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I need to write a reliable method to retrieve the answer to the following scenario...
Given a line segment AB and an arbitrary point C, how would I find the closest point to A on a line parallel to AB that passes through point C? (Reliable mentioned above refers to the algorithms ability to find D while allowing the coordinates for A, B, and C to be completely arbitrary and unpredictable. I've ran in to a quite a few solutions that I was not able to adapt to all possible scenarios, sadly...)
In the case of the data displayed in the picture below, how would I reliably find the x,y coordinates of D?
A = <425, 473>
B = <584, 533>
C = <371, 401>
D = <???, ???>
Knowing that AB and CD are parallel, that obviously means the slopes are the same.
I have tried many different formulas to no avail and have been working on this for weeks now. I'm stumped!
It's a minimization problem.
In general, the Euclidean distance between two points (A and B) in N dimensional space is given by
Dist(A,B) = sqrt((A1-B1)^2 + ... + (AN-BN)^2)
If you need to find the minimum distance between a space curve A(t) (a 1-dimensional object embedded in some N dimensional space) and a point B, then you need to solve this equation:
d Dist(A(t),B) / dt = 0 // (this is straightforward calculus: we're at either a minimum or maximum when the rate of change is 0)
and test that set of roots (t1, t2, etc) against the distance function to find which one yields the smallest value of D.
Now to find the equation for the parallel line passing through C in y=mx+b form:
m = (Ay - By)/(Ax-Bx)
b = Cy - mCx
Let's write this in space-curve form as and plug it into our formula from part 1:
Dist(D(t),A) = sqrt((t-Ax)^2 + (m*t+b-Ay)^2)
taking our derivative:
d Dist(D(t),A)/ dt = d sqrt((t-Ax)^2 + (m*t+b-Ay)^2) / dt
= (t + (m^2)*t - Ax + m*b - m*Ay)/sqrt(t^2 + (m^2)t^2 - 2*t*Ax + 2*m*t*b - 2*m*t*Ay + (Ax)^2 + (Ay)^2 + b^2 - 2*b*Ay )
= ((1+m^2)*t - Ax + m*b - m*Ay)/sqrt((1+m^2)*(t^2) + 2*t*(m*b - Ax - m*Ay) + (Ax)^2 + (Ay)^2 + b^2 - 2*b*Ay )
Settings this equal to 0 and solving for t yields:
t = (Ax-m*b+m*Ay)/(1+m^2)
as the only root (you can check this for yourself by substituting back in and verifying that everything cancels as desired).
Plugging this value of t back in to our space curve yields the following:
D=<(Ax-m*b+m*Ay)/(1+m^2),b+m*(Ax-m*b+m*Ay)/(1+m^2)>
You can then plug in your expressions for m and b if you want an explicit solution in terms A,B,C, or if you only want the numerical solution you can just compute it as a three step process:
m = (Ay - By)/(Ax-Bx)
b = Cy - mCx
D=<(Ax-m*b+m*Ay)/(1+m^2),b+m*(Ax-m*b+m*Ay)/(1+m^2)>
This will be valid for all cases with parallel straight lines. One caveat when implementing it as a numerical (rather than analytical) code: if the lines are oriented vertically, calculating m = (Ay-By)/(Ax-Bx) will result in division by 0, which will make your code not work. You can throw in a safety valve as follows:
if( Ax == Bx) {
D = <Cx,Ay>
} else {
// normal calculation here
}
For serious numerical work, you probably want to implement that in terms of tolerances rather than a direct comparison due to roundoff errors and all that fun stuff (i.e., abs(Ax-Bx) < epsilon, rather than Ax==Bx)
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I am working on a python decryption code using an encryption code which is already available.
In the encrytpion code, I have
pow (b, xyz, abc)
A number gets encrypted and is passed onto an array.
Now while decrypting, i need to get the value of "b" (from the pow function above) as i have the value in Array.
Using modulus gives the values in range and not the exact value and that is needed for my decryption logic to work.
How to continue with this?
First you factorise 928108726777524737. It has 2 prime factors, call them P and Q.
Then you need to find a value d such that d * 65539 mod (P-1)(Q-1) == 1 (use the Extended Euclidian Algorithm for this).
Once you have done that then given c = pow (b, 65539, 928108726777524737) you can calculate back to b with pow(c, d, 928108726777524737)
To help you a bit more P=948712711, Q=978282167 giving d=872653594828486879
>>> c = pow(99, 65539, 928108726777524737)
>>> pow(c, 872653594828486879, 928108726777524737)
99
Of course in real life you would start with the prime factors and make them a lot larger than this in which case it would be impractical to reverse the process without already knowing the factors. For small values such as this is it is easy to factorise and calculate the inverse.
