com.amazonaws.services.forecast.model.TimeSeriesTransformation Maven / Gradle / Ivy
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/*
* Copyright 2019-2024 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.forecast.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* A transformation function is a pair of operations that select and modify the rows in a related time series. You
* select the rows that you want with a condition operation and you modify the rows with a transformation operation. All
* conditions are joined with an AND operation, meaning that all conditions must be true for the transformation to be
* applied. Transformations are applied in the order that they are listed.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TimeSeriesTransformation implements Serializable, Cloneable, StructuredPojo {
/**
*
* An array of actions that define a time series and how it is transformed. These transformations create a new time
* series that is used for the what-if analysis.
*
*/
private Action action;
/**
*
* An array of conditions that define which members of the related time series are transformed.
*
*/
private java.util.List timeSeriesConditions;
/**
*
* An array of actions that define a time series and how it is transformed. These transformations create a new time
* series that is used for the what-if analysis.
*
*
* @param action
* An array of actions that define a time series and how it is transformed. These transformations create a
* new time series that is used for the what-if analysis.
*/
public void setAction(Action action) {
this.action = action;
}
/**
*
* An array of actions that define a time series and how it is transformed. These transformations create a new time
* series that is used for the what-if analysis.
*
*
* @return An array of actions that define a time series and how it is transformed. These transformations create a
* new time series that is used for the what-if analysis.
*/
public Action getAction() {
return this.action;
}
/**
*
* An array of actions that define a time series and how it is transformed. These transformations create a new time
* series that is used for the what-if analysis.
*
*
* @param action
* An array of actions that define a time series and how it is transformed. These transformations create a
* new time series that is used for the what-if analysis.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesTransformation withAction(Action action) {
setAction(action);
return this;
}
/**
*
* An array of conditions that define which members of the related time series are transformed.
*
*
* @return An array of conditions that define which members of the related time series are transformed.
*/
public java.util.List getTimeSeriesConditions() {
return timeSeriesConditions;
}
/**
*
* An array of conditions that define which members of the related time series are transformed.
*
*
* @param timeSeriesConditions
* An array of conditions that define which members of the related time series are transformed.
*/
public void setTimeSeriesConditions(java.util.Collection timeSeriesConditions) {
if (timeSeriesConditions == null) {
this.timeSeriesConditions = null;
return;
}
this.timeSeriesConditions = new java.util.ArrayList(timeSeriesConditions);
}
/**
*
* An array of conditions that define which members of the related time series are transformed.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setTimeSeriesConditions(java.util.Collection)} or {@link #withTimeSeriesConditions(java.util.Collection)}
* if you want to override the existing values.
*
*
* @param timeSeriesConditions
* An array of conditions that define which members of the related time series are transformed.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesTransformation withTimeSeriesConditions(TimeSeriesCondition... timeSeriesConditions) {
if (this.timeSeriesConditions == null) {
setTimeSeriesConditions(new java.util.ArrayList(timeSeriesConditions.length));
}
for (TimeSeriesCondition ele : timeSeriesConditions) {
this.timeSeriesConditions.add(ele);
}
return this;
}
/**
*
* An array of conditions that define which members of the related time series are transformed.
*
*
* @param timeSeriesConditions
* An array of conditions that define which members of the related time series are transformed.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TimeSeriesTransformation withTimeSeriesConditions(java.util.Collection timeSeriesConditions) {
setTimeSeriesConditions(timeSeriesConditions);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getAction() != null)
sb.append("Action: ").append(getAction()).append(",");
if (getTimeSeriesConditions() != null)
sb.append("TimeSeriesConditions: ").append(getTimeSeriesConditions());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TimeSeriesTransformation == false)
return false;
TimeSeriesTransformation other = (TimeSeriesTransformation) obj;
if (other.getAction() == null ^ this.getAction() == null)
return false;
if (other.getAction() != null && other.getAction().equals(this.getAction()) == false)
return false;
if (other.getTimeSeriesConditions() == null ^ this.getTimeSeriesConditions() == null)
return false;
if (other.getTimeSeriesConditions() != null && other.getTimeSeriesConditions().equals(this.getTimeSeriesConditions()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAction() == null) ? 0 : getAction().hashCode());
hashCode = prime * hashCode + ((getTimeSeriesConditions() == null) ? 0 : getTimeSeriesConditions().hashCode());
return hashCode;
}
@Override
public TimeSeriesTransformation clone() {
try {
return (TimeSeriesTransformation) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.forecast.model.transform.TimeSeriesTransformationMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}