Namespace Normalization¶

namespace
normalization
¶ Functions

af::array
decimalScalingNorm
(af::array tss)¶ Normalizes the given time series according to its maximum value and adjusts each value within the range (1, 1).
 Return
 af::array An array with the same dimensions as tss, whose values (time series in dimension 0) have been normalized by dividing each number by 10^j, where j is the number of integer digits of the max number in the time series.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

void
decimalScalingNormInPlace
(af::array &tss)¶ Same as decimalScalingNorm, but it performs the operation in place, without allocating further memory.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

af::array
maxMinNorm
(af::array tss, double high = 1.0, double low = 0.0, double epsilon = 0.00000001)¶ Normalizes the given time series according to its minimum and maximum value and adjusts each value within the range [low, high].
 Return
 af::array An array with the same dimensions as tss, whose values (time series in dimension 0) have been normalized by maximum and minimum values, and scaled as per high and low parameters.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.high
: Maximum final value (Defaults to 1.0).low
: Minimum final value (Defaults to 0.0).epsilon
: Safeguard for constant (or near constant) time series as the operation implies a unit scale operation between min and max values in the tss.

void
maxMinNormInPlace
(af::array &tss, double high = 1.0, double low = 0.0, double epsilon = 0.00000001)¶ Same as maxMinNorm, but it performs the operation in place, without allocating further memory.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.high
: Maximum final value (Defaults to 1.0).low
: Minimum final value (Defaults to 0.0).epsilon
: Safeguard for constant (or near constant) time series as the operation implies a unit scale operation between min and max values in the tss.

af::array
meanNorm
(af::array tss)¶ Normalizes the given time series according to its maximumminimum value and its mean. It follows the following formulae:
\[ \acute{x} = \frac{x  mean(x)}{max(x)  min(x)}. \] Return
 af::array An array with the same dimensions as tss, whose values (time series in dimension 0) have been normalized by substracting the mean from each number and dividing each number by \( max(x)  min(x)\), in the time series.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

void
meanNormInPlace
(af::array &tss)¶ Normalizes the given time series according to its maximumminimum value and its mean. It follows the following formulae:
\[ \acute{x} = \frac{x  mean(x)}{max(x)  min(x)}. \] Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

af::array
znorm
(af::array tss, double epsilon = 0.00000001)¶ Calculates a new set of timeseries with zero mean and standard deviation one.
 Return
 af::array With the same dimensions as tss where the time series have been adjusted for zero mean and one as standard deviation.
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.epsilon
: Minimum standard deviation to consider. It acts as a gatekeeper for those time series that may be constant or near constant.

void
znormInPlace
(af::array &tss, double epsilon = 0.00000001)¶ Adjusts the time series in the given input and performs znorm inplace (without allocating further memory).
 Parameters
tss
: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.epsilon
: Minimum standard deviation to consider. It acts as a gatekeeper for those time series that may be constant or near constant.

af::array