/code/EntryPoints.md @@ -223,7 +223,7 @@ parameter. 1. Add a `LoadableClass` assembly attribute with the `SignatureEntryPointModule` signature as shown [here](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L27). 2. Create a public static method, that: 1. Takes an object representing the arguments of the component you want to expose as shown [here](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L414) - 2. Initializes and runs the component, returning one of the nested classes of [`Microsoft.ML.Runtime.EntryPoints.CommonOutputs`](https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Data/EntryPoints/CommonOutputs.cs) + 2. Initializes and runs the component, returning one of the nested classes of [`Microsoft.ML.EntryPoints.CommonOutputs`](https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Data/EntryPoints/CommonOutputs.cs) 3. Is annotated with the [`TlcModule.EntryPoint`](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs#L407) attribute For an example of a transformer as an entrypoint, see [OneHotVectorizer](https://github.com/dotnet/machinelearning/blob/9db16c85888e7163c671543faee6ba1f47015d68/src/Microsoft.ML.Transforms/OneHotEncoding.cs#L283). diff --git a/docs/code/SchemaComprehension.md b/docs/code/SchemaComprehension.md index 37238e0c0c..93e14a02db 100644 --- a/docs/code/SchemaComprehension.md +++ b/docs/code/SchemaComprehension.md @@ -65,7 +65,7 @@ static void Main(string[] args) }; // Create the ML.NET environment. - var env = new Microsoft.ML.Runtime.Data.TlcEnvironment(); + var env = new Microsoft.ML.Data.TlcEnvironment(); // Create the data view. // This method will use the definition of IrisData to understand what columns there are in the @@ -74,7 +74,7 @@ static void Main(string[] args) // Now let's do something to the data view. For example, concatenate all four non-label columns // into 'Features' column. - dv = new Microsoft.ML.Runtime.Data.ConcatTransform(env, dv, "Features", + dv = new Microsoft.ML.Data.ConcatTransform(env, dv, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"); // Read the data into an another array, this time we read the 'Features' and 'Label' columns diff --git a/docs/code/VBufferCareFeeding.md b/docs/code/VBufferCareFeeding.md index 36c19c3184..91918c5e9b 100644 --- a/docs/code/VBufferCareFeeding.md +++ b/docs/code/VBufferCareFeeding.md @@ -224,17 +224,17 @@ ML.NET's runtime code has a number of utilities for operating over `VBuffer`s that we have written to be generally useful. We will not treat on these in detail here, but: -* `Microsoft.ML.Runtime.Data.VBuffer` itself contains a few methods for +* `Microsoft.ML.Data.VBuffer` itself contains a few methods for accessing and iterating over its values. -* `Microsoft.ML.Runtime.Internal.Utilities.VBufferUtils` contains utilities +* `Microsoft.ML.Internal.Utilities.VBufferUtils` contains utilities mainly for non-numeric manipulation of `VBuffer`s. -* `Microsoft.ML.Runtime.Numeric.VectorUtils` contains math operations +* `Microsoft.ML.Numeric.VectorUtils` contains math operations over `VBuffer` and `float[]`, like computing norms, dot-products, and whatnot. -* `Microsoft.ML.Runtime.Data.BufferBuilder` is an abstract class whose +* `Microsoft.ML.Data.BufferBuilder` is an abstract class whose concrete implementations are used throughout ML.NET to build up `VBuffer` instances. Note that if one *can* simply build a `VBuffer` oneself easily and do not need the niceties provided by the buffer builder, you should