Dimensions

VectorType.cs (3)

49Dimensions = ImmutableArray.Create(Size); 66Dimensions = dimensions.ToImmutableArray(); 84Dimensions = dimensions;

DataLoadSave\Binary\Codecs.cs (2)

806int count = _type.Dimensions.Length; 812writer.Write(_type.Dimensions[i]);

Evaluators\EvaluatorUtils.cs (1)

734type, new VectorDataViewType(keyType, ((VectorDataViewType)type).Dimensions), mapper, keyValueGetter, slotNamesGetter);

Scorers\FeatureContributionCalculation.cs (1)

344var featureContributionType = new VectorDataViewType(NumberDataViewType.Single, ((VectorDataViewType)FeatureColumn.Type).Dimensions);

Transforms\KeyToValue.cs (1)

238types[iinfo] = new VectorDataViewType((PrimitiveDataViewType)valsItemType, vectorType.Dimensions);

Transforms\KeyToVector.cs (2)

327var slotNamesType = new VectorDataViewType(TextDataViewType.Instance, _types[iinfo].Dimensions); 339var slotNamesType = new VectorDataViewType(TextDataViewType.Instance, _types[iinfo].Dimensions);

Transforms\LabelConvertTransform.cs (1)

191Interlocked.CompareExchange(ref _slotType, new VectorDataViewType(NumberDataViewType.Single, srcSlotType.Dimensions), null);

Transforms\Normalizer.cs (1)

482ctx.Writer.WriteIntArray(vectorType.Dimensions.ToArray());

Transforms\TypeConverting.cs (1)

429typeDst = new VectorDataViewType(itemType, vectorType.Dimensions);

Transforms\ValueMapping.cs (1)

1073colType = new VectorDataViewType((PrimitiveDataViewType)_parent.ValueColumnType, vectorType.Dimensions);

Transforms\ValueToKeyMappingTransformer.cs (1)

728colType = new VectorDataViewType(keyType, vectorType.Dimensions);

VectorType.cs (19)

26/// In the case where this is a multi-dimensional type, that is, a situation where <see cref="Dimensions"/> 29/// dimensions. We consider that the last dimension is the most "minor" index. In the case where <see cref="Dimensions"/> 56/// <param name="dimensions">The dimensions. Note that, like <see cref="Dimensions"/>, must be non-empty, with all 57/// non-negative values. Also, because <see cref="Size"/> is the product of <see cref="Dimensions"/>, the result of 67Size = ComputeSize(Dimensions); 74/// <param name="dimensions">The dimensions. Note that, like <see cref="Dimensions"/>, must be non-empty, with all 75/// non-negative values. Also, because <see cref="Size"/> is the product of <see cref="Dimensions"/>, the result of 85Size = ComputeSize(Dimensions); 117/// Note that this is always the product of the elements in <see cref="Dimensions"/>. 131if (Dimensions.Length != tmp.Dimensions.Length) 133for (int i = 0; i < Dimensions.Length; i++) 135if (Dimensions[i] != tmp.Dimensions[i]) 149hash = Hashing.CombineHash(hash, Dimensions.Length); 150for (int i = 0; i < Dimensions.Length; i++) 151hash = Hashing.CombineHash(hash, Dimensions[i].GetHashCode()); 160if (Dimensions.Length == 1) 167foreach (var dim in Dimensions)

ImagePixelExtractor.cs (1)

309var dims = type.Dimensions;

VectorWhitening.cs (1)

224if ((vectorType != null && !vectorType.IsKnownSize && vectorType.Dimensions.Length > 1)

OnnxUtils.cs (2)

374for (int i = 0; i < vec.Dimensions.Length; i++) 375dimsLocal.Add(vec.Dimensions[i]);

Dynamic\TensorFlow\TextClassification.cs (2)

68featuresType.ItemType.RawType, featuresType.Dimensions[0]); 74predictionType.Dimensions[0]);

docs\samples\Microsoft.ML.Samples\Dynamic\TensorFlow\TextClassification.cs (2)

68featuresType.ItemType.RawType, featuresType.Dimensions[0]); 74predictionType.Dimensions[0]);

TensorflowTransform.cs (2)

572var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray(); 582var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();

TensorFlowEstimatorTests.cs (2)

245var imageHeight = type.Dimensions[0]; 246var imageWidth = type.Dimensions[1];

TensorflowTests.cs (13)

584Assert.Equal(2, type.Dimensions.Length); 585Assert.Equal(28, type.Dimensions[0]); 586Assert.Equal(28, type.Dimensions[1]); 598Assert.Equal(new[] { 5, 5, 1, 32 }, type.Dimensions); 613Assert.Equal(new[] { 28, 28, 32 }, type.Dimensions); 628Assert.Equal(new[] { 10 }, type.Dimensions); 647Assert.Equal(new[] { 2, 2 }, type.Dimensions); 1007var imageHeight = type.Dimensions[0]; 1008var imageWidth = type.Dimensions[1]; 1066var imageHeight = type.Dimensions[0]; 1067var imageWidth = type.Dimensions[1]; 1110var imageHeight = type.Dimensions[0]; 1111var imageWidth = type.Dimensions[1];

Transformers\NormalizerTests.cs (2)

1063var dimensions1 = (transformedData.Schema["output"].Type as VectorDataViewType).Dimensions; 1064var dimensions2 = (transformedData2.Schema["output"].Type as VectorDataViewType).Dimensions;

KeyToVectorMapping.cs (2)

244var slotNamesType = new VectorDataViewType(TextDataViewType.Instance, _types[iinfo].Dimensions); 262var slotNamesType = new VectorDataViewType(TextDataViewType.Instance, _types[iinfo].Dimensions);

MissingValueIndicatorTransform.cs (1)

145types[iinfo] = new VectorDataViewType(NumberDataViewType.Single, vectorType.Dimensions.Add(2));

MissingValueIndicatorTransformer.cs (2)

197outType = new VectorDataViewType(BooleanDataViewType.Instance, vectorType.Dimensions); 548new VectorDataViewType(BooleanDataViewType.Instance, vectorType.Dimensions);

MissingValueReplacing.cs (3)

295type = new VectorDataViewType(vectorType.ItemType, vectorType.Dimensions); 569vectorType = new VectorDataViewType(vectorType.ItemType, vectorType.Dimensions); 1052new VectorDataViewType(vectorType.ItemType, vectorType.Dimensions);

DnnRetrainTransform.cs (4)

245var colTypeDims = new int[vecType.Dimensions.Length + 1]; 247for (int indexLocal = 0; indexLocal < vecType.Dimensions.Length; indexLocal += 1) 248colTypeDims[indexLocal + 1] = vecType.Dimensions[indexLocal]; 792var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();