text:
#pragma once
#include <aws/machinelearning/MachineLearning_EXPORTS.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <utility>

namespace Aws
{
namespace Utils
{
namespace Json
{
  class JsonValue;
  class JsonView;
} // namespace Json
} // namespace Utils
namespace MachineLearning
{
namespace Model
{

  /**
   *
 Describes the data specification of a DataSource.

See
   * Also:
   AWS
   * API Reference

   */
  class AWS_MACHINELEARNING_API S3DataSpec
  {
  public:
    S3DataSpec();
    S3DataSpec(Aws::Utils::Json::JsonView jsonValue);
    S3DataSpec& operator=(Aws::Utils::Json::JsonView jsonValue);
    Aws::Utils::Json::JsonValue Jsonize() const;

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline const Aws::String& GetDataLocationS3() const{ return m_dataLocationS3; }

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline bool DataLocationS3HasBeenSet() const { return m_dataLocationS3HasBeenSet; }

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline void SetDataLocationS3(const Aws::String& value) { m_dataLocationS3HasBeenSet = true; m_dataLocationS3 = value; }

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline void SetDataLocationS3(Aws::String&& value) { m_dataLocationS3HasBeenSet = true; m_dataLocationS3 = std::move(value); }

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline void SetDataLocationS3(const char* value) { m_dataLocationS3HasBeenSet = true; m_dataLocationS3.assign(value); }

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline S3DataSpec& WithDataLocationS3(const Aws::String& value) { SetDataLocationS3(value); return *this;}

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline S3DataSpec& WithDataLocationS3(Aws::String&& value) { SetDataLocationS3(std::move(value)); return *this;}

    /**
     *
The location of the data file(s) used by a DataSource. The URI
     * specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory
     * or bucket containing data files.

     */
    inline S3DataSpec& WithDataLocationS3(const char* value) { SetDataLocationS3(value); return *this;}

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline const Aws::String& GetDataRearrangement() const{ return m_dataRearrangement; }

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline bool DataRearrangementHasBeenSet() const { return m_dataRearrangementHasBeenSet; }

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline void SetDataRearrangement(const Aws::String& value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement = value; }

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline void SetDataRearrangement(Aws::String&& value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement = std::move(value); }

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline void SetDataRearrangement(const char* value) { m_dataRearrangementHasBeenSet = true; m_dataRearrangement.assign(value); }

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline S3DataSpec& WithDataRearrangement(const Aws::String& value) { SetDataRearrangement(value); return *this;}

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline S3DataSpec& WithDataRearrangement(Aws::String&& value) { SetDataRearrangement(std::move(value)); return *this;}

    /**
     *
A JSON string that represents the splitting and rearrangement processing to
     * be applied to a DataSource. If the DataRearrangement
     * parameter is not provided, all of the input data is used to create the
     * Datasource.

There are multiple parameters that control what
     * data is used to create a datasource:

     * percentBegin

Use percentBegin to indicate
     * the beginning of the range of the data used to create the Datasource. If you do
     * not include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * percentEnd

Use percentEnd to indicate the
     * end of the range of the data used to create the Datasource. If you do not
     * include percentBegin and percentEnd, Amazon ML
     * includes all of the data when creating the datasource.

     * complement

The complement parameter
     * instructs Amazon ML to use the data that is not included in the range of
     * percentBegin to percentEnd to create a datasource. The
     * complement parameter is useful if you need to create complementary
     * datasources for training and evaluation. To create a complementary datasource,
     * use the same values for percentBegin and percentEnd,
     * along with the complement parameter.

For example, the
     * following two datasources do not share any data, and can be used to train and
     * evaluate a model. The first datasource has 25 percent of the data, and the
     * second one has 75 percent of the data.

Datasource for evaluation:
     * {"splitting":{"percentBegin":0, "percentEnd":25}}

     *
Datasource for training: {"splitting":{"percentBegin":0,
     * "percentEnd":25, "complement":"true"}}

     * strategy

To change how Amazon ML splits the data for a
     * datasource, use the strategy parameter.

The default value
     * for the strategy parameter is sequential, meaning that
     * Amazon ML takes all of the data records between the percentBegin
     * and percentEnd parameters for the datasource, in the order that the
     * records appear in the input data.

