def find_predictor(user, restaurants, feature_fn):
"""Return a rating predictor (a function from restaurants to ratings),
for USER by performing least-squares linear regression using FEATURE_FN
on the items in RESTAURANTS. Also, return the R^2 value of this model.
Arguments:
user -- A user
restaurants -- A sequence of restaurants
feature_fn -- A function that takes a restaurant and returns a number
"""
reviews_by_user = {review_restaurant_name(review): review_rating(review)
for review in user_reviews(user).values()}
xs = [feature_fn(r) for r in restaurants]
ys = [reviews_by_user[restaurant_name(r)] for r in restaurants]
mean_xs = mean(xs)
mean_ys = mean(ys)
s_xx = sum([pow(x - mean_xs, 2) for x in xs])
s_yy = sum([pow(y - mean_ys, 2) for y in ys])
s_xy = sum([mul(z[0], z[1]) for z in zip([x - mean_xs for x in xs], [y - mean_ys for y in ys])])
b = s_xy / s_xx
a = mean_ys - b * mean_xs
r_squared = pow(s_xy, 2) / mul(s_xx, s_yy)
def predictor(restaurant):
return b * feature_fn(restaurant) + a
return predictor, r_squared
def generate_message(cj, cij, incoming):
''' Generate a normalized message. '''
# Compute max over xj for the message to xi.
if cj.names[0] == cij.names[0]:
edge = lambda xi, xj: (xj, xi)
xi_nstates = cij.table.shape[1]
else:
edge = lambda xi, xj: (xi, xj)
xi_nstates = cij.table.shape[0]
# Compute the total response for the states of xi.
incoming_tab = np.array([
mul(cj((xj,)), reduce(mul, (m(xj) for m in incoming), 1.))
for xj in range(cj.nstates[0])])
message = lambda xi: max(mul(cij(edge(xi, xj)), incoming_tab[xj])
for xj in range(cj.nstates[0]))
# Combine into a single table over states of xi.
table = np.fromiter((message((xi,))
for xi in xrange(xi_nstates)), dtype=float)
np.divide(table, np.sum(table), table)
return lambda xi: table[xi]
def buy_calc_amts(self, sec, amt, allo, val):
print 'buy... '
print 'price of ' + sec + 'is ', self.get_price(sec)
print 'currently have ', self.get_current_amt(sec), ' share of ', sec
print 'portfolio value is ', self.portfolio_value
print 'DESIRED ALLOCATION IS ', allo
price = self.get_price(sec)
current = self.get_current_amt(sec)
current_amt = current*price
shares = 0
allocation = 0
#amount = 0
#value = 0
if allo > 0.0:
print 'GOT HERE YOU STUPID BITCH'
amt_desired = operator.mul(allo,self.portfolio_value)
# have enough and don't have the stock --> buy all desired
if amt_desired < float(self.free_cash) and current == 0:
shares = int(amt_desired/price)
# have enough and do have the stock --> buy remaining amount to reach desired allocation
elif current != 0 and (amt_desired - current_amt) < float(self.free_cash):
shares = int((amt_desired - current_amt)/price)
# don't have enough and don't have the stock --> use all remaining cash
elif amt_desired > self.free_cash and current == 0:
shares = int(self.free_cash/price)
# don't have enough and have the stock --> use all remaining cash
elif current != 0 and (amt_desired - current_amt) > float(self.free_cash):
shares = int(self.free_cash/price)
#not 100% sure if this will ever be triggered
else:
return 0, 0, price
print "time to buy ", shares
print "buying them for ", price
amt_to_purchase = operator.mul(shares,int(price))
allocation = amt_to_purchase/float(self.portfolio_value)
print "allocation is ", allocation
elif amt > 0.0:
if current == 0 and amt*price < self.free_cash:
shares = amt
elif current < amt and (amt - current)*price < self.free_cash:
shares = amt - current
# elif val > 0:
# if val < self.free_cash and current == 0:
# shares = int(val/price)
# elif val > self.free_cash and current != 0:
# shares = int(self.free_cash/price)
# value = shares*price
return shares, 100*allocation, price
def train_epoch(self, inputs, targets, optimizer, criterion,
epoch_no=0, batch_size=64, max_step=50, max_norm=5, eval_step=10):
hidden = self.model.init_hidden(batch_size)
counter = 0
x_generator = get_batch(inputs, batch_size, max_step)
y_generator = get_batch(targets, batch_size, max_step)
for x, y in zip(x_generator, y_generator):
self.model.train()
x = Variable(torch.from_numpy(np.array(x, dtype=np.float32))).long()
y = Variable(torch.from_numpy(np.array(y, dtype=np.float32))).long()
if CUDA_AVAILABLE:
x = x.cuda()
y = y.cuda()
if isinstance(hidden, tuple):
hidden = tuple([Variable(each.data) for each in hidden])
