Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Group: Binning, Grids and Sequences
See also: bindata denest grid

Function: conv
Description: conv performs the convolution of a step kernel function and a function over a p-dimensional equidistant grid.


Usage: {xc, yc, fill} = conv (xb, yb, wx, wy {,sym})
Input:
xb m x p x d1 x ... x dn array of integers
yb m x l x d1 x ... x dn array of non-negative integers
wx r x p x d1 x ... x dn array of non-negative integers
wy r x 1x d1 x ... x dn array
sym scalar
Output:
xc k x p x d1 x ... x dn array (k >= m)
yc k x l x d1 x ... x dn array
fill k x 1 x d1 x ... x dn array of zeros or ones

Note:

Example:



x = normal (100)

{xb,yb} = bindata(x, 0.4,0)

wx = # (0,1)

wy = # (1.25,0.75)

{xc, yc, fill} = conv (xb, yb, wx, wy)

cc=createdisplay(1,1)

show (cc,1,1,xc ~ yc)  

Result:

Display of a one-dimensional kernel estimate of the normal distribution. 

Example:

randomize(0)

x = normal (100,2)

{xb,yb} = bindata(x, #(0.25,0.5),#(0,0))

library("kernel") 

x = #(0,0)

h = #(0.25,0.25)

n = #(5,5)

wx= grid(x,#(1,1),n)

wy = qua(wx.*h') 

{xc, yc, fill} = conv (xb, yb, wx, wy)

cc=createdisplay(1,1)

show (cc,1,1,xc ~ yc)

Result:

Display of a two-dimensional kernel estimate of the two-dimensional normal distribution.


Group: Binning, Grids and Sequences
See also: bindata denest grid

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 21.9.2000