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Notice, this filter does not recognize any time intervals or validities!
Parameter
ATTRIBUTES:The attributes where the filter should be applied on.SIZE:The size of the filter (look above)FUNCTION:The density function that is used as a filter. Possible values are: "gaussian", "logarithmic", "uniform", "exponential" or an expression, where x is the variable that is replaced by the value.GROUP_BY:A list of attributes that are used for grouping.OPTIONS:A map of options to configure the function: "mean", "deviation", "alpha".
Example
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// applies a normal distribution filter (gaussian filter) to the speed attribute and recognizes 3 values before and 3 values after the value that has to be filtered.
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='gaussian'}, input)
// applies a normal distribution filter (gaussian filter) to the speed attribute with mean = 0 and standard deviation = 2
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='gaussian', OPTIONS=[['mean','0.0'],['deviation','2.0']]}, input)
// applies a normal distribution filter (gaussian filter) to the speed attribute, but only considers values with same id by groupping by "id"
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='gaussian', GROUP_BY=['id']}, input)
// applies a log normal distribution filter to the speed attribute
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='logarithmic'}, input)
// applies a exponential distribution filter to the speed attribute
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='exponential'}, input)
// applies a exponential distribution filter to the speed attribute
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='exponential'}, input)
// applies a uniform distribution filter to the speed attribute
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='uniform'}, input)
// applies a self-defined density function (use x to get the value)
output = CONVOLUTION({ATTRIBUTES=['speed'], SIZE=3, FUNCTION='x+2/7'}, input) |