def __init__(self, *args, **kwargs):
super(SchonhageStorageModification, self).__init__(*args, **kwargs)
self.DEBUG = False
self.graph = DirectedGraph()
if 'word' in kwargs:
self.TEST_WORD = kwargs['word']
else:
self.TEST_WORD = 'Schonhage'
self.word = strlist(list(self.TEST_WORD)) # TEST
self.curr_key = 0
self.curr_val = self.word[self.curr_key]
self.output = self.curr_val
# Setup initial graph with default word.
self.recalculate()
# Get node after the paths have been created.
self.curr_node = self.graph[self.curr_key]
def __init__(self, *args, **kwargs):
super(SchonhageStorageModification, self).__init__(*args, **kwargs)
self.DEBUG = False
self.graph = DirectedGraph()
if 'word' in kwargs:
self.TEST_WORD = kwargs['word']
else:
self.TEST_WORD = 'Schonhage'
self.word = strlist(list(self.TEST_WORD)) # TEST
self.curr_key = 0
self.curr_val = self.word[self.curr_key]
self.output = self.curr_val
# Setup initial graph with default word.
self.recalculate()
# Get node after the paths have been created.
self.curr_node = self.graph[self.curr_key]
class SchonhageStorageModification(PointerMachine):
def __init__(self, *args, **kwargs):
super(SchonhageStorageModification, self).__init__(*args, **kwargs)
self.DEBUG = False
self.graph = DirectedGraph()
if 'word' in kwargs:
self.TEST_WORD = kwargs['word']
else:
self.TEST_WORD = 'Schonhage'
self.word = strlist(list(self.TEST_WORD)) # TEST
self.curr_key = 0
self.curr_val = self.word[self.curr_key]
self.output = self.curr_val
# Setup initial graph with default word.
self.recalculate()
# Get node after the paths have been created.
self.curr_node = self.graph[self.curr_key]
def __setitem__(self, *args):
"""Adds a new node to the graph."""
item, connections = args
self.graph[item] = connections
def __getitem__(self, key):
"""A wrapper that loops over the internal graph."""
# Skip loop if possible.
if self.curr_node['val'] == key:
return self.curr_node
# Check all other nodes.
for k, node in self.graph.iteritems():
try:
if node['val'] == key:
return node
except KeyError:
continue
def _show_edges(self, node):
"""A debug method to nicely show all edges for a given node key"""
for state, edge in self.graph[node]['edges'].iteritems():
'[SHOW EDGE] {} ... {} --> {}'.format(node, state, edge['to'])
def recalculate(self):
"""Draws/redraws the graph, taking the current value
and assigning the nodes / paths accordingly.
Assigns the edge values for each node, and
updates the value for each symbol in the word as well.
In the example {0, 1} symbol set machine, each node can have a 0, 1,
or both 0 and 1 edges, which allow for the instructions
to be dictated by traversing the graph.
In a traditional directed graph, nodes are either inbound, or outbound.
In the SMM, the edges define the direction, but they also encode
the state symbols that can be used to determine behavior in a way
that operates like tape, cells, and state in a Turing machine.
An example graph looks like this:
graph = {
0: ...,
1: {'val': 'A', 'edges': {0: {'to': 0}, 1: {'to': 2},},
2: ....
}
The key is the node, and is used as a pointer to other nodes.
The edges attribute is a dictionary, keyed by each edge
that corresponds to all symbol states (e.g. {0, 1} in the example).
Each key points to a dictionary, representing the node to move to.
