Feat: random list generator
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@ -1,7 +1,7 @@
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#!/usr/bin/env python
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# encoding: utf-8
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from .calculus import Expression, Integer, Decimal
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from .calculus import Expression, Integer, Decimal, list_generator
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# Expression.set_render('tex')
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@ -31,6 +31,7 @@ Expression is the classe wich handle all calculus. It can randomly generate or i
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"""
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from .API import Expression, Integer, Decimal
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from .core import list_generator
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from decimal import getcontext
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#getcontext().prec = 2
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@ -66,6 +66,7 @@ from .tree import Tree, AssocialTree
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from .compute import compute
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from .typing import typing, TypingError
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from .renders import tree2txt, tree2tex
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from .random import list_generator
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# -----------------------------
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@ -47,8 +47,8 @@ Tree with RdLeaf replaced by generated values
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>>> leafs
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['a', 'a*k']
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>>> rd_varia = extract_rv(leafs)
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>>> rd_varia # doctest: +SKIP
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{'a', 'k'}
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>>> sorted(list(rd_varia))
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['a', 'k']
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>>> generated = random_generator(rd_varia, conditions=['a%2+1'])
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>>> generated # doctest: +SKIP
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{'a': 7, 'k': 4}
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@ -61,8 +61,18 @@ Tree with RdLeaf replaced by generated values
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> 7
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> 28
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List generator
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--------------
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This function ignores tree structure and works with lists
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>>> values = list_generator(["a", "a*b", "b", "c"], conditions=["b%c==1"])
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>>> values # doctest: +SKIP
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{'a': -8, 'a*b': -40, 'b': 5, 'c': 4}
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"""
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__all__ = ["generator"]
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from random import choice
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from functools import reduce
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from .leaf import RdLeaf
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@ -244,3 +254,27 @@ def build_variable_scope(rd_variables, rejected, min_max, variables_scope):
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except KeyError:
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complete_scope[v]["min_max"] = min_max
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return complete_scope
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def list_generator(var_list, conditions=[], rejected=[0], min_max=(-10, 10), variables_scope={}):
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""" Generate random computed values from the list
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:param rd_variables: list of random variables to generate (can be computed value - "a*b")
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:param conditions: condition over variables
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:param rejected: Rejected values for the generator (default [0])
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:param min_max: (min, max) limits in between variables will be generated
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:param variables_scope: rejected and min_max define for individual variables
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:return: dictionnary of generated variables
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:example:
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>>> values = list_generator(["a", "a*b", "b", "c"])
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>>> values # doctest: +SKIP
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>>> values["a"] * values["b"] == values["a*b"]
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True
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>>> values["a*b"] # doctest: +SKIP
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>>> values["a"] * values["b"] # doctest: +SKIP
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"""
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rv = extract_rv(var_list)
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rv_gen = random_generator(rv, conditions, rejected, min_max, variables_scope)
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generated = compute_leafs(var_list, rv_gen)
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return generated
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