The following two
     * DataRearrangement lines are examples of sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential"}}

Datasource for training:
     * {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"sequential", "complement":"true"}}

To randomly split
     * the input data into the proportions indicated by the percentBegin and percentEnd
     * parameters, set the strategy parameter to random and
     * provide a string that is used as the seed value for the random data splitting
     * (for example, you can use the S3 path to your data as the random seed string).
     * If you choose the random split strategy, Amazon ML assigns each row of data a
     * pseudo-random number between 0 and 100, and then selects the rows that have an
     * assigned number between percentBegin and percentEnd.
     * Pseudo-random numbers are assigned using both the input seed string value and
     * the byte offset as a seed, so changing the data results in a different split.
     * Any existing ordering is preserved. The random splitting strategy ensures that
     * variables in the training and evaluation data are distributed similarly. It is
     * useful in the cases where the input data may have an implicit sort order, which
     * would otherwise result in training and evaluation datasources containing
     * non-similar data records.

The following two
     * DataRearrangement lines are examples of non-sequentially ordered
     * training and evaluation datasources:

Datasource for evaluation:
     * {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random",
     * "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for
     * training: {"splitting":{"percentBegin":70, "percentEnd":100,
     * "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
     * "complement":"true"}}

     */
    inline S3DataSpec& WithDataRearrangement(const char* value) { SetDataRearrangement(value); return *this;}

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline const Aws::String& GetDataSchema() const{ return m_dataSchema; }

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline bool DataSchemaHasBeenSet() const { return m_dataSchemaHasBeenSet; }

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline void SetDataSchema(const Aws::String& value) { m_dataSchemaHasBeenSet = true; m_dataSchema = value; }

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline void SetDataSchema(Aws::String&& value) { m_dataSchemaHasBeenSet = true; m_dataSchema = std::move(value); }

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline void SetDataSchema(const char* value) { m_dataSchemaHasBeenSet = true; m_dataSchema.assign(value); }

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline S3DataSpec& WithDataSchema(const Aws::String& value) { SetDataSchema(value); return *this;}

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline S3DataSpec& WithDataSchema(Aws::String&& value) { SetDataSchema(std::move(value)); return *this;}

    /**
     *
 A JSON string that represents the schema for an Amazon S3
     * DataSource. The DataSchema defines the structure of
     * the observation data in the data file(s) referenced in the
     * DataSource.

You must provide either the
     * DataSchema or the DataSchemaLocationS3.

Define
     * your DataSchema as a series of key-value pairs.
     * attributes and excludedVariableNames have an array of
     * key-value pairs for their value. Use the following format to define your
     * DataSchema.

{ "version": "1.0",

     *
"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

     *
"targetFieldName": "F3",

"dataFormat": "CSV",

     *
"dataFileContainsHeader": true,

"attributes": [

{ "fieldName":
     * "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, {
     * "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4",
     * "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, {
     * "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
     * "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType":
     * "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

     */
    inline S3DataSpec& WithDataSchema(const char* value) { SetDataSchema(value); return *this;}

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline const Aws::String& GetDataSchemaLocationS3() const{ return m_dataSchemaLocationS3; }

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline bool DataSchemaLocationS3HasBeenSet() const { return m_dataSchemaLocationS3HasBeenSet; }

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline void SetDataSchemaLocationS3(const Aws::String& value) { m_dataSchemaLocationS3HasBeenSet = true; m_dataSchemaLocationS3 = value; }

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline void SetDataSchemaLocationS3(Aws::String&& value) { m_dataSchemaLocationS3HasBeenSet = true; m_dataSchemaLocationS3 = std::move(value); }

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline void SetDataSchemaLocationS3(const char* value) { m_dataSchemaLocationS3HasBeenSet = true; m_dataSchemaLocationS3.assign(value); }

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline S3DataSpec& WithDataSchemaLocationS3(const Aws::String& value) { SetDataSchemaLocationS3(value); return *this;}

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline S3DataSpec& WithDataSchemaLocationS3(Aws::String&& value) { SetDataSchemaLocationS3(std::move(value)); return *this;}

    /**
     *
Describes the schema location in Amazon S3. You must provide either the
     * DataSchema or the DataSchemaLocationS3.

