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  /*
   * Copyright 2010-2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
   * 
   * Licensed under the Apache License, Version 2.0 (the "License").
   * You may not use this file except in compliance with the License.
   * A copy of the License is located at
   * 
   *  http://aws.amazon.com/apache2.0
   * 
  * or in the "license" file accompanying this file. This file is distributed
  * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
  * express or implied. See the License for the specific language governing
  * permissions and limitations under the License.
  */
 package com.amazonaws.services.machinelearning.model;
 
 
Container for the parameters to the CreateMLModel operation.

Creates a new MLModel using the data files and the recipe as information sources.

An MLModel is nearly immutable. Users can only update the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel .

CreateMLModel is an asynchronous operation. In response to CreateMLModel , Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING . After the MLModel is created and ready for use, Amazon ML sets the status to COMPLETED .

You can use the GetMLModel operation to check progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

 
 public class CreateMLModelRequest extends AmazonWebServiceRequest implements SerializableCloneable {

    
A user-supplied ID that uniquely identifies the MLModel.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

 
     private String mLModelId;

    
A user-supplied name or description of the MLModel.

Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$

 
     private String mLModelName;

    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

 
     private String mLModelType;

    
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    private java.util.Map<String,Stringparameters;

    
The DataSource that points to the training data.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

    private String trainingDataSourceId;

    
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 131071

    private String recipe;

    
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

    private String recipeUri;

    
A user-supplied ID that uniquely identifies the MLModel.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Returns:
A user-supplied ID that uniquely identifies the MLModel.
    public String getMLModelId() {
        return ;
    }
    
    
A user-supplied ID that uniquely identifies the MLModel.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Parameters:
mLModelId A user-supplied ID that uniquely identifies the MLModel.
    public void setMLModelId(String mLModelId) {
        this. = mLModelId;
    }
    
    
A user-supplied ID that uniquely identifies the MLModel.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Parameters:
mLModelId A user-supplied ID that uniquely identifies the MLModel.
Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withMLModelId(String mLModelId) {
        this. = mLModelId;
        return this;
    }

    
A user-supplied name or description of the MLModel.

Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$

Returns:
A user-supplied name or description of the MLModel.
    public String getMLModelName() {
        return ;
    }
    
    
A user-supplied name or description of the MLModel.

Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$

Parameters:
mLModelName A user-supplied name or description of the MLModel.
    public void setMLModelName(String mLModelName) {
        this. = mLModelName;
    }
    
    
A user-supplied name or description of the MLModel.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$

Parameters:
mLModelName A user-supplied name or description of the MLModel.
Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withMLModelName(String mLModelName) {
        this. = mLModelName;
        return this;
    }

    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Returns:
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See also:
MLModelType
    public String getMLModelType() {
        return ;
    }
    
    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See also:
MLModelType
    public void setMLModelType(String mLModelType) {
        this. = mLModelType;
    }
    
    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns:
A reference to this updated object so that method calls can be chained together.
See also:
MLModelType
    public CreateMLModelRequest withMLModelType(String mLModelType) {
        this. = mLModelType;
        return this;
    }

    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

See also:
MLModelType
    public void setMLModelType(MLModelType mLModelType) {
        this. = mLModelType.toString();
    }
    
    
The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS

Parameters:
mLModelType The category of supervised learning that this MLModel will address. Choose from the following types:
  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

Returns:
A reference to this updated object so that method calls can be chained together.
See also:
MLModelType
    public CreateMLModelRequest withMLModelType(MLModelType mLModelType) {
        this. = mLModelType.toString();
        return this;
    }

    
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

Returns:
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    public java.util.Map<String,StringgetParameters() {
        
        if ( == null) {
             = new java.util.HashMap<String,String>();
        }
        return ;
    }
    
    
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

Parameters:
parameters A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    public void setParameters(java.util.Map<String,Stringparameters) {
        this. = parameters;
    }
    
    
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

Returns a reference to this object so that method calls can be chained together.

Parameters:
parameters A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withParameters(java.util.Map<String,Stringparameters) {
        setParameters(parameters);
        return this;
    }

    
A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

  • sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

The method adds a new key-value pair into Parameters parameter, and returns a reference to this object so that method calls can be chained together.

Parameters:
key The key of the entry to be added into Parameters.
value The corresponding value of the entry to be added into Parameters.
    if (null == this.) {
      this. = new java.util.HashMap<String,String>();
    }
    if (this..containsKey(key))
      throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
    this..put(keyvalue);
    return this;
  }

  
Removes all the entries added into Parameters.

