NMSIS-DSP  Version 1.2.0
NMSIS DSP Software Library
Statistics Functions

Modules

 Absolute Maximum
 Computes the maximum value of absolute values of an array of data. The function returns both the maximum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
 
 Absolute Minimum
 Computes the minimum value of absolute values of an array of data. The function returns both the minimum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
 
 Accumulation functions
 Calculates the accumulation of the input vector. Sum is defined as the addition of the elements in the vector. The underlying algorithm is used:
 
 Entropy
 Computes the entropy of a distribution.
 
 Kullback-Leibler divergence
 Computes the Kullback-Leibler divergence between two distributions.
 
 LogSumExp
 LogSumExp optimizations to compute sum of probabilities with Gaussian distributions.
 
 Maximum
 Computes the maximum value of an array of data. The function returns both the maximum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
 
 Mean
 Calculates the mean of the input vector. Mean is defined as the average of the elements in the vector. The underlying algorithm is used:
 
 Minimum
 Computes the minimum value of an array of data. The function returns both the minimum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
 
 Mean Square Error
 Calculates the mean square error between two vectors.
 
 Power
 Calculates the sum of the squares of the elements in the input vector. The underlying algorithm is used:
 
 Root mean square (RMS)
 Calculates the Root Mean Square of the elements in the input vector. The underlying algorithm is used:
 
 Standard deviation
 Calculates the standard deviation of the elements in the input vector.
 
 Variance
 Calculates the variance of the elements in the input vector. The underlying algorithm used is the direct method sometimes referred to as the two-pass method:
 

Detailed Description