# Empirical Distributions¶

The pyinform.dist.Dist class provides an empirical distribution, i.e. a histogram, representing the observed frequencies of some fixed-size set of events. This class is the basis for all of the fundamental information measures on discrete probability distributions.

## Examples¶

### Example 1: Construction¶

You can construct a distribution with a specified number of unique observables. This construction method results in an invalid distribution as no observations have been made thus far.

>>> d = Dist(5)
>>> d.valid()
False
>>> d.counts()
0
>>> len(d)
5


Alternatively you can construct a distribution given a list (or NumPy array) of observation counts:

>>> d = Dist([0,0,1,2,1,0,0])
>>> d.valid()
True
>>> d.counts()
4
>>> len(d)
7


### Example 2: Making Observations¶

Once a distribution has been constructed, we can begin making observations. There are two methods for doing so. The first uses the standard indexing operations, treating the distribution similarly to a list:

>>> d = Dist(5)
>>> for i in range(len(d)):
...     d[i] = i*i
>>> list(d)
[0, 1, 4, 9, 16]


The second method is to make incremental changes to the distribution. This is useful when making observations of timeseries:

>>> obs = [1,0,1,2,2,1,2,3,2,2]
>>> d = Dist(max(obs) + 1)
>>> for event in obs:
...     assert(d[event] == d.tick(event) - 1)
...
>>> list(d)
[1, 3, 5, 1]


It is important to remember that Dist keeps track of your events as you provide them. For example:

>>> obs = [1, 1, 3, 5, 1, 3, 7, 9]
>>> d = Dist(max(obs) + 1)
>>> for event in obs:
...     assert(d[event] == d.tick(event) - 1)
...
>>> list(d)
[0, 3, 0, 2, 0, 1, 0, 1, 0, 1]
>>> d[3]
2
>>> d[7]
1


If you know there are “gaps” in your time series, e.g. no even numbers, then you can use the utility function coalesce_series() to get rid of them:

>>> from pyinform import utils
>>> obs = [1, 1, 3, 5, 1, 3, 7, 9]
>>> coal, b = utils.coalesce_series(obs)
>>> d = Dist(b)
>>> for event in coal:
...     assert(d[event] == d.tick(event) - 1)
...
>>> list(d)
[3, 2, 1, 1, 1]
>>> d[1]
2
>>> d[3]
1


This can significantly improve memory usage in situations where the range of possible states is large, but is sparsely sampled in the time series.

### Example 3: Probabilities¶

Once some observations have been made, we can start asking for probabilities. As in the previous examples, there are multiple ways of doing this. The first is to just ask for the probability of a given event.

>>> d = Dist([3,0,1,2])
>>> d.probability(0)
0.5
>>> d.probability(1)
0.0
>>> d.probability(2)
0.16666666666666666
>>> d.probability(3)
0.3333333333333333


Sometimes it is nice to just dump the probabilities out to an array:

>>> d = Dist([3,0,1,2])
>>> d.dump()
array([0.5       , 0.        , 0.16666667, 0.33333333])


### Example 4: Shannon Entropy¶

Once you have a distribution you can do lots of fun things with it. In this example, we will compute the shannon entropy of a timeseries of observed values.

from math import log2
from pyinform.dist import Dist

obs = [1,0,1,2,2,1,2,3,2,2]
d = Dist(max(obs) + 1)
for event in obs:
d.tick(event)

h = 0.
for p in d.dump():
h -= p * log2(p)

print(h)

1.6854752972273344


Of course PyInform provides a function for this: pyinform.shannon.entropy().

from pyinform.dist import Dist
from pyinform.shannon import entropy

obs = [1,0,1,2,2,1,2,3,2,2]
d = Dist(max(obs) + 1)
for event in obs:
d.tick(event)

print(entropy(d))

1.6854752972273344


## API Documentation¶

class pyinform.dist.Dist(n)[source]

Dist is class designed to represent empirical probability distributions, i.e. histograms, for cleanly logging observations of time series data.

The premise behind this class is that it allows PyInform to define the standard entropy measures on distributions. This reduces functions such as pyinform.activeinfo.active_info() to building distributions and then applying standard entropy measures.

__init__(n)[source]

Construct a distribution.

If the parameter n is an integer, the distribution is constructed with a zeroed support of size n. If n is a list or numpy.ndarray, the sequence is treated as the underlying support.

Examples:

>>> d = Dist(5)
>>> d = Dist([0,0,1,2])

Parameters

n (int, list or numpy.ndarray) – the support for the distribution

Raises
• ValueError – if support is empty or multidimensional

• MemoryError – if memory allocation fails within the C call

__len__()[source]

Determine the size of the support of the distribution.

