Now i use redispipeline to set key to redis ,and set key timeout.After running codes and timeout,and the keys are still in redis?It seemed doesn't work.Is there sth wrong with my code.
X[1] is dict,such as dict["a"]=b dict["c"]=d
redis_pool = redis.ConnectionPool(host = redis_ip, port = redis_port, decode_responses = False)
redis_connection = redis.StrictRedis(connection_pool = redis_pool)
redis_pipeline = redis_connection.pipeline(transaction = False)
sync_count = 0
for x in iterator:
key = suffix + x[0]
#value = str(x['tagid'])+"\t"+x['tag_name']
value = x[1]
redis_pipeline.hmset(key, value)
redis_pipeline.expire(key,60)
sync_count += 1
if (sync_count % 100 == 0):
result = redis_pipeline.execute()
print (result)
time.sleep(0.001)
break
if sync_count == 10000:
break
redis_pipeline.execute()
Related
Please help me, i cannot understand why X_synthetic_df returns hundreds of rows with 0 values. All of the rows have normal values fine until row 1745. From that row, all the other row values contain nothing but zeros
def nearest_neighbors(nominal_columns, numeric_columns, df, row, k):
def distance(row1, row2):
distance = 0
for col in nominal_columns:
if row1[col] != row2[col]:
distance += 1
for col in numeric_columns:
distance += (row1[col] - row2[col])**2
return distance**0.5
distances = []
for i in range(len(df)):
r = df.iloc[i]
if r.equals(row):
continue
d = distance(row, r)
if(d!=0):
distances.append((d, i))
distances.sort()
nearest = [i for d, i in distances[:k]]
return nearest
def smotenc(X, y, nominal_cols, numeric_cols, k=5, seed=None):
minority_class = y[y==1]
majority_class = y[y==0]
minority_samples = X[y == 1]
minority_target = y[y == 1]
n_synthetic_samples = len(majority_class)-len(minority_class)
synthetic_samples = np.zeros((n_synthetic_samples, X.shape[1]))
if seed is not None:
np.random.seed(seed)
for i in range(len(minority_samples)):
nn = nearest_neighbors(nominal_cols, numeric_cols, minority_samples, minority_samples.iloc[i], k=k)
for j in range(min(k, n_synthetic_samples - i*k)):
nn_idx = int(np.random.choice(a=nn))
diff = minority_samples.iloc[(nn_idx)] - minority_samples.iloc[i]
print(diff)
if (diff == 0).all():
continue
synthetic_sample = minority_samples.iloc[i] + np.random.rand() * diff
synthetic_samples[(i*k)+j, :] = synthetic_sample
X_resampled = pd.concat([X[y == 1], pd.DataFrame(synthetic_samples,columns=X.columns)], axis=0)
y_resampled = np.concatenate((y[y == 1], [1] * n_synthetic_samples))
return X_resampled, y_resampled
minority_features = df_nominal.columns.get_indexer(df_nominal.columns)
synthetic = smotenc(check_x.head(3000),check_y.head(3000),nominal_cols,numeric_cols,seed = None)
X_synthetic_df = synthetic[0]
X_synthetic_df = pd.DataFrame(X_synthetic_df,columns = X.columns)
I was a dataframe with n synthetic samples, where n is the difference between the majority samples and minority class samples
local binser = require "binser"
local log, floor, ceil, min, random = math.log, math.floor, math.ceil, math.min, math.random
local makeNode = function(value,size)
return {
value=value,
next={},
width={},
size=size
}
end
local End ={}
local NIL = makeNode(End,0)
local insert = function(self,value)
local node, chain, stepsAtLevel = self.head, {}, {}
for i=1, self.maxLevel do stepsAtLevel[i]=0 end
for level = self.maxLevel, 1, -1 do
while node.next[level] ~= NIL and node.next[level].value <= value do
stepsAtLevel[level] = ( stepsAtLevel[level] or 0 ) + node.width[level]
node = node.next[level]
--print(level, stepsAtLevel[level],value)
end
chain[level]=node
end
local nodeLevel = min( self.maxLevel, - floor(log(random()) / log(2) ) )