else:
hidden = Variable(hidden.data)
self.model.zero_grad() # 重置梯度
output, hidden = self.model.forward(x, hidden)
# 将 output 的维度进行转换:
# [batch_size, step_size, vocab_size] -> [batch_size * step_size, vocab_size]
# y 是 1D 的就好
step_size = x.size(1) # batch 里序列的长度有可能不足 max_step
cross_entropy_loss = criterion(
output.view(batch_size * step_size, -1),
y.view(batch_size * step_size).long()
)
focal_loss = FocalLoss(gamma=2)(
output.view(batch_size * step_size, -1),
y.view(batch_size * step_size).long()
)
ploss = pullaway_loss(output.view(batch_size * step_size, -1))
loss = cross_entropy_loss + focal_loss + 0.1 * ploss
loss.backward()
torch.nn.utils.clip_grad_norm(self.model.parameters(), max_norm)
optimizer.step()
counter += 1
if (counter % eval_step) == 0:
print("Epoch: {}; Step: {}; Loss: {:.4f}".format(
epoch_no + 1, counter, loss.data[0]
))
# 从 x 中随机挑选内容
pos = np.random.randint(0, mul(*x.size()) - 2)
length = np.random.randint(1, min(5, mul(*x.size()) - pos - 1))
start_tokens = x.view(-1)[pos:pos + length].data.numpy()
start_text = ''.join(self.vectorizer.inverse_transform([start_tokens])[0]).strip()
if start_text:
result = self.generate(start_text, max_len=100)
print("[%s]: %r" % (start_text, result))
def twenty_fourteen():
"""Come up with the most creative expression that evaluates to 2014,
using only numbers and the functions add(. . .) and mul(. . .).
>>> twenty_fourteen()
2014
"""
"*** YOUR CODE HERE ***"
return add(mul(20, mul(10,10)), add(mul(5,2), 4))
def main():
most = 0
best = (0, 0)
for a, b in product(range(-999, 1000), range(-999, 1000)):
formula = lambda n: n ** 2 + a * n + b
num = numPrimes(formula)
if num > most:
most = num
best = (a, b)
print mul(*best)
def power(a, b):
if b == 0:
return 1
elif b == 1:
return a
elif b == 2:
return operator.mul(a, a)
elif b % 2 == 0:
return power(power(a, b / 2), 2)
else:
return operator.mul(power(a, b - 1), a)
def fast_gpow(x, n, mul, mul_identity):
"""Raise generalized numeric x to integer power n."""
m = mul_identity
while n > 0:
if n & 1:
m = mul(m, x)
n -= 1
else:
x = mul(x, x)
n >>= 1
return m
def test_multiplication_with_autoconvert(self, input_tuple, expected):
self.ureg.autoconvert_offset_to_baseunit = True
qin1, qin2 = input_tuple
q1, q2 = self.Q_(*qin1), self.Q_(*qin2)
input_tuple = q1, q2
if expected == 'error':
self.assertRaises(OffsetUnitCalculusError, op.mul, q1, q2)
else:
expected = self.Q_(*expected)
self.assertEqual(op.mul(q1, q2).units, expected.units)
self.assertQuantityAlmostEqual(op.mul(q1, q2), expected,
atol=0.01)
def pout(gen, indi) :
message(name(indi)+"\n")
cols("",add(5,mul(4,gen)),d(add(gen,1))+"-- ")
outp(indi)
next = add(1,gen)
(fam,sp,num,iter) = families(indi)
while fam :
cols("",add(5,mul(4,gen))," sp-")
outp(sp)
if lt(next,15) :
for (no0,child) in children(fam) :
pout(next,child)
(fam,sp,num) = families(iter)
def all_values_on_form(form, value):
"""
Returns all lattice points (not necessarily coprime)
that produce the desired value on the form
Given the recurrence for the form, these values
can serve to determine *all* solutions for
the given value due to the repeating nature
of the infinite river
"""
factor_list = factors(value)
valid_factors = [factor for factor in factor_list
if is_power(value/factor, 2)]
roots = all_positive_roots(form)
found = set()
for root in roots:
candidates = seek_up_to_val(root, value)
to_add = [candidate for candidate in candidates
if candidate[1] in valid_factors] + \
[candidate for candidate in root
if candidate[1] in valid_factors]
found.update(to_add)
found = list(found)
# We may get some duplicates from since when we include
# values from the river, we don't check that they come from
# a different iteration of the river
x_mult, y_mult, _ = get_recurrence(form)
checked = found[:]
for candidate in found:
coords, val = candidate
next_x = sum([operator.mul(*pair) for pair in zip(coords, x_mult)])
next_y = sum([operator.mul(*pair) for pair in zip(coords, y_mult)])
if ((next_x, next_y), val) in found:
checked.remove(((next_x, next_y), val))
# Finally we must scale up factors to account for
# the reduction by a square multiple
result = []
for cell in checked:
(x, y), val = cell
if val < value:
ratio = int(sqrt(value/val))
x *= ratio
y *= ratio
result.append((x,y))
