I came up with this nice thing, which I am calling 'partition function for symmetric groups'
Z[0]:1;
Z[n]:=expand(sum((n-1)!/i!*z[n-i]*Z[i], i, 0, n-1));
Z[4];
6*z[4]+8*z[1]*z[3]+3*z[2]^2+6*z[1]^2*z[2]+z[1]^4
The sum of the coefficients for Z[4] is 6+8+3+6+1 = 24 = 4!
which I am hoping corresponds to the fact that the group S4 has 6 elements like (abcd), 8 like (a)(bcd), 3 like (ab)(cd), 6 like (a)(b)(cd), and 1 like (a)(b)(c)(d)
So I thought to myself, the sum of the coefficients of Z[20] should be 20!
But life being somewhat on the short side, and fingers giving trouble, I was hoping to confirm this automatically. Can anyone help?
This sort of thing points a way:
Z[20],z[1]=1,z[2]=1,z[3]=1,z[4]=1,z[5]=1,z[6]=1,z[7]=1,z[8]=1;
But really...
I don't know a straightforward way to do that; coeff seems to handle only a single variable at a time. But here's a way to get the list you want. The basic idea is to extract the terms of Z[20] as a list, and then evaluate each term with z[1] = 1, z[2] = 1, ..., z[20] = 1.
(%i1) display2d : false $
(%i2) Z[0] : 1 $
(%i3) Z[n] := expand (sum ((n - 1)!/i!*z[n - i]*Z[i], i, 0, n-1)) $
(%i4) z1 : makelist (z[i] = 1, i, 1, 20);
(%o4) [z[1] = 1,z[2] = 1,z[3] = 1,z[4] = 1,z[5] = 1,z[6] = 1,z[7] = 1, ...]
(%i5) a : args (Z[20]);
(%o5) [121645100408832000*z[20],128047474114560000*z[1]*z[19],
67580611338240000*z[2]*z[18],67580611338240000*z[1]^2*z[18],
47703960944640000*z[3]*z[17],71555941416960000*z[1]*z[2]*z[17], ...]
(%i6) a1 : ev (a, z1);
(%o6) [121645100408832000,128047474114560000,67580611338240000, ...]
(%i7) apply ("+", a1);
(%o7) 2432902008176640000
(%i8) 20!;
(%o8) 2432902008176640000
Related
I am new to CVXPY, I learn by running examples and I found this post: How to construct a SOCP problem and solve using cvxpy and cvxpylayers #ben
The author provided the codes (Below I've corrected the line that caused an error in OP's EDIT 2):
import cvxpy as cp
import numpy as np
N = 5
Q_sqrt = cp.Parameter((N, N))
Q = cp.Parameter((N, N))
x = cp.Variable(N)
z = cp.Variable(N)
p = cp.Variable()
t = cp.Variable()
objective = cp.Minimize(p - t)
constraint_soc = [z == Q # x, x.value * z >= t ** 2, z >= 0, x >= 0] # <-- my question
constraint_other = [cp.quad_over_lin(Q_sqrt # x, p) <= N * p, cp.sum(x) == 1, p >= 0, t >= 0]
constraint_all = constraint_other + constraint_soc
matrix = np.random.random((N, N))
a_matrix = matrix.T # matrix
Q.value = a_matrix
Q_sqrt.value = np.sqrt(a_matrix)
prob = cp.Problem(objective, constraint_all)
prob.solve(verbose=True)
print("status:", prob.status)
print("optimal value", prob.value)
My question is here: x.value * z >= t ** 2
why only x.value while z not?
Actually I tried x * z, x.value * z.value, they both throw out errors, only the one in the original post works, which is x.value * z.
I googled and found this page and this, looks most relevant, but still failed to find an answer.
But both x and z are variables and defined as such
x = cp.Variable(N)
z = cp.Variable(N)
why only x needs a .value after?? Maybe it's a trivial question to experienced users, but I really don't get it. Thank you.
Update: two follow-up questions
Thanks to #MichalAdamaszek the first question above is clear: x.value didn't make sense here. A suggestion is using constraint_soc = [z == Q # x] + [ z[i]>=cp.quad_over_lin(t, x[i]) for i in range(N) ]
I have two following questions:
is it better to write the second of the soc constraint as : [ x[i]>=cp.quad_over_lin(t, z[i]) for i in range(N) ]? because in the question we only know that z[i] > 0 for sure. Or it doesn't matter at all in cvxpy? I tired both, both returned a similar optimal value.
I noticed that for the two constraints:
$x^TQx <= Np^2 $ and $ x_i z_i >= t^2 $
the quadratic terms were always intentionally split into two linear terms:
cp.quad_over_lin(Q_sqrt # x, p) <= N * p and z[i]>=cp.quad_over_lin(t, x[i]) respectively.
Is it because that in cvxpy, it is not allowed to have quadratic terms in (in)equality constraints? Is there an documentation to those basic rules?
Thank you.
I try to calculate Big O complexity for this code but I always fail....
