$ys represents the list of "answers" (i.e., y = b0 + b1x1 + b2x2)
- $method is either gradient or normal
+ $method is either gradient or normal (right now)
$alpha is used in gradient descent and represents the "learning rate", set this as high as you can get away with.
$initialization is the vector of values to start your b0, b1, b2 values at during the gradient descent. if you don't pass it, will use a vector of 0s.
$repetitions is the number of times to repeat. This is required unless LL_AUTODETECT_CONVERGENCE is defined to be a floating point value, in which case, we will repeat until we're within that distance from the previous iteration.
@@ -26,6 +26,18 @@ function ll_linear_regression($xs, $ys, $method="gradient", $alpha=null, $initia