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@ -35,55 +35,56 @@ class Uniform(Distribution):
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Examples:
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Examples:
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>>> # To initialize a Uniform distribution of mean 3.0 and standard deviation 4.0
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>>> # To initialize a Uniform distribution of mean 3.0 and standard deviation 4.0
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>>> n = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
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>>> import mindspore.nn.probability.distribution as msd
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>>> u = msd.Uniform(0.0, 1.0, dtype=mstype.float32)
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>>>
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>>>
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>>> # The following creates two independent Uniform distributions
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>>> # The following creates two independent Uniform distributions
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>>> n = nn.Uniform([0.0, 0.0], [1.0, 2.0], dtype=mstype.float32)
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>>> u = msd.Uniform([0.0, 0.0], [1.0, 2.0], dtype=mstype.float32)
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>>>
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>>>
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>>> # A Uniform distribution can be initilized without arguments
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>>> # A Uniform distribution can be initilized without arguments
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>>> # In this case, high and low must be passed in through construct.
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>>> # In this case, high and low must be passed in through args during function calls.
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>>> n = nn.Uniform(dtype=mstype.float32)
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>>> u = msd.Uniform(dtype=mstype.float32)
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>>>
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>>>
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>>> # To use Uniform in a network
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>>> # To use Uniform in a network
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>>> class net(Cell):
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>>> class net(Cell):
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>>> def __init__(self)
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>>> def __init__(self)
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>>> super(net, self).__init__():
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>>> super(net, self).__init__():
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>>> self.u1 = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
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>>> self.u1 = msd.Uniform(0.0, 1.0, dtype=mstype.float32)
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>>> self.u2 = nn.Uniform(dtype=mstype.float32)
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>>> self.u2 = msd.Uniform(dtype=mstype.float32)
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>>>
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>>>
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>>> # All the following calls in construct are valid
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>>> # All the following calls in construct are valid
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>>> def construct(self, value, low_b, high_b, low_a, high_a):
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>>> def construct(self, value, low_b, high_b, low_a, high_a):
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>>>
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>>>
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>>> # Similar calls can be made to other probability functions
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>>> # Similar calls can be made to other probability functions
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>>> # by replacing 'prob' with the name of the function
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>>> # by replacing 'prob' with the name of the function
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>>> ans = self.u1('prob', value)
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>>> ans = self.u1.prob(value)
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>>> # Evaluate with the respect to distribution b
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>>> # Evaluate with the respect to distribution b
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>>> ans = self.u1('prob', value, low_b, high_b)
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>>> ans = self.u1.prob(value, low_b, high_b)
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>>>
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>>>
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>>> # High and low must be passed in through construct
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>>> # High and low must be passed in during function calls
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>>> ans = self.u2('prob', value, low_a, high_a)
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>>> ans = self.u2.prob(value, low_a, high_a)
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>>>
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>>>
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>>> # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
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>>> # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
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>>> # Will return [0.0]
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>>> # Will return 0.5
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>>> ans = self.u1('mean')
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>>> ans = self.u1.mean()
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>>> # Will return low_b
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>>> # Will return (low_b + high_b) / 2
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>>> ans = self.u1('mean', low_b, high_b)
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>>> ans = self.u1.mean(low_b, high_b)
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>>>
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>>>
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>>> # High and low must be passed in through construct
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>>> # High and low must be passed in during function calls
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>>> ans = self.u2('mean', low_a, high_a)
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>>> ans = self.u2.mean(low_a, high_a)
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>>>
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>>>
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>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
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>>> # Usage of 'kl_loss' and 'cross_entropy' are similar
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>>> ans = self.u1('kl_loss', 'Uniform', low_b, high_b)
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>>> ans = self.u1.kl_loss('Uniform', low_b, high_b)
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>>> ans = self.u1('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
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>>> ans = self.u1.kl_loss('Uniform', low_b, high_b, low_a, high_a)
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>>>
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>>>
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>>> # Additional high and low must be passed in through construct
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>>> # Additional high and low must be passed
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>>> ans = self.u2('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
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>>> ans = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
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>>>
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>>>
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>>> # Sample Usage
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>>> # Sample
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>>> ans = self.u1('sample')
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>>> ans = self.u1.sample()
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>>> ans = self.u1('sample', (2,3))
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>>> ans = self.u1.sample((2,3))
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>>> ans = self.u1('sample', (2,3), low_b, high_b)
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>>> ans = self.u1.sample((2,3), low_b, high_b)
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>>> ans = self.u2('sample', (2,3), low_a, high_a)
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>>> ans = self.u2.sample((2,3), low_a, high_a)
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"""
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"""
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def __init__(self,
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def __init__(self,
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@ -142,73 +143,64 @@ class Uniform(Distribution):
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"""
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"""
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return self._high
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return self._high
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def _range(self, name='range', low=None, high=None):
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def _range(self, low=None, high=None):
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r"""
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r"""
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Return the range of the distribution.
