def generate_fixnum_a_less_than_b():
a = random_python_int_inside_fixnum_range()
b = random_python_int_inside_fixnum_range()
while a == b:
b = random_python_int_inside_fixnum_range()
if a < b:
return Numeric(str(a)), Numeric(str(b))
else:
return Numeric(str(b)), Numeric(str(a))
def random_exact_complexnum_and_equiv_python_exactcomplex():
real_numerator = random_python_int_outside_fixnum_range()
real_denominator = random_python_int_outside_fixnum_range()
imaginary_numerator = random_python_int_outside_fixnum_range()
imaginary_denominator = random_python_int_outside_fixnum_range()
return Numeric(str(real_numerator), str(real_denominator),
str(imaginary_numerator),
str(imaginary_denominator)), ExactComplex(
Fraction(real_numerator, real_denominator),
Fraction(imaginary_numerator, imaginary_denominator))
def _prepareFGAndInitInput(self,
contentImgname,
styleImgname,
init='mixed'):
print 'prepare img_content...'
img_content = scipy.misc.imread(contentImgname)
self.ori_width = img_content.shape[0]
self.ori_height = img_content.shape[1]
self.img_content = img_content
# resize it to square
width = self.net.blobs['data'].shape[2]
img_content = scipy.misc.imresize(img_content, (width, width))
# transform it
x = self.transformer.preprocess('data', img_content)
self.F_content = Numeric().computeFandG(x, self.net, [],
self.content_layers)[0]
print 'prepare style_content...'
img_style = scipy.misc.imread(styleImgname)
self.img_style = img_style
img_style = scipy.misc.imresize(img_style, (width, width))
# transform it
x = self.transformer.preprocess('data', img_style)
self.G_style = Numeric().computeFandG(x, self.net, self.style_layers,
[])[1]
print 'prepare init input...'
if init == 'mixed':
img0 = 0.95 * self.transformer.preprocess('data', img_content) +\
0.05 * self.transformer.preprocess('data', img_style)
else:
img0 = self.transformer.preprocess('data', img_content)
# compute data bounds
data_min = -self.transformer.mean["data"][:, 0, 0]
data_max = data_min + 255
data_bounds = [(data_min[0], data_max[0])] * (img0.size / 3) + \
[(data_min[1], data_max[1])] * (img0.size / 3) + \
[(data_min[2], data_max[2])] * (img0.size / 3)
self.img0 = img0
self.data_bounds = data_bounds
def transfer(self):
"""
Do the transfer job
:return:
"""
self.tool = ST(self.model_dirs, self.content_img, self.style_img, self.device_id)
self.optimizer = Numeric()
# construct the optimize param
method = 'L-BFGS-B'
args = (self.tool.getNet(), self.tool.getLayers(), self.tool.getFcontent(),
self.tool.getGstyle(), self.tool.getStyleLayer(), self.tool.getContentLayer(),
self.tool.getRatio())
jac = True
bounds = self.tool.getBounds()
img0 = self.tool.getImg0()
options = {'maxiter': self.max_iter, 'disp': self.verbose}
res = minimize(self.optimizer.computeLossAndGradAll, img0.flatten(), args=args,
method=method, jac=jac, bounds=bounds, options=options)
data = self.tool.net.blobs['data'].data[0,...]
