Uses of Class
de.erichseifert.gral.data.filters.Kernel

Packages that use Kernel
de.erichseifert.gral.data.filters Classes for filtering data sources. 
 

Uses of Kernel in de.erichseifert.gral.data.filters
 

Methods in de.erichseifert.gral.data.filters that return Kernel
 Kernel Kernel.add(double v)
          Returns a new Kernel, where the specified value was added to each of the items.
 Kernel Kernel.add(Kernel k)
          Returns a new Kernel, where the specified kernel was added.
static Kernel KernelUtils.getBinomial(double variance)
          Returns a Kernel of specified variance with binomial coefficients.
static Kernel KernelUtils.getBinomial(int size)
          Returns a Kernel of specified size with binomial coefficients.
 Kernel Convolution.getKernel()
          Returns the Kernel.
static Kernel KernelUtils.getUniform(int size, int offset, double value)
          Returns a Kernel with the specified size and offset, filled with a single value.
 Kernel Kernel.mul(double v)
          Returns a new Kernel, where the specified value was multiplied with each of the items.
 Kernel Kernel.mul(Kernel k)
          Returns a new Kernel, where the specified kernel was multiplied.
 Kernel Kernel.negate()
          Returns a Kernel with all values being negated.
 Kernel Kernel.normalize()
          Returns a normalized Kernel so that the sum of all values equals 1.
 

Methods in de.erichseifert.gral.data.filters with parameters of type Kernel
 Kernel Kernel.add(Kernel k)
          Returns a new Kernel, where the specified kernel was added.
 Kernel Kernel.mul(Kernel k)
          Returns a new Kernel, where the specified kernel was multiplied.
 

Constructors in de.erichseifert.gral.data.filters with parameters of type Kernel
Convolution(DataSource original, Kernel kernel, Filter.Mode mode, int... cols)
          Creates a new Convolution object with the specified DataSource, Kernel, Mode and columns.
 



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