     */
    inline S3DataSpec& WithDataSchemaLocationS3(const char* value) { SetDataSchemaLocationS3(value); return *this;}

  private:

    Aws::String m_dataLocationS3;
    bool m_dataLocationS3HasBeenSet = false;

    Aws::String m_dataRearrangement;
    bool m_dataRearrangementHasBeenSet = false;

    Aws::String m_dataSchema;
    bool m_dataSchemaHasBeenSet = false;

    Aws::String m_dataSchemaLocationS3;
    bool m_dataSchemaLocationS3HasBeenSet = false;
  };

} // namespace Model
} // namespace MachineLearning
} // namespace Aws

redpajama_set_name: RedPajamaGithub


All the Posts, Districts, and Area Chairmen that helped support these students with their journey to the State Contest.
Department Oratorical Chairman, Meri West, for her dedication and support for the Oratorical Program.
The Judges, Tabulators, and Time Keepers and staff for your help and support at the State Contest.


Russian President Putin has decided that Syria is part of Russia's near abroad, no less than Ukraine it seems, a territory where some vital national interests are at stake. He has predicted the fecklessness of Western powers well. Whether he is deploying his arsenal in Syria to fight Daesh or to bolster Assad, by moving massive military presence into Syria he has made himself the one player that counts and has put himself in a position to call the shots. He does not have a strategy to end the conflict. But he has one that he thinks will guarantee Russia's influence in this pivotal country while the West has no strategy to confront him.
While Europe is witnessing the massive inflow of Syrian refugees who have made long and perilous journeys in the hope of better prospects, millions of others still remain in Syria's neighbouring countries, caught in a state of liminality or languishing in sprawling refugee camps.


text:
10 Responses to the Phrase 'Man Up'
September 24, 2014 / Guante
It's the favorite advice of patriarchal misogynists everywhere: "Man up." (See also: "Grow a pair," "Be a man," and "Stop acting like a girl.") This phrase presents a view of masculinity that is oppressive to not just women and gender non-conforming people, but to men as well.
Check out Guante's awesome poem with ten ways to respond to this so-called "advice."
The Editors at Everyday Feminism
Click for the Transcript
Ten responses to the phrase "man up."
1. F*ck you.
2. If you want to question my masculinity, like a schoolyard circle of curses, like a swordfight with light saber erections, save your breath. Because contrary to what you may believe, not every problem can be solved by "growing a pair." You cannot arm-wrestle your way out of chemical depression. The CEO of the company that just laid you off does not care how much you bench. And I promise, there is no light beer in the universe full-bodied enough to make you love yourself.
3. "Man up?" Oh that's that new superhero, right? Mild-mannered supplement salesman Mark Manstrong says the magic words "man up," and then transforms into The Five O'Clock Shadow, the massively-muscled, deep-voiced, leather-duster-wearing super-man who defends the world from… I don't know, feelings.
4. Of course. Why fight to remove our chains when we can simply compare their lengths? Why step outside the box when the box has these bad-ass flame decals on it? We men are cigarettes: dangerous and poisonous and stupid.
5. You ever notice how nobody ever says "woman up?" They just imply it. Because women and the women's movement figured out a long time ago that being directly, explicitly ordered around by commercials, magazines, and music is dehumanizing. When will men figure that out?
6. The phrase "man up" suggests that competence and perseverance are uniquely masculine traits. That women — not to mention any man who doesn't eat steak, drive a big truck, have lots of sex with women — are nothing. Nothing more than background characters, comic relief, props. More than anything, though, it suggests that to be yourself — whether you wear skinny jeans, or rock a little eyeliner, drink some other brand of light beer, or write poetry — will cost you.
7. How many boys have to kill themselves before this country acknowledges the problem? How many women have to be assaulted? How many trans people have to be murdered? We teach boys how to wear the skin of a man, but we also teach them how to raise that skin like a flag and draw blood for it.
8. Boy babies get blue socks. Girl babies get pink socks. What about purple? What about orange? What about green, what about cerulean, black, tie-dyed, buffalo plaid, rainbow…
9. I want to be free, to express myself. Man up. I want to have meaningful, emotional relationships with my brothers. Man up. I want to be weak sometimes. Man up. I want to be strong in a way that isn't about physical power or dominance. Man up. I want to talk to my son about something other than sports. Man up. I want to be who I am. Man up.
10. No.
Guante is a hip hop artist, two-time National Poetry Slam champion, social justice activist, educator and writer. He and/or his work has appeared on Upworthy, MSNBC, Racialicious, Feministing, MPR, the Progressive, City Pages' "Artists of the Year" list and URB Magazine's "Next 1000" list, and he's shared bills with Talib Kweli, Saul Williams, Brother Ali, Dead Prez, Sage Francis, Andrea Gibson, Doomtree, Zion I and more. Guante's work explores the intersections of art, media and social justice education; to that end, he facilitates workshops on identity, power and privilege, founded the MN Activist Project and Hip Hop Against Homophobia concert series, writes essays and op-eds, and works with COMPAS, TruArtSpeaks and others to engage in artist residencies around writing and performance for youth. Follow him on Twitter @elguante.
Video courtesy of Button Poetry. For more amazing spoken word performances, check them out on YouTube and Facebook.
← 5 Lies That Distort Male Sexuality and Hurt Men
Mental Illness and Sexism: What Calling Women 'Crazy' Actually Does →
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4 Reasons White People Can't Use the N-Word (No Matter What Black Folks Are Doing)
Here Are 4 Ways to Navigate Whiteness and Feminism – Without Being a White Feminist (TM)
4 Things You Need to Know Before Planning a Vacation to Hawaii
This Parody Perfectly Illustrates the Problem with Humanitarian Work Abroad
So Your Trans Friend Is Transitioning and You Want to Be Supportive – Here Are 6 Ways How
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How To Get Over Other People's Opinions of You–By Not Giving a Sh*t
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9 Things Everyone Needs to Stop Saying to Black Women Immediately
8 Lessons That Show How Emotional Labor Defines Women's Lives

