def generate_tree(data_to_compress):
# stats first contains symbols and their weights,
# then parent nodes and their weights as well, while the tree is built
stats = get_weights_and_symbols(data_to_compress)
stats = sorted(stats, key=lambda x: x["weight"])
# as long as we have more than one element to process, we'll grow the tree
while len(stats) > 1:
# let's create a parent node
parent_node = {"left0": None, "right1": None, "weight": 0}
# the 2 children are the first and second elements of the list
parent_node["left0"] = pop_entry(stats)
parent_node["right1"] = pop_entry(stats)
# and the weight is the sum of both children's
cumulative_weight = parent_node["left0"]["weight"] + parent_node[
"right1"]["weight"]
parent_node["weight"] = cumulative_weight
# let's add a new entry in the list for the recently created parent node
entry = {"node": None, "weight": 0}
entry["node"] = parent_node
entry["weight"] = cumulative_weight
stats.append(entry)
# and re-sort the list
stats = sorted(stats, key=lambda x: x["weight"])
# we just have one entry left - the root of the tree - let's return it.
root_node = pop_entry(stats)
return root_node
def generate_tree(data_to_compress):
# stats first contains symbols and their weights,
# then parent nodes and their weights as well, while the tree is built
stats = get_weights_and_symbols(data_to_compress)
stats = sorted(stats, key = lambda x:x["weight"])
# as long as we have more than one element to process, we'll grow the tree
while len(stats) > 1:
# let's create a parent node
parent_node = {"left0": None, "right1": None, "weight": 0}
# the 2 children are the first and second elements of the list
parent_node["left0"] = pop_entry(stats)
parent_node["right1"] = pop_entry(stats)
# and the weight is the sum of both children's
cumulative_weight = parent_node["left0"]["weight"] + parent_node["right1"]["weight"]
parent_node["weight"] = cumulative_weight
# let's add a new entry in the list for the recently created parent node
entry = {"node": None, "weight": 0}
entry["node"] = parent_node
entry["weight"] = cumulative_weight
stats.append(entry)
# and re-sort the list
stats = sorted(stats, key = lambda x:x["weight"])
# we just have one entry left - the root of the tree - let's return it.
root_node = pop_entry(stats)
return root_node
def encode(data_to_compress):
stats = _encoding.get_weights_and_symbols(data_to_compress)
stats = sorted(stats, key = lambda x:x["weight"], reverse = True)
tree = generate_tree(stats)
return tree
#Kabopan - Readable Algorithms. Public Domain, 2007-2009
from kbp.entro._encoding import generate_codes, get_weights_and_symbols
assert generate_codes({'left0': {'symbol': 'a', 'weight': 1},
'right1': {'symbol': 'b', 'weight': 1},
'weight': 2}) == {'a': '0', 'b': '1'}
assert get_weights_and_symbols("a") == [{"symbol":"a", "weight":1}]
assert get_weights_and_symbols("abababbc") == [ \
{"symbol":"a", "weight":3},
{"symbol":"b", "weight":4},
{"symbol":"c", "weight":1}]
#Kabopan - Readable Algorithms. Public Domain, 2007-2009
from kbp.entro._encoding import generate_codes, get_weights_and_symbols
assert generate_codes({
'left0': {
'symbol': 'a',
'weight': 1
},
'right1': {
'symbol': 'b',
'weight': 1
},
'weight': 2
}) == {
'a': '0',
'b': '1'
}
assert get_weights_and_symbols("a") == [{"symbol": "a", "weight": 1}]
assert get_weights_and_symbols("abababbc") == [ \
{"symbol":"a", "weight":3},
{"symbol":"b", "weight":4},
{"symbol":"c", "weight":1}]