return result
def test_multiplication_with_scalar(self, input_tuple, expected):
self.ureg.default_as_delta = False
in1, in2 = input_tuple
if type(in1) is tuple:
in1, in2 = self.Q_(*in1), in2
else:
in1, in2 = in1, self.Q_(*in2)
input_tuple = in1, in2 # update input_tuple for better tracebacks
if expected == 'error':
self.assertRaises(OffsetUnitCalculusError, op.mul, in1, in2)
else:
expected = self.Q_(*expected)
self.assertEqual(op.mul(in1, in2).units, expected.units)
self.assertQuantityAlmostEqual(op.mul(in1, in2), expected,
atol=0.01)
def predeal_atom2(range_rule):
atom_rule = [0] * len(range_rule)
shadows = list()
segnum = list()
base = list()
for dim in range(SF_DIM_NUM):
shadows.append(pc.shadow_rules(range_rule, dim))
segnum.append(range(len(shadows[dim]) >> 1))
print segnum
for dim in range(SF_DIM_NUM - 1):
# reduce(mul, (d[1] - d[0] + 1 for d in self.dims))
base.append(reduce(mul, (len(segnum[d]) for d in range(dim, SF_DIM_NUM))))
base.append(1)
for atom_index in product(*segnum):
atom_rect = list()
atom = 0
for dim in range(SF_DIM_NUM):
atom_rect.append([shadows[dim][atom_index[dim] << 1], shadows[dim][(atom_index[dim] << 1) + 1]])
atom += mul(atom_index[dim], base[dim])
for i in range(len(range_rule)):
if rule_le(atom_rect, range_rule[i]):
atom_rule[i] += atom
break
# print atom_index
return atom_rule
def calculate_paper(dimensions):
if not dimensions:
return 0
dimensions = get_clean_dimensions(dimensions)
l, w, h = dimensions
extra = mul(*sorted(dimensions)[:2])
return (2*l*w) + (2*w*h) + (2*h*l) + extra
def p_expression_binop(self, p):
"""expression : expression PLUS expression
| expression MINUS expression
| expression TIMES expression
| expression DIVIDE expression
| expression MODULUS expression
| expression EXPONENT expression"""
op = p[2]
left = self._sumDiceRolls(p[1])
right = self._sumDiceRolls(p[3])
if op == '+':
p[0] = operator.add(left, right)
elif op == '-':
p[0] = operator.sub(left, right)
elif op == '*':
p[0] = operator.mul(left, right)
elif op == '/':
p[0] = operator.floordiv(left, right)
elif op == '%':
p[0] = operator.mod(left, right)
elif op == '^':
if -self._MAX_EXPONENT <= left <= self._MAX_EXPONENT and -self._MAX_EXPONENT <= right <= self._MAX_EXPONENT:
p[0] = operator.pow(left, right)
else:
raise InvalidOperandsException(u'operand or exponent is larger than the maximum {}'
.format(self._MAX_EXPONENT))
def doprob():
op = choice('+-*/')
nums = [randint(1,10) for i in range(2)]
nums.sort(reverse=True)
if op != '/':
ans = ops[op](*nums)
pr = '%d %s %d =' % (nums[0],op,nums[1])
else:
ans = div(nums[0],nums[1])
if div (nums[0] * 10,nums[1]) ==ans * 10:
pr = '%d %s %d = ' %(nums[0],op,nums[1])
else:
ans = mul(num[0],nums[1])
pr = '%d %s %d =' (nums[0], ' *',nums[1])
opps =0
while True:
try:
if int(raw_input(pr)) == ans:
print 'correct'
break
if opps == MAXTRIES:
print 'answer\n %s%d'%(pr,ans)
else:
print 'incorrect ...try again'
opps +=1
except (KeyboardInterrupt, \
EOFError,ValueError):
print 'invalid input...try again'
def exp_sqr(x, n):
if n == 0:
return 1
if n % 2 == 1:
return mul(x, exp_sqr(x, n-1))
a = exp_sqr(x, n/2)
return pow(a, 2)
def index_view(request):
print(request.POST)
try:
if request.POST:
first_number_input = float(request.POST['first_number_input'])
second_number_input = float(request.POST['second_number_input'])
operation = request.POST["operator"]
if operation == "+ (add)":
answer = operator.add(first_number_input, second_number_input)
operation = "+"
elif operation == "- (subtract)":
answer = operator.sub(first_number_input, second_number_input)
operation = "-"
elif operation == "x (multiply)":
answer = operator.mul(first_number_input, second_number_input)
operation = "x"
else:
try:
answer = operator.truediv(first_number_input, second_number_input)
except ZeroDivisionError:
answer = "Answer is Undefined (You can't divide by zero)"
operation = "/"
return render(request, 'index.html', {'answer': answer,
'operation': operation,
'first_number': first_number_input,
'second_number': second_number_input})
else:
return render(request,'index.html',{})
except (ValueError, TypeError):
answer = "INVALID ENTRY"
return render(request, "index.html",{'answer':answer})
def make_anonymous_factorial():
"""Return the value of an expression that computes factorial.
