All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.tensorflow.metadata.v0.PresenceAndValencyStatistics Maven / Gradle / Ivy

The newest version!
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: tensorflow_metadata/proto/v0/statistics.proto

// Protobuf Java Version: 3.25.4
package org.tensorflow.metadata.v0;

/**
 * 
 * Statistics about the presence and valency of feature values. Feature values
 * could be nested lists. A feature in tf.Examples or other "flat" datasets has
 * values of nest level 1 -- they are lists of primitives. A nest level N
 * (N > 1) feature value is a list of lists of nest level (N - 1).
 * This proto can be used to describe the presence and valency of values at each
 * level.
 * 
* * Protobuf type {@code tensorflow.metadata.v0.PresenceAndValencyStatistics} */ public final class PresenceAndValencyStatistics extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:tensorflow.metadata.v0.PresenceAndValencyStatistics) PresenceAndValencyStatisticsOrBuilder { private static final long serialVersionUID = 0L; // Use PresenceAndValencyStatistics.newBuilder() to construct. private PresenceAndValencyStatistics(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private PresenceAndValencyStatistics() { } @java.lang.Override @SuppressWarnings({"unused"}) protected java.lang.Object newInstance( UnusedPrivateParameter unused) { return new PresenceAndValencyStatistics(); } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.metadata.v0.Statistics.internal_static_tensorflow_metadata_v0_PresenceAndValencyStatistics_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.metadata.v0.Statistics.internal_static_tensorflow_metadata_v0_PresenceAndValencyStatistics_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.metadata.v0.PresenceAndValencyStatistics.class, org.tensorflow.metadata.v0.PresenceAndValencyStatistics.Builder.class); } public static final int NUM_NON_MISSING_FIELD_NUMBER = 1; private long numNonMissing_ = 0L; /** *
   * Note: missing and non-missing counts are conditioned on the upper level
   * being non-missing (i.e. if the upper level is missing/null, all the levels
   * nested below are by definition missing, but not counted).
   * Number non-missing (not-null) values.
   * 
* * uint64 num_non_missing = 1; * @return The numNonMissing. */ @java.lang.Override public long getNumNonMissing() { return numNonMissing_; } public static final int NUM_MISSING_FIELD_NUMBER = 2; private long numMissing_ = 0L; /** *
   * Number of missing (null) values.
   * 
* * uint64 num_missing = 2; * @return The numMissing. */ @java.lang.Override public long getNumMissing() { return numMissing_; } public static final int MIN_NUM_VALUES_FIELD_NUMBER = 3; private long minNumValues_ = 0L; /** *
   * Minimum length of the values (note that nulls are not considered).
   * 
* * uint64 min_num_values = 3; * @return The minNumValues. */ @java.lang.Override public long getMinNumValues() { return minNumValues_; } public static final int MAX_NUM_VALUES_FIELD_NUMBER = 4; private long maxNumValues_ = 0L; /** *
   * Maximum length of the values.
   * 
* * uint64 max_num_values = 4; * @return The maxNumValues. */ @java.lang.Override public long getMaxNumValues() { return maxNumValues_; } public static final int TOT_NUM_VALUES_FIELD_NUMBER = 5; private long totNumValues_ = 0L; /** *
   * Total number of values.
