There are many ways to measure distance in math. Euclidean distance is one of them.
Given two points P1 and P2 the Euclidean distance ( in two dimensions, although the formula very easily generalizes to any number of dimensions) is as follows:
Let P1 have the coordiantes (x1, y1) and P2 be (x2, y2)
Then the Euclidean distance between them is the square root of
(x2-x1)2+(y2-y1)2 .
To understand some other ways of measuring "distance" I introduce the term
METRIC. A metric is a distance function. You put the points into the function (so they are its domain) and you get the distance as the output (so that is the range).
Another metric is the Taxicab Metric, formally known as the Minkowski distance.
We often use the small letter d to mean the distance between points.
So d(P1, P2) is the distance between points. Using the Taxicab Metric,
d(x, y) = |x1 - x2| + |y2 - y2|
The difference (greater minus lesser) is the distance between them.
The distance between two integers is the difference.
In classical or Euclidean plane geometry two points defines exactly one line. On a sphere two points can define infinitely many lines only one of which will represent the shortest distance between the points. On other curved surfaces, or in non-Euclidean geometries, the number of lines determined by two points can vary. Even in the Euclidean plane, two points determine infinitely many lines that are not straight!
In order to find the distance between two coordinates, you first need to find the difference between the x and y coordinates. In this case, the difference between the x coordinates is 3-(-2) = 5. The difference between the y coordinates is -4-5 = -9. To find the distance you add up the squares of these differences then find the square root. 52 = 25. -92 = 81. 25+81 = 106. Thus the distance is the square root of 106, or approximately 10.296
Depends on the metric defined on the space. The "normal" Euclidean metric for the distance between two points is the length of the shortest distance between them - ie the length of the straight line joining them. If the coordinates of the two points (in 2-dimensions) are (a,b) and (c,d) then the distance between them is sqrt([(a - c)2 + (b - d)2] This can be generalised to 3 (or more) dimensions. However, there are other metrics. One such is the "Manhattan metric" or the "Taxicab Geometry" which was developed by Minkowski. For more information on that, see http://en.wikipedia.org/wiki/Manhattan_metric
In Euclidean geometry parallel lines are always the same distance apart. In non-Euclidean geometry parallel lines are not what we think of a parallel. They curve away from or toward each other. Said another way, in Euclidean geometry parallel lines can never cross. In non-Euclidean geometry they can.
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You get a curve. If you join them along the shortest [Euclidean] distance between them, you get a straight line.
In comparing two bit patterns, the Hamming distance is the count of bits different in the two patterns. More generally, if two ordered lists of items are compared, the Hamming distance is the number of items that do not identically agree. This distance is applicable to encoded information, and is a particularly simple metric of comparison, often more useful than the city-block distance (the sum of absolute values of distances along the coordinate axes) or Euclidean distance (the square root of the sum of squares of the distances along the coordinate axes). also Metric.
Difference between outreach centre and distance education?"
i have no idea lol
Yes, the formula for the Euclidean distance. But not necessarily other distance metrics.
Pi is only constant in Euclidean Geometry, it is not the same in other Geometries. In the non-Euclidean geometry that Relativity theory uses the difference between PiE and PiNE is extremely small, approaching zero.
4 Types of Distance Metrics in Machine Learning Euclidean Distance. Manhattan Distance. Minkowski Distance. Hamming Distance.
The question is too imprecise for a simple answer. If you are given only the two points in Euclidean space you must measure the distance. If they are two points on a map, then the true (real-life) distance is the measured distance times the map's scale. If the two points, A and B, are in n-dimensional coordinate space then the Euclidean distance is given by the n-dimensional Pythagoras' equation: Dist(A, B) = sqrt[(Ax - Bx)^2 + (Ay - By)^2 + ... ] where Ax is the x coordinate of A, and so on. But there are also other metrics possible. One such is the Manhattan distance (also called the taxicab distance). Based on Manhattan's rectangular grid of avenues and streets, the distance between two points is the difference in their avenue numbers added to the difference in their street numbers. This metric can easily be extended to 3 or more dimensions.
The difference (greater minus lesser) is the distance between them.
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