def test_permutation_decoder():
gutenberg = open_data("EN-text/gutenberg.txt").read()
flatland = open_data("EN-text/flatland.txt").read()
pd = PermutationDecoder(canonicalize(gutenberg))
assert pd.decode('aba') in ('ece', 'ete', 'tat', 'tit', 'txt')
pd = PermutationDecoder(canonicalize(flatland))
assert pd.decode('aba') in ('ded', 'did', 'ece', 'ele', 'eme', 'ere', 'eve', 'eye', 'iti', 'mom', 'ses', 'tat', 'tit')
def test_permutation_decoder():
gutenberg = open_data("gutenberg.txt").read()
flatland = open_data("EN-text/flatland.txt").read()
pd = PermutationDecoder(canonicalize(gutenberg))
assert pd.decode('aba') in ('ece', 'ete', 'tat', 'tit', 'txt')
pd = PermutationDecoder(canonicalize(flatland))
assert pd.decode('aba') in ('ded', 'did', 'ece', 'ele', 'eme', 'ere', 'eve', 'eye', 'iti', 'mom', 'ses', 'tat', 'tit')
def test_samples():
story = open_data("EN-text/flatland.txt").read()
story += open_data("gutenberg.txt").read()
wordseq = words(story)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
s1 = P1.samples(10)
s2 = P3.samples(10)
s3 = P3.samples(10)
assert len(s1.split(' ')) == 10
assert len(s2.split(' ')) == 10
assert len(s3.split(' ')) == 10
def test_samples():
story = open_data("EN-text/flatland.txt").read()
story += open_data("EN-text/gutenberg.txt").read()
wordseq = words(story)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
s1 = P1.samples(10)
s2 = P3.samples(10)
s3 = P3.samples(10)
assert len(s1.split(' ')) == 10
assert len(s2.split(' ')) == 10
assert len(s3.split(' ')) == 10
def upload():
if request.method == 'POST':
training = request.files['training']
predict = request.files['predict']
if training and allowed_file(
training.filename) and predict and allowed_file(
predict.filename):
filename = secure_filename(training.filename)
training.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
predict_name = secure_filename(predict.filename)
predict.save(
os.path.join(app.config['UPLOAD_FOLDER'], predict_name))
useless_label = request.form['label'].strip().split(',')
stock_id = request.form['id']
target = request.form['target']
if target not in useless_label:
useless_label.append(target)
if stock_id not in useless_label:
useless_label.append(stock_id)
train_data = utils.open_data(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
predict_data = utils.open_data(
os.path.join(app.config['UPLOAD_FOLDER'], predict_name))
task_name = str(request.form['task'] +
time.strftime("%m%d%Y%H%M")).strip()
train_data_col = train_data.columns.tolist()
predict_data_col = predict_data.columns.tolist()
for i in useless_label:
if ' ' not in i and len(i) > 0:
try:
train_data_col.remove(i)
predict_data_col.remove(i)
except ValueError:
pass
if not utils.check_colums(train_data_col, predict_data_col):
print "error"
new_training = trainning_state(
taskName=task_name,
state="still training",
)
new_training.save()
training = threading.Thread(target=process.main,
args=(train_data, predict_data, target,
train_data_col, task_name,
stock_id))
training.start()
return redirect("/")
return ""
def __init__(self, examples=None, attrs=None, attrnames=None, target=-1,
inputs=None, values=None, distance=mean_boolean_error,
name='', source='', exclude=()):
"""Accepts any of DataSet's fields. Examples can also be a
string or file from which to parse examples using parse_csv.
Optional parameter: exclude, as documented in .setproblem().