Examples:

>>> len(Dist(5))
5
>>> len(Dist([0,1,5]))
3


See also counts().

Returns

the size of the support

Return type

int

__getitem__(event)[source]

Get the number of observations made of event.

Examples:

>>> d = Dist(2)
>>> (d[0], d[1])
(0, 0)

>>> d = Dist([0,1])
>>> (d[0], d[1])
(0, 1)

Parameters

event (int) – the observed event

Returns

the number of observations of event

Return type

int

Raises

IndexError – if event < 0 or len(self) <= event

__setitem__(event, value)[source]

Set the number of observations of event to value.

If value is negative, then the observation count is set to zero.

Examples:

>>> d = Dist(2)
>>> for i, _ in enumerate(d):
...     d[i] = i*i
...
>>> list(d)
[0, 1]

>>> d = Dist([0,1,2,3])
>>> for i, n in enumerate(d):
...     d[i] = 2 * n
...
>>> list(d)
[0, 2, 4, 6]


See also __getitem__() and tick().

Parameters
• event (int) – the observed event

• value (int) – the number of observations

Raises

IndexError – if event < 0 or len(self) <= event

resize(n)[source]

Resize the support of the distribution in place.

If the distribution…

• shrinks - the last len(self) - n elements are lost, the rest are preserved

• grows - the last n - len(self) elements are zeroed

• is unchanged - well, that sorta says it all, doesn’t it?

Examples:

>>> d = Dist(5)
>>> d.resize(3)
>>> len(d)
3
>>> d.resize(8)
>>> len(d)
8

>>> d = Dist([1,2,3,4])
>>> d.resize(2)
>>> list(d)
[1, 2]
>>> d.resize(4)
>>> list(d)
[1, 2, 0, 0]

Parameters

n (int) – the desired size of the support

Raises
• ValueError – if the requested size is zero

• MemoryError – if memory allocation fails in the C call

copy()[source]

Perform a deep copy of the distribution.

Examples:

>>> d = Dist([1,2,3])
>>> e = d
>>> e[0] = 3
>>> list(e)
[3, 2, 3]
>>> list(d)
[3, 2, 3]

>>> f = d.copy()
>>> f[0] = 1
>>> list(f)
[1, 2, 3]
>>> list(d)
[3, 2, 3]

Returns

the copied distribution

Return type

pyinform.dist.Dist

counts()[source]

Return the number of observations made thus far.

Examples:

>>> d = Dist(5)
>>> d.counts()
0

>>> d = Dist([1,0,3,2])
>>> d.counts()
6


See also __len__().

Returns

the number of observations

Return type

int

valid()[source]

Determine if the distribution is a valid probability distribution, i.e. if the support is not empty and at least one observation has been made.

Examples:

>>> d = Dist(5)
>>> d.valid()
False

>>> d = Dist([0,0,0,1])
>>> d.valid()
True


See also __len__() and counts().

Returns

a boolean signifying that the distribution is valid

Return type

bool

tick(event)[source]

Make a single observation of event, and return the total number of observations of said event.

Examples:

>>> d = Dist(5)
>>> for i, _ in enumerate(d):
...     assert(d.tick(i) == 1)
...
>>> list(d)
[1, 1, 1, 1, 1]

>>> d = Dist([0,1,2,3])
>>> for i, _ in enumerate(d):
...     assert(d.tick(i) == i + 1)
...
>>> list(d)
[1, 2, 3, 4]


See also __getitem__() and __setitem__().

Parameters

event (int) – the observed event

Returns

the total number of observations of event

Return type

int

Raises

IndexError – if event < 0 or len(self) <= event

probability(event)[source]

Compute the empiricial probability of an event.

Examples:

>>> d = Dist([1,1,1,1])
>>> for i, _ in enumerate(d):
...     assert(d.probability(i) == 1./4)
...


See also __getitem__() and dump().

Parameters

event (int) – the observed event

Returns

the empirical probability event

Return type

float

Raises
• ValueError – if not self.valid()

• IndexError – if event < 0 or len(self) <= event

dump()[source]

Compute the empirical probability of each observable event and return the result as an array.

Examples:

>>> d = Dist([1,2,2,1])
>>> d.dump()
array([0.16666667, 0.33333333, 0.33333333, 0.16666667])


See also probability().

Returns

the empirical probabilities of all o

Return type

numpy.ndarray

Raises
• ValueError – if not self.valid()

• RuntimeError – if the dump fails in the C call

• IndexError – if event < 0 or len(self) <= event