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Return the range of the distribution.
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.. math::
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.. math::
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range(U) = high -low
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range(U) = high -low
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"""
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"""
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if name == 'range':
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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return high - low
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return high - low
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return None
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def _mean(self, name='mean', low=None, high=None):
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def _mean(self, low=None, high=None):
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r"""
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r"""
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.. math::
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.. math::
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MEAN(U) = \fract{low + high}{2}.
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MEAN(U) = \fract{low + high}{2}.
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"""
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"""
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if name == 'mean':
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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return (low + high) / 2.
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return (low + high) / 2.
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return None
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def _var(self, name='var', low=None, high=None):
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def _var(self, low=None, high=None):
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r"""
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r"""
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.. math::
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.. math::
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VAR(U) = \fract{(high -low) ^ 2}{12}.
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VAR(U) = \fract{(high -low) ^ 2}{12}.
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"""
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"""
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if name in self._variance_functions:
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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return self.sq(high - low) / 12.0
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return self.sq(high - low) / 12.0
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return None
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def _entropy(self, name='entropy', low=None, high=None):
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def _entropy(self, low=None, high=None):
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r"""
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r"""
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.. math::
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.. math::
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H(U) = \log(high - low).
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H(U) = \log(high - low).
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"""
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"""
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if name == 'entropy':
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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return self.log(high - low)
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return self.log(high - low)
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return None
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def _cross_entropy(self, name, dist, low_b, high_b, low_a=None, high_a=None):
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def _cross_entropy(self, dist, low_b, high_b, low_a=None, high_a=None):
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"""
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"""
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Evaluate cross_entropy between Uniform distributoins.
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Evaluate cross_entropy between Uniform distributoins.
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Args:
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Args:
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name (str): name of the funtion.
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dist (str): type of the distributions. Should be "Uniform" in this case.
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dist (str): type of the distributions. Should be "Uniform" in this case.
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low_b (Tensor): lower bound of distribution b.
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low_b (Tensor): lower bound of distribution b.
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high_b (Tensor): upper bound of distribution b.
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high_b (Tensor): upper bound of distribution b.
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low_a (Tensor): lower bound of distribution a. Default: self.low.
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low_a (Tensor): lower bound of distribution a. Default: self.low.
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high_a (Tensor): upper bound of distribution a. Default: self.high.
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high_a (Tensor): upper bound of distribution a. Default: self.high.
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"""
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"""
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if name == 'cross_entropy' and dist == 'Uniform':
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if dist == 'Uniform':
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return self._entropy(low=low_a, high=high_a) + self._kl_loss(name, dist, low_b, high_b, low_a, high_a)
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return self._entropy(low=low_a, high=high_a) + self._kl_loss(dist, low_b, high_b, low_a, high_a)
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return None
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return None
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def _prob(self, name, value, low=None, high=None):
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def _prob(self, value, low=None, high=None):
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r"""
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r"""
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pdf of Uniform distribution.
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pdf of Uniform distribution.
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Args:
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Args:
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name (str): name of the function.
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value (Tensor): value to be evaluated.
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value (Tensor): value to be evaluated.
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low (Tensor): lower bound of the distribution. Default: self.low.
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low (Tensor): lower bound of the distribution. Default: self.low.
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high (Tensor): upper bound of the distribution. Default: self.high.
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high (Tensor): upper bound of the distribution. Default: self.high.
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@ -218,7 +210,6 @@ class Uniform(Distribution):
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pdf(x) = \fract{1.0}{high -low} if low <= x <= high;
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pdf(x) = \fract{1.0}{high -low} if low <= x <= high;
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pdf(x) = 0 if x > high;
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pdf(x) = 0 if x > high;
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"""
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"""
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if name in self._prob_functions:
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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ones = self.fill(self.dtype, self.shape(value), 1.0)
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ones = self.fill(self.dtype, self.shape(value), 1.0)
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@ -229,21 +220,19 @@ class Uniform(Distribution):
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comp_hi = self.lessequal(value, high)
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comp_hi = self.lessequal(value, high)
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less_than_low = self.select(comp_lo, zeros, prob)
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less_than_low = self.select(comp_lo, zeros, prob)
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return self.select(comp_hi, less_than_low, zeros)
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return self.select(comp_hi, less_than_low, zeros)
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return None
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def _kl_loss(self, name, dist, low_b, high_b, low_a=None, high_a=None):
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def _kl_loss(self, dist, low_b, high_b, low_a=None, high_a=None):
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"""
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"""
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Evaluate uniform-uniform kl divergence, i.e. KL(a||b).