self.tool.saveImg(data)
def random_inexact_complexnum_and_equiv_python_floats(
) -> Tuple[Numeric, float, float]:
real = random_float()
imaginary = random_float()
return Numeric(real, imaginary), real, imaginary
def random_inexact_complexnum() -> Numeric:
return Numeric(random_float(), random_float())
def random_fixnum() -> Numeric:
return Numeric(str(random_python_int_inside_fixnum_range()))
def random_ratnum_and_equiv_python_fraction():
numerator = random_python_int_outside_fixnum_range()
denominator = random_python_int_outside_fixnum_range()
return Numeric(str(numerator),
str(denominator)), Fraction(numerator, denominator)
def random_fixnum_and_equiv_python_int() -> Tuple[Numeric, int]:
py_int = random_python_int_inside_fixnum_range()
return Numeric(str(py_int)), py_int
def random_exact_complexnum():
return Numeric(str(random_python_int_outside_fixnum_range()),
str(random_python_int_outside_fixnum_range()),
str(random_python_int_outside_fixnum_range()),
str(random_python_int_outside_fixnum_range()))
from random import randint, uniform, seed
from functools import partial
from typing import Tuple
from fractions import Fraction
from test_suites.util_types import ExactComplex
MIN_FIXNUM_VALUE = -4611686018427387904
MAX_FIXNUM_VALUE = 4611686018427387903
MAX_ARBITRARY_NUM_BITS = 256
seed(9439832)
random_float = partial(uniform, -1e12, 1e12)
zero = Numeric("0")
one = Numeric("1")
negative_one = Numeric("-1")
def generate_fixnum_a_less_than_b():
a = random_python_int_inside_fixnum_range()
b = random_python_int_inside_fixnum_range()
while a == b:
b = random_python_int_inside_fixnum_range()
if a < b:
return Numeric(str(a)), Numeric(str(b))
else:
return Numeric(str(b)), Numeric(str(a))
def runWithVoice(self, rulefile):
def clearScreen():
os.system('clear')
def str2int(content):
return int(content)
self.outputLine('请说出您的名字吧');
name = self.speech.getVoiceText()
if name is not None:
names = self.nlp.getPersons(name)
if names is not None:
name = names[0]
if name is None:
name = ''
self.outputLine('欢迎您！{}'.format(name));
formulaRule = FormulaRule(rulefile)
self.outputLine('总共是{}关，您想从第几关开始？'.format(formulaRule.getSize()))
text = self.speech.getVoiceText()
if text is not None:
text = Numeric.chinese2arabic(text)
numbers = getMatchList(r'\d+', text, str2int)
if len(numbers) > 0:
formulaRule.set(numbers[0] - 1)
while True:
rule = formulaRule.createFormula()
clearScreen()
self.outputLine(u'当前第{}关，如果需要跳过或者退出，请说出跳过或者退出。'.format(formulaRule.getIndex() + 1))
text = self.speech.getVoiceText()
if text is not None and self.nlp.isSimilar('退出', text):
self.outputLine('已退出')
break
if text is None or not self.nlp.isSimilar('跳过', text):
clearScreen()
formula = Formula.parse(rule)
self.outputLine(u'第{}关题目如下：'.format(formulaRule.getIndex() + 1))
msg = '{} = 多少？'.format(formula)
for i in range(3):
if i is not 0:
self.outputLine('请再想想吧？')
self.outputLine(msg)
text = self.speech.getVoiceText()
if text is None:
continue
text = Numeric.chinese2arabic(text)
items = self.nlp.getNumbers(text)
if items is None:
continue
result = Currency.createFromReadableString(items)
if result is None:
continue
answer = result.toCurrency()
correct = False
try:
condition = '{0} == {1} or ({0} > {1} and {0} < {1} + 0.01) or ({1} > {0} and {1} < {0} + 0.01)'.format(rule, answer)
if eval(condition):
correct = True
except Exception as e:
pass
try:
answer = Formula.parse(answer)
answer = answer.getResult()
except CurrencyException as e:
pass
self.outputLine('{} = {}'.format(formula, answer))
if correct:
self.outputLine('回答正确！恭喜您通过第{}关！'.format(formulaRule.getIndex() + 1))
break
else:
self.outputLine('可能您不太理解，正确答案是：{} = {}。'.format(formula, formula.getResult()))
continue
else:
self.outputLine(u'已跳过第{}关。'.format(formulaRule.getIndex() + 1))
clearScreen()
if not formulaRule.forward():
self.outputLine('恭喜您！{}，通过全部{}关！'.format(name, formulaRule.getSize()))
break