>>> make_anonymous_factorial()(5)
120
"""
return (lambda f: lambda k: f(f, k))(lambda f, k: k if k == 1 else mul(k, f(f, sub(k, 1))))
def panlindrom(lo, hi):
global maxi
for p in product(range(lo, hi), repeat=2):
prod = str(mul(*p))
if prod == prod[::-1] and int(prod) > maxi:
maxi = int(prod)
print maxi
def make_anonymous_factorial():
"""Return the value of an expression that computes factorial.
>>> make_anonymous_factorial()(5)
120
"""
return lambda n: (lambda f, n: f(f, n))(lambda f, n: 1 if n == 1 else mul(n, f(f, sub(n, 1))), n)
def make_anonymous_factorial():
"""Return the value of an expression that computes factorial.
>>> make_anonymous_factorial()(5)
120
"""
return lambda val : (lambda f, v : f(f, v)) (lambda f, v : 1 if v == 0 else mul(v, f(f, sub(v, 1))), val)
def evaluate(self):
result = None
left = self.left.evaluate()
right = self.right.evaluate()
if self.operation == '+':
result = operator.add(left, right)
elif self.operation == '-':
result = operator.sub(left, right)
elif self.operation == '*':
result = operator.mul(left, right)
elif self.operation == '/':
result = operator.div(left, right)
elif self.operation == '^':
result = operator.pow(left, right)
elif self.operation == 'and':
result = left and right
elif self.operation == 'or':
result = left or right
elif self.operation == '<':
result = operator.lt(left, right)
elif self.operation == '<=':
result = operator.le(left, right)
elif self.operation == '==':
result = operator.eq(left, right)
elif self.operation == '!=':
result = operator.ne(left, right)
elif self.operation == '>':
result = operator.gt(left, right)
elif self.operation == '>=':
result = operator.ge(left, right)
elif self.operation == 'in':
result = (left in right)
return result
def _call(self, arguments, delta_time, computing_context):
try:
result = OperatorComputingResult(mul(*arguments), NoneComputingContext())
except:
result = NoneComputingResult()
return result
def train(self, images, statuses):
numStatuses = len(self.possibleStatuses)
ds = self.datasetMethod(mul(*self.imageSize), 1, numStatuses)
[ds.addSample(self._loadToArray(i), e.value) for i, e in izip(images, statuses)]
#convert to one output per class. Apparently this is a better format?
# http://pybrain.org/docs/tutorial/fnn.html
ds._convertToOneOfMany()
trainer = self.trainMethod(self.net, dataset=ds)
start = time.clock()
trainErrors, validationErrors = trainer.trainUntilConvergence(convergence_threshold=4)
trainTime = time.clock() - start
iterations = len(trainErrors) + len(validationErrors)
print "Training took {} iterations".format(iterations)
if trainErrors:
print "Errors: {}, {}".format(trainErrors[-1], validationErrors[-1])
else:
print "Training unsuccesfull. Trainerrors is empty."
self.trainTime = float(trainTime) / iterations
self.error = validationErrors[-1]
return trainErrors, validationErrors
def _loadToArray(self, imagePath):
"""
Creates input array. Applies scale factor to each image.
"""
try:
image = PIL.Image.open(imagePath)
except IOError as e:
#print("Trying to open by converting to png")
png = os.path.splitext(imagePath)[0] + '.png'
wand.image.Image(filename=imagePath).convert('PNG').save(filename=png)
image = PIL.Image.open(png)
#resize
scaleFactor = np.divide(self.imageSize, image.size)
newSize = tuple(round(x * s) for x, s in zip(image.size, scaleFactor))
image.thumbnail(newSize)
#greyscale
image = image.convert('L')
# neurolab seems to expect 1d input, so rescale the images in the
# input array as linear (the network does't know about shape anyway)
imageArray = np.array(image)
newSize = mul(*imageArray.shape)
return imageArray.reshape(newSize)
def make_anonymous_factorial():
"""Return the value of an expression that computes factorial.