   * 
* * uint64 tot_num_values = 5; * @return The totNumValues. */ @java.lang.Override public long getTotNumValues() { return totNumValues_; } private byte memoizedIsInitialized = -1; @java.lang.Override public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } @java.lang.Override public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { if (numNonMissing_ != 0L) { output.writeUInt64(1, numNonMissing_); } if (numMissing_ != 0L) { output.writeUInt64(2, numMissing_); } if (minNumValues_ != 0L) { output.writeUInt64(3, minNumValues_); } if (maxNumValues_ != 0L) { output.writeUInt64(4, maxNumValues_); } if (totNumValues_ != 0L) { output.writeUInt64(5, totNumValues_); } getUnknownFields().writeTo(output); } @java.lang.Override public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (numNonMissing_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeUInt64Size(1, numNonMissing_); } if (numMissing_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeUInt64Size(2, numMissing_); } if (minNumValues_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeUInt64Size(3, minNumValues_); } if (maxNumValues_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeUInt64Size(4, maxNumValues_); } if (totNumValues_ != 0L) { size += com.google.protobuf.CodedOutputStream .computeUInt64Size(5, totNumValues_); } size += getUnknownFields().getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.metadata.v0.PresenceAndValencyStatistics)) { return super.equals(obj); } org.tensorflow.metadata.v0.PresenceAndValencyStatistics other = (org.tensorflow.metadata.v0.PresenceAndValencyStatistics) obj; if (getNumNonMissing() != other.getNumNonMissing()) return false; if (getNumMissing() != other.getNumMissing()) return false; if (getMinNumValues() != other.getMinNumValues()) return false; if (getMaxNumValues() != other.getMaxNumValues()) return false; if (getTotNumValues() != other.getTotNumValues()) return false; if (!getUnknownFields().equals(other.getUnknownFields())) return false; return true; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + NUM_NON_MISSING_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getNumNonMissing()); hash = (37 * hash) + NUM_MISSING_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getNumMissing()); hash = (37 * hash) + MIN_NUM_VALUES_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getMinNumValues()); hash = (37 * hash) + MAX_NUM_VALUES_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getMaxNumValues()); hash = (37 * hash) + TOT_NUM_VALUES_FIELD_NUMBER; hash = (53 * hash) + com.google.protobuf.Internal.hashLong( getTotNumValues()); hash = (29 * hash) + getUnknownFields().hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } @java.lang.Override public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.metadata.v0.PresenceAndValencyStatistics prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } @java.lang.Override public Builder toBuilder() { return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this); } @java.lang.Override protected Builder newBuilderForType( com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** *
   * Statistics about the presence and valency of feature values. Feature values
   * could be nested lists. A feature in tf.Examples or other "flat" datasets has
   * values of nest level 1 -- they are lists of primitives. A nest level N
   * (N > 1) feature value is a list of lists of nest level (N - 1).
   * This proto can be used to describe the presence and valency of values at each
   * level.
   * 
* * Protobuf type {@code tensorflow.metadata.v0.PresenceAndValencyStatistics} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.metadata.v0.PresenceAndValencyStatistics) org.tensorflow.metadata.v0.PresenceAndValencyStatisticsOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.metadata.v0.Statistics.internal_static_tensorflow_metadata_v0_PresenceAndValencyStatistics_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.metadata.v0.Statistics.internal_static_tensorflow_metadata_v0_PresenceAndValencyStatistics_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.metadata.v0.PresenceAndValencyStatistics.class, org.tensorflow.metadata.v0.PresenceAndValencyStatistics.Builder.class); } // Construct using org.tensorflow.metadata.v0.PresenceAndValencyStatistics.newBuilder() private Builder() { } private Builder( com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); } @java.lang.Override public Builder clear() { super.clear(); bitField0_ = 0; numNonMissing_ = 0L; numMissing_ = 0L; minNumValues_ = 0L; maxNumValues_ = 0L; totNumValues_ = 0L; return this; } @java.lang.Override public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.metadata.v0.Statistics.internal_static_tensorflow_metadata_v0_PresenceAndValencyStatistics_descriptor; } @java.lang.Override public org.tensorflow.metadata.v0.PresenceAndValencyStatistics getDefaultInstanceForType() { return org.tensorflow.metadata.v0.PresenceAndValencyStatistics.getDefaultInstance(); } @java.lang.Override