GP-CONFIDENCES-AS-PLOT-DATA (GP INPUTS &KEY MEANS COVARIANCES (LEVELS-AND-OPTIONS '((0 title 'mean' with lines) (-1.96 title 'mean - 1.96 * stddev' with lines) (1.96 title 'mean + 1.96 * stddev' with lines))))
Return a list of MGL-GNUPLOT:DATA-MAPPINGs, one for each level in LEVELS-AND-OPTIONS (a list of (LEVEL OPTIONS)). Each mapping contains INPUTS in its first column, and MEANS + LEVEL*VARIANCES in the second.
GP-SAMPLES-AS-PLOT-DATA (GP INPUTS &KEY MEANS COVARIANCES OPTIONS)
Returns a matrix that contains INPUTS in its first column, and a sample taken with SAMPLE-GP in its second.
UPDATE-GP (GP INPUTS OUTPUTS &KEY MEANS COVARIANCES)
Update GP with the evidence embodied by INPUTS and the corresponding OUTPUTS. Return a new POSTERIOR-GP. If MEANS are COVARIANCES are given, then the call to GP-MEANS-AND-COVARIANCES is not made.
->GP (&REST ARGS)
GP-COVARIANCES (GP X1 &OPTIONAL (X2 X1))
GP-MEANS-AND-COVARIANCES (GP X1 &OPTIONAL (X2 X1))
SAMPLE-GP (GP INPUTS &KEY MEANS COVARIANCES)
Return a sample from the multivariate normal distribution defined by GP at INPUTS as a column vector.
EXTRACT-COVARIANCES (LUMP STRIPE N-ROWS N-COLS)
EXTRACT-MEANS (LUMP STRIPE)
GAUSSIAN-KERNEL (X1 X2 &KEY SIGNAL-VARIANCE (BIAS-VARIANCE 0) LENGTH-SCALE (ROUGHNESS 2))
GP-DATA-MATRIX (INPUTS OUTPUTS)
GP-DATA-MATRIX-FOR-LEVEL (INPUTS MEANS COVARIANCES LEVEL)
MAKE-MATRIX-FROM-LUMP-STRIPE (LUMP N-ROWS N-COLS STRIPE)
MAKE-VECTOR-FROM-LUMP-STRIPE (LUMP STRIPE)
POSTERIOR-GP-MEANS-AND-COVARIANCES (GP X1 X2 &KEY COMPUTE-COVARIANCES-P)
UPDATE-GP* (GP INPUTS OUTPUTS MEANS COVARIANCES)
GP-MEANS (GP X)
Returns the vector of means for the vector of inputs X. X is a vector of arbitrary objects.
GP-MEANS-AND-COVARIANCES* (GP X1 X2)
Returns two values: the means and the covariances as matrices.
GP-COVARIANCES* (GP X1 X2)
Returns the matrix of covariances between X1 and X2. X1 and X2 are vectors of arbitrary objects. Noise is assumed to be included in the covariance function.
SETFPOSTERIOR-GPS (NEW-VALUE OBJECT)
SETFSAMPLES (NEW-VALUE OBJECT)
A GP whose mean and covariance are defined by two lisp functions. Can be updated, but it's not trainable.