>>> DataSet(examples='1, 2, 3')
<DataSet(): 1 examples, 3 attributes>
"""
self.name = name
self.source = source
self.values = values
self.distance = distance
if values is None:
self.got_values_flag = False
else:
self.got_values_flag = True
# Initialize .examples from string or list or data directory
if isinstance(examples, str):
self.examples = parse_csv(examples)
elif examples is None:
self.examples = parse_csv(open_data(name + '.csv').read())
else:
self.examples = examples
# Attrs are the indices of examples, unless otherwise stated.
if attrs is None and self.examples is not None:
attrs = list(range(len(self.examples[0])))
self.attrs = attrs
# Initialize .attrnames from string, list, or by default
if isinstance(attrnames, str):
self.attrnames = attrnames.split()
else:
self.attrnames = attrnames or attrs
self.setproblem(target, inputs=inputs, exclude=exclude)
def __init__(self, examples=None, attrs=None, attrnames=None, target=-1,
inputs=None, values=None, distance=mean_boolean_error,
name='', source='', exclude=()):
"""Accepts any of DataSet's fields. Examples can also be a
string or file from which to parse examples using parse_csv.
Optional parameter: exclude, as documented in .setproblem().
>>> DataSet(examples='1, 2, 3')
<DataSet(): 1 examples, 3 attributes>
"""
self.name = name
self.source = source
self.values = values
self.distance = distance
if values is None:
self.got_values_flag = False
else:
self.got_values_flag = True
# Initialize .examples from string or list or data directory
if isinstance(examples, str):
self.examples = parse_csv(examples)
elif examples is None:
self.examples = parse_csv(open_data(name + '.csv').read())
else:
self.examples = examples
# Attrs are the indices of examples, unless otherwise stated.
if attrs is None and self.examples is not None:
attrs = list(range(len(self.examples[0])))
self.attrs = attrs
# Initialize .attrnames from string, list, or by default
if isinstance(attrnames, str):
self.attrnames = attrnames.split()
else:
self.attrnames = attrnames or attrs
self.setproblem(target, inputs=inputs, exclude=exclude)
def test_viterbi_segmentation():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P = UnigramWordModel(wordseq)
text = "itiseasytoreadwordswithoutspaces"
s, p = viterbi_segment(text, P)
assert s == [
'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces']
def test_viterbi_segmentation():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P = UnigramWordModel(wordseq)
text = "itiseasytoreadwordswithoutspaces"
s, p = viterbi_segment(text, P)
assert s == [
'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces']
def test_text_models():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
# Test top
assert P1.top(5) == [(2081, 'the'), (1479, 'of'),
(1021, 'and'), (1008, 'to'),
(850, 'a')]
assert P2.top(5) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is'))]
assert P3.top(5) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as'))]
# Test isclose
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
# Test cond_prob.get
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
# Test dictionary
test_string = 'unigram'
wordseq = words(test_string)
P1 = UnigramWordModel(wordseq)
assert P1.dictionary == {('unigram'): 1}
test_string = 'bigram text'
wordseq = words(test_string)
P2 = NgramWordModel(2, wordseq)
assert P2.dictionary == {('bigram', 'text'): 1}
test_string = 'test trigram text here'
wordseq = words(test_string)
P3 = NgramWordModel(3, wordseq)
assert ('test', 'trigram', 'text') in P3.dictionary
assert ('trigram', 'text', 'here') in P3.dictionary
def test_text_models():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
# Test top
assert P1.top(5) == [(2081, 'the'), (1479, 'of'),
(1021, 'and'), (1008, 'to'),
(850, 'a')]
assert P2.top(5) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is'))]
assert P3.top(5) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as'))]
# Test isclose
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
# Test cond_prob.get
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
# Test dictionary
test_string = 'unigram'
wordseq = words(test_string)
P1 = UnigramWordModel(wordseq)
assert P1.dictionary == {('unigram'): 1}
test_string = 'bigram text'
wordseq = words(test_string)
P2 = NgramWordModel(2, wordseq)
assert P2.dictionary == {('bigram', 'text'): 1}
test_string = 'test trigram text here'
wordseq = words(test_string)
P3 = NgramWordModel(3, wordseq)
assert ('test', 'trigram', 'text') in P3.dictionary
assert ('trigram', 'text', 'here') in P3.dictionary
def test_shift_decoding():
flatland = open_data("EN-text/flatland.txt").read()
ring = ShiftDecoder(flatland)
msg = ring.decode('Kyzj zj r jvtivk dvjjrxv.')