local newNode = makeNode( value, nodeLevel)
local steps, prevNode = 0
for level= 1, nodeLevel do
prevNode = chain[level]
newNode.next[level] = prevNode.next[level]
prevNode.next[level] = newNode
newNode.width[level] = prevNode.width[level] - steps
prevNode.width[level] = steps + 1
steps = steps + stepsAtLevel[level]
end
for level = nodeLevel + 1, self.maxLevel do
chain[level].width[level] = chain[level].width[level] +1
end
self.size = self.size + 1
end
local first = function(self)
return self.head.next[1].value
end
local tostring = function (self)
local t = {}
for k,v in self:ipairs() do table.insert(t,v) end
return "( "..table.concat(t,", ").. " )"
end
local islMT = {
__index = function(self,i)
if type(i) ~= "number" then return end
if i > self.size then return end
local node = self.head
for level=self.maxLevel, 1, -1 do
while node.width[level] <= i do
i = i - node.width[level]
node = node.next[level]
end
end
return node.value
end,
__tostring=tostring
}
local ipairs = function (self)
local node, size = self.head.next[1] , self.size
count = 0
return function()
value=node.value
node = node.next[1]
count = count+1
return count <= size and count or nil, value
end
end
math.randomseed(os.time())
local size = expected_size or 16
if not expected_size then
expected_size = 16
end
local maxLevel = floor( log(expected_size) / log(2) )
local head = makeNode("HEAD",maxLevel)
for i=1,maxLevel do
head.next[i] = NIL
head.width[i] = 1
end
local insdel = setmetatable( {
size = 0,
head = head,
maxLevel = maxLevel,
insert = insert,
tostring = tostring,
ipairs=ipairs,
}, islMT
)
insdel:insert('foo')
print(insdel)
-- how to serialize metatable insdel? this fail
local ser = binser.serialize(insdel)
insdel = binser.deserialize(ser)
insdel:insert('bar')
print(insdel)
I have tried countless serialization module (serpent, json, pluto) and none works with metatable, does someone know how to serialize metatables? Which module to use to serialize/deserialize metatable and how?
The full code is included, the binser module is from luarocks :
https://luarocks.org/modules/bakpakin/binser
I have found many times a solution for my problems from here, but this time I am totally baffled. I don't know what's wrong at my code.
I made a code to create a box with charged particles inside with Vpython. As I launch the program, I get only a grey screen and the program crash. No error message, nothing.
from visual import *
from random import *
def electronizer(num):
list = []
electron_charge = -1.60217662e-19
electron_mass = 9.10938356e-31
for i in range(num):
another_list = []
e = sphere(pos=(random(), random(),random()), radius=2.818e-15,
color=color.cyan)
e.v = vector(random(), random(), random())
another_list.append(e)
another_list.append(e.v)
another_list.append(electron_charge)
another_list.append(electron_mass)
list.append(another_list)
return list
def protonizer(num):
list = []
proton_charge = 1.60217662e-19
proton_mass = 1.6726219e-27
for i in range(num):
another_list = []
p = sphere(pos=(random(), random(),random()), radius=0.8408739e-15, color=color.red)
p.v = vector(random(), random(), random())
another_list.append(p)
another_list.append(p.v)
another_list.append(proton_charge)
another_list.append(proton_mass)
list.append(another_list)
return list
def cross(a, b):
c = vector(a[1]*b[2] - a[2]*b[1],
a[2]*b[0] - a[0]*b[2],
a[0]*b[1] - a[1]*b[0])
return c
def positioner(work_list):
k = 8.9875517873681764e3 #Nm2/C2
G = 6.674e-11 # Nm2/kg2
vac_perm = 1.2566370614e-6 # H/m
pi = 3.14159265
dt = 0.1e-3
constant = 1
force = vector(0,0,0)
for i in range(len(work_list)):
for j in range(len(work_list)):
if i != j:
