def _calculate_score(self):
for result in self.results:
adverb_values = [float(x['value']) for x in result['terms'] if x['type'] == 'adverb']
substantive_values = [float(x['value']) for x in result['terms'] if x['type'] == 'substantive']
adjective_values = [float(x['value']) for x in result['terms'] if x['type'] == 'adjective']
score = util.mult(adverb_values) * util.mult(substantive_values) * util.mult(adjective_values)
result['score'] = score
def edgeFrame(self,edge):
p=util.mult(self.perpendicularTo(edge),self.frameWidth)
v0=edge.Vertexes[0].Point
v1=edge.Vertexes[1].Point
pts = [v0+p,v1+p,v1-p,v0-p]
face = util.faceFromVectors(pts)
return self.extrudeInCenter(face,self.frameThickness) \
def part2():
field_rules, your_ticket, nearby_tickets = parse_input(read_input())
valid_tickets = list(
filter(lambda t: ticket_is_valid(t, field_rules), nearby_tickets))
fields = {
field_name: Field(field_name)
for field_name in field_rules.keys()
}
positions = [Position(i) for i in range(len(fields))]
for position in positions:
possible_fieldnames_str = set.intersection(
*(find_possible_fields(t[position.index], field_rules)
for t in valid_tickets))
position.possible_fields = [
fields[field_name] for field_name in possible_fieldnames_str
]
for field_name in fields.values():
field_name.possible_positions = list(
filter(lambda p: field_name in p.possible_fields, positions))
location_per_fieldname = find_field_positions(positions,
list(fields.values()))
return mult(your_ticket[location_per_fieldname[f]]
for f in location_per_fieldname.keys()
if f.startswith('departure'))
def legHolder(self,toCut=False):
points = []
c1 = Base.Vector(0,0,0)
c2 = c1 + Base.Vector(self.legHolderLength,0,0)
c3 = c2 + Base.Vector(0,self.horseWidth,0)
c4 = c1 + Base.Vector(0,self.horseWidth,0)
attachMargin = (self.legHolderLength- self.attachWidth)/2
t11 = c1 + Base.Vector(attachMargin,0,0)
t12 = t11+ Base.Vector(self.attachWidth)
t21 = c4 + Base.Vector(attachMargin,0,0)
t22 = t21+ Base.Vector(self.attachWidth)
#block between legs
b0 = util.mult((c3 - c2),0.5,c2)
b11 = b0 + Base.Vector(0,-self.legSafeWidth)
b21 = b0 + Base.Vector(0,self.legSafeWidth)
b12 = b11 + Base.Vector(self.legSafeHeight/2,self.legSafeCurve1)
b22 = b21 + Base.Vector(self.legSafeHeight/2,-self.legSafeCurve1)
b13 = b11 + Base.Vector(self.legSafeHeight)
b23 = b21 + Base.Vector(self.legSafeHeight)
bt = b0 + Base.Vector(self.legSafeCurve2 + self.legSafeHeight)
spPoints = [b11,b12,b13,bt,b23,b22,b21]
points.append(b21)
points.append(c3)
points.append(c4)
points.append(c1)
points.append(c2)
points.append(b11)
p = util.partFromVectors(points,Base.Vector(0,0,self.thickness))
lines = util.linesFromPoints(points)
sp = Part.BSplineCurve()
sp.interpolate(spPoints,False)
lines.append(sp.toShape())
w = Part.Wire(lines)
f = Part.Face(w)
p = f.extrude(Base.Vector(0,0,self.thickness))
a1 = self.attachPart(t11,t12,Base.Vector(0,-1,0),toCut=toCut)
a2 = self.attachPart(t21,t22,Base.Vector(0,1,0),toCut=toCut)
part = p.fuse(a1).fuse(a2)
part.translate(Base.Vector(0,self.sideThickness,0))
return part
def generate_deformations(img, theta_step=1, deformations=30):
H, W = np.shape(img)[:2]
center = np.float32(H / 2.0), np.float32(W / 2.0)
rotation_matrices = [
cv2.getRotationMatrix2D((center[1], center[0]), theta, 1.0)
for theta in range(0, 361)
]
for theta in range(0, 360, theta_step):
Rt = rotation_matrices[theta]
N = deformations
r_phi = np.random.randint(0, 360, N)
r_lambda1 = np.random.uniform(0.6, 1.5, N)
r_lambda2 = np.random.uniform(0.6, 1.5, N)
r_noise_ratio = np.random.uniform(0, 0.1, N)
for noise_ratio, lambda1, lambda2, phi in zip(r_noise_ratio, r_lambda1,
r_lambda2, r_phi):
Rp = rotation_matrices[phi]
