Python _random.Random() Examples

The following are 30 code examples of _random.Random(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module _random , or try the search function .
Example #1
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Change the internal state to one that is likely far away
        from the current state.  This method will not be in Py3.x,
        so it is better to simply reseed.
        """
        # The super.jumpahead() method uses shuffling to change state,
        # so it needs a large and "interesting" n to work with.  Here,
        # we use hashing to create a large n for the shuffle.
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha512', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------
Example #2
Source File: random.py    From ironpython2 with Apache License 2.0 6 votes vote down vote up 
def jumpahead(self, n):
        """Change the internal state to one that is likely far away
        from the current state.  This method will not be in Py3.x,
        so it is better to simply reseed.
        """
        # The super.jumpahead() method uses shuffling to change state,
        # so it needs a large and "interesting" n to work with.  Here,
        # we use hashing to create a large n for the shuffle.
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha512', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------
Example #3
Source File: random.py    From ironpython2 with Apache License 2.0 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
Example #4
Source File: random.py    From ironpython2 with Apache License 2.0 6 votes vote down vote up 
def jumpahead(self, n):
        """Act as if n calls to random() were made, but quickly.

        n is an int, greater than or equal to 0.

        Example use:  If you have 2 threads and know that each will
        consume no more than a million random numbers, create two Random
        objects r1 and r2, then do
            r2.setstate(r1.getstate())
            r2.jumpahead(1000000)
        Then r1 and r2 will use guaranteed-disjoint segments of the full
        period.
        """

        if not n >= 0:
            raise ValueError("n must be >= 0")
        x, y, z = self._seed
        x = int(x * pow(171, n, 30269)) % 30269
        y = int(y * pow(172, n, 30307)) % 30307
        z = int(z * pow(170, n, 30323)) % 30323
        self._seed = x, y, z
Example #5
Source File: random.py    From kobo-predict with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.
Example #6
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def seed(self, a=None):
        """Initialize internal state from hashable object.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If a is not None or an int or long, hash(a) is used instead.
        """

        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None
Example #7
Source File: random.py    From Coursera-Stanford-Divide-and-Conquer-Sorting-and-Searching-and-Randomized-Algorithms with MIT License 6 votes vote down vote up 
def seed(self, a=None):
        """Initialize internal state from hashable object.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If a is not None or an int or long, hash(a) is used instead.
        """

        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None
Example #8
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
Example #9
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Change the internal state to one that is likely far away
        from the current state.  This method will not be in Py3.x,
        so it is better to simply reseed.
        """
        # The super.jumpahead() method uses shuffling to change state,
        # so it needs a large and "interesting" n to work with.  Here,
        # we use hashing to create a large n for the shuffle.
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha512', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------
Example #10
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Act as if n calls to random() were made, but quickly.

        n is an int, greater than or equal to 0.

        Example use:  If you have 2 threads and know that each will
        consume no more than a million random numbers, create two Random
        objects r1 and r2, then do
            r2.setstate(r1.getstate())
            r2.jumpahead(1000000)
        Then r1 and r2 will use guaranteed-disjoint segments of the full
        period.
        """

        if not n >= 0:
            raise ValueError("n must be >= 0")
        x, y, z = self._seed
        x = int(x * pow(171, n, 30269)) % 30269
        y = int(y * pow(172, n, 30307)) % 30307
        z = int(z * pow(170, n, 30323)) % 30323
        self._seed = x, y, z
Example #11
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def whseed(self, a=None):
        """Seed from hashable object's hash code.

        None or no argument seeds from current time.  It is not guaranteed
        that objects with distinct hash codes lead to distinct internal
        states.

        This is obsolete, provided for compatibility with the seed routine
        used prior to Python 2.1.  Use the .seed() method instead.
        """

        if a is None:
            self.__whseed()
            return
        a = hash(a)
        a, x = divmod(a, 256)
        a, y = divmod(a, 256)
        a, z = divmod(a, 256)
        x = (x + a) % 256 or 1
        y = (y + a) % 256 or 1
        z = (z + a) % 256 or 1
        self.__whseed(x, y, z)

## --------------- Operating System Random Source  ------------------
Example #12
Source File: random.py    From BinderFilter with MIT License 6 votes vote down vote up 
def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.
Example #13
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def seed(self, a=None):
        """Initialize internal state from hashable object.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If a is not None or an int or long, hash(a) is used instead.
        """

        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None
Example #14
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.
Example #15
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
Example #16
Source File: random.py    From Coursera-Stanford-Divide-and-Conquer-Sorting-and-Searching-and-Randomized-Algorithms with MIT License 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
Example #17
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Act as if n calls to random() were made, but quickly.

        n is an int, greater than or equal to 0.

        Example use:  If you have 2 threads and know that each will
        consume no more than a million random numbers, create two Random
        objects r1 and r2, then do
            r2.setstate(r1.getstate())
            r2.jumpahead(1000000)
        Then r1 and r2 will use guaranteed-disjoint segments of the full
        period.
        """

        if not n >= 0:
            raise ValueError("n must be >= 0")
        x, y, z = self._seed
        x = int(x * pow(171, n, 30269)) % 30269
        y = int(y * pow(172, n, 30307)) % 30307
        z = int(z * pow(170, n, 30323)) % 30323
        self._seed = x, y, z
Example #18
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def whseed(self, a=None):
        """Seed from hashable object's hash code.

        None or no argument seeds from current time.  It is not guaranteed
        that objects with distinct hash codes lead to distinct internal
        states.

