[oneDAL] build warnings fix

source/elements/oneDAL/source/data_management/index.rst

@@ -56,7 +56,7 @@ Dataset
 --------
 
 The main data-related concept that |dal_short_name| works with is a
-:term:`dataset`. It is an in-memory or out-of-memory tabular view of data, where
+:term:`Dataset`. It is an in-memory or out-of-memory tabular view of data, where
 table rows represent the :term:`observations <Observation>` and columns
 represent the :term:`features <Feature>`.
 
@@ -82,7 +82,7 @@ example:
 Data source
 -----------
 
-Data source is a concept of an out-of-memory storage for a :term:`dataset`. It is
+Data source is a concept of an out-of-memory storage for a :term:`Dataset`. It is
 used at the data acquisition and data preparation stages for the following:
 
 - To extract datasets from external sources such as databases, files, remote
@@ -92,11 +92,11 @@ used at the data acquisition and data preparation stages for the following:
   the local memory, especially when processing with accelerators. A data source
   provides the ability to load data by batches and extracts it directly into the
   device's local memory. Therefore, a data source enables complex data analytics
-  scenarios, such as :term:`online computations <Online mode>`.
+  scenarios, such as :term:`online computations <Online Mode>`.
 
-- To filter and normalize :term:`feature` values that are being extracted.
+- To filter and normalize :term:`Feature` values that are being extracted.
 
-- To recover missing :term:`feature` values.
+- To recover missing :term:`Feature` values.
 
 - To detect :term:`outliers <Outlier>` and recover the abnormal data.
 
@@ -112,7 +112,7 @@ For details, see :ref:`data-sources` section.
 Table
 -----
 
-Table is a concept of a :term:`dataset` with in-memory numerical data. It is
+Table is a concept of a :term:`Dataset` with in-memory numerical data. It is
 used at the data preparation and data processing stages for the following:
 
 - To store heterogeneous in-memory data in various
@@ -130,19 +130,19 @@ used at the data preparation and data processing stages for the following:
 
 - To support streaming of the data to the algorithm.
 
-- To access the underlying data on a device in a required :term:`data format`,
-  e.g. by blocks with the defined :term:`data layout`.
+- To access the underlying data on a device in a required :term:`Data format`,
+  e.g. by blocks with the defined :term:`Data layout`.
 
 For thread-safety reasons and better integration with external entities, a table
 provides a read-only access to the data within it, thus, table concept
 implementations shall be :term:`immutable <Immutability>`.
 
 This concept has different logical organization and physical
-:term:`format of the data <data format>`:
+:term:`format of the data <Data format>`:
 
 - Logically, a table is a :ref:`dataset` with :math:`n` rows and
-  :math:`p` columns. Each row represents an :term:`observation` and each column
-  is a :term:`feature` of a dataset. Physical amount of bytes needed to store
+  :math:`p` columns. Each row represents an :term:`Observation` and each column
+  is a :term:`Feature` of a dataset. Physical amount of bytes needed to store
   the data differ from the number of elements :math:`n \times p` within
   a table.
 
@@ -169,24 +169,24 @@ For each dataset, its metadata shall contain:
 
 - The number of rows :math:`n` and columns :math:`p` in a dataset.
 
-- The type of each :term:`feature` (e.g. :term:`nominal <Nominal feature>`,
+- The type of each :term:`Feature` (e.g. :term:`nominal <Nominal feature>`,
   :term:`interval <Interval feature>`).
 
-- The :term:`data type` of each feature (e.g. :code:`float` or :code:`double`).
+- The :term:`Data type` of each feature (e.g. :code:`float` or :code:`double`).
 
 .. note::
   Metadata can contain both compile-time and run-time information. For example,
   basic compile-time metadata is the type of a dataset - whether it is a
   particular :ref:`data-source` or a :ref:`table`. Run-time information can
-  contain the :term:`feature` types and :term:`data types <Data type>` of a
+  contain the :term:`Feature` types and :term:`data types <Data type>` of a
   dataset.
 
