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Projection documentation - #3232
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| // Convert training data to IDataView, the general data type used in ML.NET. | ||
| var data = mlContext.Data.LoadFromEnumerable(samples); | ||
| // NormalizeLpNorm normalize rows individually by rescaling them to unit norm. | ||
| // Performs the following operaion on a row X: Y = (X - M) / D where M is mean, and D is selected norm. |
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What is the selected norm? Is it norm of the feature vector in a row being processed? Also, what are the shapes of X, Y, M, and D? #Pending
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mean -> mean vectorD is selected norm -> D is calculated value of selected norm parameter
Does that sound better?
In reply to: 273152443 [](ancestors = 273152443)
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Norm on what? A column? A row? Is it a scalar? In tensor computation, norm operation can produce another tensor.
Say, if I have rows, x_1, x_2, x_3. Is M=1/3 (x_1 + x_2 + x_3) true? Or M=ReduceSum(x_i) for the x subscripted by i? In addition, is D=||x_i||_2 for the x subscripted by i?
In reply to: 273153905 [](ancestors = 273153905,273152443)
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Same here. Please move the final answer to what NormalizeLpNorm does' to the section for the estimator.
In reply to: 273181155 [](ancestors = 273181155,273153905,273152443)
| { | ||
| public static class ApproximatedKernelMap | ||
| { | ||
| public static void Example() |
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| publicstaticvoidExample() | |
| // Transform feature vector to another non-linear space. See https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf. | |
| publicstaticvoidExample() |
This transform is non-trivial, so some references are required. #Resolved
| private class DataPoint | ||
| { | ||
| [VectorType(7)] |
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7 [](start = 24, length = 1)
It shouldn't work! #Resolved
| private class DataPoint | ||
| { | ||
| [VectorType(7)] |
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it shouldn't work! #Resolved
| //-0.0119, 0.5867, 0.4942, 0.7041 | ||
| // 0.4720, 0.5639, 0.4346, 0.2671 | ||
| //-0.2243, 0.7071, 0.7053, -0.1681 | ||
| // 0.0846, 0.5836, 0.6575, 0.0581 |
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Could you move these lines below the foreach loop and use:
// Expected output:
Could you do the same for the other files? #Resolved
Codecov Report
@@ Coverage Diff @@## master #3232 +/- ##
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- Coverage 72.62% 72.62% -0.01%
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Files 807 807 Lines 145080 145080 Branches 16213 16213 ==========================================
- Hits 105369 105365 -4 - Misses 35294 35297 +3 - Partials 4417 4418 +1
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| /// <format type="text/markdown"> | ||
| /// <] | ||
| /// [!code-csharp[GlobalContrastNormalize](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs)] |
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GlobalContrastNormalize [](start = 26, length = 23)
NormalizeGlobalContrast #Resolved
| /// <format type="text/markdown"> | ||
| /// <] | ||
| /// [!code-csharp[LpNormalize](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLpNorm.cs)] |
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LpNormalize [](start = 26, length = 11)
NormalizeLpNorm #Resolved
| // Performs the following operaion on a row X: Y = (X - M(X)) / D(X) | ||
| // where M(X) is scalar value of mean for current row, | ||
| // and D(X) is scalar value of selected `norm` parameter . | ||
| var approximation = mlContext.Transforms.NormalizeLpNorm("Features", norm: LpNormNormalizingEstimatorBase.NormFunction.L1, ensureZeroMean: true); |
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ensureZeroMean [](start = 135, length = 14)
What does EnsureZeroMean do? Subtract the mean? #Resolved
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Let's move parameter details to xml docstring.
In reply to: 273741392 [](ancestors = 273741392,273740225)
| /// <format type="text/markdown"> | ||
| /// <] | ||
| /// [!code-csharp[CreateRandomFourierFeatures](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs)] |
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CreateRandomFourierFeatures [](start = 26, length = 27)
ApproximatedKernelMap #Resolved
| foreach (var row in column) | ||
| Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4")))); | ||
| // Expected output: | ||
| // -0.0119, 0.5867, 0.4942, 0.7041 |
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[](start = 14, length = 1)
Space Space #ByDesign
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I prefer to align numbers, so one space was taken by minus sign.
In reply to: 273740487 [](ancestors = 273740487)
| // NormalizeLpNorm normalize rows individually by rescaling them to unit norm. | ||
| // Performs the following operaion on a row X: Y = scale *(X - M(X)) / D(X) | ||
| // where M(X) is scalar value of mean for current row, | ||
| // and D(X) is scalar value of either Standard deviation or L2 norm. |
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This comment looks like a copy/paste holdover. #Resolved
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| }; | ||
| // Convert training data to IDataView, the general data type used in ML.NET. | ||
| var data = mlContext.Data.LoadFromEnumerable(samples); | ||
| // NormalizeLpNorm normalize rows individually by rescaling them to unit norm. |
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NormalizeLpNorm [](start = 15, length = 15)
old name? #Resolved
| }; | ||
| // Convert training data to IDataView, the general data type used in ML.NET. | ||
| var data = mlContext.Data.LoadFromEnumerable(samples); | ||
| // NormalizeLpNorm normalize rows individually by rescaling them to unit norm. |
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normalize [](start = 31, length = 9)
normalizes #Resolved
| // NormalizeLpNorm normalize rows individually by rescaling them to unit norm. | ||
| // Performs the following operaion on a row X: Y = scale *(X - M(X)) / D(X) | ||
| // where M(X) is scalar value of mean for current row if ensureZeroMean = true or 0 othewise | ||
| // and D(X) is scalar value of either Standard deviation or L2 norm. |
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let's actually drop detailed algorithm descriptions inside examples. such details belong to the section and we don't want to repeat them again here. #Resolved
| /// <param name="scale">Scale features by this value.</param> | ||
| /// <remarks> | ||
| /// This transform performs the following operation on a row X: Y = scale * (X - M(X)) / D(X) | ||
| /// where M(X) is scalar value of mean for current row if <paramref name="ensureZeroMean"/>set to <see langword="true"/> or <value>0</value> othewise |
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| /// where M(X) is scalar value of mean for current row if <paramref name="ensureZeroMean"/>set to <see langword="true"/> or <value>0</value> othewise | |
| /// where M(X) is scalar value of mean for all elements in the current row if <paramref name="ensureZeroMean"/>set to <see langword="true"/> or <value>0</value> othewise | |
| ``` #Resolved |
| /// This transform performs the following operation on a row X: Y = scale * (X - M(X)) / D(X) | ||
| /// where M(X) is scalar value of mean for current row if <paramref name="ensureZeroMean"/>set to <see langword="true"/> or <value>0</value> othewise | ||
| /// D(X) is scalar value of standard deviation for row if <paramref name="ensureUnitStandardDeviation"/> set to <see langword="true"/> or | ||
| /// L2 norm value for this row if it set to <see langword="false"/> and scale is <paramref name="scale"/>. |
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| /// L2 norm value for this row if it set to <see langword="false"/> and scale is <paramref name="scale"/>. | |
| /// L2 norm of this row vector if <paramref name="ensureUnitStandardDeviation"/> set to <see langword="false"/>. "scale" is defined by <paramref name="scale"/>. | |
| ``` #Resolved |
Towards #1209