Module pyreport.maths.variable

A module for tracking variables and their values in mathematical expressions.

Functions

def binarytrack(lhs, rhs, result_var_name, template)

def choice(a, size=None, replace=True, p=None)

Generates a random sample from a given 1-D array

Added in version: 1.7.0

Note

New code should use the ~numpy.random.Generator.choice method of a ~numpy.random.Generator instance instead; please see the :ref:random-quick-start.

Parameters

a : 1-D array-like or int

If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if it were np.arange(a)

size : int or tuple of ints, optional

Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

replace : boolean, optional

Whether the sample is with or without replacement. Default is True, meaning that a value of a can be selected multiple times.

p : 1-D array-like, optional

The probabilities associated with each entry in a. If not given, the sample assumes a uniform distribution over all entries in a.

Returns

samples : single item or ndarray

The generated random samples

Raises

ValueError

If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size

See Also

randint, shuffle, permutation random.Generator.choice: which should be used in new code

Notes

Setting user-specified probabilities through p uses a more general but less efficient sampler than the default. The general sampler produces a different sample than the optimized sampler even if each element of p is 1 / len(a).

Sampling random rows from a 2-D array is not possible with this function, but is possible with Generator.choice through its axis keyword.

Examples

Generate a uniform random sample from np.arange(5) of size 3:

>>> np.random.choice(5, 3)
array([0, 3, 4]) # random
>>> #This is equivalent to np.random.randint(0,5,3)

Generate a non-uniform random sample from np.arange(5) of size 3:

>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0]) # random

Generate a uniform random sample from np.arange(5) of size 3 without replacement:

>>> np.random.choice(5, 3, replace=False)
array([3,1,0]) # random
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:

>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0]) # random

Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance:

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
      dtype='<U11')

def generate_tmp(value)

def ternarytrack(var1, var2, var3, result_var_name, template)

def timestamp()

Classes

class Log

class Variable (name, value)

Instance variables

var identity

var imag

var name

var real

var value

Methods

def assign(self, _Variable__value)

def conjugate(self)

def is_watching(self, _Variable__variable)

def log(self, message)

def unwatch(self, *_Variable__variables)

def watch(self, *_Variable__variables)