def test_get_sequence_nodes():
hds = Hydraseq('_')
hds.insert('a b c d e f g h i j')
nxt = hds.look_ahead('a b c d e f g h i').next_nodes.pop()
assert nxt.get_sequence() == 'a b c d e f g h i j'
assert str(nxt.get_sequence_nodes()
) == '[[a], [b], [c], [d], [e], [f], [g], [h], [i]]'
def test_get_sequence_multiple():
hds2 = Hydraseq('_')
hds2.insert([['a'], ['B'], ['c']])
hds2.insert([['a'], ['b'], ['c']])
nxts = hds2.look_ahead([['a'], ['b', 'B']]).next_nodes
assert sorted([str(nxt.get_sequence_nodes())
for nxt in nxts]) == ['[[a], [B]]', '[[a], [b]]']
def test_streaming():
hdr = Hydraseq('streaming')
hdr.insert("the quick brown fox")
assert hdr.look_ahead("the quick").get_next_values() == ["brown"]
hdr.reset()
assert hdr.look_ahead("the").get_next_values() == ["quick"]
assert hdr.hit("quick", None).get_next_values() == ["brown"]
def test_autocomplete():
ac = Hydraseq('auto')
for name in ['efrain', 'efrom', 'efren', 'ephrem', 'efrainium']:
ac.insert(expand(name + '$'))
def autocomp(st):
def _compact(seq):
return "".join([c for c in seq[:-1]]).replace(' ', '')
ac.look_ahead(expand(st))
hits = ac.forward_prediction()
hits = [_compact(hit.get_sequence()) for hit in hits]
return hits
hits = autocomp('efra')
assert sorted(hits) == sorted(['efrainium', 'efrain'])
def test_get_downwards():
hds = Hydraseq("_")
hds.insert("a b c _D")
hds.insert("e f g _D")
hds.insert("c e f _D")
assert hds.get_downwards(["_D"]) == ['a', 'b', 'c', 'e', 'f', 'g']
def test_01_02_01_B_sequence():
hdr = Hydraseq('main')
hdr.insert([['a'], ['b'], ['d']])
hdr.insert([['a'], ['c'], ['d']])
check_active(hdr, 1, ['a c d'], ['d'])
check_next(hdr, 0, [], [])
hdr.look_ahead([['a'], ['b']])
check_active(hdr, 1, ['a b'], ['b'])
check_next(hdr, 1, ['a b d'], ['d'])
hdr.look_ahead([['a'], ['c']])
check_active(hdr, 1, ['a c'], ['c'])
check_next(hdr, 1, ['a c d'], ['d'])
hdr.look_ahead([['a'], ['b', 'c'], ['d']])
check_active(hdr, 2, ['a b d', 'a c d'], ['d'])
check_next(hdr, 0, [], [])
def test_surpise_flag():
hdr = Hydraseq('main')
assert hdr.surprise == False
hdr.insert("This is a test")
assert hdr.surprise == True
hdr.insert("This is a test")
assert hdr.surprise == False
hdr.insert("This is NOT a test")
assert hdr.surprise == True
hdr.insert("This is a test")
assert hdr.surprise == False
def test_run_convolutions_json():
hdr = Hydraseq('_')
hdr.insert("a b c _ALPHA")
hdr.insert("1 2 3 _DIGIT")
assert hdr.convolutions("a b c 1 2 3".split()) == [{
'convo': '_ALPHA',
'end': 3,
'start': 0,
'words': ['a', 'b', 'c']
}, {
'convo': '_DIGIT',
'end': 6,
'start': 3,
'words': ['1', '2', '3']
}]
assert hdr.convolutions("a b 1 2 3 a b".split()) == [{
'convo':
'_DIGIT',
'end':
5,
'start':
2,
'words': ['1', '2', '3']
}]
def test_self_insert():
hdq = Hydraseq('_')
hdq.self_insert(
"Burger King wants people to download its app. So it's sending them to McDonald's for access to a one-cent Whopper."