Calculation of d:
def egcd(a, b):
x,y, u,v = 0,1, 1,0
while a != 0:
q, r = b//a, b%a
m, n = x-u*q, y-v*q
b,a, x,y, u,v = a,r, u,v, m,n
gcd = b
return gcd, x, y
First find the prime factors:
>>> P, Q = 948712711, 978282167
>>> P*Q
928108726777524737
>>> egcd(65539, (P-1)*(Q-1))
(1, -55455130022042981, 3916)
We want the middle value x:
>>> gcd, x, y = egcd(65539, (P-1)*(Q-1))
But we have to make it positive which we can do by adding on the (P-1)*(Q-1) value:
>>> x + (P-1)*(Q-1)
872653594828486879
Given the line segment AB, how do I find the point Pn where the normal to AB passes through the point P? I also need to know if there is no normal that passes through the point (e.g. the point Q).
If R is any point on the normal line passing through P (different from P), then Pn is the point where AB and PR intersect.
One way to generate point R is to rotate segment AB by 90 degrees and then translate it so that A coincides with P. The the translated B is your R:
Rx = Px + (By - Ay)
Ry = Py - (Bx - Ax)
Once you have your point R, it becomes a simple line-line intersection problem, which will give you your Pn (the formulas can be simplified for a specific case of perpendicular lines).
Then you can easily check whether Pn lies between A and B or not.
P.S. Note that solution provided in #MBo's answer is a more direct and efficient one (and if you combine and simplify/optimize the formulas required for my answer you will eventually arrive at the same thing). What I describe above might make good sense if you already have a primitive function that calculates the intersection of two lines, say,
find_intersection(Ax, Ay, Bx, By, Cx, Cy, Dx, Dy)
// Intersect AB and CD
In that case finding Pn becomes a simple one-liner
Pn = find_intersection(Ax, Ay, Bx, By, Px, Py, Px + (By - Ay), Py - (Bx - Ax))
But if you don't have such primitive function at your disposal and/or care for making your code more efficient, then you might want to opt for a more direct dedicated sequence of calculations like the one in #MBo's answer.
Find vectors
AB = (B.X-A.X, B.Y-A.Y)
AP = (P.X-A.X, P.Y-A.Y)
Projection of P to AB is:
APn = AB * (AB.dot.AP) / (AB.dot.AB);
where .dot. is scalar product
In coordinates:
cf = ((B.X-A.X)*(P.X-A.X)+(B.Y-A.Y)*(P.Y-A.Y))/((B.X-A.X)^2+(B.Y-A.Y)^2)
if cf < 0 or cf > 1 then projection lies outside AB segment
Pn.X = A.X + (B.X-A.X) * cf
Pn.Y = A.Y + (B.Y-A.Y) * cf
I found this code snippet on raywenderlich.com, however the link to the explanation wasn't valid anymore. I "translated" the answer into Swift, I hope you can understand, it's actually quite easy even without knowing the language. Could anyone explain what exactly is going on here? Thanks for any help.
class func linesCross(#line1: Line, line2: Line) -> Bool {
let denominator = (line1.end.y - line1.start.y) * (line2.end.x - line2.start.x) -
(line1.end.x - line1.start.x) * (line2.end.y - line2.start.y)
if denominator == 0 { return false } //lines are parallel
let ua = ((line1.end.x - line1.start.x) * (line2.start.y - line1.start.y) -
(line1.end.y - line1.start.y) * (line2.start.x - line1.start.x)) / denominator
let ub = ((line2.end.x - line2.start.x) * (line2.start.y - line1.start.y) -
(line2.end.y - line2.start.y) * (line2.start.x - line1.start.x)) / denominator
//lines may touch each other - no test for equality here
return ua > 0 && ua < 1 && ub > 0 && ub < 1
}
You can find a detailed segment-intersection algorithm
in the book Computational Geometry in C, Sec. 7.7.
The SegSegInt code described there is available here.
I recommend avoiding slope calculations.
There are several "degenerate" cases that require care: collinear segments
overlapping or not, one segment endpoint in the interior of the other segments,
etc. I wrote the code to return an indication of these special cases.
This is what the code is doing.
Every point P in the segment AB can be described as:
P = A + u(B - A)
for some constant 0 <= u <= 1. In fact, when u=0 you get P=A, and you getP=B when u=1. Intermediate values of u will give you intermediate values of P in the segment. For instance, when u = 0.5 you will get the point in the middle. In general, you can think of the parameter u as the ratio between the lengths of AP and AB.
