Need help

[Cortana]

So here's the question:

I have a dataset of one-dimensional vectors, each of those has a corresponding N-dimensional vector. The task is: given a one-dimensional vector from outside the dataset, predict the corresponding N-dimensional vector.
In other words, I somehow need to learn a function f(x)->(y1, y2,..., yN) or f(x)->([a1,b1], [a2,b2],..., [aN,bN]), where y1 belongs to [a1,b1] and so on (a and b as close together as possible).
Would really appreciate any suggestions, I'm kinda stuck - the methods I've tried didn't perform well.

[ByTheWay]

have you tried to drain a glass of brandy, it can be helpful

[Cortana]

i have, thank u, and how about smth to the point?

[Junior]

Cortana, could you please formulate the problem with more detail? And give 2-3 examples?

[ByTheWay]

Originally Posted by Junior

Cortana, could you please formulate the problem with more detail? And give 2-3 examples?

You know, after a glass of brandy, it's not so easy task

[Cortana]

Wow, I see a lot of useful suggestions..lol
Junior, what kind of details do u want?

[ByTheWay]

Originally Posted by Cortana

Wow, I see a lot of useful suggestions..

Yeh, no thanks required, you know, you're welcome
can you translate your question into Russian
cause my don't understooding English very wall

[Satyricon]

Please give an example with real numbers

[Cortana]

Originally Posted by ByTheWay

Yeh, no thanks required, you know, you're welcome
can you translate your question into Russian
cause my don't understooding English very wall

Ha, qo asacn a, cavd tanem

[Cortana]

Originally Posted by Satyricon

Please give an example with real numbers

Not sure how this will help, but anyways, here's a sample of my data:

1: x = -28.240891 y = (310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3)
2: x = -42.423779 y = (349.184, 3.89, -1.74, -60.42, 0, 3, 7, 8, -1, 5)
3: x = -37.312016 y = (296.355, 2.03, -0.54, -22.76, 0, 0, 5, 2, 0, 5)

and so on, about 4M of them.
What I need is a function, which given, say, x = -28.240891 (first data point), will give me back some y very close to y = (310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3).
In other words, the points in the database r not random, so I need to find a rule that describes them.

[W_z_rd]

Did I understand it right that, based on a dataset you have, you need to calculate set of functions Y1(x)...YN(x), which, for any given X, will return you all the Y1-Yn-s closely matching those in dataset ?

There are numbers of interpolation methods (polinomial, b-spline, etc.), every one is good for a specific type of data/task. Which one of those have you tried and why the result was not satisfactory ?

One thing is clear that you can consider the problem as N intependednt x-y relationships, so we have to calculate N functions independently.

[ByTheWay]

Originally Posted by Cortana

What I need is a function, which given, say, x = -28.240891 (first data point), will give me back some y very close to y = (310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3).

public static void main(String[] args) {
double x = -28.240891;
double y[] = {310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3};

System.out.println( getMoreCloseElement(x, y) );
}

static double getMoreCloseElement(double x, double y[]) {
double ret = y[0];
double dif = Math.abs(x - y[0]);

for (int i = 1; i < y.length; i++) {
double temp = Math.abs(x - y[i]);
if( temp < dif ) {
dif = temp;
ret = y[i];
}
}
return ret;
}

[AvDav]

Originally Posted by Cortana

Not sure how this will help, but anyways, here's a sample of my data:

1: x = -28.240891 y = (310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3)
2: x = -42.423779 y = (349.184, 3.89, -1.74, -60.42, 0, 3, 7, 8, -1, 5)
3: x = -37.312016 y = (296.355, 2.03, -0.54, -22.76, 0, 0, 5, 2, 0, 5)

and so on, about 4M of them.
What I need is a function, which given, say, x = -28.240891 (first data point), will give me back some y very close to y = (310.747, 3.01, -1.4, -40.58, 0, 4, 5, 8, 1, 3).
In other words, the points in the database r not random, so I need to find a rule that describes them.

Hmm.. what you need is just 2 arrays, 1-st one is one dimensional, second one - two, just use linear search by given x value and take the corresponding y[i]-th vector, what's the problem ?

[AvDav]

Ooops, didn't mention 4M data stuff ... well pre-sort the array to use binary search so will be somewhat O(log(n)) instead of O(N)

[Intel]

Theoretical, we can manage to have just 1 number, instead of Y1, Y2, ..., Yn. And after that all we have to do is to find a function that supplies a single(and also correct) Y value corresponding to a single X, thats much more easier cuz, as it was mentioned before by W_z_rd, there are several methods available for that case, and it doesn't require separate calculations for each Y value.