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https://github.com/AdaCore/cvc5.git
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bf2d64336c
This moves RoundingMode to cvc5_types.h and switches to using the auto-generated enum bindings. It also fixes the Java-bindings generator for enums (certain parts were previously hardcoded for Kind) and extends the unit tests for Solver::mkRoundingMode() to actually check the value being created.
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
#!/usr/bin/env python
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###############################################################################
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# Top contributors (to current version):
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# Makai Mann, Andres Noetzli
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#
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# This file is part of the cvc5 project.
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#
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# Copyright (c) 2009-2021 by the authors listed in the file AUTHORS
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# in the top-level source directory and their institutional affiliations.
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# All rights reserved. See the file COPYING in the top-level source
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# directory for licensing information.
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# #############################################################################
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#
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# A simple demonstration of the solving capabilities of the cvc5
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# floating point solver through the Python API contributed by Eva
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# Darulova. This requires building cvc5 with symfpu.
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##
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import cvc5
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from cvc5 import Kind, RoundingMode
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if __name__ == "__main__":
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slv = cvc5.Solver()
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slv.setOption("produce-models", "true")
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slv.setLogic("QF_FP")
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# single 32-bit precision
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fp32 = slv.mkFloatingPointSort(8, 24)
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# the standard rounding mode
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rm = slv.mkRoundingMode(RoundingMode.RoundNearestTiesToEven)
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# create a few single-precision variables
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x = slv.mkConst(fp32, 'x')
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y = slv.mkConst(fp32, 'y')
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z = slv.mkConst(fp32, 'z')
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# check floating-point arithmetic is commutative, i.e. x + y == y + x
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commutative = slv.mkTerm(Kind.FPEq, slv.mkTerm(Kind.FPAdd, rm, x, y), slv.mkTerm(Kind.FPAdd, rm, y, x))
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slv.push()
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slv.assertFormula(slv.mkTerm(Kind.Not, commutative))
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print("Checking floating-point commutativity")
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print("Expect SAT (property does not hold for NaN and Infinities).")
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print("cvc5:", slv.checkSat())
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print("Model for x:", slv.getValue(x))
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print("Model for y:", slv.getValue(y))
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# disallow NaNs and Infinities
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slv.assertFormula(slv.mkTerm(Kind.Not, slv.mkTerm(Kind.FPIsNan, x)))
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slv.assertFormula(slv.mkTerm(Kind.Not, slv.mkTerm(Kind.FPIsInf, x)))
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slv.assertFormula(slv.mkTerm(Kind.Not, slv.mkTerm(Kind.FPIsNan, y)))
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slv.assertFormula(slv.mkTerm(Kind.Not, slv.mkTerm(Kind.FPIsInf, y)))
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print("Checking floating-point commutativity assuming x and y are not NaN or Infinity")
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print("Expect UNSAT.")
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print("cvc5:", slv.checkSat())
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# check floating-point arithmetic is not associative
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slv.pop()
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# constrain x, y, z between -3.14 and 3.14 (also disallows NaN and infinity)
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a = slv.mkFloatingPoint(
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8,
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24,
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slv.mkBitVector(32, "11000000010010001111010111000011", 2)) # -3.14
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b = slv.mkFloatingPoint(
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8,
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24,
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slv.mkBitVector(32, "01000000010010001111010111000011", 2)) # 3.14
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bounds_x = slv.mkTerm(Kind.And, slv.mkTerm(Kind.FPLeq, a, x), slv.mkTerm(Kind.FPLeq, x, b))
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bounds_y = slv.mkTerm(Kind.And, slv.mkTerm(Kind.FPLeq, a, y), slv.mkTerm(Kind.FPLeq, y, b))
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bounds_z = slv.mkTerm(Kind.And, slv.mkTerm(Kind.FPLeq, a, z), slv.mkTerm(Kind.FPLeq, z, b))
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slv.assertFormula(slv.mkTerm(Kind.And, slv.mkTerm(Kind.And, bounds_x, bounds_y), bounds_z))
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# (x + y) + z == x + (y + z)
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lhs = slv.mkTerm(Kind.FPAdd, rm, slv.mkTerm(Kind.FPAdd, rm, x, y), z)
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rhs = slv.mkTerm(Kind.FPAdd, rm, x, slv.mkTerm(Kind.FPAdd, rm, y, z))
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associative = slv.mkTerm(Kind.Not, slv.mkTerm(Kind.FPEq, lhs, rhs))
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slv.assertFormula(associative)
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print("Checking floating-point associativity")
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print("Expect SAT.")
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print("cvc5:", slv.checkSat())
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print("Model for x:", slv.getValue(x))
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print("Model for y:", slv.getValue(y))
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print("Model for z:", slv.getValue(z))
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