>>> make_anonymous_factorial()(5)
120
"""
return lambda x: 1 if x <= 1 else mul(x, make_anonymous_factorial()(sub(x, 1)))
def parse_dealer(self, img, coords):
"""Parses to see who is the dealer
Template will be image with button
Low MSE is true"""
# load template
tpl = self.img['dealer']
tpl_data = np.array(tpl)
self.logger.debug('loaded dealer template {}'.format(tpl))
tpl.save(os.path.join(self.DEBUG_PATH, 'dealer_tpl.png'))
dealers = {}
for seat, bb in coords['dealers'].items():
self.logger.debug('parsing seat {} for dealer within {}'.format(seat, bb))
# crop out box
box = img.crop(bb[0:4])
box_data = np.array(box)
self.logger.debug('dealer box {} from {}'.format(box, bb[:4]))
box.save(os.path.join(self.DEBUG_PATH, 'dealer_{}.png'.format(seat)))
# MSE
mse = np.sum((box_data.astype(np.float) - tpl_data.astype(np.float)) ** 2)
mse /= mul(*box.size)
is_dealer = mse <= bb[-1]
self.logger.info('{}: {} calculated from MSE = {} (threshold {})'.format(
seat, is_dealer, int(mse), bb[-1]))
dealers[seat] = is_dealer
self.logger.info('dealers {}'.format(dealers))
return dealers
def euler_27(max_a, max_b):
a = range(-max_a, max_a + 1)
#n = 0 means that the equation simplifies to b, so b must be prime.
b = filter(lambda x: lib.is_prime(abs(x)), range(-max_b, max_b + 1))
vars = filter(a_filter, itertools.product(a, b))
solution = max((consec_primes_for_eq(*var), var) for var in vars)[1]
return operator.mul(*solution)
def ch_to_op(c, x, y):
if c == '+':
return operator.add(x, y)
elif c == '-':
return operator.sub(x, y)
elif c == 'x':
return operator.mul(x, y)
def make_anonymous_factorial():
"""Return the value of an expression that computes factorial.
>>> make_anonymous_factorial()(5)
120
>>> from construct_check import check
>>> # ban any assignments or recursion
>>> check(HW_SOURCE_FILE, 'make_anonymous_factorial', ['Assign', 'AugAssign', 'FunctionDef', 'Recursion'])
True
"""
"""
return (lambda f: lambda n: f(f, n))(lambda f, n: 1 if not n else mul(n, f(f, sub(n, 1)))) #普通递归, 匿名函数的递归
"""
return (lambda f: lambda n: f(f, n))(lambda f, n, y=1: y if not n else f(f, sub(n, 1), mul(n, y))) #尾递归优化
def solve1(input, n=256):
return mul(*hash_round([int(l)
for l in input.split(',')], list(range(n)))[0][:2])
def create_transformer_class(row, transformer_mapping):
"""
Creates a transformer class from the provided mapping overrides.
:param row: The row to transform
:param transformer_mapping: The overrides for the transform functions
:return: The new transformer class
"""
transformer_mapping = {
"lookup": lambda r, name: r[name],
"add": lambda r, lhs, rhs: add(lhs, rhs),
"subtract": lambda r, lhs, rhs: sub(lhs, rhs),
"multiply": lambda r, lhs, rhs: mul(lhs, rhs),
"divide": lambda r, lhs, rhs: div(lhs, rhs),
"eq": lambda r, lhs, rhs: lhs == rhs,
"not_eq": lambda r, lhs, rhs: lhs != rhs,
"is_in": default_in_transformer,
"not_in": default_not_in_transformer,
"gt": lambda r, lhs, rhs: lhs > rhs,
"gte": lambda r, lhs, rhs: lhs >= rhs,
"lt": lambda r, lhs, rhs: lhs < rhs,
"lte": lambda r, lhs, rhs: lhs <= rhs,
"logical_or": lambda r, lhs, rhs: lhs or rhs,
"logical_and": lambda r, lhs, rhs: lhs and rhs,
"logical_not": lambda r, v: not v,
"any": lambda r, v: AnyWrapper(v),
"all": lambda r, v: AllWrapper(v),
"str_join": default_join,
"str_replace": default_replace,
"str_match": default_match,
"str_search": default_search,
**(transformer_mapping or {}),
}
def mapped_function(name, *args, **kwargs):
return transformer_mapping[name](row, *args, **kwargs)
@v_args(inline=True)
class TreeTransformer(BaseTreeTransformer):
lookup = partial(mapped_function, "lookup")
add = partial(mapped_function, "add")
subtract = partial(mapped_function, "subtract")
multiply = partial(mapped_function, "multiply")
divide = partial(mapped_function, "divide")
eq = partial(mapped_function, "eq")
not_eq = partial(mapped_function, "not_eq")
is_in = partial(mapped_function, "is_in")
not_in = partial(mapped_function, "not_in")
gt = partial(mapped_function, "gt")
