Various kernel functions.
Various kernel functions. * RBF (double -> double functions) * vector kernels (vector x vector -> double function; may be positive definite, conditional positive definite, positive semi-definite, mercer) * density estimation * some kernel operations
(bandwidth kernel data h)Returns infered bandwidth (h).
h can be one of:
:nrd - rule-of-thumb (scale=1.06):nrd0 - rule-of-thumb (scake=0.9):nrd-adjust - kernel specific adjustment of :nrd, doesn't work for silverman and cauchy:rlcv - robust likelihood cross-validation:lcv - likelihood cross-validation:lscv - least squares cross-validationReturns infered bandwidth (h). h can be one of: * `:nrd` - rule-of-thumb (scale=1.06) * `:nrd0` - rule-of-thumb (scake=0.9) * `:nrd-adjust` - kernel specific adjustment of `:nrd`, doesn't work for `silverman` and `cauchy` * `:rlcv` - robust likelihood cross-validation * `:lcv` - likelihood cross-validation * `:lscv` - least squares cross-validation
(exp k)(exp k t)Kernel wraper. exp of kernel k with optional scaling value t.
Kernel wraper. exp of kernel `k` with optional scaling value `t`.
Create vector kernel.
Vector kernel returns a number for two vectors (or numbers)
Kernels can be Mercer, positive definite, conditional positive definite, positive semi-definite or other.
Create vector kernel. Vector kernel returns a number for two vectors (or numbers) Kernels can be Mercer, positive definite, conditional positive definite, positive semi-definite or other.
(kernel-density kernel data)(kernel-density kernel data bandwidth)(kernel-density kernel data bandwidth info?)Returns kernel density estimation function, 1d
Arguments:
kernel - kernel name or kernel functiondata - dataparams - a map containing:
:bandwidth - bandwidth h:binned? - if data should be binned, if true the width of the bin is bandwidth divided by 5, if is a number then it will be used as denominator. Default: false.:bandwidth can be a number or one of:
:nrd - rule-of-thumb (scale=1.06):nrd0 - rule-of-thumb (scake=0.9):nrd-adjust - kernel specific adjustment of :nrd, doesn't work for silverman and cauchy:rlcv - robust likelihood cross-validation:lcv - likelihood cross-validation:lscv - least squares cross-validationReturns kernel density estimation function, 1d
Arguments:
* `kernel` - kernel name or kernel function
* `data` - data
* `params` - a map containing:
* `:bandwidth` - bandwidth h
* `:binned?` - if data should be binned, if `true` the width of the bin is `bandwidth` divided by 5, if is a number then it will be used as denominator. Default: `false`.
`:bandwidth` can be a number or one of:
* `:nrd` - rule-of-thumb (scale=1.06)
* `:nrd0` - rule-of-thumb (scake=0.9)
* `:nrd-adjust` - kernel specific adjustment of `:nrd`, doesn't work for `silverman` and `cauchy`
* `:rlcv` - robust likelihood cross-validation
* `:lcv` - likelihood cross-validation
* `:lscv` - least squares cross-validation(kernel-density-ci kernel data)(kernel-density-ci kernel data bandwidth-or-params)Create function which returns confidence intervals for given kde method.
Check 6.1.5 http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode33.html
Arguments:
kernel - kernel namedata - sequence of data valuesparams - map with other parameters (including kernel-density parameters)
alpha - confidence level, default: 0.05Returns three values: density, lower confidence value and upper confidence value
Create function which returns confidence intervals for given kde method.
Check 6.1.5 http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode33.html
Arguments:
* `kernel` - kernel name
* `data` - sequence of data values
* `params` - map with other parameters (including [[kernel-density]] parameters)
* `alpha` - confidence level, default: 0.05
Returns three values: density, lower confidence value and upper confidence value(mult k1)(mult k1 k2)(mult k1 k2 k3)(mult k1 k2 k3 & r)Kernel wrapper. Multiply two or more kernels.
Kernel wrapper. Multiply two or more kernels.
RBF kernel creator. RBF is double->double function.
Refer fastmath.kernel.rbf namespace for details.
RBF kernel creator. RBF is double->double function. Refer `fastmath.kernel.rbf` namespace for details.
(scale k scale)Kernel wrapper. Scale kernel result.
Kernel wrapper. Scale kernel result.
(wmean kernels)(wmean kernels weights)Kernel wrapper. (Weighted) mean of kernel results.
Kernel wrapper. (Weighted) mean of kernel results.
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