NMath Reference Guide

## Float |

Class FloatPCA performs a principal component analysis on a given single-precision
data matrix.

Inheritance Hierarchy

Syntax

The FloatPCA type exposes the following members.

Constructors

Name | Description | |
---|---|---|

FloatPCA(FloatMatrix) | Constructs a FloatPCA instance from the given data. | |

FloatPCA(FloatMatrix, Boolean, Boolean) | Constructs a FloatPCA instance from the given data, optionally centering and scaling the data before analysis takes place. |

Properties

Name | Description | |
---|---|---|

CumulativeVarianceProportions | Gets the cumulative variance proportions. | |

Data | Gets the data matrix. | |

Eigenvalues | Gets the eigenvalues of the covariance/correlation matrix, though the calculation is actually performed using the singular values of the data matrix. | |

IsCentered | Returns true if the data supplied at construction time was shifted to be zero-centered. | |

IsScaled | Returns true if the data supplied at construction time was scaled to have unit variance. | |

Item | Gets the specified principal component. | |

Loadings | Gets the loading matrix. Each column is a principal component. | |

Means | Gets the column means of the data matrix. | |

Norms | Gets the column norms (1-norm). | |

NumberOfObservations | Gets the number of observations in the data matrix. | |

NumberOfVariables | Gets the number of variables in the data matrix. | |

Scores | Gets the score matrix. | |

StandardDeviations | Gets the standard deviations of the principal components. | |

VarianceProportions | Gets the proportion of the total variance accounted for by each principal component. |

Methods

Name | Description | |
---|---|---|

Clone | Creates a deep copy of this principal component analysis. | |

Threshold | Gets the number of principal components required to account for the given proportion of the total variance. |

Fields

Name | Description | |
---|---|---|

center_ | If true, the data supplied at construction time will be shifted to be zero-centered. | |

d_ | Eigenvalues. | |

means_ | Column means. Used for centering. | |

norms_ | Column 1-norms. Used for scaling. | |

scale_ | If true, the data supplied at construction time will be scaled to have unit variance. | |

scores_ | Scores matrix. | |

v_ | Right eigenvectors. | |

x_ | The data matrix. |

Remarks

The first principal component accounts for as much of the variability
in the data as possible, and each succeeding component accounts for as
much of the remaining variability as possible.

The calculation is performed using a singular value decomposition of the data matrix. The data may optionally be centered and scaled before analysis takes place.

The calculation is performed using a singular value decomposition of the data matrix. The data may optionally be centered and scaled before analysis takes place.

See Also