the object variable in link list reverse codes - variables

Hi this is a link list reverse code. Anyone can help me understand that the differecen between r = Solution().reverseList(n1) and r = Solution().reverseList(ListNode(1)).
I wonder why the output which is [1] and [3,2,1] respectively and what is the purpose of to assign the ListNode object to a variable.
class Solution(object):
def reverseList(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
#if not head:
# return None
prev = head
curr = prev.next
while curr:
next =curr.next
curr.next = prev
prev = curr
curr = next
head.next = None
return prev
class ListNode(object):
def __init__(self,x):
self.val = x
self.next = None
def to_list(self):
return[self.val] + self.next.to_list() if self.next else [self.val]
if __name__ == "__main__":
n1 = ListNode(1)
n2 = ListNode(2)
n3 = ListNode(3)
n1.next = n2
n2.next = n3
r = Solution().reverseList(n1)
print r.to_list()
#assert r.to_list() == [3,2,1]

I understood now.
ListNode(1) has no "next" set, so it will forever return 1 as it's list, whereas n1 starts the same way (as just node of 1), but the "next" attribute is set to node of 2 whose "next" is set to node of 3, so n1 carries all of this information with it, and ListNode(1) does not.
welcome any comments.

Related

Binary Search Template Leetcode, The meaning of it?

I Found a Binary Search Template here in leetcode
def binarySearch(nums, target):
"""
:type nums: List[int]
:type target: int
:rtype: int
"""
if len(nums) == 0:
return -1
left, right = 0, len(nums)
while left < right:
mid = (left + right) // 2
if nums[mid] == target:
return mid
elif nums[mid] < target:
left = mid + 1
else:
right = mid
# Post-processing:
# End Condition: left == right
if left != len(nums) and nums[left] == target:
return left
return -1
They say that "Template #2 is an advanced form of Binary Search. It is used to search for an element or condition which requires accessing the current index and its immediate right neighbor's index in the array."
I am struggling to understand what the information above means. In a usual binary search right would be
right = len(nums) - 1 # before while loop
right = mid - 1 # inside while loop
and the while loop would be
while left <= right:
You are performing a binary search on an array.
Lets say the array has 100 elements.
# left, right = 0, len(nums)
left = 0, right = 99
# mid = (left + right) // 2
mid = 49
# elif nums[mid] < target:
We need to move right.
# left = mid + 1
left = 50 and right = 99
# else:
We need to move left.
# right = mid
Left = 0, right = 49

How to fix "submatrix incorrectly defined" in Scilab?

I am trying to find three parameters (a, b, c) to fit my experimental data using ODE solver and optimization by least squares using Scilab in-built functions.
However, I keep having the message "submatrix incorrectly defined" at line "y_exp(:,1) = [0.135 ..."
When I try another series of data (t, yexp) such as the one used in the original template I get no error messages. The template I use was found here: https://wiki.scilab.org/Non%20linear%20optimization%20for%20parameter%20fitting%20example
function dy = myModel ( t , y , a , b, c )
// The right-hand side of the Ordinary Differential Equation.
dy(1) = -a*y(1) - b*y(1)*y(2)
dy(2) = a*y(1) - b*y(1)*y(2) - c*y(2)
endfunction
function f = myDifferences ( k )
// Returns the difference between the simulated differential
// equation and the experimental data.
global MYDATA
t = MYDATA.t
y_exp = MYDATA.y_exp
a = k(1)
b = k(2)
c = k(3)
y0 = y_exp(1,:)
t0 = 0
y_calc=ode(y0',t0,t,list(myModel,a,b,c))
diffmat = y_calc' - y_exp
// Make a column vector
f = diffmat(:)
MYDATA.funeval = MYDATA.funeval+ 1
endfunction
// Experimental data
t = [0,20,30,45,75,105,135,180,240]';
y_exp(:,1) =
[0.135,0.0924,0.067,0.0527,0.0363,0.02445,0.01668,0.012,0.009]';
y_exp(:,2) =
[0,0.00918,0.0132,0.01835,0.0261,0.03215,0.0366,0.0393,0.0401]';
// Store data for future use
global MYDATA;
MYDATA.t = t;
MYDATA.y_exp = y_exp;
MYDATA.funeval = 0;
function val = L_Squares ( k )
// Computes the sum of squares of the differences.
f = myDifferences ( k )
val = sum(f.^2)
endfunction
// Initial guess
a = 0;
b = 0;
c = 0;
x0 = [a;b;c];
[fopt ,xopt]=leastsq(myDifferences, x0)
Does anyone know how to approach this problem?
Just rewrite lines 28,29 as
y_exp = [0.135,0.0924,0.067,0.0527,0.0363,0.02445,0.01668,0.012,0.009
0,0.00918,0.0132,0.01835,0.0261,0.03215,0.0366,0.0393,0.0401]';
or insert a clear at line 1 (you may have defined y_exp before with a different size).

Vpython greyscreen crash

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.

How to declare constraints with variable as array index in Z3Py?

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.

Passing variables in python from radio buttons

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