 .. _table-builder:
 
 Table builder
 -------------
 
-A table :term:`builder` is a concept that is associated with a particular
+A table :term:`Builder` is a concept that is associated with a particular
 :ref:`table` type and is used at the data preparation and data processing stages
 for:
 
@@ -226,13 +226,13 @@ in-memory numerical :ref:`dataset`. It allows:
 - To convert a variety of numeric :term:`data formats <Data format>` into a
   smaller set of formats.
 
-- To provide a :term:`flat <flat data>` view on the data blocks of a
+- To provide a :term:`flat <Flat data>` view on the data blocks of a
   :ref:`dataset` for better a data locality. For example, some accessor
-  implementation returns :term:`feature` values as a contiguous array, while the
+  implementation returns :term:`Feature` values as a contiguous array, while the
   original dataset stored row-by-row (there are strides between values of a
   single feature).
 
-- To acquire data in a desired :term:`data format` for which
+- To acquire data in a desired :term:`Data format` for which
   a specific set of operations is defined.
 
 - To have read-only, read-write and write-only access to the data. Accessor
@@ -282,7 +282,7 @@ within some table, a builder object can be constructed for this. Data inside a
 table builder can be retrieved by read-only, write-only or read-write accessors.
 
 Accessors shown on the diagram allow to get data from tables and table builders
-as :term:`flat <flat data>` blocks of rows.
+as :term:`flat <Flat data>` blocks of rows.
 
 Details
 =======

source/elements/oneDAL/source/glossary.rst

@@ -11,13 +11,13 @@ Machine learning terms
     :sorted:
 
     Categorical feature
-        A :term:`feature` with a discrete domain. Can be :term:`nominal <Nominal
+        A :term:`Feature` with a discrete domain. Can be :term:`nominal <Nominal
         feature>` or :term:`ordinal <Ordinal feature>`.
 
         **Synonyms:** discrete feature, qualitative feature
 
     Nominal feature
-        A :term:`categorical feature` without ordering between values. Only
+        A :term:`Categorical feature` without ordering between values. Only
         equality operation is defined for nominal features.
 
         **Examples:** a person's gender, color of a car
@@ -38,7 +38,7 @@ Machine learning terms
         **Example:** find big star clusters in the space images
 
     Continuous feature
-        A :term:`feature` with values in a domain of real numbers. Can be
+        A :term:`Feature` with values in a domain of real numbers. Can be
         :term:`interval <Interval feature>` or :term:`ratio <Ratio feature>`
 
         **Synonyms:** quantitative feature, numerical feature
@@ -57,51 +57,51 @@ Machine learning terms
 
     Feature vector
         A vector that encodes information about real object, an event or a group
-        of objects or events. Contains at least one :term:`feature`.
+        of objects or events. Contains at least one :term:`Feature`.
 
         **Example:** A rectangle can be described by two features: its width and
         height
 
     Inference
-        A process of applying a :term:`trained <Training>` :term:`model` to the
-        :term:`dataset` in order to predict :term:`response`
+        A process of applying a :term:`trained <Training>` :term:`Model` to the
+        :term:`Dataset` in order to predict :term:`Response`
         values based on input :term:`feature vectors <Feature vector>`.
 
         **Synonym:** prediction
 
     Inference set
-        A :term:`dataset` used at the :term:`inference` stage.
+        A :term:`Dataset` used at the :term:`Inference` stage.
         Usually without :term:`responses <Response>`.
 
     Interval feature
-        A :term:`continuous feature` with values that can be compared, added or
+        A :term:`Continuous feature` with values that can be compared, added or
         subtracted, but cannot be multiplied or divided.
 
         **Examples:** a timeframe scale, a temperature in Celcius or Fahrenheit
 
     Label
-        A :term:`response` with :term:`categorical <Categorical feature>` or
+        A :term:`Response` with :term:`categorical <Categorical feature>` or
         :term:`ordinal <Ordinal feature>` values. This is an output in
-        :term:`classification` and :term:`clustering` problems.
+        :term:`Classification` and :term:`Clustering` problems.
 
         **Example:** the spam-detection problem has a binary label indicating
         whether the email is spam or not
 
     Model
-        An entity that stores information necessary to run :term:`inference`
-        on a new :term:`dataset`. Typically a result of a :term:`training`
+        An entity that stores information necessary to run :term:`Inference`
+        on a new :term:`Dataset`. Typically a result of a :term:`Training`
         process.
 