r = work_list[i][0].pos - work_list[j][0].pos
r_mag = mag(r)
r_norm = norm(r)
F = k * ((work_list[i][2] * work_list[j][2]) / (r_mag**2)) * r_norm
force += F
B = constant*(vac_perm / 4*pi) * (cross(work_list[j][2] * work_list[j][1], norm(r)))/r_mag**2
F = cross(work_list[i][2] * work_list[i][1], B)
force += F
F = -(G * work_list[i][3] * work_list[j][3]) / r_mag**2 * r_norm
force += F
acceleration = force / work_list[i][3]
difference_in_velocity = acceleration * dt
work_list[i][1] += difference_in_velocity
difference_in_position = work_list[i][1] * dt
work_list[i][0].pos += difference_in_position
if abs(work_list[i][0].pos[0]) > 2.5e-6:
work_list[i][1][0] = -work_list[i][1][0]
elif abs(work_list[i][0][1]) > 2.5e-6:
work_list[i][1][1] = -work_list[i][1][1]
elif abs(work_list[i][0][2]) > 2.5e-6:
work_list[i][1][2] = -work_list[i][1][2]
return work_list
box = box(pos=(0, 0, 0), length = 5e-6, width = 5e-6, height = 5e-6, opacity = 0.5)
protons_num = raw_input("number of protons: ")
electrons_num = raw_input("number of electrons: ")
list_of_electrons = electronizer(int(electrons_num))
list_of_protons = protonizer(int(protons_num))
work_list = list_of_electrons + list_of_protons
while True:
work_list = positioner(work_list)
You should ask your question on the VPython.org forum where the VPython experts hang out and will be able to answer your question. You should mention which operating system you are using and which version of python you are using. From your code I see that you are using classic VPython. There is a newer version of VPython 7 that just came out but the VPython syntax has changed.
Suppose x,y,z are int variables and A is a matrix, I want to express a constraint like:
z == A[x][y]
However this leads to an error:
TypeError: object cannot be interpreted as an index
What would be the correct way to do this?
=======================
A specific example:
I want to select 2 items with the best combination score,
where the score is given by the value of each item and a bonus on the selection pair.
For example,
for 3 items: a, b, c with related value [1,2,1], and the bonus on pairs (a,b) = 2, (a,c)=5, (b,c) = 3, the best selection is (a,c), because it has the highest score: 1 + 1 + 5 = 7.
My question is how to represent the constraint of selection bonus.
Suppose CHOICE[0] and CHOICE[1] are the selection variables and B is the bonus variable.
The ideal constraint should be:
B = bonus[CHOICE[0]][CHOICE[1]]
but it results in TypeError: object cannot be interpreted as an index
I know another way is to use a nested for to instantiate first the CHOICE, then represent B, but this is really inefficient for large quantity of data.
Could any expert suggest me a better solution please?
If someone wants to play a toy example, here's the code:
from z3 import *
items = [0,1,2]
value = [1,2,1]
bonus = [[1,2,5],
[2,1,3],
[5,3,1]]
choices = [0,1]
# selection score
SCORE = [ Int('SCORE_%s' % i) for i in choices ]
# bonus
B = Int('B')
# final score
metric = Int('metric')
# selection variable
CHOICE = [ Int('CHOICE_%s' % i) for i in choices ]
# variable domain
domain_choice = [ And(0 <= CHOICE[i], CHOICE[i] < len(items)) for i in choices ]
# selection implication
constraint_sel = []
for c in choices:
for i in items:
constraint_sel += [Implies(CHOICE[c] == i, SCORE[c] == value[i])]
# choice not the same
constraint_neq = [CHOICE[0] != CHOICE[1]]
# bonus constraint. uncomment it to see the issue
# constraint_b = [B == bonus[val(CHOICE[0])][val(CHOICE[1])]]
# metric definition
constraint_sumscore = [metric == sum([SCORE[i] for i in choices ]) + B]
constraints = constraint_sumscore + constraint_sel + domain_choice + constraint_neq + constraint_b