redpajama_set_name: RedPajamaCommonCrawl


The ex-Muslim journalist baptised by Pope Benedict XVI this year has warned the pontiff that Catholic dialogue with Muslims is based on naive misconceptions about the "peaceful" nature of Islam.
Magdi Allam, whom Benedict baptised in St Peter's this Easter, has told the Pope that Cardinal Jean-Louis Tauran, the Vatican's head of inter-religious dialogue, was wrong to claim this year that Islam "promotes peace" and that Islamist extremists betray their faith.
I'm not sure I'd put it quite like that. But will anyone dare discuss the hate-filled rhetoric of mainstream Islam when the Vatican hosts its first Catholic-Muslim Forum next week? Or will both sides maintain the doctrine – promulgated by every public institution in Britain – that the jihadist and anti-semitic sentiments encouraged by Arab governments and Muslim community leaders are a distortion of Islam?


Patrick John Cleary was born on September 19, 1922 to Mr. and Mrs. Albert Cleary of Kent England. Patrick grew up in a town close to the ocean in Kent.
Patrick joined the British Navy in 1940 during WW2. Patrick served aboard an English destroyer during WW2. This destroyer was one of the ships involved in the hunt for the famed German battleship the Bismarck in 1941. Patrick Immigrated to Montreal Canada in 1951. Patrick married his wife Patricia in Canada and they had their only child, Colleen Patrice Cleary in 1954. Patrick received his Bachelor's degree in engineering in 1955. Shortly afterward Patrick, Patricia, and Colleen left for an oil field job in Venezuela. The family left Venezuela in 1957 moved to Seattle Washington to take a position with Boeing Aircraft. Patrick and Patricia became U.S. Citizens in 1965. Patrick worked for the next 25 years as a design engineer for the Boeing Corporation. In his time at Boeing, he worked on the design team on the cockpit of the 737. He also received several patents for his designs while working at Boeing. Patrick liked working with his hands. After retirement from Boeing he built his own house, and a bridge across the creek to get to the house. Patrick was preceded in death by his parents, his wife Patricia and his daughter Colleen. Patrick is survived by his Granddaughter Kaitlin who is a medical student in Omaha Nebraska.


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text:
Тара́сівка — пасажирський залізничний зупинний пункт Полтавської дирекції Південної залізниці.