assert msg == 'This is a secret message.'
def test_text_models():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramTextModel(wordseq)
P2 = NgramTextModel(2, wordseq)
P3 = NgramTextModel(3, wordseq)
# The most frequent entries in each model
assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'),
(1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'),
(478, 'that'), (399, 'is'), (348, 'you')]
assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is')), (71, ('by', 'the')),
(68, ('for', 'the')), (68, ('and', 'the')),
(62, ('on', 'the')), (60, ('to', 'be'))]
assert P3.top(10) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as')),
(12, ('of', 'the', 'circles')),
(12, ('of', 'sight', 'recognition')),
(11, ('the', 'number', 'of')),
(11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))]
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
assert P2.cond_prob.get(('went', )) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
test_string = 'unigram'
wordseq = words(test_string)
P1 = UnigramTextModel(wordseq)
assert P1.dictionary == {('unigram'): 1}
test_string = 'bigram text'
wordseq = words(test_string)
P2 = NgramTextModel(2, wordseq)
assert (P2.dictionary == {
('', 'bigram'): 1,
('bigram', 'text'): 1
} or P2.dictionary == {
('bigram', 'text'): 1,
('', 'bigram'): 1
})
test_string = 'test trigram text'
wordseq = words(test_string)
P3 = NgramTextModel(3, wordseq)
assert ('', '', 'test') in P3.dictionary
assert ('', 'test', 'trigram') in P3.dictionary
assert ('test', 'trigram', 'text') in P3.dictionary
assert len(P3.dictionary) == 3
def test_rot13_decoding():
flatland = open_data("EN-text/flatland.txt").read()
ring = ShiftDecoder(flatland)
msg = ring.decode(rot13('Hello, world!'))
assert msg == 'Hello, world!'
def test_shift_decoding():
flatland = open_data("EN-text/flatland.txt").read()
ring = ShiftDecoder(flatland)
msg = ring.decode('Kyzj zj r jvtivk dvjjrxv.')
assert msg == 'This is a secret message.'
def build():
# STEP: clone FruityMod
if not os.path.exists(mod_dir):
print("Downloading {}".format("FruityMod"))
fruity_url = r"https://github.com/gskleres/FruityMod-StS/archive/v0.6.2b.zip"
utils.mkdir("cache")
download_file = tempfile.NamedTemporaryFile(suffix=".zip",
dir="cache",
delete=False).name
with urllib.request.urlopen(fruity_url) as response, open(
download_file, "wb") as out_file:
shutil.copyfileobj(response, out_file)
utils.unzip(download_file, mod_dir, shift=1, remove=True)
# STEP: fetch libs
mod_jar = os.path.join(spire_dir, "ModTheSpire.jar")
if not os.path.exists(mod_jar):
print("Downloading ModTheSpire")
download_file = tempfile.NamedTemporaryFile(suffix=".zip",
dir="..",
delete=False).name
urllib.request.urlretrieve(
"https://github.com/kiooeht/ModTheSpire/releases/download/v2.6.0/ModTheSpire.zip",
download_file)
with zipfile.ZipFile(download_file,
"r") as archive, open(mod_jar, "wb") as file:
jar_data = archive.read("ModTheSpire.jar")
file.write(jar_data)
os.remove(download_file)
base_jar = os.path.join(spire_dir, "mods", "BaseMod.jar")
if not os.path.exists(base_jar):
print("Downloading BaseMod")
urllib.request.urlretrieve(
"https://github.com/daviscook477/BaseMod/releases/download/v2.9.1/BaseMod.jar",
base_jar)
from spire import name_id
import textwrap
import io
import json
print("Generating data")
image_dir = os.path.join("assets", "images")
if os.path.exists(os.path.join("cache", "DEBUG")):
image_dir = os.path.join("todo", "images")
# STEP: generate cards
from engi_mod import cards
with open(os.path.join("templates", "card.java"),
encoding="utf-8") as file:
card_template = file.read()
for card in cards:
with open(os.path.join(mod_dir,
*r"src\main\java\fruitymod\cards".split("\\"),
name_id(card["name"]) + ".java"),
"w",
encoding="utf-8") as file:
file.write(format(card_template, card))
# STEP: patch code
templates_cache = os.path.join("cache", "templates")
if not os.path.exists(templates_cache):
utils.mkdir(templates_cache)
shutil.copy(
os.path.join(
mod_dir,
*r"src\main\java\fruitymod\FruityMod.java".split("\\")),
os.path.join(templates_cache, "FruiyMod.java"))
shutil.copy(
os.path.join(
mod_dir,
*r"src\main\java\fruitymod\characters\TheSeeker.java".split(
"\\")), os.path.join(templates_cache, "TheSeeker.java"))
shutil.copy(
os.path.join(
mod_dir,
*r"src\main\resources\localization\FruityMod-CardStrings.json".
