我试图将一个范围的数字转换为另一个，保持比率。数学不是我的强项。
I have an image file where point values may range from -16000.00 to 16000.00 though the typical range may be much less. What I want to do is compress these values into the integer range 0-100, where 0 is the value of the smallest point, and 100 is the value of the largest. All points in between should keep a relative ratio even though some precision is being lost I'd like to do this in python but even a general algorithm should suffice. I'd prefer an algorithm where the min/max or either range can be adjusted (ie, the second range could be -50 to 800 instead of 0 to 100).
在由PenguinTD提供的清单中，我不明白为什么范围是颠倒的，它不需要颠倒范围就能工作。线性范围转换基于线性方程Y=Xm+n，其中m和n是从给定的范围推导出来的。与其将范围称为min和max，不如将它们称为1和2。所以公式是:
Y = (((X - x1) * (y2 - y1)) / (x2 - x1)) + y1
当X=x1时Y=y1，当X=x2时Y=y2。X1, x2, y1和y2可以取任意正值或负值。在宏中定义表达式使其更有用，它可以与任何参数名称一起使用。
#define RangeConv(X, x1, x2, y1, y2) (((float)((X - x1) * (y2 - y1)) / (x2 - x1)) + y1)
在所有实参都是整数值的情况下，浮点强制转换将确保浮点除法。
根据应用程序的不同，可能不需要检查x1=x2和y1==y2的范围。
NewValue = (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin
或者更容易读懂:
OldRange = (OldMax - OldMin)
NewRange = (NewMax - NewMin)
NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin
或者如果你想保护旧范围为0的情况(OldMin = OldMax):
OldRange = (OldMax - OldMin)
if (OldRange == 0)
NewValue = NewMin
else
{
NewRange = (NewMax - NewMin)
NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin
}
注意，在这种情况下，我们被迫任意选择一个可能的新范围值。根据上下文，明智的选择可能是:NewMin(见示例)，NewMax或(NewMin + NewMax) / 2
我写了一个函数用R来做这个，方法和上面一样，但是我需要在R中做很多次，所以我想分享一下，以防它对任何人有帮助。
convertRange <- function(
oldValue,
oldRange = c(-16000.00, 16000.00),
newRange = c(0, 100),
returnInt = TRUE # the poster asked for an integer, so this is an option
){
oldMin <- oldRange[1]
oldMax <- oldRange[2]
newMin <- newRange[1]
newMax <- newRange[2]
newValue = (((oldValue - oldMin)* (newMax - newMin)) / (oldMax - oldMin)) + newMin
if(returnInt){
return(round(newValue))
} else {
return(newValue)
}
}
