org.tensorflow.metadata.v0.MultiDimensionalRegression Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow_metadata/proto/v0/problem_statement.proto
// Protobuf Java Version: 3.25.5
package org.tensorflow.metadata.v0;
/**
*
* A multi-dimensional regression task.
* Similar to OneDimensionalRegression, MultiDimensionalRegression predicts
* continuous real numbers. However instead of predicting a single scalar value
* per example, we predict a fixed dimensional vector of values. By default the
* range is any float -inf to inf, but specific sub-types (e.g. probability)
* define more narrow ranges.
*
*
* Protobuf type {@code tensorflow.metadata.v0.MultiDimensionalRegression}
*/
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/**
*
* By default, MultiDimensionalRegression assumes that each value in the
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*
*
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/**
*
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*
*
* Protobuf type {@code tensorflow.metadata.v0.MultiDimensionalRegression.Probability}
*/
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*
*
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/**
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public Builder clearWeight() {
weight_ = getDefaultInstance().getWeight();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
/**
*
* (optional) The weight column.
*
*
* string weight = 2;
* @param value The bytes for weight to set.
* @return This builder for chaining.
*/
public Builder setWeightBytes(
com.google.protobuf.ByteString value) {
if (value == null) { throw new NullPointerException(); }
checkByteStringIsUtf8(value);
weight_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability, org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.Builder, org.tensorflow.metadata.v0.MultiDimensionalRegression.ProbabilityOrBuilder> probabilityBuilder_;
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
* @return Whether the probability field is set.
*/
@java.lang.Override
public boolean hasProbability() {
return labelTypeCase_ == 4;
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
* @return The probability.
*/
@java.lang.Override
public org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability getProbability() {
if (probabilityBuilder_ == null) {
if (labelTypeCase_ == 4) {
return (org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability) labelType_;
}
return org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.getDefaultInstance();
} else {
if (labelTypeCase_ == 4) {
return probabilityBuilder_.getMessage();
}
return org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.getDefaultInstance();
}
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
public Builder setProbability(org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability value) {
if (probabilityBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
labelType_ = value;
onChanged();
} else {
probabilityBuilder_.setMessage(value);
}
labelTypeCase_ = 4;
return this;
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
public Builder setProbability(
org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.Builder builderForValue) {
if (probabilityBuilder_ == null) {
labelType_ = builderForValue.build();
onChanged();
} else {
probabilityBuilder_.setMessage(builderForValue.build());
}
labelTypeCase_ = 4;
return this;
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
public Builder mergeProbability(org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability value) {
if (probabilityBuilder_ == null) {
if (labelTypeCase_ == 4 &&
labelType_ != org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.getDefaultInstance()) {
labelType_ = org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.newBuilder((org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability) labelType_)
.mergeFrom(value).buildPartial();
} else {
labelType_ = value;
}
onChanged();
} else {
if (labelTypeCase_ == 4) {
probabilityBuilder_.mergeFrom(value);
} else {
probabilityBuilder_.setMessage(value);
}
}
labelTypeCase_ = 4;
return this;
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
public Builder clearProbability() {
if (probabilityBuilder_ == null) {
if (labelTypeCase_ == 4) {
labelTypeCase_ = 0;
labelType_ = null;
onChanged();
}
} else {
if (labelTypeCase_ == 4) {
labelTypeCase_ = 0;
labelType_ = null;
}
probabilityBuilder_.clear();
}
return this;
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
public org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.Builder getProbabilityBuilder() {
return getProbabilityFieldBuilder().getBuilder();
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
@java.lang.Override
public org.tensorflow.metadata.v0.MultiDimensionalRegression.ProbabilityOrBuilder getProbabilityOrBuilder() {
if ((labelTypeCase_ == 4) && (probabilityBuilder_ != null)) {
return probabilityBuilder_.getMessageOrBuilder();
} else {
if (labelTypeCase_ == 4) {
return (org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability) labelType_;
}
return org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.getDefaultInstance();
}
}
/**
*
* When set means the label is a probability in range [0..1].
*
*
* .tensorflow.metadata.v0.MultiDimensionalRegression.Probability probability = 4;
*/
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability, org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.Builder, org.tensorflow.metadata.v0.MultiDimensionalRegression.ProbabilityOrBuilder>
getProbabilityFieldBuilder() {
if (probabilityBuilder_ == null) {
if (!(labelTypeCase_ == 4)) {
labelType_ = org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.getDefaultInstance();
}
probabilityBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability, org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability.Builder, org.tensorflow.metadata.v0.MultiDimensionalRegression.ProbabilityOrBuilder>(
(org.tensorflow.metadata.v0.MultiDimensionalRegression.Probability) labelType_,
getParentForChildren(),
isClean());
labelType_ = null;
}
labelTypeCase_ = 4;
onChanged();
return probabilityBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.metadata.v0.MultiDimensionalRegression)
}
// @@protoc_insertion_point(class_scope:tensorflow.metadata.v0.MultiDimensionalRegression)
private static final org.tensorflow.metadata.v0.MultiDimensionalRegression DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.metadata.v0.MultiDimensionalRegression();
}
public static org.tensorflow.metadata.v0.MultiDimensionalRegression getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public MultiDimensionalRegression parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.metadata.v0.MultiDimensionalRegression getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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