Now, if you have another segment CD you can describe the points Q on it in the same way, but with a different u, which I will call v:
Q = C + v(D - C)
Again, keep in mind that Q lies between C and D if, and only if, 0 <= v <= 1 (same as above for P).
To find the intersection between the two segments you have to equate P=Q. In other words, you need to find u and v, both between 0 and 1 such that:
A + u(B - A) = C + v(D - C)
So, you have this equation and you have to see if it is solvable within the given constraints on u and v.
Given that A, B, C and D are points with two coordinates x,y each, you can open the equation above into two equations:
ax + u(bx - ax) = cx + v(dx - cx)
ay + u(by - ay) = cy + v(dy - cy)
where ax = A.x, ay = A.y, etc., are the coordinates of the points.
Now we are left with a 2x2 linear system. In matrix form:
|bx-ax cx-dx| |u| = |cx-ax|
|by-ay cy-dy| |v| |cy-ay|
The determinant of the matrix is
det = (bx-ax)(cy-dy) - (by-ay)(cx-dx)
This quantity corresponds to the denominator of the code snippet (please check).
Now, multiplying both sides by the cofactor matrix:
|cy-dy dx-cx|
|ay-by bx-ax|
we get
det*u = (cy-dy)(cx-ax) + (dx-cx)(cy-ay)
det*v = (ay-by)(cx-ax) + (bx-ax)(cy-ay)
which correspond to the variables ua and ub defined in the code (check this too!)
Finally, once you have u and v you can check whether they are both between 0 and 1 and in that case return that there is intersection. Otherwise, there isn't.
For a given line the slope is
m=(y_end-y_start)/(x_end-x_start)
if two slopes are equal, the lines are parallel
m1=m1
(y1_end-y_start)/(x1_end-x1_start)=(y2_end-y2_start)/(x2_end-x2_start)
And this is equivalent to checking that the denominator is not zero,
Regarding the rest of the code, find the explanation on wikipedia under "Given two points on each line"
Local alignment between X and Y, with at least one column aligning a C
to a W.
Given two sequences X of length n and Y of length m, we
are looking for a highest-scoring local alignment (i.e., an alignment
between a substring X' of X and a substring Y' of Y) that has at least
one column in which a C from X' is aligned to a W from Y' (if such an
alignment exists). As scoring model, we use a substitution matrix s
and linear gap penalties with parameter d.
Write a code in order to solve the problem efficiently. If you use dynamic
programming, it suffices to give the equations for computing the
entries in the dynamic programming matrices, and to specify where
traceback starts and ends.
My Solution:
I've taken 2 sequences namely, "HCEA" and "HWEA" and tried to solve the question.
Here is my code. Have I fulfilled what is asked in the question? If am wrong kindly tell me where I've gone wrong so that I will modify my code.
Also is there any other way to solve the question? If its available can anyone post a pseudo code or algorithm, so that I'll be able to code for it.
public class Q1 {
public static void main(String[] args) {
// Input Protein Sequences
String seq1 = "HCEA";
String seq2 = "HWEA";
// Array to store the score
int[][] T = new int[seq1.length() + 1][seq2.length() + 1];
// initialize seq1
for (int i = 0; i <= seq1.length(); i++) {
T[i][0] = i;
}
// Initialize seq2
for (int i = 0; i <= seq2.length(); i++) {
T[0][i] = i;
}
// Compute the matrix score
for (int i = 1; i <= seq1.length(); i++) {
for (int j = 1; j <= seq2.length(); j++) {
if ((seq1.charAt(i - 1) == seq2.charAt(j - 1))
|| (seq1.charAt(i - 1) == 'C') && (seq2.charAt(j - 1) == 'W')) {
T[i][j] = T[i - 1][j - 1];
} else {
T[i][j] = Math.min(T[i - 1][j], T[i][j - 1]) + 1;
}
}
}
// Strings to store the aligned sequences
StringBuilder alignedSeq1 = new StringBuilder();
StringBuilder alignedSeq2 = new StringBuilder();
// Build for sequences 1 & 2 from the matrix score
for (int i = seq1.length(), j = seq2.length(); i > 0 || j > 0;) {
if (i > 0 && T[i][j] == T[i - 1][j] + 1) {
alignedSeq1.append(seq1.charAt(--i));
alignedSeq2.append("-");
} else if (j > 0 && T[i][j] == T[i][j - 1] + 1) {
alignedSeq2.append(seq2.charAt(--j));
alignedSeq1.append("-");
} else if (i > 0 && j > 0 && T[i][j] == T[i - 1][j - 1]) {
alignedSeq1.append(seq1.charAt(--i));
alignedSeq2.append(seq2.charAt(--j));
}
}
// Display the aligned sequence
System.out.println(alignedSeq1.reverse().toString());
System.out.println(alignedSeq2.reverse().toString());
}
}
#Shole
The following are the two question and answers provided in my solved worksheet.
