Description: Project a given set of data onto a given set of eigenvectors.
Usage:
gist-project [options] -train <filename> -learned <filename> -test <filename>Input:
- -train <filename> - an tab-delimited file of training data. The first column contains labels, and the remaining columns containing real-valued features.
- -learned <filename> - a tab-delimited file of eigenvectors, as produced by
kernel-pca. Each column corresponds to an eigenvector.- -test <filename> - a tab-delimited file of test data to be projected onto the training data's eigenvectors.
Output: A tab-delimited matrix in which the given data has been projected onto the given set of eigenvectors.
Options:
- -selftrain <filename> - Read from the given file a series of n values of the form K(x,x), where K is the base kernel function and x is an element in the training set. This option is only necessary if the base kernel function is supplied from a file and the kernel is normalized or radial basis. The input file should be in tab-delimited format, with data labels in the first column and values in the second column.
- -selftest <filename> - Similar to '-selftrain', but for the test set.
- -rdb - Allow the program to read and create RDB formatted files, which contain an additional format line after the first line of text.
- -kernelout - Compute and print the kernel matrix to stdout. Do not compute the classifications.
- -notime - Do not include timing information in the output header.
- -precision <value> - Number of digits after the decimal place in the output file. The default is 6.
- -verbose 1|2|3|4|5 - Set the verbosity level of the output to stderr. The default level is 2.