Загальний опис
Розташований у селі Тарасівка, Гребінківського району Полтавської області на лінії Прилуки — Гребінка між станціями Гребінка (11 км) та Пирятин (5 км).

На платформі зупиняються приміські поїзди.

Примітки

Джерела

Зупинні пункти Південної залізниці
Зупинні пункти Полтавської області
Транспорт Гребінківського району

redpajama_set_name: RedPajamaWikipedia


text:
Real/full name:
Ville Hermanni Valo
43 (born Nov 22nd, 1976)
Valo is best known as the lead vocalist and frontman of the Finnish rock band HIM, who became the first Finnish rock band to achieve a gold record (more than 500,000 copies) in the United States.
Valo was born in a district of Helsinki called Vallila. His father, Kari, is Finnish and his mother, Anita, is of Hungarian descent. Soon after his birth, the family moved to the riverside community ...
Received the "Golden God" award in 2004 by the heavy metal magazine Metal Hammer.
Has a baritone vocal range, spanning A1 to A6.
Is close friends with CKY, Jackass and Viva La Bam star Bam Margera, who shares many of Valo's tattoos.
Portrayed Jim Morrison in the 69 Eyes music video "Wasting the Dawn".
Voices Motto Motto in the Finnish dub of Madagascar 2.
Past Bands
Guest/Session
Daniel Lioneye
2001 The King of Rock'n Roll Drums
Vocals (1991-2017)
2010 We're Here Because We're Here Vocals (backing) (track 5)
2004 Bittersweet (Single) Vocals
2005 Apocalyptica Vocals (track 4)
2006 Thornography Vocals (clean) (track 5)
Added by: Eagleheart Modified by: Jesse James
Duplicate? Please file a report for merging.