Aligning a suffix of X to a prefix of Y
Given two sequences X and Y, we are looking for a highest-scoring alignment between any suffix of X and any prefix of Y. As a scoring model, we use a substitution matrix s and linear gap penalties with parameter d.
Give an efficient algorithm to solve this problem optimally in time O(nm), where n is the length of X and m is the length of Y. If you use a dynamic programming approach, it suffices to give the equations that are needed to compute the dynamic programming matrix, to explain what information is stored for the traceback, and to state where the traceback starts and ends.
Solution:
Let X_i be the prefix of X of length i, and let Y_j denote the prefix of Y of length j. We compute a matrix F such that F[i][j] is the best score of an alignment of any suffix of X_i and the string Y_j. We also compute a traceback matrix P. The computation of F and P can be done in O(nm) time using the following equations:
F[0][0]=0
for i = 1..n: F[i][0]=0
for j = 1..m: F[0][j]=-j*d, P[0][j]=L
for i = 1..n, j = 1..m:
F[i][j] = max{ F[i-1][j-1]+s(X[i-1],Y[j-1]), F[i-1][j]-d, F[i][j-1]-d }
P[i][j] = D, T or L according to which of the three expressions above is the maximum
Once we have computed F and P, we find the largest value in the bottom row of the matrix F. Let F[n][j0] be that largest value. We start traceback at F[n][j0] and continue traceback until we hit the first column of the matrix. The alignment constructed in this way is the solution.
Aligning Y to a substring of X, without gaps in Y
Given a string X of length n and a string Y of length m, we want to compute a highest-scoring alignment of Y to any substring of X, with the extra constraint that we are not allowed to insert any gaps into Y. In other words, the output is an alignment of a substring X' of X with the string Y, such that the score of the alignment is the largest possible (among all choices of X') and such that the alignment does not introduce any gaps into Y (but may introduce gaps into X'). As a scoring model, we use again a substitution matrix s and linear gap penalties with parameter d.
Give an efficient dynamic programming algorithm that solves this problem optimally in polynomial time. It suffices to give the equations that are needed to compute the dynamic programming matrix, to explain what information is stored for the traceback, and to state where the traceback starts and ends. What is the running-time of your algorithm?
Solution:
Let X_i be the prefix of X of length i, and let Y_j denote the prefix of Y of length j. We compute a matrix F such that F[i][j] is the best score of an alignment of any suffix of X_i and the string Y_j, such that the alignment does not insert gaps in Y. We also compute a traceback matrix P. The computation of F and P can be done in O(nm) time using the following equations:
F[0][0]=0
for i = 1..n: F[i][0]=0
for j = 1..m: F[0][j]=-j*d, P[0][j]=L
for i = 1..n, j = 1..m:
F[i][j] = max{ F[i-1][j-1]+s(X[i-1],Y[j-1]), F[i][j-1]-d }
P[i][j] = D or L according to which of the two expressions above is the maximum
Once we have computed F and P, we find the largest value in the rightmost column of the matrix F. Let F[i0][m] be that largest value. We start traceback at F[i0][m] and continue traceback until we hit the first column of the matrix. The alignment constructed in this way is the solution.
Hope you get some idea about wot i really need.
I think it's quite easy to find resources or even the answer by google...as the first result of the searching is already a thorough DP solution.
However, I appreciate that you would like to think over the solution by yourself and are requesting some hints.
Before I give out some of the hints, I would like to say something about designing a DP solution
(I assume you know this can be solved by a DP solution)
A dp solution basically consisting of four parts:
1. DP state, you have to self define the physical meaning of one state, eg:
a[i] := the money the i-th person have;
a[i][j] := the number of TV programmes between time i and time j; etc
2. Transition equations
3. Initial state / base case
4. how to query the answer, eg: is the answer a[n]? or is the answer max(a[i])?
Just some 2 cents on a DP solution, let's go back to the question :)
Here's are some hints I am able to think of:
What is the dp state? How many dimensions are enough to define such a state?
Thinking of you are solving problems much alike to common substring problem (on 2 strings),
1-dimension seems too little and 3-dimensions seems too many right?
As mentioned in point 1, this problem is very similar to common substring problem, maybe you should have a look on these problems to get yourself some idea?