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Evaluate uniform-uniform kl divergence, i.e. KL(a||b).
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Args:
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Args:
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name (str): name of the funtion.
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dist (str): type of the distributions. Should be "Uniform" in this case.
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dist (str): type of the distributions. Should be "Uniform" in this case.
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low_b (Tensor): lower bound of distribution b.
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low_b (Tensor): lower bound of distribution b.
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high_b (Tensor): upper bound of distribution b.
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high_b (Tensor): upper bound of distribution b.
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low_a (Tensor): lower bound of distribution a. Default: self.low.
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low_a (Tensor): lower bound of distribution a. Default: self.low.
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high_a (Tensor): upper bound of distribution a. Default: self.high.
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high_a (Tensor): upper bound of distribution a. Default: self.high.
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"""
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"""
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if name in self._divergence_functions and dist == 'Uniform':
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if dist == 'Uniform':
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low_a = self.low if low_a is None else low_a
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low_a = self.low if low_a is None else low_a
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high_a = self.high if high_a is None else high_a
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high_a = self.high if high_a is None else high_a
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kl = self.log(high_b - low_b) / self.log(high_a - low_a)
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kl = self.log(high_b - low_b) / self.log(high_a - low_a)
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@ -251,12 +240,11 @@ class Uniform(Distribution):
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return self.select(comp, kl, self.log(self.zeroslike(kl)))
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return self.select(comp, kl, self.log(self.zeroslike(kl)))
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return None
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return None
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def _cdf(self, name, value, low=None, high=None):
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def _cdf(self, value, low=None, high=None):
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r"""
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r"""
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cdf of Uniform distribution.
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cdf of Uniform distribution.
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Args:
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Args:
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name (str): name of the function.
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value (Tensor): value to be evaluated.
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value (Tensor): value to be evaluated.
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low (Tensor): lower bound of the distribution. Default: self.low.
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low (Tensor): lower bound of the distribution. Default: self.low.
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high (Tensor): upper bound of the distribution. Default: self.high.
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high (Tensor): upper bound of the distribution. Default: self.high.
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@ -266,7 +254,6 @@ class Uniform(Distribution):
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cdf(x) = \fract{x - low}{high -low} if low <= x <= high;
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cdf(x) = \fract{x - low}{high -low} if low <= x <= high;
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cdf(x) = 1 if x > high;
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cdf(x) = 1 if x > high;
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"""
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"""
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if name in self._cdf_survival_functions:
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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prob = (value - low) / (high - low)
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prob = (value - low) / (high - low)
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@ -277,14 +264,12 @@ class Uniform(Distribution):
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comp_hi = self.less(value, high)
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comp_hi = self.less(value, high)
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less_than_low = self.select(comp_lo, zeros, prob)
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less_than_low = self.select(comp_lo, zeros, prob)
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return self.select(comp_hi, less_than_low, ones)
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return self.select(comp_hi, less_than_low, ones)
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return None
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def _sample(self, name, shape=(), low=None, high=None):
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def _sample(self, shape=(), low=None, high=None):
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"""
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"""
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Sampling.
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Sampling.
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Args:
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Args:
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name (str): name of the function. Should always be 'sample' when passed in from construct.
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shape (tuple): shape of the sample. Default: ().
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shape (tuple): shape of the sample. Default: ().
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low (Tensor): lower bound of the distribution. Default: self.low.
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low (Tensor): lower bound of the distribution. Default: self.low.
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high (Tensor): upper bound of the distribution. Default: self.high.
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high (Tensor): upper bound of the distribution. Default: self.high.
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@ -292,7 +277,6 @@ class Uniform(Distribution):
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Returns:
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Returns:
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Tensor, shape is shape + batch_shape.
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Tensor, shape is shape + batch_shape.
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"""
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"""
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if name == 'sample':
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low = self.low if low is None else low
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low = self.low if low is None else low
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high = self.high if high is None else high
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high = self.high if high is None else high
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broadcast_shape = self.shape(low + high)
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broadcast_shape = self.shape(low + high)
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@ -301,4 +285,3 @@ class Uniform(Distribution):
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sample_uniform = self.uniform(shape + broadcast_shape, l_zero, h_one)
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sample_uniform = self.uniform(shape + broadcast_shape, l_zero, h_one)
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sample = (high - low) * sample_uniform + low
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sample = (high - low) * sample_uniform + low
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return sample
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return sample
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return None
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Xun Deng
5 years ago