Rp1 = rotation_matrices[360 - phi]
Rl = np.matrix([[lambda1, 0, 0], [0, lambda2, 0]])
Rz = mult(Rp, mult(Rl, Rp1))
R = mult(Rt, Rz)
R_inv = cv2.invertAffineTransform(R)
warped = cv2.warpAffine(img,
R,
dsize=(H, W),
borderMode=cv2.BORDER_REFLECT101)
# add gaussian noise
noise = np.uint8(np.random.normal(0, 25, (W, H)))
blurred = warped # cv2.GaussianBlur(warped, (7, 7), 25)
noised = cv2.addWeighted(blurred, 1 - noise_ratio, noise,
noise_ratio, 0)
yield R_inv, noised
def _calculate_score(self):
for result in self.results:
adverb_values = [
float(x['value']) for x in result['terms']
if x['type'] == 'adverb'
]
substantive_values = [
float(x['value']) for x in result['terms']
if x['type'] == 'substantive'
]
adjective_values = [
float(x['value']) for x in result['terms']
if x['type'] == 'adjective'
]
score = util.mult(adverb_values) * util.mult(
substantive_values) * util.mult(adjective_values)
result['score'] = score
def setup(data, test_data=None, processes=1, maxtasks=10000):
Evaluator.close()
max_train_size = data.size()
max_test_size = test_data.size() if test_data is not None else (0, 0)
max_size = max_train_size if util.mult(max_train_size) > util.mult(
max_test_size) else max_test_size
Evaluator._inputs = mp.sharedctypes.RawArray(ctypes.c_double,
util.mult(max_size))
Evaluator._targets = mp.sharedctypes.RawArray(ctypes.c_double,
max_size[0])
Evaluator._test_inputs = mp.sharedctypes.RawArray(ctypes.c_double, util.mult(max_test_size)) \
if test_data is not None else None
Evaluator._test_targets = mp.sharedctypes.RawArray(ctypes.c_double, max_test_size[0]) \
if test_data is not None else None
Evaluator.multiprocessing = processes > 1
Evaluator._pool = concurrent.futures.ProcessPoolExecutor(max_workers=processes) \
if Evaluator.multiprocessing else None
Evaluator._maxtasks = maxtasks
Evaluator._tasks = 0
Evaluator._processes = processes
def attachPart(self,p1,p2,direction,size=attachSize,holeWidth=thickness, toCut=False):
p12 = util.mult(direction,size-self.attachCurveDepth,p1)
p22 = util.mult(direction,size-self.attachCurveDepth,p2)
c = util.mult((p2 - p1),0.5,p1) + util.mult(direction,size)
a = Part.Arc(p12,c,p22)
lines = util.linesFromPoints([p22,p2, p1,p12])
lines += [a.toShape()]
w = Part.Wire(lines)
p = Part.Face(w).extrude(Base.Vector(0,0,self.thickness))
distHoleStart = util.mult(direction,self.attachHoleMarginStart)
distHoleEnd = util.mult(direction,size-self.attachHoleMarginEnd)
pi1 = util.mult((p2 - p1),1.0/3,p1) + distHoleStart
pi2 = util.mult((p2 - p1),2.0/3,p1) + distHoleStart
pi3 = util.mult((p2 - p1),1.0/3,p1) + distHoleEnd
pi4 = util.mult((p2 - p1),2.0/3,p1) + distHoleEnd
hole = util.partFromVectors([pi1,pi3,pi4,pi2], Base.Vector(0,0,self.thickness))
if not toCut:
attach = p.cut(hole)
else:
attach = p
return attach
def _set_data(data, test_data):
size = data.size()
flat_len = util.mult(size)
assert flat_len <= len(Evaluator._inputs) and size[0] <= len(
Evaluator._targets)
try:
Evaluator._inputs[:flat_len] = data.inputs.ravel()[:flat_len]
Evaluator._targets[:size[0]] = data.targets[:size[0]]
except TypeError:
raise TypeError('Data must be numeric')
test_size = test_data.size() if test_data is not None else None
if test_size is not None:
test_flat_len = util.mult(test_size)
assert test_flat_len <= len(
Evaluator._test_inputs) and test_size[0] <= len(
Evaluator._test_targets)
Evaluator._test_inputs[:test_flat_len] = test_data.inputs.ravel(
)[:test_flat_len]
Evaluator._test_targets[:test_size[
0]] = test_data.targets[:test_size[0]]
return size, test_size
def find_sum_combo_with_z3(numbers, n, target=2020):
solver = z3.Solver()
symvars = [z3.Int(f'sv_{i}') for i in range(n)]
solver.add(z3.Sum(symvars) == target)