Opening Doors Worldwide improves lives and delivers hope to the poorest parts of the world by building medical and maternity clinics, providing safe drinking water, educating children, empowering women, fighting hunger and poverty through micro-loans and business training.
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text:
HomeCOVER STORY Story
India's Best CEOs: Natarajan Chandrasekaran has kept TCS on a high-growth path
Goutam Das | Edition:Jan 5, 2014
The IT czar, who bagged the Business Today's Best CEO 2013 award, has never attended any management school. His lessons in running a company have come from real-life experiences.
Natarajan Chandrasekaran, CEO/TATA Consultancy Services (TCS) Photo: Vivan Mehra
Natarajan Chandrasekaran loves his daily run and often talks about it. "Today, I ran 16 km till Nariman Point," he says with a smile when Business Today meets him at his home-office at Worli, Mumbai. The CEO and managing director of Tata Consultancy Services (TCS), who has a family history of diabetes, started running in 2007 to improve his fitness level. The marathon runner has changed in many other ways since then as well. He has become more persevering, patient and observant - desirable qualities for one who leads a company of 285,250 people. He is also more relaxed. "I used to be more tense as chief operating officer and in my earlier years," he says. He is dressed simply in a plaid shirt, jeans and slippers. "I am not tense at all now."
Chandrasekaran, or Chandra as he is popularly known, may not be the only TCS executive enjoying peace of mind. The fast-paced growth India's largest software services exporter has recorded since Chandra took over as CEO in October 2009 would make any company executive stress-free. TCS has nearly doubled its revenue in the past four years to $11.6 billion for the year ended March 2013. It has grown at a compound annual rate of 21 per cent despite a crippling slowdown in its main markets of North America and Europe. The company also led Business Today's list of 500 most valuable companies in India for the second year in a row and has widened the gap with second-placed Reliance Industries.
Chandra's old friends believe he is a corporate legend in the making. His story has the familiar 'filmi' trappings of a small-town guy making it big - from a village called Mohanur in Tamil Nadu where he grew up, to his current sea-facing apartment, Sagar Darshan, in the country's business capital.
Chandra is not a micromanager. He gives executives enough freedom to operate. But he sets them ambitious targets and his reviews are intense
Chandra lives on the second floor of the apartment with wife Lalitha and 15-year-old son Pranav. The office is on the first floor. It is intricately designed in classic European style with vintage porcelain vases, statues, candle stands, clocks and chandeliers complementing vintage-like furniture. The only Indian touch are the paintings on the walls, one of them - of the ghats of Varanasi - by Pune-based landscape artist Govind Dumbre. The home-office also has video conferencing facilities, a gym, bedrooms, and a bar where Chandra settles down with single malt in the evenings. The only things modest about the apartment are an old, manually operated, two-door elevator and the CEO's yellow Nano minicar, which is parked beside his black Mercedes.
All this is a world apart from his early life. Chandra was born in an agricultural family and had five siblings. His father, Srinivasan Natarajan, was a lawyer by profession but also managed the family farm that grew bananas, rice and sugarcane. Even Chandra tried farming for six months before realising it was not his cup of tea. He studied in a Tamil-medium government school and switched to English-medium for the senior secondary exams. He graduated from the Coimbatore Institute of Technology with a degree in applied sciences and completed his postgraduate degree in computer applications from Regional Engineering College, Tiruchirappalli.
"I remember Chandru (his friends called him Chandru) as a focused and hard-working student during our college days," says Nandakumar V., his hostel mate at REC Tiruchirappalli, in an email. Chandra was not athletic at this stage, though he had played some cricket, batting a bit, bowling a bit, in school. Nandakumar moved to the United States in 1989 and now runs a company called Yes-M Systems LLC in Atlanta. He kept interacting with his college friend whenever Chandra visited North America to handle TCS projects in the late 1980s and early 1990s. Even at the time, says Nandakumar, Chandra aspired for leadership roles.
Chandra never attended any management school. Nor does he read much management literature. His lessons in running a company have come from real-life experiences. Of course, he benefited immensely from the platform he inherited from S. Ramadorai, his predecessor. A strong delivery mindset was already part of the company's DNA when he took over as CEO. He also flourished because TCS is part of the Tata group. "The group is professionally managed, so I get to run the show. For me, it is the enormous freedom to operate. Then there is the strong value system of the group," says Chandra.
One of Chandrasekaran's early influences was his father, who wanted him to document every rupee he spent. He learnt lessons in frugality, honesty and perseverance from his father. "Every time I wanted to do something, he would ask if I am committing myself for the long term or was it for the short term. He would say whenever you have to do a one-off thing, you don't have to think much. Long-term commitments have to be thought through," says Chandra.
Chandra remembers an incident that influenced the way he looks after his people. In the government school in Mohanur, when he was in the sixth grade, Chandra could not attend classes for more than two months because of a surgery he underwent. His class teacher, Kannammal, kept for him the complete notes of what was taught in every subject during his absence. "The teacher didn't have to do that. I didn't think about it so much at that time. Many of these things come to my mind now," he says in a reflective tone. "Now I think about what it means to take care of people. You work in a team.
Somebody or the other will always need help. It is important to spend the time to help him."
In the early stages of his career at TCS, Chandra learnt vital management lessons from customers. Around 1993, he was working on a very large US federal government programme. In the first phase of the project, TCS did a spectacular job. On the day the second phase was about to go live, the client manager came up to Chandra and asked him out to lunch. Chandra was alarmed. The system was going live and he felt he had to stay in the office. But the client manager was persistent. "I was very restless in the car. There were no mobile phones those days - no one could have reached me," he says. "The client manager then said he needed to find out who could step into my shoes. Somebody needed to handle the job when I wasn't around." That was a lesson in delegating, trusting people and risk taking.
A recent incident highlights Chandra's risk-taking spirit. In September this year TCS was up against four competitors in Saudi Arabia to set up a business process outsourcing (BPO) centre that would service clients such as state-run energy giant Saudi Aramco and US conglomerate General Electric. Abid Ali Neemuchwala, who heads TCS's BPO business, was speaking to Chandra about the project when the CEO suddenly popped the question - why not set up an all-women centre? This was unheard of in the deeply conservative Islamic kingdom. "It created a competitive advantage and became a very unique proposition. This shows his ability to think out of the box," says Neemuchwala.
Pradeep Udhas, Head of Markets at consultancy KPMG India, feels Chandra's grip over whatever is happening in the company is awesome. Brother of ghazal singer Pankaj Udhas, Pradeep is a singer and composer himself and Chandra has attended his performances. "There are two different types of brains. Some leaders are very good with the macro picture and the vision. Some are more detailoriented," says Udhas, who has known Chandra for 23 years. "He has this great ability to see the big picture and, at the same time, go down to minute levels of detail. Even while leading a $12 billion company, he would know about a $2 million project TCS is pitching for."
Chandra's control over the firm does not mean he is a micro-manager. His trusting nature allows his executives enough freedom to operate. But he sets ambitious targets and his reviews are intense. "I feel I am in review every day. He knows every single thing about my unit," says Pratik Pal, head of TCS's retail business. "When I go to make a presentation to a customer in the US - when it's 3 a.m. in India - I get an SMS from him, 'How is the preparation?' After two hours, I get another SMS, 'How was the presentation?' He remembers a presentation even if I had told him about it two weeks before," says Pal.
Chandra never attended management school. Nor does he read much management literature. His lessons in running a company come from real-life experiences
Chandra also has a phenomenal capacity for remembering people - some executives claim he knows at least 5,000 of them by their names. Corridor conversations with him can revolve around not only the executives themselves but also their families. The personal touch allows the CEO to remain connected with employees. While remembering so many names appears superhuman, research from Kansas State University last year said it was not brain power alone that determined a person's ability to remember names. It was his level of interest in people and the desire to understand them that mattered. This ability is found in people who are more socially aware. Chandra definitely is. He also has a soft side. He once flew back from the US to attend to his dog Ray, a Beagle, who had cancer. Ray died earlier this year at age seven, after struggling with the disease for many months. "He was a good guy," Chandra says. "We have been thinking of getting a new dog. We will wait till my son finishes his Class X exams."
Agrarian Challenge
More from COVER STORY