         **Example:** in linear regression algorithm, the model contains weight
         values for each input feature and a single bias value
 
     Observation
-        A :term:`feature vector` and zero or more :term:`responses<Response>`.
+        A :term:`Feature vector` and zero or more :term:`responses<Response>`.
 
         **Synonyms:** instance, sample
 
     Ordinal feature
-        A :term:`categorical feature` with defined operations of equality and
+        A :term:`Categorical feature` with defined operations of equality and
         ordering between values.
 
         **Example:** student's grade
@@ -111,46 +111,46 @@ Machine learning terms
         observations.
 
     Ratio feature
-        A :term:`continuous feature` with defined operations of equality,
+        A :term:`Continuous feature` with defined operations of equality,
         comparison, addition, subtraction, multiplication, and division.
         Zero value element means the absence of any value.
 
         **Example:** the height of a tower
 
     Regression
         A :term:`supervised machine learning problem <Supervised learning>` of
-        assigning :term:`continuous <Continuous feature>`
-        :term:`responses<Response>` for :term:`feature vectors <Feature vector>`.
+        assigning :term:`continuous <Continuous feature>` :term:`responses
+        <Response>` for :term:`feature vectors <Feature vector>`.
 
         **Example:** predict temperature based on weather conditions
 
     Response
         A property of some real object or event which dependency from
-        :term:`feature vector` need to be defined in :term:`supervised learning`
-        problem. While a :term:`feature` is an input in the machine learning
+        :term:`Feature vector` need to be defined in :term:`Supervised learning`
+        problem. While a :term:`Feature` is an input in the machine learning
         problem, the response is one of the outputs can be made by the
-        :term:`model` on the :term:`inference` stage.
+        :term:`Model` on the :term:`Inference` stage.
 
         **Synonym:** dependent variable
 
     Supervised learning
-        :term:`Training` process that uses a :term:`dataset` with information
+        :term:`Training` process that uses a :term:`Dataset` with information
         about dependencies between :term:`features <Feature>` and
-        :term:`responses <Response>`. The goal is to get a :term:`model` of
-        dependencies between input :term:`feature vector` and
+        :term:`responses <Response>`. The goal is to get a :term:`Model` of
+        dependencies between input :term:`Feature vector` and
         :term:`responses <Response>`.
 
     Training
-        A process of creating a :term:`model` based on information extracted
-        from a :term:`training set`. Resulting :term:`model` is selected in
+        A process of creating a :term:`Model` based on information extracted
+        from a :term:`Training set`. Resulting :term:`Model` is selected in
         accordance with some quality criteria.
 
     Training set
-        A :term:`dataset` used at the :term:`training` stage to create a
-        :term:`model`.
+        A :term:`Dataset` used at the :term:`Training` stage to create a
+        :term:`Model`.
 
     Unsupervised learning
-        :term:`Training` process that uses a :term:`training set` with no
+        :term:`Training` process that uses a :term:`Training set` with no
         :term:`responses <Response>`. The goal is to find hidden patters inside
         :term:`feature vectors <Feature vector>` and dependencies between them.
 
@@ -162,7 +162,7 @@ Machine learning terms
 
     Accessor
         A |dal_short_name| concept for an object that provides access to the
-        data of another object in the special :term:`data format`. It abstracts
+        data of another object in the special :term:`Data format`. It abstracts
         data access from interface of an object and provides uniform access to
         the data stored in objects of different types.
 
@@ -177,7 +177,7 @@ Machine learning terms
 
     Contiguous data
         Data that are stored as one contiguous memory block. One of the
-        characteristics of a :term:`data format`.
+        characteristics of a :term:`Data format`.
 
     Data format
         Representation of the internal structure of the data.
@@ -186,8 +186,8 @@ Machine learning terms
         compressed-sparse-row format
 
     Data layout
-        A characteristic of :term:`data format` which describes the
-        order of elements in a :term:`contiguous data` block.
+        A characteristic of :term:`Data format` which describes the
+        order of elements in a :term:`Contiguous data` block.
 
