distribution

Distribution

Abstract base class for a distribution.

Source code in hfppl/distributions/distribution.py

class Distribution:
    """Abstract base class for a distribution."""

    async def sample(self):
        """Generate a random sample from the distribution.

        Returns:
            x: a value randomly sampled from the distribution."""
        raise NotImplementedError()

    async def log_prob(self, x):
        """Compute the log probability of a value under this distribution,
        or the log probability density if the distribution is continuous.

        Args:
            x: the point at which to evaluate the log probability.
        Returns:
            logprob (float): the log probability of `x`."""
        raise NotImplementedError()

    async def argmax(self, n):
        """Return the nth most probable outcome under this distribution (assuming this is a discrete distribution).

        Args:
            n (int): which value to return to, indexed from most probable (n=0) to least probable (n=|support|).
        Returns:
            x: the nth most probable outcome from this distribution."""
        raise NotImplementedError()

argmax(n) async

Return the nth most probable outcome under this distribution (assuming this is a discrete distribution).

Parameters:

Name	Type	Description	Default
n	int	which value to return to, indexed from most probable (n=0) to least probable (n=|support|).	required

Returns: x: the nth most probable outcome from this distribution.

Source code in hfppl/distributions/distribution.py

async def argmax(self, n):
    """Return the nth most probable outcome under this distribution (assuming this is a discrete distribution).

    Args:
        n (int): which value to return to, indexed from most probable (n=0) to least probable (n=|support|).
    Returns:
        x: the nth most probable outcome from this distribution."""
    raise NotImplementedError()

log_prob(x) async

Compute the log probability of a value under this distribution, or the log probability density if the distribution is continuous.

Parameters:

Name	Type	Description	Default
x		the point at which to evaluate the log probability.	required

Returns: logprob (float): the log probability of x.

Source code in hfppl/distributions/distribution.py

async def log_prob(self, x):
    """Compute the log probability of a value under this distribution,
    or the log probability density if the distribution is continuous.

    Args:
        x: the point at which to evaluate the log probability.
    Returns:
        logprob (float): the log probability of `x`."""
    raise NotImplementedError()

sample() async

Generate a random sample from the distribution.

Returns:

Name	Type	Description
x		a value randomly sampled from the distribution.

Source code in hfppl/distributions/distribution.py

async def sample(self):
    """Generate a random sample from the distribution.

    Returns:
        x: a value randomly sampled from the distribution."""
    raise NotImplementedError()