Description Usage Arguments Details Value Author(s) See Also Examples

Function for making predictions from some subset of views to the remaining ones. This can be used, for example, for multi-output regression and classification tasks.

1 2 |

`pred` |
A vector of binary indicators telling which of the views are observed (1), and which are to be predicted (0). |

`Y` |
The input data as a list of M elements, N times D[m] matrices. |

`model` |
A list of model parameters as returned by |

`sample` |
Boolean indicator telling whether to also draw samples from the predictive distribution. |

`nSample` |
How many samples to draw if |

Estimates the conditional distribution of Z given the observed view and then estimates the expected predictions for the unobserved view. It is also possible to draw samples from the full predictive distribution, which cannot be specified in analytic form.

`Y` |
The mean predictions. Also the observed input data is
returned, so that Y is in the same format as the input data
for |

`Z` |
The mean of the latent variables given the observed data. |

`covZ` |
The covariance of the latent variables given the observed data. |

`sam` |
List that contain |

Seppo Virtanen and Arto Klami

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
#
# Assume we have a variable model which has been learned with
# CCAexperiment() or CCA().
#
# Predict the 2nd view:
#
# predictedY <- CCApred(c(1,0),Y,model)$Y
#
# Draw some samples from the conditional distribution of the
# first view given the second
#
# sampled <- CCApred(c(0,1),Y,model,sample=TRUE,nSample=10)$sam$Y
#
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.