[SPARK-2479][MLlib] Comparing floating-point numbers using relative error in UnitTests

Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors. Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result. That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.
See the following famous article for detail.
http://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
For example:
float a = 0.15 + 0.15
float b = 0.1 + 0.2
if(a == b) // can be false!
if(a >= b) // can also be false!

(ps, not all the tests involving floating point comparisons are changed to use almostEquals) 
1.0e-6 is way bigger than an ulp for a double; 1.0e-12 is more like it. I understand a complex calculation might legitimately vary by significantly more than an ulp depending on the implementation. As @mengxr says where you mean to allow significantly more than machine precision worth of noise, that's probably good to do with an explicitly larger epsilon. But this is certainly a good step forward already.