155 lines
6.2 KiB
Python
155 lines
6.2 KiB
Python
"""
|
|
The following determines the next state of a given cell in a CAM.
|
|
|
|
The ruleset takes in a collection of rules specifying neighborhoods, as well as the configurations of
|
|
said neighborhood that yield an "on" or "off" state on the cell a ruleset is being applied to.
|
|
|
|
@author: jrpotter
|
|
@date: May 31st, 2015
|
|
"""
|
|
import enum
|
|
import itertools as it
|
|
|
|
import numpy as np
|
|
|
|
|
|
class Ruleset:
|
|
"""
|
|
The primary class of this module, which saves configurations of cells that yield the next state.
|
|
|
|
The ruleset will take in configurations defined by the user that specify how a cell's state should change,
|
|
depending on the given neighborhood and current state. For example, if I have a configuration that states
|
|
|
|
[[0, 0, 0]
|
|
,[1, 0, 1]
|
|
,[1, 1, 1]
|
|
]
|
|
|
|
must match exactly for the center cell to be a 1, then each cell is checked for this configuration, and its
|
|
state is updated afterward (note the above is merely for clarity; a configuration is not defined as such). Note
|
|
configurations are checked until a match occurs, in a FIFO manner.
|
|
"""
|
|
|
|
class Method(enum.Enum):
|
|
"""
|
|
Specifies how a ruleset should be applied to a given cell.
|
|
|
|
* A match declares that a given configuration must match exactly for the cell to be considered on.
|
|
* A tolerance specifies that a configuration must match within a given percentage to be considered on.
|
|
* A specification allows the user to define a custom function which must return a boolean, declaring
|
|
whether a cell should be on or off. This function is given the current cell's state, as well as
|
|
the state of the cell's neighbors.
|
|
|
|
"""
|
|
MATCH = 0
|
|
TOLERATE = 1
|
|
SATISFY = 2
|
|
ALWAYS_PASS = 3
|
|
|
|
def __init__(self, method):
|
|
"""
|
|
@grid: Every ruleset is bound to a grid, which a ruleset is applied to.
|
|
@method: One of the values defined in the RulesetMethod enumeration. View class for description.
|
|
"""
|
|
self.method = method
|
|
self.configurations = []
|
|
|
|
def addConfiguration(self, grid, next_state, offsets):
|
|
"""
|
|
Creates a configuration and saves said configuration.
|
|
"""
|
|
config = Configuration(grid, next_state, offsets)
|
|
self.configurations.append(config)
|
|
|
|
def applyTo(self, plane, *args):
|
|
"""
|
|
Depending on the set method, applies ruleset to each cell in the plane.
|
|
|
|
Note we first compute all neighborhoods in a batch manner and then test that a configuration
|
|
passes on the supplied neighborhood.
|
|
|
|
@args: If our method is TOLERATE, we pass in a value in set [0, 1]. This specifies the threshold between a
|
|
passing (i.e. percentage of matches in a configuration is > arg) and failing. If our method is SATISFY,
|
|
arg should be a function returning a BOOL, which takes in a current cell's value, and the
|
|
value of its neighbors.
|
|
"""
|
|
master = plane.grid.flat
|
|
|
|
for config in self.configurations:
|
|
|
|
# Construct neighborhoods
|
|
#
|
|
# After profiling with a previous version, I found that going through each index and totaling the number
|
|
# of active states was taking much longer than I liked. Instead, we compute as many neighborhoods as possible
|
|
# simultaneously, avoiding explicit summation via the "sum" function, at least for each state separately.
|
|
#
|
|
# Because the states are now represented as numbers, we instead convert each number to their binary representation
|
|
# and add the binary representations together. We do this in chunks of 9, depending on the number of offsets, so
|
|
# no overflowing of a single column can occur. We can then find the total of the ith neighborhood by checking the
|
|
# sum of the ith index of the summation of every 9 chunks of numbers (this is done a row at a time).
|
|
|
|
# TODO: Config offsets should be flat index, bit offset
|
|
|
|
|
|
|
|
neighborhoods = []
|
|
|
|
values = []
|
|
for f_index, offset in config.offsets:
|
|
val = plane.f_bits([f_index])
|
|
values.append(int(val[offset+1:] + val[:offset]))
|
|
|
|
# Chunk into groups of 9 and sum all values
|
|
chunks = [values[i:i+9] for i in range(0, len(values), 9)]
|
|
summands = map(sum, chunks)
|
|
|
|
# Construct neighborhoods for each value in list
|
|
|
|
|
|
|
|
|
|
if self.method == Ruleset.Method.MATCH:
|
|
vfunc = self._matches
|
|
elif self.method == Ruleset.Method.TOLERATE:
|
|
vfunc = self._tolerates
|
|
elif self.method == Ruleset.Method.SATISFY:
|
|
vfunc = self._satisfies
|
|
elif self.method == Ruleset.Method.ALWAYS_PASS:
|
|
vfunc = lambda *args: True
|
|
|
|
# Apply the function if possible
|
|
passed, state = config.passes(f_index, grid, vfunc, *args)
|
|
if passed:
|
|
return state
|
|
|
|
# If no configuration passes, we leave the state unchanged
|
|
return grid.flat[f_index]
|
|
|
|
def _matches(self, f_index, f_grid, indices, states):
|
|
"""
|
|
Determines that neighborhood matches expectation exactly.
|
|
|
|
Note this functions like the tolerate method with a tolerance of 1.
|
|
"""
|
|
return not np.count_nonzero(f_grid[indices] ^ states)
|
|
|
|
def _tolerates(self, f_index, f_grid, indices, states, tolerance):
|
|
"""
|
|
Determines that neighborhood matches expectation within tolerance.
|
|
|
|
We see that the percentage of actual matches are greater than or equal to the given tolerance level. If so, we
|
|
consider this cell to be alive. Note tolerance must be a value 0 <= t <= 1.
|
|
"""
|
|
non_matches = np.count_nonzero(f_grid[indices] ^ states)
|
|
return (non_matches / len(f_grid)) >= tolerance
|
|
|
|
def _satisfies(self, f_index, f_grid, indices, states, valid_func):
|
|
"""
|
|
Allows custom function to relay next state of given cell.
|
|
|
|
The passed function is supplied the list of 2-tuple elements, of which the first is a Cell and the second is
|
|
the expected state as declared in the Neighborhood, as well as the grid and cell in question.
|
|
"""
|
|
return valid_func(f_index, f_grid, indices, states)
|
|
|