solver.add(z3.Distinct(symvars))
for sv in symvars:
solver.add(z3.Or([sv == number for number in numbers]))
if str(solver.check()) == 'sat':
print(f'Model = {solver.model()}')
return mult(solver.model()[sv].as_long() for sv in symvars)
else:
print('No solution')
return -1
def back(self,toCut=False):
points = []
c1 = Base.Vector(0,0,0)
c2 = c1 + Base.Vector(self.backLength,0,0)
c3 = c2 + Base.Vector(0,self.horseWidth,0)
c4 = c1 + Base.Vector(0,self.horseWidth,0)
#arc in the top
av1 = util.mult((c3 - c2),0.5,c2) + Base.Vector(self.backCurveDepth)
c3c = c3 + Base.Vector(0,self.sideThickness,0)
c2c = c2 + Base.Vector(0,-self.sideThickness,0)
points.append(c3c)
points.append(c3 + Base.Vector(-self.tenonLength,self.sideThickness,0))
points.append(c3 + Base.Vector(-self.tenonLength,0,0))
points.append(c4)
points.append(c1)
points.append(c2 + Base.Vector(-self.tenonLength,0,0))
points.append(c2 + Base.Vector(-self.tenonLength,-self.sideThickness,0))
points.append(c2c)
lines = util.linesFromPoints(points)
lines.append(Part.Arc(c2c,av1,c3c).toShape())
w = Part.Wire(lines)
f = Part.Face(w)
p = f.extrude(Base.Vector(0,0,self.thickness))
offset = Base.Vector((self.backLength - self.tenonLength - self.attachWidth)/2)
att1v1 = offset + c1
att1v2 = offset + c1+Base.Vector(self.attachWidth)
att2v1 = offset + c4
att2v2 = offset + c4+Base.Vector(self.attachWidth)
a1 = self.attachPart(att1v1,att1v2,Base.Vector(0,-1,0),toCut=toCut)
a2 = self.attachPart(att2v1,att2v2,Base.Vector(0,1,0),toCut=toCut)
part = p.fuse(a1)
part = part.fuse(a2)
part.translate(Base.Vector(0,self.sideThickness,0))
return part
def enumPossibleMoves(self):
self.moveLookUp={}
self.currentIndex=self.getCurrentIndex() #hack, to be removed
self.previousZ=self.z[self.currentIndex]
pz = self.crpSampler(self.alpha,self.m,self.currentIndex)
zi = copy.deepcopy(self.z)
lambd=[]
for j in range(len(pz)-1):
zi[self.currentIndex]=j
if self.kplus==self.numberVariables:
numberClasses=len(pz)
else:
numberClasses=len(pz)-1
nplus,nminus = computeEdge(self.childDensity.dag,zi,numberClasses)
pGz = priorG(self.beta,nplus,nminus)
cePrior = self.classEdgePrior(self.beta,self.kplus,self.classOrder)
ep = bernoulli(self.childDensity.dag,cePrior,zi)
lambd.append(pGz*ep)
if self.kplus<self.numberVariables:
for k in range(len(self.classOrder)+1):
newClassOrder=copy.deepcopy(self.classOrder)
zi[self.currentIndex]=self.kplus
newClassOrder.insert(k,self.kplus)
nplus,nminus = computeEdge(self.childDensity.dag,zi,len(pz))
pGz2 = priorG(self.beta,nplus,nminus)
ceprior2 = self.classEdgePrior(self.beta,self.kplus+1,newClassOrder)
ep2 = bernoulli(self.childDensity.dag,ceprior2,zi)
lambd.append(pGz2*ep2)
if k>0:
pz.append(pz[j+1])
probVector =mult(pz, lambd)
out=[]
for i in range(len(probVector)):
out.append(i)
self.moveLookUp[i]=probVector[i]
return out
def part2():
return mult(
get_n_combinations(partition)
for partition in partition_input(read_all_voltages_sorted()))
def find_sum_combo(numbers, n, target=2020):
for perm in itertools.combinations(numbers, n):
if sum(perm) == target:
return mult(perm)
def normalO(self,size=1):
params=self.face.Surface.parameter(self.refPoint())
normal=self.face.normalAt(params[0],params[1])
return util.mult(normal,size)
def part2():
return mult(
find_n_tree_in_path(read_input_as_lines(), x_d, y_d)
for x_d, y_d in ((1, 1), (3, 1), (5, 1), (7, 1), (1, 2)))
def part1():
tile_data = parse_input_as_tiles(read_input())
match_borders(tile_data)
return mult(t.tile_id for t in find_corner_tiles(tile_data))
def tenonPoints(self,p1,p2,direction,depth=sideThickness):
translationVector = util.mult(direction,depth)
p11 = p1 + translationVector
p21 = p2 + translationVector
return [p1,p11,p21,p2]