opt = Optimize()
opt.add(constraints)
opt.maximize(metric)
s = []
if opt.check() == sat:
m = opt.model()
print [ m.evaluate(CHOICE[i]) for i in choices ]
print m.evaluate(metric)
else:
print "failed to solve"
Turns out the best way to deal with this problem is to actually not use arrays at all, but simply create integer variables. With this method, the 317x317 item problem originally posted actually gets solved in about 40 seconds on my relatively old computer:
[ 0.01s] Data loaded
[ 2.06s] Variables defined
[37.90s] Constraints added
[38.95s] Solved:
c0 = 19
c1 = 99
maxVal = 27
Note that the actual "solution" is found in about a second! But adding all the required constraints takes the bulk of the 40 seconds spent. Here's the encoding:
from z3 import *
import sys
import json
import sys
import time
start = time.time()
def tprint(s):
global start
now = time.time()
etime = now - start
print "[%ss] %s" % ('{0:5.2f}'.format(etime), s)
# load data
with open('data.json') as data_file:
dic = json.load(data_file)
tprint("Data loaded")
items = dic['items']
valueVals = dic['value']
bonusVals = dic['bonusVals']
vals = [[Int("val_%d_%d" % (i, j)) for j in items if j > i] for i in items]
tprint("Variables defined")
opt = Optimize()
for i in items:
for j in items:
if j > i:
opt.add(vals[i][j-i-1] == valueVals[i] + valueVals[j] + bonusVals[i][j])
c0, c1 = Ints('c0 c1')
maxVal = Int('maxVal')
opt.add(Or([Or([And(c0 == i, c1 == j, maxVal == vals[i][j-i-1]) for j in items if j > i]) for i in items]))
tprint("Constraints added")
opt.maximize(maxVal)
r = opt.check ()
if r == unsat or r == unknown:
raise Z3Exception("Failed")
tprint("Solved:")
m = opt.model()
print " c0 = %s" % m[c0]
print " c1 = %s" % m[c1]
print " maxVal = %s" % m[maxVal]
I think this is as fast as it'll get with Z3 for this problem. Of course, if you want to maximize multiple metrics, then you can probably structure the code so that you can reuse most of the constraints, thus amortizing the cost of constructing the model just once, and incrementally optimizing afterwards for optimal performance.
I want to set values depends on the selected radio button and to use that values in other function.
Whatever i try, i always get the same answer
NameError: global name 'tX' is not defined #
import maya.cmds as cmds
from functools import partial
winID='MSDKID'
def init(*args):
print tX
print tY
print tZ
print rX
print rY
print rZ
return
def prozor():
if cmds.window(winID, exists = True):
cmds.deleteUI(winID);
cmds.window()
cmds.columnLayout( adjustableColumn=True, rowSpacing=10 )
cmds.button(label = "Init")
cmds.button(label = "MirrorSDK",command=init)
cmds.setParent( '..' )
cmds.setParent( '..' )
cmds.frameLayout( label='Position' )
cmds.columnLayout()
collection2 = cmds.radioCollection()
RButton0 = cmds.radioButton( label='Behavior' )
RButton1 = cmds.radioButton( label='Orientation' )
cmds.button(l='Apply', command = partial(script,RButton0,RButton1,))
cmds.setParent( '..' )
cmds.setParent( '..' )
print script(RButton0,RButton1)
cmds.showWindow()
def script(RButton0,RButton1,*_cb_val):
X = 0
rb0 = cmds.radioButton(RButton0, q = True, sl = True)
rb1 = cmds.radioButton(RButton1,q = True, sl = True)
if (rb0 == True):
tX = -1
tY = -1
tZ = -1
rX = 1
rY = 1
rZ = 1
if (rb1 == True):
tX = -1
tY = 1
tZ = 1
rX = 1
rY = -1
rZ = -1
return tX,tY,tZ,rX,rY,rZ
prozor()
The variables you are defining in script() are local to that function. The other functions don't see them.
If you need multiple UI elements to share data, you'll probably need to create a class to let them share variables. Some reference here and here