gte = partial(mapped_function, "gte")
lt = partial(mapped_function, "lt")
lte = partial(mapped_function, "lte")
logical_not = partial(mapped_function, "logical_not")
logical_or = partial(mapped_function, "logical_or")
logical_and = partial(mapped_function, "logical_and")
any = partial(mapped_function, "any")
all = partial(mapped_function, "all")
str_join = partial(mapped_function, "str_join")
str_replace = partial(mapped_function, "str_replace")
str_match = partial(mapped_function, "str_match")
str_search = partial(mapped_function, "str_search")
return TreeTransformer
def square(x):
return mul(x, x)
[2, 0, 'one', 'seven'] # Operators 2000 + 17 2001 + 4**2 4034 // 2 -1 + 0 + 1 * 2**3 * 4 // 5 * 6 * 7 * 8 * 9 // 10 + 11 + 12 + 13 * 14 - 1 'Hello ' + 'world!' [2, 0] + ['one', 'seven'] # Call expressions from operator import add, mul add(2000, 17) mul(1009, 2) abs(-2017) pow(2, 100) from math import sqrt sqrt(2017) from math import sin, pi sin(pi) # Nested call expressions add(add(6, mul(4, 6)), mul(3, 5)) # Shakespeare demo # Note: Download from http://composingprograms.com/shakespeare.txt
def vector_count(self):
return mul(map(Range.value_count, self))
def multiply(t):
return mul(*t)
def mul(*args):
ans = 1
for arg in args:
ans = op.mul(ans, arg)
return ans
from operator import mul
import random
total = 0
incs = []
mistakes = False
qs = int(input("Number of questions:"))
for num in range(0, qs):
x = random.randint(6, 8)
y = random.randint(13, 20)
print("What is " + str(x) + " x " + str(y) + "?")
answer = int(input("Your Answer:"))
if answer == (mul(x, y)):
print("Correct!")
total = total + 1
elif answer == (000):
exit()
else:
print("Wrong, the answer is " + str(x * y))
mistakes = True
incs.append("You said " + str(x) + " times " + str(y) + " = " +
str(answer) + " but the answer is actually " +
str(mul(x, y)))
print("You got " + str(total) + " out of " + str(qs) + " questions correct!")
if mistakes == True:
print("Here are your mistakes:")
z = 0
for num in range(0, len(incs)):
print(incs[z])
z += 1
def square(square):
return mul(square, square)
def solver():
with open('input.txt', 'r') as f:
data = f.read().strip()
print('Part 1:', play_game(data, 100)) # 34952786
print('Part 2:', mul(*play_game2(data))) # 505334281774
adict = {'a': 1, 'b': 5, 'c': 1}
dict((i, dict(v)) for i, v in groupby(adict.items(), itemgetter(1)))
# Output: {1: {'a': 1, 'c': 1}, 5: {'b': 5}}
#which is equivalent (but faster) to a lambda function like this:
dict((i, dict(v)) for i, v in groupby(adict.items(), lambda x: x[1]))
#Or sorting a list of tuples by the second element first the first element as secondary
alist_of_tuples = [(5, 2), (1, 3), (2, 2)]
sorted(alist_of_tuples, key=itemgetter(1, 0))
#Section 48.2: Operators as alternative to an infix operator
print(
"-----Section 48.2: Operators as alternative to an infix operator---------"
)
print(add(1, 1))
print(mul('a', 10))
print(mul([3], 3))
#Section 48.3: Methodcaller
print("-------Section 48.3: Methodcaller---------")
alist = ['wolf', 'sheep', 'duck']
list(filter(lambda x: x.startswith('d'),
alist)) # Keep only elements that start with 'd'
# Output: ['duck']
# or
list(filter(methodcaller('startswith', 'd'),
alist)) # Does the same but is faster
def __mul__(self, other):
return operator.mul(*self._get_operands(other))
def imp(consequence_crisp):
return operator.mul(antecedent_out,
consequence_membershipf(consequence_crisp))
def multiply(self, a: int, b: int) -> int:
return mul(a, b)
def test_reformer2_one_step(self):
d_model = 1024
vocab_size = 14041
max_len = 16384
pos_axial = (128, 128) # should multiply to max_len
pos_d_axial_embs = (512, 512) # sum to d model
assert operator.mul(*pos_axial) == max_len
assert sum(pos_d_axial_embs) == d_model
d_ff = 4096
n_heads = 8
d_attn = d_model // n_heads
n_buckets = 128
encoder_chunk_len = (2 * max_len) // n_buckets # 256
decoder_chunk_len = 2 * encoder_chunk_len # 512
encoder_n_chunks_after = 1 # since its not causal.
