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matlab normpdf用法,matlab
Warning: Function C:\Users\HP\Dropbox\海南\相似日的相似度计算\三亚台风\三亚台风负荷预测96点\sum.m has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.
Warning: Function C:\Users\HP\Dropbox\海南\相似日的相似度计算\海口台风\海口台风负荷预测修正\sum.m has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.
>> help ksdensity
ksdensity Compute kernel density or distribution estimate
[F,XI]=ksdensity(X) computes a probability density estimate of the sample
in the vector X. ksdensity evaluates the density estimate at 100 points
covering the range of the data. F is the vector of density values and XI
is the set of 100 points. The estimate is based on a normal kernel
function, using a window parameter (bandwidth) that is a function of the
number of points in X.
F=ksdensity(X,XI) specifies the vector XI of values where the density
estimate is to be evaluated.
[F,XI,U]=ksdensity(...) also returns the bandwidth of the kernel smoothing
window.
ksdensity(...) without output arguments produces a plot of the results.
ksdensity(AX,...) plots into axes AX instead of GCA.
[...]=ksdensity(...,'PARAM1',val1,'PARAM2',val2,...) specifies parameter
name/value pairs to control the density estimation. Valid parameters
are the following:
Parameter Value
'censoring' A logical vector of the same length of X, indicating which
entries are censoring times (default is no censoring).
'kernel' The type of kernel smoother to use, chosen from among
'normal' (default), 'box', 'triangle', and
'epanechnikov'.
'npoints' The number of equally-spaced points in XI.
'support' Either 'unbounded' (default) if the density can extend
over the whole real line, or 'positive' to restrict it to
positive values, or a two-element vector giving finite
lower and upper limits for the support of the density.
'weights' Vector of the same length as X, giving the weight to
assign to each X value (default is equal weights).
'bandwidth' The bandwidth of the kernel smoothing window. The
default is optimal for estimating normal densities, but
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