LCS, LIS, Edit Distance, etc.
Supplement part: not directly related to the OP
DP is easy to learn, but hard to master. I know a very little about it, really cannot share much. I think "Introduction to algorithm" is a quite standard book to start with, you can find many resources, especially some ppt/ pdf tutorials of some colleges / universities to learn some basic examples of DP.(Learn these examples is useful and I'll explain below)
A problem can be solved by many different DP solutions, some of them are much better (less time / space complexity) due to a well-defined DP state.
So how to design a better DP state or even get the sense that one problem can be solved by DP? I would say it's a matter of experiences and knowledge. There are a set of "well-known" DP problems which I would say many other DP problems can be solved by modifying a bit of them. Here is a post I just got accepted about another DP problem, as stated in that post, that problem is very similar to a "well-known" problem named "matrix chain multiplication". So, you cannot do much about the "experience" part as it has no express way, yet you can work on the "knowledge" part by studying these standard DP problems first maybe?
Lastly, let's go back to your original question to illustrate my point of view:
As I knew LCS problem before, I have a sense that for similar problem, I may be able to solve it by designing similar DP state and transition equation? The state s(i,j):= The optimal cost for A(1..i) and B(1..j), given two strings A & B
What is "optimal" depends on the question, and how to achieve this "optimal" value in each state is done by the transition equation.
With this state defined, it's easy to see the final answer I would like to query is simply s(len(A), len(B)).
Base case? s(0,0) = 0 ! We can't really do much on two empty string right?
So with the knowledge I got, I have a rough thought on the 4 main components of designing a DP solution. I know it's a bit long but I hope it helps, cheers.
I have a class implementing an audio stream that can be read at varying speed (including reverse and fast varying / "scratching")... I use linear interpolation for the read part and everything works quite decently..
But now I want to implement writing to the stream at varying speed as well and that requires me to implement a kind of "reverse interpolation" i.e. Deduce the input sample vector Z that, interpolated with vector Y will produce the output X (which I'm trying to write)..
I've managed to do it for constant speeds, but generalising for varying speeds (e.g accelerating or decelerating) is proving more complicated..
I imagine this problem has been solved repeatedly, but I can't seem to find many clues online, so my specific question is if anyone has heard of this problem and can point me in the right direction (or, even better, show me a solution :)
Thanks!
I would not call it "reverse interpolation" as that does not exists (my first thought was you were talking about extrapolation!). What you are doing is still simply interpolation, just at an uneven rate.
Interpolation: finding a value between known values
Extrapolation: finding a value beyond known values
Interpolating to/from constant rates is indeed much much simpler than the generic quest of "finding a value between known values". I propose 2 solutions.
1) Interpolate to a significantly higher rate, and then just sub-sample to the nearest one (try adding dithering)
2) Solve the generic problem: for each point you need to use the neighboring N points and fit a order N-1 polynomial to them.
N=2 would be linear and would add overtones (C0 continuity)
N=3 could leave you with step changes at the halfway point between your source samples (perhaps worse overtones than N=2!)
N=4 will get you C1 continuity (slope will match as you change to the next sample), surely enough for your application.
Let me explain that last one.
For each output sample use the 2 previous and 2 following input samples. Call them S0 to S3 on a unit time scale (multiply by your sample period later), and you are interpolating from time 0 to 1. Y is your output and Y' is the slope.
Y will be calculated from this polynomial and its differential (slope)
Y(t) = At^3 + Bt^2 + Ct + D
Y'(t) = 3At^2 + 2Bt + C
The constraints (the values and slope at the endpoints on either side)
Y(0) = S1
Y'(0) = (S2-S0)/2
Y(1) = S2
Y'(1) = (S3-S1)/2
Expanding the polynomial
Y(0) = D
Y'(0) = C
Y(1) = A+B+C+D
Y'(1) = 3A+2B+C
Plugging in the Samples
D = S1
C = (S2-S0)/2
A + B = S2 - C - D
3A+2B = (S3-S1)/2 - C
The last 2 are a system of equations that are easily solvable. Subtract 2x the first from the second.
3A+2B - 2(A+B)= (S3-S1)/2 - C - 2(S2 - C - D)
A = (S3-S1)/2 + C - 2(S2 - D)
Then B is
B = S2 - A - C - D
Once you have A, B, C and D you can put in an time 't' in the polynomial to find a sample value between your known samples.
Repeat for every output sample, reuse A,B,C&D if the next output sample is still between the same 2 input samples. Calculating t each time is similar to Bresenham's line algorithm, you're just advancing by a different amount each time.