Read Isaiah 6 two or three times. What words or phrases stand out to you?
How dose God's revelation in Isaiah 6 challenge some of our culture's assumptions about God and human goodness? Read Ephesians 2:1-16. What are parallel themes?
Isaiah was given this vision "during the year King Uzziah died". The emphasis is upon the historic circumstances. What do you suppose the relationship was between the timing and the content of Isaiah's vision?
Read Revelation 1, 4-5. These are visions of the resurrection Christ written for us to realize His preeminence and position over all creation. Where do you need to see this vision in your life circumstances?
Jesus taught the Holy Spirit moves as freely as the wind (John 3). We can't corral the Holy Spirit, yet we are dependent upon the Holy Spirit to encounter God. Would you commit to pray every Sunday morning for the next 6 months: 1) "God, show yourself to me as I come to worship you… Encounter me" and 2) "God, show yourself to your people when we gather to worship you… Shake us." How will you remind yourself?


text:
#include "shared-bindings/_bleio/Attribute.h"

// Convert a _bleio security mode to a ble_gap_conn_sec_mode_t setting.
void bleio_attribute_gatts_set_security_mode(ble_gap_conn_sec_mode_t *perm, bleio_attribute_security_mode_t security_mode) {
    switch (security_mode) {
        case SECURITY_MODE_NO_ACCESS:
            BLE_GAP_CONN_SEC_MODE_SET_NO_ACCESS(perm);
            break;

        case SECURITY_MODE_OPEN:
            BLE_GAP_CONN_SEC_MODE_SET_OPEN(perm);
            break;

        case SECURITY_MODE_ENC_NO_MITM:
            BLE_GAP_CONN_SEC_MODE_SET_ENC_NO_MITM(perm);
            break;

        case SECURITY_MODE_ENC_WITH_MITM:
            BLE_GAP_CONN_SEC_MODE_SET_ENC_WITH_MITM(perm);
            break;

        case SECURITY_MODE_LESC_ENC_WITH_MITM:
            BLE_GAP_CONN_SEC_MODE_SET_LESC_ENC_WITH_MITM(perm);
            break;

        case SECURITY_MODE_SIGNED_NO_MITM:
            BLE_GAP_CONN_SEC_MODE_SET_SIGNED_NO_MITM(perm);
            break;

        case SECURITY_MODE_SIGNED_WITH_MITM:
            BLE_GAP_CONN_SEC_MODE_SET_SIGNED_WITH_MITM(perm);
            break;
    }
}