gradient($start_value, $end_value, $steps)
This function will return an array of evenly distributed values between $start_value and $end_value.
All three values supplied should be numeric. $steps should be the number of steps that should occur
between the two points; for instance, gradient(0, 10, 4) would return the array (2, 4, 6, 8); the 4
evenly-distributed steps necessary to get from 0 to 10, whereas gradient(0, 1, 3) would return (0.25,
0.5, 0.75). This is the basest function in the Math::Gradient module and isn't very exciting, but all
of the other functions below derive their work from it.
array_gradient($start_value, $end_value, $steps)
While gradient() takes numeric values for $start_value and $end_value, array_gradient() takes
arrayrefs instead. The arrays supplied are expected to be lists of numerical values, and all of the
arrays should contain the same number of elements. array_gradient() will return a list of arrayrefs
signifying the gradient of all values on the lists $start_value and $end_value.
For example, calling array_gradient([ 0, 100, 2 ], [ 100, 50, 70], 3) would return: ([ 25, 87.5, 19
], [ 50, 75, 36 ], [ 75, 62.5, 53 ]).
multi_gradient($steps, @values)
multi_gradient() calculates multiple gradients at once, returning one list that is an even transition
between all points, with the values supplied interpolated evenly within the list. If $steps is less
than the number of entries in the list @values, items are deleted from @values instead.
For example, calling multi_gradient(10, 0, 100, 50) would return: (0, 25, 50, 75, 100, 90, 80, 70,
60, 50)
multi_array_gradient($steps, @values)
multi_array_gradient() is the same as multi_gradient, except that it works on arrayrefs instead of
scalars (like array_gradient() is to gradient()).