I tried to nest SUM's or to get the number of steps for each case like:
i=1 j=1 k=1 (1 step)
i=2 j=1,2 k=1,2,3,4 (4 steps)
. . . . . . . . . . . . . . .
i=n (i said n = 2^(log n) j = 1,2,4,8,16,.....,n k=1,2,3,4,.....n^2 (n^2 steps)
then sum all the steps together, I need help.
for (int i=1; i<=n; i*=2)
for (int j=1; j<=i; j*=2)
for(int k=1; k<=j*j; k++)
//code line with complexity code O(1)
Let's take a look at the number of times the inner loop runs: j2. But j steps along in powers of 2 up to i. i in turn steps in powers of 2 up to n. So let's "draw" a little graphic of the terms of the sum that would give us the total number of iterations:
---- 1
^ 1 4
| 1 4 16
log2(n) ...
| 1 4 16 ... n2/16
v 1 4 16 ... n2/16 n2/4
---- 1 4 16 ... n2/16 n2/4 n2
|<------log2(n)------>|
The graphic can be interpreted as follows: each value of i corresponds to a row. Each value of j is a column within that row. The number itself is the number of iterations k goes through. The values of j are the square roots of the numbers. The values of i are the square roots of the last element in each row. The sum of all the numbers is the total number of iterations.
Looking at the bottom row, the terms of the sum are (2z)2 = 22z for z = 1 ... log2(n). The number of times that the terms appear in the sum is modulated by the height of the column. The height for a given term is log2(n) + 1 - z (basically a count down from log2(n)).
So the final sum is
log2(n)
Σ 22z(log2(n) + 1 - z)
z = 1
Here is what Wolfram Alpha has to say about evaluating the sum: http://m.wolframalpha.com/input/?i=sum+%28%28log%5B2%2C+n%5D%29+%2B+1+-+z%29%282%5E%282z%29%29%2C+z%3D1+to+log%5B2%2C+n%5D:
C1n2 - C2log(n) - C3
Cutting out all the less significant terms and constants, the result is
O(n2)
For the outermost loop:
sum_{i in {1, 2, 4, 8, 16, ...}} 1, i <= n (+)
<=>
sum_{i in {2^0, 2^1, 2^2, ... }} 1, i <= n
Let 2^I = i:
2^I = i <=> e^{I log 2} = i <=> I log 2 = log i <=> I = (log i)/(log 2)
Thus, (+) is equivalent to
sum_{I in {0, 1, ... }} 1, I <= floor((log n)/(log 2)) ~= log n (*)
Second outermost loop:
sum_{j in {1, 2, 4, 8, 16, ...}} 1, j <= i (++)
As above, 2^I = i, and let 2^J = j. Similarly to above,
(++) is equivalent to:
sum_{J in {0, 1, ... }} 1, J <= floor((log (2^I))/(log 2)) = floor(I/(log 2)) ~= I (**)
To touch base, only the outermost and second outermost
have now been reduced to
sum_{I in {0, 1, ... }}^{log n} sum_{J in {0, 1, ...}}^{I} ...
Which is (if there would be no innermost loop) O((log n)^2)
Innermost loop is a trivial one if we can express the largest bound in terms of `n`.
sum_{k in {1, 2, 3, 4, ...}} 1, k <= j^2 (+)
As above, let 2^J = j and note that j^2 = 2^(2J)
sum_{k in {1, 2, 3, 4, ...}} 1, k <= 2^(2J)
Thus, k is bounded by 2^(2 max(J)) = 2^(2 max(I)) = 2^(2 log(n) ) = 2n^2 (***)
Combining (*), (**) and (***), the asymptotic complexity of the three nested loops is:
O(n^2 log^2 n) (or, O((n log n)^2)).
I'm trying to re-implement the rma function from TradingView pinescript but I cannot make it output the same result as the original function.
Here is the code I developed, the code is basically the ema function, but it differs greatly from the rma function plot result when charting:
//#version=3
study(title = "test", overlay=true)
rolling_moving_average(data, length) =>
alpha = 2 / (length + 1)
sum = 0.0
for index = length to 0
if sum == 0.0
sum := data[index]
else
sum := alpha * data[index] + (1 - alpha) * sum
atr2 = rolling_moving_average(close, 5)
plot(atr2, title="EMAUP2", color=blue)
atr = rma(close, 5)
plot(atr, title="EMAUP", color=red)
So my question is how is the rma function works internally so I can implement a clone of it?
PS. Here is the link to the documentation https://www.tradingview.com/study-script-reference/#fun_rma It does show a possible implementation, but it does not work when running it.
Below is the correct implementation:
plot(rma(close, 15))
// same on pine, but much less efficient
pine_rma(x, y) =>
alpha = 1/y
sum = 0.0
sum := alpha * x + (1 - alpha) * nz(sum[1])
plot(pine_rma(close, 15))
There is a mistake in the code on TradingView, the alpha should be 1/y not y. This Wikipedia page has the correct formula for RMA
Wikipedia - moving averages
I have tried to write a function that takes in notes in MIDI form (C2,A4,Bb6) and returns their respective frequencies in hertz. I'm not sure what the best method of doing this should be. I am torn between two approaches. 1) a list based one where I can switch on an input and return hard-coded frequency values given that I may only have to do this for 88 notes (in the grand piano case). 2) a simple mathematical approach however my math skills are a limitation as well as converting the input string into a numerical value. Ultimately I've been working on this for a while and could use some direction.