        This is obsolete, provided for compatibility with the seed routine
        used prior to Python 2.1.  Use the .seed() method instead.
        """

        if a is None:
            self.__whseed()
            return
        a = hash(a)
        a, x = divmod(a, 256)
        a, y = divmod(a, 256)
        a, z = divmod(a, 256)
        x = (x + a) % 256 or 1
        y = (y + a) % 256 or 1
        z = (z + a) % 256 or 1
        self.__whseed(x, y, z)

## --------------- Operating System Random Source  ------------------
Example #19
Source File: random.py    From Computable with MIT License 6 votes vote down vote up 
def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.
Example #20
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def whseed(self, a=None):
        """Seed from hashable object's hash code.

        None or no argument seeds from current time.  It is not guaranteed
        that objects with distinct hash codes lead to distinct internal
        states.

        This is obsolete, provided for compatibility with the seed routine
        used prior to Python 2.1.  Use the .seed() method instead.
        """

        if a is None:
            self.__whseed()
            return
        a = hash(a)
        a, x = divmod(a, 256)
        a, y = divmod(a, 256)
        a, z = divmod(a, 256)
        x = (x + a) % 256 or 1
        y = (y + a) % 256 or 1
        z = (z + a) % 256 or 1
        self.__whseed(x, y, z)

## --------------- Operating System Random Source  ------------------
Example #21
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def seed(self, a=None):
        """Initialize internal state from hashable object.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If a is not None or an int or long, hash(a) is used instead.
        """

        if a is None:
            try:
                # Seed with enough bytes to span the 19937 bit
                # state space for the Mersenne Twister
                a = long(_hexlify(_urandom(2500)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None
Example #22
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def jumpahead(self, n):
        """Act as if n calls to random() were made, but quickly.

        n is an int, greater than or equal to 0.

        Example use:  If you have 2 threads and know that each will
        consume no more than a million random numbers, create two Random
        objects r1 and r2, then do
            r2.setstate(r1.getstate())
            r2.jumpahead(1000000)
        Then r1 and r2 will use guaranteed-disjoint segments of the full
        period.
        """

        if not n >= 0:
            raise ValueError("n must be >= 0")
        x, y, z = self._seed
        x = int(x * pow(171, n, 30269)) % 30269
        y = int(y * pow(172, n, 30307)) % 30307
        z = int(z * pow(170, n, 30323)) % 30323
        self._seed = x, y, z
Example #23
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)
Example #24
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Change the internal state to one that is likely far away
        from the current state.  This method will not be in Py3.x,
        so it is better to simply reseed.
        """
        # The super.jumpahead() method uses shuffling to change state,
        # so it needs a large and "interesting" n to work with.  Here,
        # we use hashing to create a large n for the shuffle.
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha512', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------
Example #25
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def jumpahead(self, n):
        """Act as if n calls to random() were made, but quickly.

        n is an int, greater than or equal to 0.

        Example use:  If you have 2 threads and know that each will
        consume no more than a million random numbers, create two Random
        objects r1 and r2, then do
            r2.setstate(r1.getstate())
            r2.jumpahead(1000000)
        Then r1 and r2 will use guaranteed-disjoint segments of the full
        period.
        """

        if not n >= 0:
            raise ValueError("n must be >= 0")
        x, y, z = self._seed
        x = int(x * pow(171, n, 30269)) % 30269
        y = int(y * pow(172, n, 30307)) % 30307
        z = int(z * pow(170, n, 30323)) % 30323
        self._seed = x, y, z
Example #26
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def whseed(self, a=None):
        """Seed from hashable object's hash code.

        None or no argument seeds from current time.  It is not guaranteed
        that objects with distinct hash codes lead to distinct internal
        states.

        This is obsolete, provided for compatibility with the seed routine
        used prior to Python 2.1.  Use the .seed() method instead.
        """

        if a is None:
            self.__whseed()
            return
        a = hash(a)
        a, x = divmod(a, 256)
        a, y = divmod(a, 256)
        a, z = divmod(a, 256)
        x = (x + a) % 256 or 1
        y = (y + a) % 256 or 1
        z = (z + a) % 256 or 1
        self.__whseed(x, y, z)

## --------------- Operating System Random Source  ------------------
Example #27
Source File: random.py    From oss-ftp with MIT License 6 votes vote down vote up 
def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))

# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
#(both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.
Example #28
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def jumpahead(self, n):
        """Change the internal state to one that is likely far away
        from the current state.  This method will not be in Py3.x,
        so it is better to simply reseed.
        """
        # The super.jumpahead() method uses shuffling to change state,
        # so it needs a large and "interesting" n to work with.  Here,
        # we use hashing to create a large n for the shuffle.
        s = repr(n) + repr(self.getstate())
        n = int(_hashlib.new('sha512', s).hexdigest(), 16)
        super(Random, self).jumpahead(n)

## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------
Example #29
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def seed(self, a=None):
        """Initialize internal state from hashable object.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If a is not None or an int or long, hash(a) is used instead.
        """

        if a is None:
            try:
                a = long(_hexlify(_urandom(16)), 16)
            except NotImplementedError:
                import time
                a = long(time.time() * 256) # use fractional seconds

        super(Random, self).seed(a)
        self.gauss_next = None
Example #30
Source File: random.py    From pmatic with GNU General Public License v2.0 6 votes vote down vote up 
def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super(Random, self).setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple( long(x) % (2**32) for x in internalstate )
            except ValueError, e:
                raise TypeError, e
            super(Random, self).setstate(internalstate)