Visible to Intel only — GUID: GUID-F32AA7F4-27FB-415C-BCEE-35A0FA879EF2
Visible to Intel only — GUID: GUID-F32AA7F4-27FB-415C-BCEE-35A0FA879EF2
Z-score
Z-score normalization is an algorithm that produces data with each feature (column) having zero mean and unit variance.
Details
Given a set X of n feature vectors of dimension p, the problem is to compute the matrix of dimension as following:
where:
is the mean of j-th component of set , where
value of depends omn a computation mode
oneDAL provides two modes for computing the result matrix. You can enable the mode by setting the flag doScale to a certain position (for details, see Algorithm Parameters). The mode may include:
Centering only. In this case, and no scaling is performed. After normalization, the mean of j-th component of result set will be zero.
Centering and scaling. In this case, , where is the standard deviation of j-th component of set . After normalization, the mean of j-th component of result set will be zero and its variance will get a value of one.
Batch Processing
Algorithm Input
Z-score normalization algorithm accepts an input as described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Input ID |
Input |
---|---|
data |
Pointer to the numeric table of size .
NOTE:
This table can be an object of any class derived from NumericTable.
|
Algorithm Parameters
Z-score normalization algorithm has the following parameters. Some of them are required only for specific values of the computation method parameter method:
Parameter |
method |
Default Value |
Description |
---|---|---|---|
algorithmFPType |
defaultDense or sumDense |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
Not applicable |
defaultDense |
Available computation methods:
|
moments |
defaultDense |
SharedPtr<low_order_moments::Batch<algorithmFPType, low_order_moments::defaultDense> > |
Pointer to the low order moments algorithm that computes means and standard deviations to be used for Z-score normalization with the defaultDense method. |
doScale |
defaultDense or sumDense |
true |
If true, the algorithm applies both centering and scaling. Otherwise, the algorithm provides only centering. |
resultsToCompute |
defaultDense or sumDense |
Not applicable |
Optional. Pointer to the data collection containing the following key-value pairs for Z-score:
Provide one of these values to request a single characteristic or use bitwise OR to request a combination of them. |
Algorithm Output
Z-score normalization algorithm calculates the result as described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID |
Result |
---|---|
normalizedData |
Pointer to the numeric table that stores the result of normalization.
NOTE:
By default, the result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
|
means |
Optional. Pointer to the numeric table that contains mean values for each feature. If the function result is not requested through the resultsToCompute parameter, the numeric table contains a NULL pointer. |
variances |
Optional. Pointer to the numeric table that contains variance values for each feature. If the function result is not requested through the resultsToCompute parameter, the numeric table contains a NULL pointer. - |
Examples
C++ (CPU)
Batch Processing:
Java*
Batch Processing:
Python*
Batch Processing: