def reward(self, sVals, action, bounds, goal, trans):
di = hcl.scalar(0, "di")
dj = hcl.scalar(0, "dj")
dk = hcl.scalar(0, "dk")
dl = hcl.scalar(0, "dl")
dm = hcl.scalar(0, "dm")
mag = hcl.scalar(0, "mag")
rwd = hcl.scalar(0, "rwd")
# Check if moving from a collision state, if so, assign a penalty
with hcl.if_(hcl.or_(sVals[0] <= bounds[0,0], sVals[0] >= bounds[0,1])):
rwd[0] = -400
with hcl.elif_(hcl.or_(sVals[1] <= bounds[1,0], sVals[1] >= bounds[1,1])):
rwd[0] = -400
with hcl.elif_(hcl.or_(sVals[2] <= bounds[2,0], sVals[2] >= bounds[2,1])):
rwd[0] = -400
with hcl.elif_(hcl.or_(sVals[3] <= bounds[3,0], sVals[3] >= bounds[3,1])):
rwd[0] = -400
with hcl.elif_(hcl.or_(sVals[4] <= bounds[4,0], sVals[4] >= bounds[4,1])):
rwd[0] = -400
with hcl.else_():
# Check if moving from a goal state
di[0] = (sVals[0] - goal[0]) * (sVals[0] - goal[0])
dj[0] = (sVals[1] - goal[1]) * (sVals[1] - goal[1])
dk[0] = (sVals[2] - goal[2]) * (sVals[2] - goal[2])
dl[0] = (sVals[3] - goal[3]) * (sVals[3] - goal[3])
dm[0] = (sVals[4] - goal[4]) * (sVals[4] - goal[4])
mag[0] = hcl.sqrt(di[0] + dj[0] + dk[0] + dl[0] + dm[0])
with hcl.if_(mag[0] <= 5.0):
rwd[0] = 1000
# Standard move
with hcl.else_():
rwd[0] = 0
return rwd[0]
def reward(sVals, action, bounds, goal, trans):
dx = hcl.scalar(0, "dx")
dy = hcl.scalar(0, "dy")
mag = hcl.scalar(0, "mag")
rwd = hcl.scalar(0, "rwd")
# Check if moving from a collision state, if so, assign a penalty
with hcl.if_(
hcl.or_(sVals[0] < bounds[0, 0] + 0.2,
sVals[0] > bounds[0, 1] - 0.2)):
rwd[0] = -400
with hcl.elif_(
hcl.or_(sVals[1] < bounds[1, 0] + 0.2,
sVals[1] > bounds[1, 1] - 0.2)):
rwd[0] = -400
with hcl.else_():
# Check if moving from a goal state
dx[0] = sVals[0] - goal[0, 0]
dy[0] = sVals[1] - goal[0, 1]
mag[0] = hcl.sqrt((dx[0] * dx[0]) + (dy[0] * dy[0]))
with hcl.if_(
hcl.and_(mag[0] <= 1.0, sVals[2] <= goal[1, 1],
sVals[2] >= goal[1, 0])):
rwd[0] = 1000
# Standard move
with hcl.else_():
rwd[0] = 0
return rwd[0]
def update(l, prototype, prototypeCounter, max):
hcl.print((l + 1),
"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((hdTrainData.shape[1], ), lambda x: 0,
'distance')
hamming_dist = hcl.compute((numClasses, ), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, hdTrainData.shape[1], "m")
###
with hcl.for_(0, hdTrainData.shape[0]) as i:
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance,
lambda x: hdTrainData[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hamming_dist[n] = hcl.sum(distance[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, hdTrainData.shape[1]) as m:
prototypeCounter[trainLabels[i]][m] += hdTrainData[i][m]
prototypeCounter[pred][m] -= hdTrainData[i][m]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 == 0):
prototype[trainLabels[i]][m] &= 1
with hcl.else_():
prototype[trainLabels[i]][m] = hcl.select(
prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m] -
max[pred] / 2 == 0):
prototype[pred][m] &= 1
with hcl.else_():
prototype[pred][m] = hcl.select(
prototypeCounter[pred][m] - max[pred] / 2 > 0, 1,
0)
#print the accuracy
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTrainData, trainLabels, 1),
'training_update')
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTestData, testLabels, 2),
'testing_update')
def update(l, prototype, prototypeCounter, max):
hcl.print((l+1),"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((in_train.shape[1],), lambda x: 0, 'distance', dtype=hcl.UInt(in_bw))
pre_dist = hcl.compute((in_train.shape[1],), lambda x: 0, "pre_dist")
hamming_dist = hcl.compute((numClasses,), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, in_train.shape[1], "m")
###
with hcl.for_(0, in_train.shape[0]) as i:
hcl.print((i),"%d suc\n")
# pack_proto = hcl.pack(prototype, axis=1, dtype=hcl.UInt(in_bw), name="pack_proto")
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance, lambda x: in_train[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hcl.update(pre_dist, lambda x: popcount(distance[x]))
hcl.print((),"sum of 1s suc")
hamming_dist[n] = hcl.sum(pre_dist[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, in_train.shape[1]) as m:
with hcl.for_(0, in_bw) as bit:
# with hcl.if_(in_train[i][m][bit] == 1):
# ###########
# prototypeCounter[trainLabels[i]][m*in_bw+bit] += 1
# prototypeCounter[pred][m*in_bw+bit] -= 1
prototypeCounter[trainLabels[i]][m*in_bw+bit] += in_train[i][m][bit]
prototypeCounter[pred][m*in_bw+bit] -= in_train[i][m][bit]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 == 0):
prototype[trainLabels[i]][m][bit] &= 1
with hcl.else_():
prototype[trainLabels[i]][m][bit] = hcl.select(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 == 0):
prototype[pred][m][bit] &= 1
with hcl.else_():
prototype[pred][m][bit] = hcl.select(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 > 0, 1, 0)
#print the accuracy
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_train, trainLabels, 1), 'training_update')
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_test, testLabels, 2), 'testing_update')
def align(curr_i, curr_j, next_i, next_j):
outA = hcl.local(0, "a")
outB = hcl.local(0, "b")
with hcl.if_(next_i[0] == curr_i[0]):
outA[0] = 0
with hcl.else_():
outA[0] = seqA[curr_i[0] - 1]
with hcl.if_(next_j[0] == curr_j[0]):
outB[0] = 0
with hcl.else_():
outB[0] = seqB[curr_j[0] - 1]
return outA[0], outB[0]
def kernel(trainData, testData, itemMem, idMem, rdv1, rdv2):
def train_encoding(m, preTrainData):
train_temp = hcl.compute((trainData.shape[1], dim), lambda x, y: itemMem[trainData[m][x]][y] ^ idMem[x][y], name = "train_temp")
k1 = hcl.reduce_axis(0, trainData.shape[1], 'k1')
train_result = hcl.compute((dim,), lambda x: hcl.sum(train_temp[k1, x], axis = k1, dtype=hcl.Int()), name = "train_result")
with hcl.for_(0, dim) as n:
preTrainData[m][n] = train_result[n]
with hcl.if_((m + 1) % 1000 == 0):
hcl.print((m+1), "Finish encoding %d training data\n")
def test_encoding(m, preTestData):
test_temp = hcl.compute((testData.shape[1], dim), lambda x, y: itemMem[testData[m][x]][y]^idMem[x][y], name = "test_temp")
k2 = hcl.reduce_axis(0, testData.shape[1], 'k2')
test_result = hcl.compute((dim,), lambda x: hcl.sum(test_temp[k2, x], axis = k2, dtype=hcl.Int()), name = "test_result")
with hcl.for_(0, dim) as n:
preTestData[m][n] = test_result[n]
with hcl.if_((m+1)%100 == 0):
hcl.print((m+1), "Finish encoding %d testing data\n")
#Encoding
hcl.print((), "Encoding the training data into HDVs.\n")
preTrainData = hcl.compute((trainData.shape[0], dim), lambda x, y: 0, "preTrainData")
hcl.mutate((trainData.shape[0], ), lambda x: train_encoding(x, preTrainData))
hdTrainData = hcl.compute((trainData.shape[0], dim), lambda x, y: 0, "hdTrainData", dtype=hcl.UInt(1))
with hcl.Stage("S1"):
with hcl.if_(trainData.shape[1] % 2 == 0):
hcl.print((), "Use the random vector\n")
hcl.update(hdTrainData, lambda x, y: hcl.select(preTrainData[x][y] + rdv1[x][y] - trainData.shape[1]/2 > 0, 1, 0))
with hcl.else_():
hcl.update(hdTrainData, lambda x, y: hcl.select(preTrainData[x][y] - trainData.shape[1]/2 > 0, 1, 0))
hcl.print((),"Encoding the testing data into HDVs.\n")
preTestData = hcl.compute((testData.shape[0], dim), lambda x, y: 0, "preTestData")
hcl.mutate((testData.shape[0], ), lambda x: test_encoding(x, preTestData))
hdTestData = hcl.compute((testData.shape[0], dim), lambda x, y: 0, "hdTestData", dtype=hcl.UInt(1))
with hcl.Stage("S2"):
with hcl.if_(testData.shape[1] % 2 == 0):
hcl.print((), "Use the random vector\n")
hcl.update(hdTestData, lambda x, y: hcl.select(preTestData[x][y] + rdv2[x][y] - testData.shape[1]/2 > 0, 1, 0))
with hcl.else_():
hcl.update(hdTestData, lambda x, y: hcl.select(preTestData[x][y] - testData.shape[1]/2 > 0, 1, 0))
###data_packing
pack_train = hcl.pack(hdTrainData, axis=1, dtype=hcl.UInt(bw), name="pack_train")
pack_test = hcl.pack(hdTestData, axis=1, dtype=hcl.UInt(bw), name="pack_test")
return pack_train, pack_test
def optDstb(self, spat_deriv):
"""
:param spat_deriv: tuple of spatial derivative in all dimensions
:return: a tuple of optimal disturbances
"""
# Graph takes in 4 possible inputs, by default, for now
d1 = hcl.scalar(0, "d1")
d2 = hcl.scalar(0, "d2")
# Just create and pass back, even though they're not used
d3 = hcl.scalar(0, "d3")
d4 = hcl.scalar(0, "d4")
with hcl.if_(self.dMode == "max"):
with hcl.if_(spat_deriv[0] > 0):
d1[0] = self.dMax[0]
with hcl.elif_(spat_deriv[0] < 0):
d1[0] = self.dMin[0]
with hcl.if_(spat_deriv[1] > 0):
d2[0] = self.dMax[1]
with hcl.elif_(spat_deriv[1] < 0):
d2[0] = self.dMin[1]
with hcl.else_():
with hcl.if_(spat_deriv[0] > 0):
d1[0] = self.dMin[0]
with hcl.elif_(spat_deriv[0] < 0):
d1[0] = self.dMax[0]
with hcl.if_(spat_deriv[1] > 0):
d2[0] = self.dMin[1]
with hcl.elif_(spat_deriv[1] < 0):
d2[0] = self.dMax[1]
return (d1[0], d2[0], d3[0], d4[0])
def absolute(A, B):
with hcl.for_(0, A.shape[0], name="x") as x:
with hcl.for_(0, A.shape[1], name="y") as y:
with hcl.if_(A[x, y] >= 0):
B[x, y] = A[x, y]
with hcl.else_():
B[x, y] = -A[x, y]
def loop_body(x, y):
q = 255
r = 255
c1 = hcl.and_((0 <= angle[x][y]), (angle[x][y] < 22.5))
c2 = hcl.and_((157.5 <= angle[x][y]), (angle[x][y] <= 180))
c3 = hcl.and_((22.5 <= angle[x][y]), (angle[x][y] < 67.5))
c4 = hcl.and_((67.5 <= angle[x][y]), (angle[x][y] < 112.5))
c5 = hcl.and_((112.5 <= angle[x][y]), (angle[x][y] < 157.5))
#angle 0
with hcl.if_(hcl.or_(c1, c2)):
q = image[x][y + 1]
r = image[x][y - 1]
#angle 45
with hcl.elif_(c3):
q = image[x + 1][y - 1]
r = image[x - 1][y + 1]
#angle 90
with hcl.elif_(c4):
q = image[x + 1][y]
r = image[x - 1, ][y]
#angle 135
with hcl.elif_(c5):
q = image[x - 1, y - 1]
r = image[x + 1, y + 1]
with hcl.if_(hcl.and_((image[x, y] >= q), (image[x, y] >= r))):
Z[x][y] = image[x][y]
with hcl.else_():
Z[x][y] = 0
def freduce(x, Y):
with hcl.if_(x < Y[0]):
Y[1] = Y[0]
Y[0] = x
with hcl.else_():
with hcl.if_(x < Y[1]):
Y[1] = x
def pad(x, y, z):
out = hcl.scalar(0, "out")
with hcl.if_(hcl.and_(x > 0, y > 0)):
out.v = imgF[x - 1, y - 1, z]
with hcl.else_():
out.v = 0
return out.v
def my_min(a, b):
result = hcl.scalar(0, "result")
with hcl.if_(a < b):
result[0] = a
with hcl.else_():
result[0] = b
return result[0]
def loop_body(x):
with hcl.if_(A[x] > M[0]):
with hcl.if_(A[x] > M[1]):
M[0] = M[1]
M[1] = A[x]
with hcl.else_():
M[0] = A[x]
def my_max(a, b):
result = hcl.scalar(0, "result")
with hcl.if_(a > b):
result.v = a
with hcl.else_():
result.v = b
return result.v
def transition(sVals, action, bounds, trans, goal):
dx = hcl.scalar(0, "dx")
dy = hcl.scalar(0, "dy")
mag = hcl.scalar(0, "mag")
# Check if moving from a goal state
dx[0] = sVals[0] - goal[0, 0]
dy[0] = sVals[1] - goal[0, 1]
mag[0] = hcl.sqrt((dx[0] * dx[0]) + (dy[0] * dy[0]))
with hcl.if_(
hcl.and_(mag[0] <= 1.0, sVals[2] <= goal[1, 1],
sVals[2] >= goal[1, 0])):
trans[0, 0] = 0
# Check if moving from an obstacle
with hcl.elif_(
hcl.or_(sVals[0] < bounds[0, 0] + 0.2,
sVals[0] > bounds[0, 1] - 0.2)):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[1] < bounds[1, 0] + 0.2,
sVals[1] > bounds[1, 1] - 0.2)):
trans[0, 0] = 0
# Standard move
with hcl.else_():
trans[0, 0] = 1.0
trans[0, 1] = sVals[0] + (0.6 * action[0] * hcl.cos(sVals[2]))
trans[0, 2] = sVals[1] + (0.6 * action[0] * hcl.sin(sVals[2]))
trans[0, 3] = sVals[2] + (0.6 * action[1])
# Adjust for periodic dimension
with hcl.while_(trans[0, 3] > 3.141592653589793):
trans[0, 3] -= 6.283185307179586
with hcl.while_(trans[0, 3] < -3.141592653589793):
trans[0, 3] += 6.283185307179586
def learn(k, hdTrainData, prototype, prototypeCounter):
#Find samples that have the label k
match = hcl.compute(
hdTrainData.shape,
lambda x, y: hcl.select(trainLabels[x] == k, hdTrainData[x][y], 0),
"match")
#Record the number of these samples
with hcl.for_(0, hdTrainData.shape[0]) as a:
with hcl.if_(trainLabels[a] == k):
max[k] += 1
#Do hdc sum on these samples' hdv
r = hcl.reduce_axis(0, hdTrainData.shape[0], 'r')
result = hcl.compute((hdTrainData.shape[1], ),
lambda y: hcl.sum(match[r][y], axis=r), "result")
#Do the binary voting
sum1 = hcl.compute((hdTrainData.shape[1], ), lambda x: 0, "sum1")
with hcl.if_(max[k] % 2 == 0):
hcl.update(
sum1, lambda x: hcl.select(
result[x] + rdv3[k][x] - max[k] / 2 > 0, 1, 0))
with hcl.else_():
hcl.update(sum1,
lambda x: hcl.select(result[x] - max[k] / 2 > 0, 1, 0))
#Push the binary sum to prototype and the original sum to prototypeCounter
with hcl.for_(0, hdTrainData.shape[1]) as t:
prototype[k][t] = sum1[t]
prototypeCounter[k][t] = result[t]
def new_abs(my_x):
my_value = hcl.scalar(0, "my_value", dtype=hcl.Float())
with hcl.if_(my_x > 0):
my_value.v = my_x
with hcl.else_():
my_value.v = -my_x
return my_value.v
def kernel(A):
with hcl.if_(A[0] > 5):
A[0] = 5
with hcl.elif_(A[0] > 3):
A[0] = 3
with hcl.else_():
A[0] = 0
def my_abs(my_x):
abs_value = hcl.scalar(0, "abs_value", dtype=hcl.Float())
with hcl.if_(my_x > 0):
abs_value.v = my_x
with hcl.else_():
abs_value.v = -my_x
return abs_value.v
def loop_body(x):
with hcl.if_(A[x]> M[0]):
with hcl.if_(A[x]> M[1]):
hcl.assert_(x == 2, "assert error in if--value of x: %d", x)
M[0] = M[1]
M[1] = A[x]
with hcl.else_():
M[0] = A[x]
def Sigmoid(exponent):
ret = hcl.scalar(0.0, "sigmoid", FTYPE)
with hcl.if_(exponent > hcl.cast(FTYPE, 4.0)):
ret[0] = 1.0
with hcl.elif_(exponent < hcl.cast(FTYPE, -4.0)):
ret[0] = 0.0
with hcl.else_():
with hcl.if_(exponent < hcl.cast(FTYPE, 0.0)):
num = hcl.scalar(0, dtype=hcl.UFixed(18, 8))
num[0][18:0] = exponent[29:11]
num[0] = ~(num[0] << 8) + 1
index = 2047.0 - num[0]
ret[0] = lut[hcl.cast(hcl.Int(32), index)]
with hcl.else_():
index = exponent[21:11]
ret[0] = lut[hcl.cast(hcl.Int(32), index)]
return ret[0]
def pe(a, b, x):
with hcl.if_(x == 0):
result = a * b
hcl.return_(a)
with hcl.elif_(x == 1):
hcl.return_(b)
with hcl.else_():
hcl.return_(result)
def use_lut(lut, in_, val):
temp_in = hcl.scalar(in_, dtype=hcl.Int())
with hcl.if_(in_ < 0):
index = LUT_SIZE - (temp_in) << (LUTIN_TWIDTH - LUTIN_IWIDTH)
with hcl.else_():
index = (temp_in) << (LUTIN_TWIDTH - LUTIN_IWIDTH)
val[0] = lut[index]
def Sigmoid(lut, exponent, prob):
with hcl.if_(exponent[0] > 4):
prob[0] = 1.0
with hcl.elif_(exponent[0] < -4):
prob[0] = 0.0
with hcl.else_():
use_lut(lut, exponent[0], prob)
def update_B(A, x):
with hcl.if_(A[x] > 5):
with hcl.if_(A[x] > 7):
hcl.return_(-2)
hcl.return_(-1)
with hcl.else_():
with hcl.if_(A[x] > 3):
hcl.return_(-3)
hcl.return_(A[x] + 1)
def transition(self, sVals, action, bounds, trans, goal):
di = hcl.scalar(0, "di")
dj = hcl.scalar(0, "dj")
dk = hcl.scalar(0, "dk")
dl = hcl.scalar(0, "dl")
dm = hcl.scalar(0, "dm")
dn = hcl.scalar(0, "dn")
do = hcl.scalar(0, "do")
mag = hcl.scalar(0, "mag")
# Check if moving from a goal state
di[0] = (sVals[0] - goal[0]) * (sVals[0] - goal[0])
dj[0] = (sVals[1] - goal[1]) * (sVals[1] - goal[1])
dk[0] = (sVals[2] - goal[2]) * (sVals[2] - goal[2])
dl[0] = (sVals[3] - goal[3]) * (sVals[3] - goal[3])
dm[0] = (sVals[4] - goal[4]) * (sVals[4] - goal[4])
dn[0] = (sVals[5] - goal[5]) * (sVals[5] - goal[5])
do[0] = (sVals[6] - goal[6]) * (sVals[6] - goal[6])
mag[0] = hcl.sqrt(di[0] + dj[0] + dk[0] + dl[0] + dm[0] + dn[0] +
do[0])
# Check if moving from an obstacle
with hcl.if_(mag[0] <= 5.0):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[0] <= bounds[0, 0], sVals[0] >= bounds[0, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[1] <= bounds[1, 0], sVals[1] >= bounds[1, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[2] <= bounds[2, 0], sVals[2] >= bounds[2, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[3] <= bounds[3, 0], sVals[3] >= bounds[3, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[4] <= bounds[4, 0], sVals[4] >= bounds[4, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[5] <= bounds[5, 0], sVals[5] >= bounds[5, 1])):
trans[0, 0] = 0
with hcl.elif_(
hcl.or_(sVals[6] <= bounds[6, 0], sVals[6] >= bounds[6, 1])):
trans[0, 0] = 0
# Standard move
with hcl.else_():
trans[0, 0] = 1.0
trans[0, 1] = sVals[0] + action[0]
trans[0, 2] = sVals[1] + action[1]
trans[0, 3] = sVals[2] + action[2]
trans[0, 4] = sVals[3] + action[3]
trans[0, 5] = sVals[4] + action[4]
trans[0, 6] = sVals[5] + action[5]
trans[0, 7] = sVals[6] + action[6]
def update_B(A, x):
with hcl.if_(A[x] > 5):
hcl.print(0, "print if 1\n")
hcl.assert_(A[x] <= 5, "assert in if")
hcl.print(0, "print if 2\n")
hcl.return_(-1)
with hcl.else_():
hcl.print(0, "print else 1\n")
hcl.assert_(A[x] <= 5, "assert in else")
hcl.print(0, "print else 2\n")
hcl.return_(A[x] + 1)
def step_loop(step):
with hcl.if_(theta[0] > current[0]):
T[0] = X[0] - (Y[0] >> step)
Y[0] = Y[0] + (X[0] >> step)
X[0] = T[0]
current[0] = current[0] + C[step]
with hcl.else_():
T[0] = X[0] + (Y[0] >> step)
Y[0] = Y[0] - (X[0] >> step)
X[0] = T[0]
current[0] = current[0] - C[step]
def sort_knn(knn_mat, i, j):
val = hcl.local(0, "val")
with hcl.if_( j == 1 ):
with hcl.if_( knn_mat[i][1] > knn_mat[i][2] ):
val.v = knn_mat[i][1]
knn_mat[i][1] = knn_mat[i][2]
knn_mat[i][2] = val.v
with hcl.else_():
with hcl.if_( knn_mat[i][0] > knn_mat[i][1] ):
val.v = knn_mat[i][0]
knn_mat[i][0] = knn_mat[i][1]
knn_mat[i][1] = val.v
def two_stage(A):
var = hcl.scalar(0, "v", dtype=hcl.UInt(32))
var.v = 1
with hcl.if_(var == 0):
hcl.print((), "A\n")
with hcl.else_():
var.v = var - 1
# this condition should not be optimized away
with hcl.if_(var == 0):
hcl.print((), "B\n")
A[0] = var
return A
