# Interface MultivariateStatisticalSummary

All Known Implementing Classes:
`MultivariateOnlineSummarizer`

public interface MultivariateStatisticalSummary
Trait for multivariate statistical summary of a data matrix.
• ## Method Summary

Modifier and Type
Method
Description
`long`
`count()`
Sample size.
`Vector`
`max()`
Maximum value of each column.
`Vector`
`mean()`
Sample mean vector.
`Vector`
`min()`
Minimum value of each column.
`Vector`
`normL1()`
L1 norm of each column
`Vector`
`normL2()`
Euclidean magnitude of each column
`Vector`
`numNonzeros()`
Number of nonzero elements (including explicitly presented zero values) in each column.
`Vector`
`variance()`
Sample variance vector.
`double`
`weightSum()`
Sum of weights.
• ## Method Details

• ### count

long count()
Sample size.
Returns:
(undocumented)
• ### max

Vector max()
Maximum value of each column.
Returns:
(undocumented)
• ### mean

Vector mean()
Sample mean vector.
Returns:
(undocumented)
• ### min

Vector min()
Minimum value of each column.
Returns:
(undocumented)
• ### normL1

Vector normL1()
L1 norm of each column
Returns:
(undocumented)
• ### normL2

Vector normL2()
Euclidean magnitude of each column
Returns:
(undocumented)
• ### numNonzeros

Vector numNonzeros()
Number of nonzero elements (including explicitly presented zero values) in each column.
Returns:
(undocumented)
• ### variance

Vector variance()
Sample variance vector. Should return a zero vector if the sample size is 1.
Returns:
(undocumented)
• ### weightSum

double weightSum()
Sum of weights.
Returns:
(undocumented)