cuda_ml_pca {cuda.ml} | R Documentation |

## Perform principal component analysis.

### Description

Compute principal component(s) of the input data. Each feature from the input will be mean-centered (but not scaled) before the SVD computation takes place.

### Usage

```
cuda_ml_pca(
x,
n_components = NULL,
eig_algo = c("dq", "jacobi"),
tol = 1e-07,
n_iters = 15L,
whiten = FALSE,
transform_input = TRUE,
cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)
```

### Arguments

`x` |
The input matrix or dataframe. Each data point should be a row and should consist of numeric values only. |

`n_components` |
Number of principal component(s) to keep. Default: min(nrow(x), ncol(x)). |

`eig_algo` |
Eigen decomposition algorithm to be applied to the covariance matrix. Valid choices are "dq" (divid-and-conquer method for symmetric matrices) and "jacobi" (the Jacobi method for symmetric matrices). Default: "dq". |

`tol` |
Tolerance for singular values computed by the Jacobi method. Default: 1e-7. |

`n_iters` |
Maximum number of iterations for the Jacobi method. Default: 15. |

`whiten` |
If TRUE, then de-correlate all components, making each component have unit variance and removing multi-collinearity. Default: FALSE. |

`transform_input` |
If TRUE, then compute an approximate representation of the input data. Default: TRUE. |

`cuML_log_level` |
Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off. |

### Value

A PCA model object with the following attributes:
- "components": a matrix of `n_components`

rows containing the top
principal components.
- "explained_variance": amount of variance within the input data explained
by each component.
- "explained_variance_ratio": fraction of variance within the input data
explained by each component.
- "singular_values": singular values (non-negative) corresponding to the
top principal components.
- "mean": the column wise mean of `x`

which was used to mean-center
`x`

first.
- "transformed_data": (only present if "transform_input" is set to TRUE)
an approximate representation of input data based on principal
components.
- "pca_params": opaque pointer to PCA parameters which will be used for
performing inverse transforms.

The model object can be used as input to the inverse_transform() function to map a representation based on principal components back to the original feature space.

### Examples

```
library(cuda.ml)
iris.pca <- cuda_ml_pca(iris[1:4], n_components = 3)
print(iris.pca)
```

*cuda.ml*version 0.3.2 Index]