)
assert len(hdq.columns) == 38
assert sorted(
hdq.look_ahead("Burger King wants").get_next_values()) == sorted(
['people', '_3'])
assert sorted(
hdq.look_ahead("Burger King wants").get_active_values()) == sorted(
['wants'])
def test_run_convolutions_overlap():
hdr = Hydraseq('_')
hdr.insert("b a d _1")
hdr.insert("a n d _2")
hdr.insert("a d a _3")
hdr.insert("a n d y _4")
hdr.insert("a d a n _5")
tester = "b a d a n d y".split()
assert hdr.convolutions(tester) == [{
'convo': '_1',
'end': 3,
'start': 0,
'words': ['b', 'a', 'd']
}, {
'convo': '_3',
'end': 4,
'start': 1,
'words': ['a', 'd', 'a']
}, {
'convo': '_5',
'end': 5,
'start': 1,
'words': ['a', 'd', 'a', 'n']
}, {
'convo': '_2',
'end': 6,
'start': 3,
'words': ['a', 'n', 'd']
}, {
'convo': '_4',
'end': 7,
'start': 3,
'words': ['a', 'n', 'd', 'y']
}]
def test_cloning_hydra():
hdr0 = Hydraseq('zero')
hdr0.insert("the quick brown fox")
hdr1 = Hydraseq('one', hdr0)
assert hdr1.look_ahead("the quick").get_next_values() == ["brown"]
hdr1.reset()
assert hdr1.look_ahead("the").get_next_values() == ["quick"]
assert hdr1.hit("quick", None).get_next_values() == ["brown"]
def initialize_seq(self, lst_typs, fpath, id):
seq = Hydraseq(id)
for typ in lst_typs:
self.train_sequences_from_file(seq, '{}/{}.csv'.format(fpath, typ),
[['_{}_'.format(typ.upper())]])
return seq
def test_get_sequence_nodes_out_of_order():
hds1 = Hydraseq('_')
hds1.insert('C A B')
nxt1 = hds1.look_ahead('C A').next_nodes.pop()
assert nxt1.get_sequence() == 'C A B'
def test_compare():
hq = Hydraseq('_')
hq.insert('small large GROWING')
hq.insert('large small SHRINKING')
hq.insert('left right EAST')
hq.insert('right left WEST')
hq.insert('circle square SHAPER')
hq.insert('square circle BLUNTER')
hq.look_ahead([['small', 'left', 'circle'], ['small', 'right', 'circle']])
assert hq.get_next_values() == ['EAST']
hq.look_ahead([['small', 'left', 'square'], ['small', 'right', 'circle']])
assert hq.get_next_values() == ['BLUNTER', 'EAST']
hq.look_ahead([['small', 'left', 'circle'], ['large', 'right', 'circle']])
assert hq.get_next_values() == ['EAST', 'GROWING']
hq.look_ahead([['large', 'right', 'circle'], ['small', 'left', 'circle']])
assert hq.get_next_values() == ['SHRINKING', 'WEST']
0 0 0 0 0
0 o o 0 0
0 0 db 0 0
0 v u v 0
0 0 0 0 0
_end
"""
face = """
o o
db
v u v
_end
"""
hdq1 = Hydraseq('0_')
for pattern in [
"o 0_eye",
"db 0_nose",
"v 0_left_mouth",
"u 0_mid_mouth",
"v 0_right_mouth",
]:
hdq1.insert(pattern)
hdq2 = Hydraseq('1_')
for pattern in [
"0_eye 0_eye 1_eyes",
"0_nose 1_nose",
"0_left_mouth 0_mid_mouth 0_right_mouth 1_mouth",
]:
def test_sentence():
hdr = Hydraseq('main')
hdr.insert("The quick brown fox jumped over the lazy dog")
assert hdr.look_ahead("The quick brown").get_next_values() == ['fox']
hdr.insert("The quick brown cat jumped over the lazy dog")
assert hdr.look_ahead("The quick brown").get_next_values() == [
'cat', 'fox'
]
hdr.insert("The quick brown cat jumped over the lazy hound")
assert hdr.look_ahead("The quick brown").get_next_values() == [
'cat', 'fox'
]
hdr.look_ahead([['The'], ['quick'], ['brown'], ['fox', 'cat']])
check_active(hdr, 2, ['The quick brown cat', 'The quick brown fox'],
['cat', 'fox'])
check_next(hdr, 2,
['The quick brown cat jumped', 'The quick brown fox jumped'],
['jumped'])
def test_hydra_depths():
hdr = Hydraseq('main')
assert len(
hdr.d_depths) == 0, "Initially a hydraseq should have no depth sets"
