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pricom__define.pro
Class description for pricom
Routines
result = pricom::project(data [, nterm=nterm] [, coeffs=coeffs])Projects a given vector onto the vector space defined by the principal compents, and returns the result
result = pricom::get_pc()Return the principal components.
result = pricom::get_eval()result = pricom::get_mean()result = pricom::init(data)Build a new PCA object.
pricom::cleanuppricom__defineThis class provides a more convenient interface for working with principal component analysis.
Routine details
toppricom::project
result = pricom::project(data [, nterm=nterm] [, coeffs=coeffs])
Projects a given vector onto the vector space defined by the principal compents, and returns the result
Return value
The projection of the input onto the principal components. This is equivalent to sum(coeff[i] * PC_i) where coeff[i] = sum(data * PC_i)
Parameters
- data in required
A vector to project
Keywords
- nterm in optional
Set to an integer to project onto only the first nterm principal components.
- coeffs in optional
Set to a variable to hold the coefficients of the linear combination of principal components. In other words, result = sum( coeff[i] * PC_i )
Author information
- History:
March 2010: Written by Chris Beaumont April 2010: Fixed a bug that crashed this function when called with only one data point. cnb April 2010: Number of returned coeffs matches nterm. cnb. August 2010: Truncated intermediate arrays when nterm < ndim. Speeds up execution. cnb. August 10 2010: Further optimization when nterm < ndim. cnb. August 18 2010: Fixed spelling error in definition of get_mean. cnb.
toppricom::get_eval
result = pricom::get_eval()
toppricom::get_mean
result = pricom::get_mean()
toppricom::init
result = pricom::init(data)
Build a new PCA object.
Parameters
- data in required
an (ndim) by (nobj) array of training data. Contains (nobj) vectors, each with (ndim) elements. These are the data used to generate the principal components
toppricom::cleanup
pricom::cleanup
toppricom__define
pricom__define
This class provides a more convenient interface for working with principal component analysis. The user supplies a training data set, which is used to generate principal components via the IDL procedure PCOMP. The methods in this class then allow a simple way to project new vectors onto PCA space, obtain the principal components, etc.
Author information
- History:
April 2010: Written by Chris Beaumont June 11 2010: Class now uses the cnb_pca function to calculate principal components. cnb.
File attributes
| Modifcation date: | Wed Aug 18 19:40:51 2010 |
| Lines: | 144 |