Package Bio :: Module LogisticRegression
[hide private]
[frames] | no frames]

Module LogisticRegression

source code

This module provides code for doing logistic regressions.

Classes: LogisticRegression Holds information for a LogisticRegression classifier.

Functions: train Train a new classifier. calculate Calculate the probabilities of each class, given an observation. classify Classify an observation into a class.

Classes [hide private]
  LogisticRegression
Holds information necessary to do logistic regression classification.
Functions [hide private]
LogisticRegression
train(xs, ys, update_fn=...)
Train a logistic regression classifier on a training set.
source code
list of probabilities
calculate(lr, x)
Calculate the probability for each class.
source code
1 or 0
classify(lr, x)
Classify an observation into a class.
source code
Variables [hide private]
  Complex0 = 'F'
  Complex16 = 'F'
  Complex32 = 'F'
  Complex64 = 'D'
  Complex8 = 'F'
  Float0 = 'f'
  Float16 = 'f'
  Float32 = 'f'
  Float64 = 'd'
  Float8 = 'f'
  Int0 = '1'
  Int16 = 's'
  Int32 = 'i'
  Int8 = '1'
  absolute = <ufunc 'absolute'>
  add = <ufunc 'add'>
  arccos = <ufunc 'arccos'>
  arccosh = <ufunc 'arccosh'>
  arcsin = <ufunc 'arcsin'>
  arcsinh = <ufunc 'arcsinh'>
  arctan = <ufunc 'arctan'>
  arctan2 = <ufunc 'arctan2'>
  arctanh = <ufunc 'arctanh'>
  bitwise_and = <ufunc 'bitwise_and'>
  bitwise_or = <ufunc 'bitwise_or'>
  bitwise_xor = <ufunc 'bitwise_xor'>
  ceil = <ufunc 'ceil'>
  conjugate = <ufunc 'conjugate'>
  cos = <ufunc 'cos'>
  cosh = <ufunc 'cosh'>
  divide = <ufunc 'divide'>
  divide_safe = <ufunc 'divide_safe'>
  e = 2.71828182846
  equal = <ufunc 'equal'>
  exp = <ufunc 'exp'>
  fabs = <ufunc 'fabs'>
  floor = <ufunc 'floor'>
  floor_divide = <ufunc 'floor_divide'>
  fmod = <ufunc 'fmod'>
  greater = <ufunc 'greater'>
  greater_equal = <ufunc 'greater_equal'>
  hypot = <ufunc 'hypot'>
  invert = <ufunc 'invert'>
  left_shift = <ufunc 'left_shift'>
  less = <ufunc 'less'>
  less_equal = <ufunc 'less_equal'>
  log = <ufunc 'log'>
  log10 = <ufunc 'log10'>
  logical_and = <ufunc 'logical_and'>
  logical_not = <ufunc 'logical_not'>
  logical_or = <ufunc 'logical_or'>
  logical_xor = <ufunc 'logical_xor'>
  maximum = <ufunc 'maximum'>
  minimum = <ufunc 'minimum'>
  multiply = <ufunc 'multiply'>
  negative = <ufunc 'negative'>
  not_equal = <ufunc 'not_equal'>
  pi = 3.14159265359
  power = <ufunc 'power'>
  remainder = <ufunc 'remainder'>
  right_shift = <ufunc 'right_shift'>
  sin = <ufunc 'sin'>
  sinh = <ufunc 'sinh'>
  sqrt = <ufunc 'sqrt'>
  subtract = <ufunc 'subtract'>
  tan = <ufunc 'tan'>
  tanh = <ufunc 'tanh'>
  true_divide = <ufunc 'true_divide'>
Function Details [hide private]

train(xs, ys, update_fn=...)

source code 

Train a logistic regression classifier on a training set. xs is a list of observations and ys is a list of the class assignments, which should be 0 or 1. xs and ys should contain the same number of elements. update_fn is an optional callback function that takes as parameters that iteration number and log likelihood.

Returns: LogisticRegression

calculate(lr, x)

source code 

Calculate the probability for each class. lr is a LogisticRegression object. x is the observed data. Returns a list of the probability that it fits each class.

Returns: list of probabilities