MParzenSample: Sample from MParzen model
In rudazhang/plmr: Probabilistic Learning on Manifolds in R
Description
Usage
Arguments
Value
Description
Sample from MParzen model
Usage
1
MParzenSample(N, X, model, sigma)
Arguments
N
Number of samples.
X
matrix of training data.
model
Top d-th order local covariance structure; output of 'MParzenTrain()'.
sigma
Maximum noise level added to the normal space of each kernel,
i.e. marginal standard deviation of an isotropic Gaussian. Can be zero.
Value
A sample of size N from the Manifold Parzen model.
rudazhang/plmr documentation built on Aug. 30, 2021, 5:43 p.m.
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Description Usage Arguments Value
Description
Sample from MParzen model
Usage
1 | MParzenSample(N, X, model, sigma)
|
Arguments
N |
Number of samples. |
X |
matrix of training data. |
model |
Top d-th order local covariance structure; output of 'MParzenTrain()'. |
sigma |
Maximum noise level added to the normal space of each kernel, i.e. marginal standard deviation of an isotropic Gaussian. Can be zero. |
Value
A sample of size N from the Manifold Parzen model.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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