lsh_self_attention = functools.partial(self._lsh_self_attention_fn(),
n_buckets=n_buckets)
encoder_lsh_self_attention = functools.partial(
lsh_self_attention, n_chunks_after=encoder_n_chunks_after,
chunk_len=encoder_chunk_len)
decoder_lsh_self_attention = functools.partial(
lsh_self_attention, n_chunks_after=0,
chunk_len=decoder_chunk_len)
model = reformer.Reformer2(
vocab_size,
d_model=d_model,
d_ff=d_ff,
d_attention_key=d_attn,
d_attention_value=d_attn,
n_encoder_layers=1,
n_decoder_layers=1,
n_heads=n_heads,
dropout=0.05,
max_len=max_len,
encoder_attention_type=encoder_lsh_self_attention,
encoder_decoder_attention_type=decoder_lsh_self_attention,
pos_axial_shape=pos_axial,
pos_d_axial_embs=pos_d_axial_embs,
ff_activation=tl.Relu,
ff_use_sru=0,
mode='train',
)
def random_sentence():
return np.random.randint(low=1, high=vocab_size - 1, size=(1, max_len),
dtype=np.int32)
x = [random_sentence(), random_sentence()]
weights, state = model.init(shapes.signature(x))
@fastmath.jit
def mock_training_step(x, weights, state, rng):
def compute_mock_loss(weights):
logits_and_dec_toks, new_state = model.pure_fn(x, weights, state, rng)
# This returns [logits, decoder tokens]
logits = logits_and_dec_toks[0]
loss = fastmath.numpy.mean(logits[..., 0])
return loss, (new_state, logits)
gradients, (new_state, logits) = fastmath.grad(
compute_mock_loss, has_aux=True)(weights)
new_weights = fastmath.nested_map_multiarg(
lambda w, g: w - 1e-4 * g, weights, gradients)
return new_weights, new_state, logits
weights, state, logits = mock_training_step(
x, weights, state, fastmath.random.get_prng(0))
self.assertEqual(logits.shape, (1, max_len, vocab_size))
def __hash__(self):
return mul(map(hash, [self._min, self._max]))
# ext_srcs = filter(lambda block: block not in text, page)
sample_list = [
[1, 2],
3, 4, 5,
[6, 7, 8],
[9, 10]
]
def compose(*funcs):
return reduce(lambda f, g: lambda x: f(g(x)), funcs, lambda x: x)
add = curry(add)
mul = curry(mul)
add_and_mul = compose(neg, mul(2), add(2))
@curry
def calc(li):
return reduce(lambda acc, i: acc + add_and_mul(i), li)
@curry
def odd(li):
return filter(lambda i: i % 2 == 1, li)
def i_range(stop):
i = 0
while(i < stop):
# Numeric expressions
2019
2000 + 19
0 + 1 // 2 + 3 + 4 * ((5 // 6) + 7 * 8 * 9)
# Call expressions
max(3, 4.5)
pow(100, 2)
pow(2, 100)
max(1, -2, 3, -4)
max(pow(10, 2), pow(2, 10), 1010)
# Importing and arithmetic with call expressions
from operator import add, mul
add(1, 2)
mul(4, 6)
mul(add(4, mul(4, 6)), add(3, 5))
add(3, mul(9, mul(add(4, mul(4, 6)), add(3, 5))))
### DEMO 2: "Functions, Values, Objects, Interpreters, and Data"
# Objects
# Note: Download from http://composingprograms.com/shakespeare.txt
# downloaded
shakes = open('01/shakespeare.txt')
text = shakes.read().split()
len(text)
text[:25]
text.count('the')
text.count('thou')
def test_next_bus(self):
next = pkg.next_bus(*pkg.load_bus_info('test_input/day13_t1.txt'))
self.assertEqual(mul(*next), 295)
# Callable : 호출 연산자 -> 메소드 형태로 호출 가능한지 확인 # 호출 가능 확인 print(callable(str), callable(list), callable(var_func), callable(3.14)) from inspect import signature sg = signature(var_func) print(sg) print(sg.parameters) print() print() # partial 사용법 : 인수 고정 -> 콜백 함수에 사용 from operator import mul from functools import partial print(mul(10, 10)) # 인수 고정 five = partial(mul, 5) # 고정 추가 six = partial(five, 6) print(five(10)) print(six()) print([five(i) for i in range(1, 11)]) print(list(map(five, range(1, 11))))
def op(a, b):
return int64(mul(a, b))
def init_leftmost_tick(self):
if self.leftmost_tick is None:
self.leftmost_tick = op.mul(
self.tick_frequency, np.ceil(self.x_min / self.tick_frequency)
)
# Operator: realizando operações em variaveis
import operator
def soma(x):
return operator.add(x, 2)
print(soma(3))
# Python code to demonstrate working of
# add(), sub(), mul()
a = 4
b = 3
# using add() to add two numbers
print("The addition of numbers is :", end="")
print(operator.add(a, b))
# using sub() to subtract two numbers
print("The difference of numbers is :", end="")
print(operator.sub(a, b))
# using mul() to multiply two numbers
print("The product of numbers is :", end="")
print(operator.mul(a, b))
def dot(wi, x):
from functools import reduce
from operator import mul, add
return reduce(add, map(lambda i: mul(wi[i], x[i]), x))
def kinetic_energy_jacobi(self, x, **kwargs):
r"""Compute the value of the kinetic enery using the Jacobi Formula.