"""
end = len(self.word)
# Special case for single nodes.
if end == 1:
self.graph[0] = {
'key': 0,
'val': self.word, # guaranteed to only be one character
'edges': {
0: {
'to': 0
},
}
}
else:
for key, val in enumerate(self.word):
self.graph[key] = {'key': key, 'val': val, 'edges': {}}
# The first nodes 0-edge points to itself,
# and the 1-edge points to the next node.
if key == 0:
self.graph[key]['edges'] = {
0: {
'to': 0
},
1: {
'to': key + 1
}
}
# The last nodes 0 and 1 edges
# BOTH point to the previous node.
elif key == end - 1:
self.graph[key]['edges'] = {
0: {
'to': key - 1
},
1: {
'to': key - 1
}
}
# All others; 0-edge points to the previous,
# 1-edge points to the next.
else:
self.graph[key]['edges'] = {
0: {
'to': key - 1
},
1: {
'to': key + 1
}
}
if self.DEBUG:
prnt('Graph', self.graph.vertices)
def new(self, val):
"""Adds a new value to the word"""
# Handle recalculation by simply adding the
# new words and calling function again.
for token in list(val):
self.word += token
# Adjust edges to / from each node
self.recalculate()
if self.DEBUG:
print('word with new item(s) {}'.format(self.word))
# Make this one chain-able.
return self
def _get_path(self, word):
"""Returns a list of nodes representing the
path traveled for a given word."""
path = []
for char in word:
path.append(self.graph[char][1])
return path
def jump(self, word1, word2, jump_node):
"""Get the two paths to compare their ending nodes.
Compare the two paths to see if they end in the same node. If so, jump.
"""
if self._get_path(word1)[-1:] == self._get_path(word2)[-1:]:
self.curr_node = jump_node
self._run_step()
if self.DEBUG:
print('{} {} paths are equal. Jumping to node {}'.format(
word1, word2, jump_node))
def set(self, node, val):
"""Sets a nodes value to the new value."""
self.graph[node]['val'] = val
# Recalculation to determine where nodes
# point is done in one place, using the same technique
# as when the graph is initially created.
self.recalculate()
def _update_state(self):
# Take the 1-edge, neo
new_node_key = self.curr_node['edges'][1]['to']
self.curr_node = self.graph[new_node_key]
self.curr_val = self.curr_node['val']
self.curr_key = self.curr_node['key']
def _run_step(self):
if self.DEBUG:
prnt('curr node (before):', self.curr_node)
time.sleep(self.DELAY)
# Visually update
if self.DEBUG:
prnt('curr node (after):', self.curr_node)
self._update_state()
if self.curr_node is not None:
# Add to the string as the program runs.
self.output += self.curr_val
def traverse_word(self):
prnt('Graph nodes beginning', self.graph.vertices)
start, end = 0, len(self.word)
while start < end:
# Print must come before next step,
# otterwise it will print the letters shifted.
print('Counter: {}, Output: {}, Curr val: {}, Curr key: {}'.format(
start, self.output, self.curr_val, self.curr_key))
self._run_step()
start += 1
def run(self):
self.traverse_word()
class SchonhageStorageModification(PointerMachine):
def __init__(self, *args, **kwargs):
super(SchonhageStorageModification, self).__init__(*args, **kwargs)
self.DEBUG = False
self.graph = DirectedGraph()
if 'word' in kwargs:
self.TEST_WORD = kwargs['word']
else:
self.TEST_WORD = 'Schonhage'
self.word = strlist(list(self.TEST_WORD)) # TEST
self.curr_key = 0
self.curr_val = self.word[self.curr_key]
self.output = self.curr_val
# Setup initial graph with default word.
self.recalculate()
# Get node after the paths have been created.
self.curr_node = self.graph[self.curr_key]
def __setitem__(self, *args):
"""Adds a new node to the graph."""
item, connections = args
self.graph[item] = connections
def __getitem__(self, key):
"""A wrapper that loops over the internal graph."""
# Skip loop if possible.
if self.curr_node['val'] == key:
return self.curr_node
# Check all other nodes.
for k, node in self.graph.iteritems():
try:
if node['val'] == key:
return node
except KeyError:
continue
def _show_edges(self, node):
"""A debug method to nicely show all edges for a given node key"""
for state, edge in self.graph[node]['edges'].iteritems():
'[SHOW EDGE] {} ... {} --> {}'.format(node, state, edge['to'])
def recalculate(self):
"""Draws/redraws the graph, taking the current value
and assigning the nodes / paths accordingly.
