class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable
MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector format in an online fashion.
Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of the corresponding joint dataset.
A numerically stable algorithm is implemented to compute the mean and variance of instances: Reference: variance-wiki Zero elements (including explicit zero values) are skipped when calling add(), to have time complexity O(nnz) instead of O(n) for each column.
For weighted instances, the unbiased estimation of variance is defined by the reliability weights: see Reliability weights (Wikipedia).
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- new MultivariateOnlineSummarizer()
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def
add(sample: Vector): MultivariateOnlineSummarizer.this.type
Add a new sample to this summarizer, and update the statistical summary.
Add a new sample to this summarizer, and update the statistical summary.
- sample
The sample in dense/sparse vector format to be added into this summarizer.
- returns
This MultivariateOnlineSummarizer object.
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def
count: Long
Sample size.
Sample size.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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def
max: Vector
Maximum value of each dimension.
Maximum value of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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def
mean: Vector
Sample mean of each dimension.
Sample mean of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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def
merge(other: MultivariateOnlineSummarizer): MultivariateOnlineSummarizer.this.type
Merge another MultivariateOnlineSummarizer, and update the statistical summary.
Merge another MultivariateOnlineSummarizer, and update the statistical summary. (Note that it's in place merging; as a result,
this
object will be modified.)- other
The other MultivariateOnlineSummarizer to be merged.
- returns
This MultivariateOnlineSummarizer object.
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def
min: Vector
Minimum value of each dimension.
Minimum value of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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ne(arg0: AnyRef): Boolean
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def
normL1: Vector
L1 norm of each dimension.
L1 norm of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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def
normL2: Vector
L2 (Euclidean) norm of each dimension.
L2 (Euclidean) norm of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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notifyAll(): Unit
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def
numNonzeros: Vector
Number of nonzero elements in each dimension.
Number of nonzero elements in each dimension.
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def
toString(): String
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def
variance: Vector
Unbiased estimate of sample variance of each dimension.
Unbiased estimate of sample variance of each dimension.
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- MultivariateOnlineSummarizer → MultivariateStatisticalSummary
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def
weightSum: Double
Sum of weights.
Sum of weights.
- Definition Classes
- MultivariateOnlineSummarizer → MultivariateStatisticalSummary