Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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92 lines
2.7 KiB
92 lines
2.7 KiB
4 years ago
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#!/usr/bin/env python3
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"""Prints type-coercion tables for the built-in NumPy types
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"""
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import numpy as np
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# Generic object that can be added, but doesn't do anything else
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class GenericObject:
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def __init__(self, v):
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self.v = v
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def __add__(self, other):
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return self
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def __radd__(self, other):
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return self
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dtype = np.dtype('O')
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def print_cancast_table(ntypes):
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print('X', end=' ')
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for char in ntypes:
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print(char, end=' ')
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print()
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for row in ntypes:
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print(row, end=' ')
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for col in ntypes:
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print(int(np.can_cast(row, col)), end=' ')
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print()
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def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
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print('+', end=' ')
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for char in ntypes:
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print(char, end=' ')
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print()
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for row in ntypes:
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if row == 'O':
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rowtype = GenericObject
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else:
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rowtype = np.obj2sctype(row)
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print(row, end=' ')
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for col in ntypes:
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if col == 'O':
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coltype = GenericObject
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else:
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coltype = np.obj2sctype(col)
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try:
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if firstarray:
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rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
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else:
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rowvalue = rowtype(inputfirstvalue)
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colvalue = coltype(inputsecondvalue)
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if use_promote_types:
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char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
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else:
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value = np.add(rowvalue, colvalue)
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if isinstance(value, np.ndarray):
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char = value.dtype.char
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else:
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char = np.dtype(type(value)).char
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except ValueError:
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char = '!'
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except OverflowError:
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char = '@'
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except TypeError:
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char = '#'
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print(char, end=' ')
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print()
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if __name__ == '__main__':
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print("can cast")
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print_cancast_table(np.typecodes['All'])
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print()
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print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")
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print()
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print("scalar + scalar")
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print_coercion_table(np.typecodes['All'], 0, 0, False)
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print()
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print("scalar + neg scalar")
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print_coercion_table(np.typecodes['All'], 0, -1, False)
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print()
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print("array + scalar")
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print_coercion_table(np.typecodes['All'], 0, 0, True)
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print()
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print("array + neg scalar")
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print_coercion_table(np.typecodes['All'], 0, -1, True)
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print()
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print("promote_types")
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print_coercion_table(np.typecodes['All'], 0, 0, False, True)
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