public org.tensorflow.metadata.v0.PresenceAndValencyStatistics build() { org.tensorflow.metadata.v0.PresenceAndValencyStatistics result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } @java.lang.Override public org.tensorflow.metadata.v0.PresenceAndValencyStatistics buildPartial() { org.tensorflow.metadata.v0.PresenceAndValencyStatistics result = new org.tensorflow.metadata.v0.PresenceAndValencyStatistics(this); if (bitField0_ != 0) { buildPartial0(result); } onBuilt(); return result; } private void buildPartial0(org.tensorflow.metadata.v0.PresenceAndValencyStatistics result) { int from_bitField0_ = bitField0_; if (((from_bitField0_ & 0x00000001) != 0)) { result.numNonMissing_ = numNonMissing_; } if (((from_bitField0_ & 0x00000002) != 0)) { result.numMissing_ = numMissing_; } if (((from_bitField0_ & 0x00000004) != 0)) { result.minNumValues_ = minNumValues_; } if (((from_bitField0_ & 0x00000008) != 0)) { result.maxNumValues_ = maxNumValues_; } if (((from_bitField0_ & 0x00000010) != 0)) { result.totNumValues_ = totNumValues_; } } @java.lang.Override public Builder clone() { return super.clone(); } @java.lang.Override public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return super.setField(field, value); } @java.lang.Override public Builder clearField( com.google.protobuf.Descriptors.FieldDescriptor field) { return super.clearField(field); } @java.lang.Override public Builder clearOneof( com.google.protobuf.Descriptors.OneofDescriptor oneof) { return super.clearOneof(oneof); } @java.lang.Override public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return super.setRepeatedField(field, index, value); } @java.lang.Override public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return super.addRepeatedField(field, value); } @java.lang.Override public Builder mergeFrom(com.google.protobuf.Message other) { if (other instanceof org.tensorflow.metadata.v0.PresenceAndValencyStatistics) { return mergeFrom((org.tensorflow.metadata.v0.PresenceAndValencyStatistics)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.metadata.v0.PresenceAndValencyStatistics other) { if (other == org.tensorflow.metadata.v0.PresenceAndValencyStatistics.getDefaultInstance()) return this; if (other.getNumNonMissing() != 0L) { setNumNonMissing(other.getNumNonMissing()); } if (other.getNumMissing() != 0L) { setNumMissing(other.getNumMissing()); } if (other.getMinNumValues() != 0L) { setMinNumValues(other.getMinNumValues()); } if (other.getMaxNumValues() != 0L) { setMaxNumValues(other.getMaxNumValues()); } if (other.getTotNumValues() != 0L) { setTotNumValues(other.getTotNumValues()); } this.mergeUnknownFields(other.getUnknownFields()); onChanged(); return this; } @java.lang.Override public final boolean isInitialized() { return true; } @java.lang.Override public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; case 8: { numNonMissing_ = input.readUInt64(); bitField0_ |= 0x00000001; break; } // case 8 case 16: { numMissing_ = input.readUInt64(); bitField0_ |= 0x00000002; break; } // case 16 case 24: { minNumValues_ = input.readUInt64(); bitField0_ |= 0x00000004; break; } // case 24 case 32: { maxNumValues_ = input.readUInt64(); bitField0_ |= 0x00000008; break; } // case 32 case 40: { totNumValues_ = input.readUInt64(); bitField0_ |= 0x00000010; break; } // case 40 default: { if (!super.parseUnknownField(input, extensionRegistry, tag)) { done = true; // was an endgroup tag } break; } // default: } // switch (tag) } // while (!done) } catch (com.google.protobuf.InvalidProtocolBufferException e) { throw e.unwrapIOException(); } finally { onChanged(); } // finally return this; } private int bitField0_; private long numNonMissing_ ; /** *
     * Note: missing and non-missing counts are conditioned on the upper level
     * being non-missing (i.e. if the upper level is missing/null, all the levels
     * nested below are by definition missing, but not counted).
     * Number non-missing (not-null) values.
     * 
* * uint64 num_non_missing = 1; * @return The numNonMissing. */ @java.lang.Override public long getNumNonMissing() { return numNonMissing_; } /** *
     * Note: missing and non-missing counts are conditioned on the upper level
     * being non-missing (i.e. if the upper level is missing/null, all the levels
     * nested below are by definition missing, but not counted).
     * Number non-missing (not-null) values.
     * 
* * uint64 num_non_missing = 1; * @param value The numNonMissing to set. * @return This builder for chaining. */ public Builder setNumNonMissing(long value) { numNonMissing_ = value; bitField0_ |= 0x00000001; onChanged(); return this; } /** *
     * Note: missing and non-missing counts are conditioned on the upper level
     * being non-missing (i.e. if the upper level is missing/null, all the levels
     * nested below are by definition missing, but not counted).
     * Number non-missing (not-null) values.
