object ImputeNormalWin extends Imputation
The ImputeNormalWin object imputes the missing values in the vector using
Normal Distribution for a sliding window.
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DAMPEN: Double
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def
findLastMissing(x: VectoD, i: Int = -1): (Int, Double)
Return the index of last missing value in vector 'x' from index 'i' and the new imputed value.
Return the index of last missing value in vector 'x' from index 'i' and the new imputed value.
- x
the vector with missing values
- i
the starting index to look for missing value
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def
findMissing(x: VectoD, i: Int = 0): (Int, Double)
Return the index of first missing value in vector 'x' from index 'i' and the new imputed value.
Return the index of first missing value in vector 'x' from index 'i' and the new imputed value.
- x
the vector with missing values
- i
the starting index to look for missing value
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def
impute(x: MatriD): MatriD
Replace all missing values in matrix 'x' with imputed values.
Replace all missing values in matrix 'x' with imputed values. Will change the values in matrix 'x'. Make a copy to preserve values 'x.copy ()'.
- x
the matrix with missing values
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def
impute(x: VectoD, i: Int = 0): (Int, Double)
Impute a value for the first missing value in vector 'x' from index 'i'.
Impute a value for the first missing value in vector 'x' from index 'i'. The type (Int, Double) returns (vector index for imputation, imputed value). Does not modify the vector.
- x
the vector with missing values
- i
the starting index to look for missing values
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def
imputeAll(x: VectoD): VectoD
Impute all the missing values in vector 'x' using Normal Distribution for a sliding window.
Impute all the missing values in vector 'x' using Normal Distribution for a sliding window.
- x
the vector with missing values@param q size of the sliding window
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- ImputeNormalWin → Imputation
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def
imputeAt(x: VectoD, i: Int): Double
Impute a value for vector 'x' at index 'i'.
Impute a value for vector 'x' at index 'i'. Does not modify the vector.
- x
the vector with missing values
- i
the index position for which to impute a value
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- ImputeNormalWin → Imputation
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def
imputeCol(c: Vec, i: Int = 0): (Int, Any)
Impute a value for the first missing value in column 'c' from index 'i'.
Impute a value for the first missing value in column 'c' from index 'i'. The type (Int, Double) returns (vector index for imputation, imputed value). Does not modify the column.
- c
the column with missing values
- i
the starting index to look for missing values
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val
missVal: Double
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ne(arg0: AnyRef): Boolean
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def
nextVal(x: VectoD, i: Int): Double
Return the next non-missing value in vector 'x' from index 'i'.
Return the next non-missing value in vector 'x' from index 'i'.
- x
the vector to be searched for a non-missing value
- i
the starting index to look for non-missing value
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def
normalMedian(mu: Double, sig2: Double): Double
Return the median of three normally distributed random numbers.
Return the median of three normally distributed random numbers.
- mu
the mean
- sig2
the variance
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def
prevVal(x: VectoD, i: Int): Double
Return the previous non-missing value in vector 'x' from index 'i'.
Return the previous non-missing value in vector 'x' from index 'i'.
- x
the vector to be searched (backwards) for a non-missing value
- i
the starting index to look for non-missing value
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val
q: Int
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def
setDist(dist_: Int): Unit
Set the distance 'dist' to the new value 'dist_'.
Set the distance 'dist' to the new value 'dist_'.
- dist_
the new value for the distance
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def
setMissVal(missVal_: Double): Unit
Set the missing value 'missVal' to the new missing value indicator 'missVal_'.
Set the missing value 'missVal' to the new missing value indicator 'missVal_'.
- missVal_
the new missing value indicator
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