split("\\")),
os.path.join(templates_cache, "FruityMod-CardStrings.json"))
image_code = io.StringIO()
add_code = io.StringIO()
unlock_code = io.StringIO()
for card in cards:
id = name_id(card["name"], upper=True).lower()
image_file = os.path.join(image_dir, id + ".png")
image_file = "cards/{}.png".format(
id if os.path.exists(image_file) else "runic_binding")
image_code.write(
format(
'public static final String {{ name_id(card["name"], upper=True) }} = "{{ image_file }}";'
) + "\n")
if card["rarity"] != "special":
add_code.write(
format('BaseMod.addCard(new {{ name_id(card["name"]) }}());') +
"\n")
unlock_code.write(
format('UnlockTracker.unlockCard("{{ card["name"] }}");') +
"\n")
with open(os.path.join(templates_cache, "FruiyMod.java"),
encoding="utf-8") as file:
fruity_lines = [line for line in file]
for i, line in enumerate(fruity_lines):
if "public static final String PHASE_COIL" in line:
fruity_lines.insert(
i + 1, "\n" + textwrap.indent(image_code.getvalue(), " " * 4))
break
for i, line in enumerate(fruity_lines):
if "BaseMod.addCard(new Nexus())" in line:
fruity_lines.insert(
i + 1,
"\n" + textwrap.indent(add_code.getvalue(), " " * 4 * 2))
fruity_lines.insert(
i + 2,
"\n" + textwrap.indent(unlock_code.getvalue(), " " * 4 * 2))
break
with open(os.path.join(
mod_dir, *r"src\main\java\fruitymod\FruityMod.java".split("\\")),
"w",
encoding="utf-8") as file:
file.write("".join(fruity_lines))
with open(os.path.join(templates_cache, "TheSeeker.java"),
encoding="utf-8") as file:
seeker_lines = [line for line in file]
# STEP: starting relic
from engi_mod import relic
for i, line in enumerate(seeker_lines):
if "Arcanosphere" in line:
del seeker_lines[i:i + 2]
seeker_lines.insert(
i, "\n{}\n\n".format(
textwrap.indent(
textwrap.dedent(
format("""
retVal.add("{{ relic }}");
UnlockTracker.markRelicAsSeen("{{ relic }}");
""")).strip(), " " * 4 * 2)))
break
# STEP: starting deck
from engi_mod import deck
if not deck:
deck = [card["name"] for card in cards if card["rarity"] != "special"]
for i, line in enumerate(seeker_lines):
if "Strike_P" in line:
for j, line in enumerate(seeker_lines):
if "AstralHaze" in line:
break
del seeker_lines[i:j + 1]
seeker_lines.insert(
i, "\n{}\n\n".format(
textwrap.indent(
"\n".join('retVal.add("{}");'.format(card)
for card in deck), " " * 4 * 2)))
break
with open(os.path.join(
mod_dir,
*r"src\main\java\fruitymod\characters\TheSeeker.java".split("\\")),
"w",
encoding="utf-8") as file:
file.write("".join(seeker_lines))
card_strings = json.load(
open(os.path.join(templates_cache, "FruityMod-CardStrings.json"),
encoding="utf-8"))
for card in cards:
data = {
"NAME": card["name"],
"DESCRIPTION": card["desc"],
}
desc = card.get("upgrade_desc")
if desc:
data["UPGRADE_DESCRIPTION"] = desc
card_strings[card["name"]] = data
json.dump(
card_strings,
open(os.path.join(
mod_dir,
*r"src\main\resources\localization\FruityMod-CardStrings.json".
