/usr/share/octave/packages/data-smoothing-1.3.0/doc-cache is in octave-data-smoothing 1.3.0-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | # doc-cache created by Octave 4.0.0
# name: cache
# type: cell
# rows: 3
# columns: 4
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
ddmat
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 330
-- Function File: D = ddmat (X, O)
Compute divided differencing matrix of order O
Input
X: vector of sampling positions
O: order of diffferences
Output
D: the matrix; D * Y gives divided differences of order O
References: Anal. Chem. (2003) 75, 3631.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 46
Compute divided differencing matrix of order O
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 13
regdatasmooth
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1940
-- Function File: [YHAT, LAMBDA] = regdatasmooth (X, Y, [OPTIONS])
Smooths the Y vs. X values of 1D data by Tikhonov regularization.
The smooth y-values are returned as YHAT. The regularization
parameter LAMBDA that was used for the smoothing may also be
returned.
Note: the options have changed! Currently supported input options
are (multiple options are allowed):
'"d", VALUE'
the smoothing derivative to use (default = 2)
'"lambda", VALUE'
the regularization paramater to use
'"stdev", VALUE'
the standard deviation of the measurement of Y; an optimal
value for lambda will be determined by matching the provided
VALUE with the standard devation of YHAT-Y; if the option
"relative" is also used, then a relative standard deviation is
inferred
'"gcv"'
use generalized cross-validation to determine the optimal
value for lambda; if neither "lambda" nor "stdev" options are
given, this option is implied
'"lguess", VALUE'
the initial value for lambda to use in the iterative
minimization algorithm to find the optimal value (default = 1)
'"xhat", VECTOR'
A vector of x-values to use for the smooth curve; must be
monotonically increasing and must at least span the data
'"weights", VECTOR'
A vector of weighting values for fitting each point in the
data.
'"relative"'
use relative differences for the goodnes of fit term.
Conflicts with the "weights" option.
'"midpointrule"'
use the midpoint rule for the integration terms rather than a
direct sum; this option conflicts with the option "xhat"
Please run the demos for example usage.
References: Anal. Chem. (2003) 75, 3631; AIChE J. (2006) 52, 325
See also: rgdtsmcorewrap, rgdtsmcore.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 17
Smooths the Y vs.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
rgdtsmcore
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1276
-- Function File: [YHAT, V] = rgdtsmcore (X, Y, D, LAMBDA, [OPTIONS])
Smooths Y vs. X values by Tikhonov regularization. Although this
function can be used directly, the more feature rich function
"regdatasmooth" should be used instead. In addition to X and Y,
required input includes the smoothing derivative D and the
regularization parameter LAMBDA. The smooth y-values are returned
as YHAT. The generalized cross validation variance V may also be
returned.
Note: the options have changed! Currently supported input options
are (multiple options are allowed):
'"xhat", VECTOR'
A vector of x-values to use for the smooth curve; must be
monotonically increasing and must at least span the data
'"weights", VECTOR'
A vector of weighting values for fitting each point in the
data.
'"relative"'
use relative differences for the goodnes of fit term.
Conflicts with the "weights" option.
'"midpointrule"'
use the midpoint rule for the integration terms rather than a
direct sum; this option conflicts with the option "xhat"
References: Anal. Chem. (2003) 75, 3631; AIChE J. (2006) 52, 325
See also: regdatasmooth.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 13
Smooths Y vs.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 14
rgdtsmcorewrap
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 482
-- Function File: CVE = rgdtsmcorewrap (LOG10LAMBDA, X, Y, D, MINCELL,
OPTIONS)
-- Function File: STDEVDIF = rgdtsmcorewrap (LOG10LAMBDA, X, Y, D,
MINCELL, OPTIONS)
Wrapper function for rgdtsmcore in order to minimize over LAMBDA
w.r.t. cross-validation error OR the squared difference between
the standard deviation of (Y-YHAT) and the given standard
deviation. This function is called from regdatasmooth.
See also: regdatasmooth.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 71
Wrapper function for rgdtsmcore in order to minimize over LAMBDA w.r.t.
|