Portability | portable |
---|---|

Stability | experimental |

Maintainer | bos@serpentine.com |

Functions for computing histograms of sample data.

# Documentation

:: (Vector v0 Double, Vector v1 Double, Num b, Vector v1 b) | |

=> Int | Number of bins (must be positive). |

-> v0 Double | Sample data (cannot be empty). |

-> (v1 Double, v1 b) |

*O(n)* Compute a histogram over a data set.

The result consists of a pair of vectors:

- The lower bound of each interval. * The number of samples within the interval.

Interval (bin) sizes are uniform, and the upper and lower bounds
are chosen automatically using the `range`

function. To specify
these parameters directly, use the `histogram_`

function.

# Building blocks

:: (Num b, RealFrac a, Vector v0 a, Vector v1 b) | |

=> Int | Number of bins. This value must be positive. A zero or negative value will cause an error. |

-> a | Lower bound on interval range. Sample data less than this will cause an error. |

-> a | Upper bound on interval range. This value must not be less than the lower bound. Sample data that falls above the upper bound will cause an error. |

-> v0 a | Sample data. |

-> v1 b |

*O(n)* Compute a histogram over a data set.

Interval (bin) sizes are uniform, based on the supplied upper and lower bounds.

:: Vector v Double | |

=> Int | Number of bins (must be positive). |

-> v Double | Sample data (cannot be empty). |

-> (Double, Double) |

*O(n)* Compute decent defaults for the lower and upper bounds of
a histogram, based on the desired number of bins and the range of
the sample data.

The upper and lower bounds used are `(lo-d, hi+d)`

, where

d = (maximum sample - minimum sample) / ((bins - 1) * 2)