split("\\")),
"w",
encoding="utf-8"),
sort_keys=True,
indent=4)
# STEP: generate powers
from engi_mod import powers
with open(os.path.join("templates", "power.java"),
encoding="utf-8") as file:
power_template = file.read()
for power in powers:
with open(os.path.join(mod_dir,
*r"src\main\java\fruitymod\powers".split("\\"),
power["id"] + ".java"),
"w",
encoding="utf-8") as file:
file.write(format(power_template, power))
# STEP: generate actions
from engi_mod import actions
with open(os.path.join("templates", "action.java"),
encoding="utf-8") as file:
action_template = file.read()
for action in actions:
with open(os.path.join(
mod_dir,
*r"src\main\java\fruitymod\actions\unique".split("\\"),
action["id"] + ".java"),
"w",
encoding="utf-8") as file:
file.write(format(action_template, action))
# STEP: generate java files
from engi_mod import javas
with open(os.path.join("templates", "java.java"),
encoding="utf-8") as file:
java_template = file.read()
for java in javas:
with open(os.path.join(mod_dir, *r"src\main\java".split("\\"),
*java["package"], java["name"] + ".java"),
"w",
encoding="utf-8") as file:
file.write(format(java_template, java))
# STEP: card images
print("Generating images")
import numpy as np
portrait_masks = {}
for type in "attack skill power".split():
image = utils.open_data(
os.path.join("templates",
"1024Portraits_{}_mask.png".format(type)))
image = image / 255
image = np.repeat(image[:, :, :1], 4, axis=-1)
portrait_masks[type] = image
for card in cards:
id = name_id(card["name"], upper=True).lower()
image_file = os.path.join(image_dir, id + ".png")
target_p_file = os.path.join(
mod_dir, *r"src\main\resources\img\cards".split("\\"),
id + "_p" + ".png")
target_file = os.path.join(
mod_dir, *r"src\main\resources\img\cards".split("\\"), id + ".png")
if os.path.exists(target_p_file):
continue
if os.path.exists(image_file):
image = utils.open_data(image_file)
from skimage.transform import resize
target = 500, 380
r = image.shape[0] / image.shape[1]
if r >= target[0] / target[1]:
size = np.ceil(target[1] * r).astype("int"), target[1]
x = np.round((size[0] - target[0]) / 2).astype("int")
image = resize(image, size, mode="edge")[x:x + target[0]]
else:
size = target[0], np.ceil(target[0] / r).astype("int")
image = resize(image, size, mode="edge")[:, :target[1]]
image *= portrait_masks[card["type"]]
from PIL import Image
img = Image.fromarray(
np.round(image * 255).astype("uint8").transpose((1, 0, 2)))
img.save(target_p_file)
target = 250, 190
image = resize(image, target, mode="edge")
img = Image.fromarray(
np.round(image * 255).astype("uint8").transpose((1, 0, 2)))
img.save(target_file)
# STEP: card borders
utils.sync(
os.path.join("assets", "512"),
os.path.join(mod_dir, *r"src\main\resources\img\512".split("\\")))
utils.sync(
os.path.join("assets", "1024"),
os.path.join(mod_dir, *r"src\main\resources\img\1024".split("\\")))
# STEP: keywords
from engi_mod import keywords
keyword_code = io.StringIO()
for name, keyword in keywords.items():
words = ", ".join('"{}"'.format(word)
for word in [name.lower()] + keyword["words"])
keyword_code.write(
format(
'BaseMod.addKeyword(new String[] {"{{ name }}", {{ words }}}, "{{ keyword["desc"] }}");'
) + "\n")
with open(os.path.join(
mod_dir, *r"src\main\java\fruitymod\FruityMod.java".split("\\")),
encoding="utf-8") as file:
