Tools

jajapy provides also few functions that can be useful in some contexts.

jajapy.resolveRandom(m: list) → int

Given a list of probabilities it returns the index of the one choosen according to the probabilities. Example: if m=[0.7,0.3], it will returns 0 with probability 0.7, and 1 with probability 0.3.

Parameters

m: list of float: list of probabilities.

Returns

int: the chosen index.

Examples

>>> import jajapy as ja
>>> p = [0.2,0.6,0.15,0.05]
>>> chosen = ja.resolveRandom(p)
>>> print(chosen)
1

jajapy.normalize(ll)

Normalizes a list of float such that the sum is equal to 1.0.

Parameters

ll: list of float or 1-D narray: list of probabilities to normalize.

Returns

list or 1-D narray: normalized list.

jajapy.randomProbabilities(size: int) → ndarray

Return of ndarray of length size of probabilities.

Parameters

size: int: size of the output list.

Returns

list of float: list of probabilities.

Raises

ValueError: If size is strictly lower than 1.0.
TypeError: If size is not an int.

Examples

>>> import jajapy as ja
>>> p = ja.randomProbabilities(4)
>>> print(p)
array([0.3155861636575178, 0.11453783121165262, 0.5686125794652406, 0.001263425665589013])

jajapy.normpdf(x: float, params: list, variation: float = 0.01) → float

Returns the probability of x under a normal distribution returns of parameters params. Since this probability should be 0 it returns in fact the probability that the normal distribution gives us a value between x-variation and x+variation.

Parameters

x: float: the value of the normal distribution.
params: list of two float.: the parameters of the distribution: [mean,sd].
variation: float: the vicinity.

Returns

float: the probability of x under a normal distribution returns of parameters params.