Assigns the edge values for each node, and
updates the value for each symbol in the word as well.
In the example {0, 1} symbol set machine, each node can have a 0, 1,
or both 0 and 1 edges, which allow for the instructions
to be dictated by traversing the graph.
In a traditional directed graph, nodes are either inbound, or outbound.
In the SMM, the edges define the direction, but they also encode
the state symbols that can be used to determine behavior in a way
that operates like tape, cells, and state in a Turing machine.
An example graph looks like this:
graph = {
0: ...,
1: {'val': 'A', 'edges': {0: {'to': 0}, 1: {'to': 2},},
2: ....
}
The key is the node, and is used as a pointer to other nodes.
The edges attribute is a dictionary, keyed by each edge
that corresponds to all symbol states (e.g. {0, 1} in the example).
Each key points to a dictionary, representing the node to move to.
"""
end = len(self.word)
# Special case for single nodes.
if end == 1:
self.graph[0] = {
'key': 0,
'val': self.word, # guaranteed to only be one character
'edges': {0: {'to': 0}, }
}
else:
for key, val in enumerate(self.word):
self.graph[key] = {
'key': key,
'val': val,
'edges': {}
}
# The first nodes 0-edge points to itself,
# and the 1-edge points to the next node.
if key == 0:
self.graph[key]['edges'] = {
0: {'to': 0},
1: {'to': key + 1}}
# The last nodes 0 and 1 edges
# BOTH point to the previous node.
elif key == end - 1:
self.graph[key]['edges'] = {
0: {'to': key - 1},
1: {'to': key - 1}}
# All others; 0-edge points to the previous,
# 1-edge points to the next.
else:
self.graph[key]['edges'] = {
0: {'to': key - 1},
1: {'to': key + 1}}
if self.DEBUG:
prnt('Graph', self.graph.vertices)
def new(self, val):
"""Adds a new value to the word"""
# Handle recalculation by simply adding the
# new words and calling function again.
for token in list(val):
self.word += token
# Adjust edges to / from each node
self.recalculate()
if self.DEBUG:
print('word with new item(s) {}'.format(self.word))
# Make this one chain-able.
return self
def _get_path(self, word):
"""Returns a list of nodes representing the
path traveled for a given word."""
path = []
for char in word:
path.append(self.graph[char][1])
return path
def jump(self, word1, word2, jump_node):
"""Get the two paths to compare their ending nodes.
Compare the two paths to see if they end in the same node. If so, jump.
"""
if self._get_path(word1)[-1:] == self._get_path(word2)[-1:]:
self.curr_node = jump_node
self._run_step()
if self.DEBUG:
print('{} {} paths are equal. Jumping to node {}'.format(
word1, word2, jump_node))
def set(self, node, val):
"""Sets a nodes value to the new value."""
self.graph[node]['val'] = val
# Recalculation to determine where nodes
# point is done in one place, using the same technique
# as when the graph is initially created.
self.recalculate()
def _update_state(self):
# Take the 1-edge, neo
new_node_key = self.curr_node['edges'][1]['to']
self.curr_node = self.graph[new_node_key]
self.curr_val = self.curr_node['val']
self.curr_key = self.curr_node['key']
def _run_step(self):
if self.DEBUG:
prnt('curr node (before):', self.curr_node)
time.sleep(self.DELAY)
# Visually update
if self.DEBUG:
prnt('curr node (after):', self.curr_node)
self._update_state()
if self.curr_node is not None:
# Add to the string as the program runs.
self.output += self.curr_val
def traverse_word(self):
prnt('Graph nodes beginning', self.graph.vertices)
start, end = 0, len(self.word)
while start < end:
# Print must come before next step,
# otterwise it will print the letters shifted.
print('Counter: {}, Output: {}, Curr val: {}, Curr key: {}'.format(
start, self.output, self.curr_val, self.curr_key))
self._run_step()
start += 1
def run(self):
self.traverse_word()
