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# Miscellaneous

## Confidence Interval

`mlpy.``percentile_ci_median`(x, nboot=1000, alpha=0.025, rseed=0)

  Percentile confidence interval for the median of a sample x and unknown distribution.

  Input

  > - x - [1D numpy array] sample
  > - nboot - [integer] (>1) number of resamples
  > - alpha - [float] confidence level is 100*(1-2*alpha) (0.0<alpha<1.0)
  > - rseed - [integer] random seed

  Output

  > - ci - (cimin, cimax) confidence interval

  Example:

      >>> from numpy import *
      >>> from mlpy import *
      >>> x = array([1,2,4,3,2,2,1,1,2,3,4,3,2])
      >>> percentile_ci_median(x, nboot = 100)
      (1.8461538461538463, 2.8461538461538463)

## Peaks Detection

`mlpy.``span_pd`(x, span)

  span peaks detection.

  Input

  > - x - [1D numpy array float] data
  > - span - [odd int] span

  Output

  > - idx - [1D numpy array integer] peaks indexes

  New in version 2.0.7.

## Functions from GSL

`mlpy.``gamma`(x)

  Gamma Function.

  Input

  > - x - [float] data

  Output

  > - gx - [float] gamma(x)

`mlpy.``fact`(x)

  Factorial x!. The factorial is related to the gamma function by x! = gamma(x+1)

  Input

  > - x - [int] data

  Output

  > - fx - [float] factorial x!

`mlpy.``quantile`(x, f)

  Quantile value of sorted data. The elements of the array must be in ascending numerical order. The quantile is determined by the f, a fraction between 0 and 1. The quantile is found by interpolation, using the formula: quantile = (1 - delta) x_i + delta x_{i+1} where i is floor((n - 1)f) and delta is (n-1)f - i.

  Input

  > - x - [1D numpy array float] sorted data
  > - f - [float] fraction between 0 and 1

  Output

  > - q - [float] quantile

`mlpy.``cdf_gaussian_P`(x, sigma)

  Cumulative Distribution Functions (CDF) P(x) for the Gaussian distribution.

  Input

  > - x - [float] data
  > - sigma - [float] standard deviation

  Output

  > - p - [float]

  New in version 2.0.2.

## Other

`mlpy.``away`(a, b, d)

  Given numpy 1D array a and numpy 1D array b compute c = { bi : | bi - aj | > d for each i, j}

  Input

  > - a - [1D numpy array float]
  > - b - [1D numpy array float]
  > - d - [double]

  Output

  > - c - [1D numpy array float]

  New in version 2.0.3.

`mlpy.``is_power`(n, b)

  Return True if ‘n’ is power of ‘b’, False otherwise.

  New in version 2.0.6.

`mlpy.``next_power`(n, b)

  Returns the smallest integer, greater than or equal to ‘n’ which can be obtained as power of ‘b’.

  New in version 2.0.6.

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