Returns a reference to this object so that method calls can be chained together.

    this. = null;
    return this;
  }
  
    
The DataSource that points to the training data.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Returns:
The DataSource that points to the training data.
    public String getTrainingDataSourceId() {
        return ;
    }
    
    
The DataSource that points to the training data.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Parameters:
trainingDataSourceId The DataSource that points to the training data.
    public void setTrainingDataSourceId(String trainingDataSourceId) {
        this. = trainingDataSourceId;
    }
    
    
The DataSource that points to the training data.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+

Parameters:
trainingDataSourceId The DataSource that points to the training data.
Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withTrainingDataSourceId(String trainingDataSourceId) {
        this. = trainingDataSourceId;
        return this;
    }

    
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 131071

Returns:
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
    public String getRecipe() {
        return ;
    }
    
    
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 131071

Parameters:
recipe The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
    public void setRecipe(String recipe) {
        this. = recipe;
    }
    
    
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Length: 0 - 131071

Parameters:
recipe The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withRecipe(String recipe) {
        this. = recipe;
        return this;
    }

    
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Returns:
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
    public String getRecipeUri() {
        return ;
    }
    
    
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Parameters:
recipeUri The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
    public void setRecipeUri(String recipeUri) {
        this. = recipeUri;
    }
    
    
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.

Returns a reference to this object so that method calls can be chained together.

Constraints:
Length: 0 - 2048
Pattern: s3://([^/]+)(/.*)?

Parameters:
recipeUri The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don???t specify a recipe or its URI, Amazon ML creates a default.
Returns:
A reference to this updated object so that method calls can be chained together.
    public CreateMLModelRequest withRecipeUri(String recipeUri) {
        this. = recipeUri;
        return this;
    }

    
Returns a string representation of this object; useful for testing and debugging.

Returns:
A string representation of this object.
See also:
java.lang.Object.toString()
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("{");
        if (getMLModelId() != nullsb.append("MLModelId: " + getMLModelId() + ",");
        if (getMLModelName() != nullsb.append("MLModelName: " + getMLModelName() + ",");
        if (getMLModelType() != nullsb.append("MLModelType: " + getMLModelType() + ",");
        if (getParameters() != nullsb.append("Parameters: " + getParameters() + ",");
        if (getTrainingDataSourceId() != nullsb.append("TrainingDataSourceId: " + getTrainingDataSourceId() + ",");
        if (getRecipe() != nullsb.append("Recipe: " + getRecipe() + ",");
        if (getRecipeUri() != nullsb.append("RecipeUri: " + getRecipeUri() );
        sb.append("}");
        return sb.toString();
    }
    
    @Override
    public int hashCode() {
        final int prime = 31;
        int hashCode = 1;
        
        hashCode = prime * hashCode + ((getMLModelId() == null) ? 0 : getMLModelId().hashCode()); 
        hashCode = prime * hashCode + ((getMLModelName() == null) ? 0 : getMLModelName().hashCode()); 
        hashCode = prime * hashCode + ((getMLModelType() == null) ? 0 : getMLModelType().hashCode()); 
        hashCode = prime * hashCode + ((getParameters() == null) ? 0 : getParameters().hashCode()); 
        hashCode = prime * hashCode + ((getTrainingDataSourceId() == null) ? 0 : getTrainingDataSourceId().hashCode()); 
        hashCode = prime * hashCode + ((getRecipe() == null) ? 0 : getRecipe().hashCode()); 
        hashCode = prime * hashCode + ((getRecipeUri() == null) ? 0 : getRecipeUri().hashCode()); 
        return hashCode;
    }
    
    @Override
    public boolean equals(Object obj) {
        if (this == objreturn true;
        if (obj == nullreturn false;
        if (obj instanceof CreateMLModelRequest == falsereturn false;
        CreateMLModelRequest other = (CreateMLModelRequest)obj;
        
        if (other.getMLModelId() == null ^ this.getMLModelId() == nullreturn false;
        if (other.getMLModelId() != null && other.getMLModelId().equals(this.getMLModelId()) == falsereturn false
        if (other.getMLModelName() == null ^ this.getMLModelName() == nullreturn false;
        if (other.getMLModelName() != null && other.getMLModelName().equals(this.getMLModelName()) == falsereturn false
        if (other.getMLModelType() == null ^ this.getMLModelType() == nullreturn false;
        if (other.getMLModelType() != null && other.getMLModelType().equals(this.getMLModelType()) == falsereturn false
        if (other.getParameters() == null ^ this.getParameters() == nullreturn false;
        if (other.getParameters() != null && other.getParameters().equals(this.getParameters()) == falsereturn false
        if (other.getTrainingDataSourceId() == null ^ this.getTrainingDataSourceId() == nullreturn false;
        if (other.getTrainingDataSourceId() != null && other.getTrainingDataSourceId().equals(this.getTrainingDataSourceId()) == falsereturn false
        if (other.getRecipe() == null ^ this.getRecipe() == nullreturn false;
        if (other.getRecipe() != null && other.getRecipe().equals(this.getRecipe()) == falsereturn false
        if (other.getRecipeUri() == null ^ this.getRecipeUri() == nullreturn false;
        if (other.getRecipeUri() != null && other.getRecipeUri().equals(this.getRecipeUri()) == falsereturn false
        return true;
    }
    
    @Override
    public CreateMLModelRequest clone() {
        
            return (CreateMLModelRequestsuper.clone();
    }
}
    
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