         **Example:** row-major format, where elements are stored row by row
 
@@ -199,8 +199,8 @@ Machine learning terms
         **Examples:** ``int32_t``, ``float``, ``double``
 
     Flat data
-        A block of :term:`contiguous <contiguous data>` :term:`homogeneous
-        <homogeneous data>` data.
+        A block of :term:`contiguous <Contiguous data>` :term:`homogeneous
+        <Homogeneous data>` data.
 
     Getter
         A method that returns the value of the private member variable.
@@ -215,21 +215,21 @@ Machine learning terms
     Heterogeneous data
         Data which contain values either of different :term:`data types <Data
         type>` or different sets of operations defined on them. One of the
-        characteristics of a :term:`data format`.
+        characteristics of a :term:`Data format`.
 
-        **Example:** A :term:`dataset` with 100
-        :term:`observations <Observation>` of three :term:`interval features <Interval
-        feature>`. The first two features are of float32 :term:`data type`, while the
-        third one is of float64 data type.
+        **Example:** A :term:`Dataset` with 100 :term:`observations
+        <Observation>` of three :term:`interval features <Interval feature>`.
+        The first two features are of float32 :term:`Data type`, while the third
+        one is of float64 data type.
 
     Homogeneous data
-        Data with values of single :term:`data type` and the same set of
+        Data with values of single :term:`Data type` and the same set of
         available operations defined on them. One of the characteristics of a
-        :term:`data format`.
+        :term:`Data format`.
 
-        **Example:** A :term:`dataset` with 100
-        :term:`observations <Observation>` of three  :term:`interval features <Interval
-        feature>`, each of type float32
+        **Example:** A :term:`Dataset` with 100 :term:`observations
+        <Observation>` of three  :term:`interval features <Interval feature>`,
+        each of type float32
 
     Immutability
         The object is immutable if it is not possible to change its state after
@@ -241,7 +241,7 @@ Machine learning terms
         possible objects of a given type. Metadata do not expose information
         that is not a part of a type definition, e.g. implementation details.
 
-        **Example:** :term:`table` object can contain three :term:`nominal features
+        **Example:** :term:`Table` object can contain three :term:`nominal features
         <Nominal feature>` with 100 :term:`observations <Observation>` (logical
         part of metadata). This object can store data as sparse csr array and
         provides direct access to them (physical part)
@@ -270,14 +270,14 @@ Machine learning terms
 
 
     Table
-        A |dal_short_name| concept for a :term:`dataset` that contains only
+        A |dal_short_name| concept for a :term:`Dataset` that contains only
         numerical data, :term:`categorical <Categorical feature>` or
         :term:`continuous <Continuous feature>`. Serves as a transfer of data
         between user's application and computations inside |dal_short_name|.
-        Hides details of :term:`data format` and generalizes access to the data.
+        Hides details of :term:`Data format` and generalizes access to the data.
 
     Workload
-        A problem of applying a |dal_short_name| algorithm to a :term:`dataset`.
+        A problem of applying a |dal_short_name| algorithm to a :term:`Dataset`.
 
 Common oneAPI terms
 ===================
@@ -291,7 +291,7 @@ Common oneAPI terms
     DPC++
         Data Parallel C++ (DPC++) is a high-level language designed for data
         parallel programming productivity. DPC++ is based on :term:`SYCL*
-        <sycl>` from the Khronos* Group to support data parallelism and
+        <SYCL>` from the Khronos* Group to support data parallelism and
         heterogeneous programming.
 
     Host/Device

source/elements/oneDAL/source/introduction.rst

@@ -48,11 +48,11 @@ communication technology and, therefore, can be used within different end-to-end
    - Prediction algorithms typically work with the trained data model and with a working data set.
 
  - The **Utilities** component includes auxiliary functionality intended to be used for design of
- classes and implementation of methods such as memory allocators or type traits.
+   classes and implementation of methods such as memory allocators or type traits.
 
  - The **Miscellaneous** component includes functionality intended to be used by |dal_short_name|
- algorithms and applications for algorithm customization and optimization on various stages of the
- analytical pipeline. Examples of such algorithms include solvers and random number generators.
+   algorithms and applications for algorithm customization and optimization on various stages of the
+   analytical pipeline. Examples of such algorithms include solvers and random number generators.
 
 Classes in Data Management, Algorithms, Utilities, and Miscellaneous components cover the most
 important usage scenarios and allow seamless implementation of complex data analytics workflows