You can use a function based on this formula:
The basic formula for the frequencies of the notes of the equal
tempered scale is given by
fn = f0 * (a)n
where
f0 = the frequency of one fixed note which must be defined. A common choice is setting the A above middle C (A4) at f0 = 440 Hz.
n = the number of half steps away from the fixed note you are. If you are at a higher note, n is positive. If you are on a lower note, n is negative.
fn = the frequency of the note n half steps away. a = (2)1/12 = the twelth root of 2 = the number which when multiplied by itself 12 times equals 2 = 1.059463094359...
http://www.phy.mtu.edu/~suits/NoteFreqCalcs.html
In Objective-C, this would be:
+ (double)frequencyForNote:(Note)note withModifier:(Modifier)modifier inOctave:(int)octave {
int halfStepsFromA4 = note - A;
halfStepsFromA4 += 12 * (octave - 4);
halfStepsFromA4 += modifier;
double frequencyOfA4 = 440.0;
double a = 1.059463094359;
return frequencyOfA4 * pow(a, halfStepsFromA4);
}
With the following enums defined:
typedef enum : int {
C = 0,
D = 2,
E = 4,
F = 5,
G = 7,
A = 9,
B = 11,
} Note;
typedef enum : int {
None = 0,
Sharp = 1,
Flat = -1,
} Modifier;
https://gist.github.com/NickEntin/32c37e3d31724b229696
Why don't you use a MIDI pitch?
where f is the frequency, and d the MIDI data.
I've written some code below to check if two line segments intersect and if they do to tell me where. As input I have the (x,y) coordinates of both ends of each line. It appeared to be working correctly but now in the scenario where line A (532.87,787.79)(486.34,769.85) and line B (490.89,764.018)(478.98,783.129) it says they intersect at (770.136, 487.08) when the lines don't intersect at all.
Has anyone any idea what is incorrect in the below code?
double dy[2], dx[2], m[2], b[2];
double xint, yint, xi, yi;
WsqT_Location_Message *location_msg_ptr = OPC_NIL;
FIN (intersect (<args>));
dy[0] = y2 - y1;
dx[0] = x2 - x1;
dy[1] = y4 - y3;
dx[1] = x4 - x3;
m[0] = dy[0] / dx[0];
m[1] = dy[1] / dx[1];
b[0] = y1 - m[0] * x1;
b[1] = y3 - m[1] * x3;
if (m[0] != m[1])
{
//slopes not equal, compute intercept
xint = (b[0] - b[1]) / (m[1] - m[0]);
yint = m[1] * xint + b[1];
//is intercept in both line segments?
if ((xint <= max(x1, x2)) && (xint >= min(x1, x2)) &&
(yint <= max(y1, y2)) && (yint >= min(y1, y2)) &&
(xint <= max(x3, x4)) && (xint >= min(x3, x4)) &&
(yint <= max(y3, y4)) && (yint >= min(y3, y4)))
{
if (xi && yi)
{
xi = xint;
yi = yint;
location_msg_ptr = (WsqT_Location_Message*)op_prg_mem_alloc(sizeof(WsqT_Location_Message));
location_msg_ptr->current_latitude = xi;
location_msg_ptr->current_longitude = yi;
}
FRET(location_msg_ptr);
}
}
FRET(location_msg_ptr);
}
There is an absolutely great and simple theory about lines and their intersections that is based on adding an extra dimensions to your points and lines. In this theory a line can be created from two points with one line of code and the point of line intersection can be calculated with one line of code. Moreover, points at the Infinity and lines at the Infinity can be represented with real numbers.
You probably heard about homogeneous representation when a point [x, y] is represented as [x, y, 1] and the line ax+by+c=0 is represented as [a, b, c]?
The transitioning to Cartesian coordinates for a general homogeneous representation of a point [x, y, w] is [x/w, y/w]. This little trick makes all the difference including representation of lines at infinity (e.g. [1, 0, 0]) and making line representation look similar to point one. This introduces a GREAT symmetry into formulas for numerous line/point manipulation and is an
absolute MUST to use in programming. For example,
It is very easy to find line intersections through vector product
p = l1xl2
A line can be created from two points is a similar way:
l=p1xp2
In the code of OpenCV it it just:
line = p1.cross(p2);
p = line1.cross(line2);
Note that there are no marginal cases (such as division by zero or parallel lines) to be concerned with here. My point is, I suggest to rewrite your code to take advantage of this elegant theory about lines and points.
Finally, if you don't use openCV, you can use a 3D point class and create your own cross product function similar to this one:
template<typename _Tp> inline Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const
{
return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x);
}