fruity_lines = [line for line in file]
for i, line in enumerate(fruity_lines):
if '{"intangible", "Intangible"}, "All damage and HP loss you suffer is reduced to 1."' in line:
fruity_lines.insert(
i + 1,
"\n" + textwrap.indent(keyword_code.getvalue(), " " * 4 * 2))
break
with open(os.path.join(
mod_dir, *r"src\main\java\fruitymod\FruityMod.java".split("\\")),
"w",
encoding="utf-8") as file:
file.write("".join(fruity_lines))
# STEP: mod info
old_info = os.path.join(
mod_dir, *r"src\main\resources\ModTheSpire.config".split("\\"))
if os.path.exists(old_info):
os.remove(old_info)
from engi_mod import info
json.dump(info,
open(os.path.join(
mod_dir,
*r"src\main\resources\ModTheSpire.json".split("\\")),
"w",
encoding="utf-8"),
indent=4)
# STEP: maven project
pom_template = os.path.join(templates_cache, "pom.xml")
if not os.path.exists(pom_template):
shutil.copy(os.path.join(mod_dir, "pom.xml"), pom_template)
with open(pom_template, encoding="utf-8") as file:
pom = file.read()
pom = pom.replace(
"${basedir}/../lib/ModTheSpire.jar",
"/".join(spire_dir.split(os.path.sep) + ["ModTheSpire.jar"]))
pom = pom.replace(
"${basedir}/../lib/BaseMod.jar",
"/".join(spire_dir.split(os.path.sep) + ["mods", "BaseMod.jar"]))
pom = pom.replace(
"${basedir}/../lib/desktop-1.0.jar",
"/".join(spire_dir.split(os.path.sep) + ["desktop-1.0.jar"]))
jar_file = os.path.join(spire_dir, "mods", "EngiMod.jar")
pom = pom.replace("../_ModTheSpire/mods/FruityMod.jar",
"/".join(jar_file.split(os.path.sep)))
with open(os.path.join(mod_dir, "pom.xml"), "w", encoding="utf-8") as file:
file.write(pom)
# STEP: compile
if os.path.exists(jar_file):
os.remove(jar_file)
with utils.cd(mod_dir):
os.system("mvn package")
if not os.path.exists(jar_file):
print("Compilation failed")
return
# STEP: test
with utils.cd(spire_dir):
os.system("ModTheSpire.jar")
def test_rot13_decoding():
flatland = open_data("EN-text/flatland.txt").read()
ring = ShiftDecoder(flatland)
msg = ring.decode(rot13('Hello, world!'))
assert msg == 'Hello, world!'
def test_parse_csv():
Iris = open_data('iris.csv').read()
assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa']
def __init__(self, board=None):
if BoggleFinder.wordlist is None:
BoggleFinder.wordlist = Wordlist(open_data("EN-text/wordlist.txt"))
self.found = {}
if board:
self.set_board(board)
def test_parse_csv():
Iris = open_data('iris.csv').read()
assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa']
with open(path, 'w', encoding='utf-8') as f:
for i in range(len(x)):
content = x[i] + '\t' + y[i] + '\n'
f.write(content)
if __name__ == '__main__':
# # “每个类别一个文件” 的 版本
# questions, classes = open_data('data_orig/train_data.txt')
# for i in range(len(questions)):
# c = classes[i]
# with open('data/'+c+'.txt', 'a', encoding='utf-8') as f:
# f.write(questions[i] + '\n')
# 加载原始数据
x_train, c_train = open_data('data_orig/train_data.txt')
x_dev, c_dev = open_data('data_orig/dev_data.txt')
x_test, c_test = open_data('data_orig/test_data.txt')
# 识别所有类，生成类别列表和字典，并保存类别列表
if not os.path.exists('data'):
os.mkdir('data')
class_list = list(set(c_train))
with open('data/class.txt', 'w', encoding='utf-8') as f:
f.writelines(content + '\n' for content in class_list)
class_dict = {}
for i, item in enumerate(class_list):
class_dict[item] = str(i)
# 类别转换为标签, 打乱顺序， 保存
x_train, y_train = pro_data(x_train, c_train, 'train')