     * 
* * uint64 num_non_missing = 1; * @return This builder for chaining. */ public Builder clearNumNonMissing() { bitField0_ = (bitField0_ & ~0x00000001); numNonMissing_ = 0L; onChanged(); return this; } private long numMissing_ ; /** *
     * Number of missing (null) values.
     * 
* * uint64 num_missing = 2; * @return The numMissing. */ @java.lang.Override public long getNumMissing() { return numMissing_; } /** *
     * Number of missing (null) values.
     * 
* * uint64 num_missing = 2; * @param value The numMissing to set. * @return This builder for chaining. */ public Builder setNumMissing(long value) { numMissing_ = value; bitField0_ |= 0x00000002; onChanged(); return this; } /** *
     * Number of missing (null) values.
     * 
* * uint64 num_missing = 2; * @return This builder for chaining. */ public Builder clearNumMissing() { bitField0_ = (bitField0_ & ~0x00000002); numMissing_ = 0L; onChanged(); return this; } private long minNumValues_ ; /** *
     * Minimum length of the values (note that nulls are not considered).
     * 
* * uint64 min_num_values = 3; * @return The minNumValues. */ @java.lang.Override public long getMinNumValues() { return minNumValues_; } /** *
     * Minimum length of the values (note that nulls are not considered).
     * 
* * uint64 min_num_values = 3; * @param value The minNumValues to set. * @return This builder for chaining. */ public Builder setMinNumValues(long value) { minNumValues_ = value; bitField0_ |= 0x00000004; onChanged(); return this; } /** *
     * Minimum length of the values (note that nulls are not considered).
     * 
* * uint64 min_num_values = 3; * @return This builder for chaining. */ public Builder clearMinNumValues() { bitField0_ = (bitField0_ & ~0x00000004); minNumValues_ = 0L; onChanged(); return this; } private long maxNumValues_ ; /** *
     * Maximum length of the values.
     * 
* * uint64 max_num_values = 4; * @return The maxNumValues. */ @java.lang.Override public long getMaxNumValues() { return maxNumValues_; } /** *
     * Maximum length of the values.
     * 
* * uint64 max_num_values = 4; * @param value The maxNumValues to set. * @return This builder for chaining. */ public Builder setMaxNumValues(long value) { maxNumValues_ = value; bitField0_ |= 0x00000008; onChanged(); return this; } /** *
     * Maximum length of the values.
     * 
* * uint64 max_num_values = 4; * @return This builder for chaining. */ public Builder clearMaxNumValues() { bitField0_ = (bitField0_ & ~0x00000008); maxNumValues_ = 0L; onChanged(); return this; } private long totNumValues_ ; /** *
     * Total number of values.
     * 
* * uint64 tot_num_values = 5; * @return The totNumValues. */ @java.lang.Override public long getTotNumValues() { return totNumValues_; } /** *
     * Total number of values.
     * 
* * uint64 tot_num_values = 5; * @param value The totNumValues to set. * @return This builder for chaining. */ public Builder setTotNumValues(long value) { totNumValues_ = value; bitField0_ |= 0x00000010; onChanged(); return this; } /** *
     * Total number of values.
     * 
* * uint64 tot_num_values = 5; * @return This builder for chaining. */ public Builder clearTotNumValues() { bitField0_ = (bitField0_ & ~0x00000010); totNumValues_ = 0L; onChanged(); return this; } @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.PresenceAndValencyStatistics) } // @@protoc_insertion_point(class_scope:tensorflow.metadata.v0.PresenceAndValencyStatistics) private static final org.tensorflow.metadata.v0.PresenceAndValencyStatistics DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.metadata.v0.PresenceAndValencyStatistics(); } public static org.tensorflow.metadata.v0.PresenceAndValencyStatistics getDefaultInstance() { return DEFAULT_INSTANCE; } private static final com.google.protobuf.Parser PARSER = new com.google.protobuf.AbstractParser() { @java.lang.Override public PresenceAndValencyStatistics 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.PresenceAndValencyStatistics getDefaultInstanceForType() { return DEFAULT_INSTANCE; } }




© 2015 - 2025 Weber Informatics LLC | Privacy Policy