st = "one two three four five six seven eight nine ten"
hdr.insert(st)
assert len(
hdr.d_depths) == 10, "there should be one set per depht traversed"
st_lst = st.split()
for idx, set_item in hdr.d_depths.items():
assert len(hdr.d_depths[idx]
) == 1, "there should be one item in each depth level"
assert next(node for node in hdr.d_depths[idx]).key == st_lst[
idx - 1], "the nodes should be in depth order"
hdr.look_ahead("one two three four five six seven eight nine")
last_node = next(node for node in hdr.next_nodes)
assert last_node.depth == 10
hdr.insert("one two three four five six siete ocho nueve diez")
hdr.insert("one two three four five six siete ocho nueve")
last_node = next(node for node in hdr.next_nodes)
assert last_node.depth == 10
hdr.insert("one two three four five six siete")
last_node = next(node for node in hdr.next_nodes)
assert last_node.depth == 8
def test_activate_node_pathway():
hdr = Hydraseq('main')
hdr.insert("a b c d e f LETTERS")
hdr.insert("1 2 3 4 5 6 NUMBERS")
hdr.insert("a1 2b b4 MIXED")
hdr.activate_node_pathway('LETTERS')
assert {node.key
for node in hdr.path_nodes
} == {"a", "b", "c", "d", "e", "f", "LETTERS"}
assert hdr.look_ahead("a b c d").get_active_values() == ["d"]
assert hdr.look_ahead("a b c d").get_next_values() == ["e"]
assert {node.key
for node in hdr.path_nodes
} == {"a", "b", "c", "d", "e", "f", "LETTERS"}
assert hdr.look_ahead("1 2 3 4").get_active_values() == []
assert hdr.look_ahead("1 2 3 4").get_next_values() == []
hdr.reset_node_pathway()
assert {node.key for node in hdr.path_nodes} == set()
assert hdr.look_ahead("a b c d").get_active_values() == ["d"]
assert hdr.look_ahead("a b c d").get_next_values() == ["e"]
assert {node.key for node in hdr.path_nodes} == set()
assert hdr.look_ahead("1 2 3 4").get_active_values() == ["4"]
assert hdr.look_ahead("1 2 3 4").get_next_values() == ["5"]
def test_active_synapses():
hdr = Hydraseq('main')
hdr.insert("a b c d e f")
hdr.insert("1 2 3 4 5 6")
assert hdr.look_ahead("a b c d e").get_active_values() == ['e']
assert hdr.look_ahead("1 2 3 4 5").get_active_values() == ['5']
hdr.set_active_synapses(['f'])
assert hdr.look_ahead("a b c d e").get_active_values() == ['e']
assert hdr.look_ahead("1 2 3 4 5").get_active_values() == []
assert hdr.look_ahead("a b c d").get_active_values() == ['d']
hdr.reset_active_synapses()
assert hdr.look_ahead("a b c d e").get_active_values() == ['e']
assert hdr.look_ahead("1 2 3 4 5").get_active_values() == ['5']
assert hdr.look_ahead("a b c d").get_active_values() == ['d']
filepath - csv two column file, second column 'SEQUENCE' contains lists of lists
lst_lst_identifier - what identifier to use to cap seqennces, [['mysequence']] for example
"""
with open(filepath, 'r') as source:
csv_file = csv.DictReader(source)
for row in csv_file:
str_sequence = row['SEQUENCE'].strip()
if len(str_sequence.strip()) == 0:
continue
else:
lst_sequence = eval(str_sequence)
train_with_provided_list(_seq,
lst_sequence + lst_lst_identifier)
seq = Hydraseq('input')
for typ in ['suite', 'address', 'dir', 'pobox', 'attn']:
train_sequences_from_file(seq, 'data/address_{}.csv'.format(typ),
[['_{}_'.format(typ.upper())]])
def encode_from_word_list(arr_st):
"""Expects ['123', 'main', 'st]"""
assert isinstance(arr_st, list)
if arr_st: assert isinstance(arr_st[0], str)
return [encoder(word) for word in arr_st]
def is_address(seq, arr_st):
"""Expects ["123","main","st"]"""
return any([