.. math::
\\frac{\Delta (J(R) \Psi(R))}{ J(R) \Psi(R)} = \\frac{\\Delta J(R)}{J(R}
+ 2 \\frac{\\nabla J(R)}{J(R)} \\frac{\\nabla \\Psi(R)}{\\Psi(R)}
+ \\frac{\\Delta \\Psi(R)}{\\Psi(R)}
The lapacian of the determinental part is computed via
.. math::
\\Delta_i \\Psi(R) \\sum_n c_n ( \\frac{\\Delta_i D_n^{u}}{D_n^{u}} +
\\frac{\\Delta_i D_n^{d}}{D_n^{d}} +
2 \\frac{\\nabla_i D_n^{u}}{D_n^{u}} \\frac{\\nabla_i D_n^{d}}{D_n^{d}} )
D_n^{u} D_n^{d}
Since the backflow orbitals are multi-electronic the laplacian of the determinants
are obtained
.. math::
\\frac{\\Delta det(A)}{det(A)} = Tr(A^{-1} \\Delta A) +
Tr(A^{-1} \\nabla A) Tr(A^{-1} \\nabla A) +
Tr( (A^{-1} \\nabla A) (A^{-1} \\nabla A ))
Args:
x (torch.tensor): sampling points (Nbatch, 3*Nelec)
Returns:
torch.tensor: values of the kinetic energy at each sampling points
"""
# get ao values
ao, dao, d2ao = self.ao(
x, derivative=[0, 1, 2], sum_grad=False)
# get the mo values
mo = self.ao2mo(ao)
dmo = self.ao2mo(dao)
d2mo = self.ao2mo(d2ao)
# compute the value of the slater det
slater_dets = self.pool(mo)
sum_slater_dets = self.fc(slater_dets)
# compute ( tr(A_u^-1\Delta A_u) + tr(A_d^-1\Delta A_d) )
hess = self.pool.operator(mo, d2mo)
# compute (tr(A_u^-1\nabla A_u) and tr(A_d^-1\nabla A_d))
grad = self.pool.operator(mo, dmo, op=None)
# compute (tr((A_u^-1\nabla A_u)^2) + tr((A_d^-1\nabla A_d))^2)
grad2 = self.pool.operator(mo, dmo, op_squared=True)
# assemble the total second derivative term
hess = (hess.sum(0)
+ operator.add(*[(g**2).sum(0) for g in grad])
- grad2.sum(0)
+ 2 * operator.mul(*grad).sum(0))
hess = self.fc(hess * slater_dets) / sum_slater_dets
if self.use_jastrow is False:
return -0.5 * hess
# compute the Jastrow terms
jast, djast, d2jast = self.jastrow(x,
derivative=[0, 1, 2],
sum_grad=False)
# prepare the second derivative term d2Jast/Jast
# Nbatch x Nelec
d2jast = d2jast / jast
# prepare the first derivative term
djast = djast / jast.unsqueeze(-1)
# -> Nelec x Ndim x Nbatch
djast = djast.permute(2, 1, 0)
# -> [Nelec*Ndim] x Nbatch
djast = djast.reshape(-1, djast.shape[-1])
# prepare the grad of the dets
# [Nelec*Ndim] x Nbatch x 1
grad_val = self.fc(operator.add(*grad) *
slater_dets) / sum_slater_dets
# [Nelec*Ndim] x Nbatch
grad_val = grad_val.squeeze()
# assemble the derivaite terms
out = d2jast.sum(-1) + 2*(grad_val * djast).sum(0) + \
hess.squeeze(-1)
return -0.5 * out.unsqueeze(-1)
def pi_term(k):
return 8 / mul(k * 4 - 3, k * 4 - 1)
import operator as op
a = 5
b = 3
print('add : ',op.add(a,b))
print('sub : ',op.sub(a, b))
print('mul : ',op.mul(a, b))
print('true div : ',op.truediv(a,b)) # a/b
print('floor div : ',op.floordiv(a,b)) # a//b
print('pow : ',op.pow(a,b)) # a**b
print('MOD : ',op.mod(a,b))
#lt () : less than
# using lt() to check if a is less than b
if(op.lt(a,b)):
print (str(a)+" is less than "+str(b))
else :
print (str(a)+" is not less than "+str(b))
#le () : less than or equal
# using le() to check if a is less than or equal b
if(op.le(a,b)):
print (str(a)+" is less than or equal to "+str(b))
else :
print (str(a)+" is not less than or equal to "+str(b))
#eq () : equal
#using eq() to check if a is equal to b
if (op.eq(a,b)):
def square(x): # x is formal parameter
return mul(x, x) # returns x * x using built in func mul
def squared(squared):
return mul(squared, squared)
