algorithm.py

import math


def calculate(new, old, net_worth):
	new = float(new)
	old = float(old)
	net_worth = float(net_worth)

	# Treat anything below 0 upvotes as 0 upvotes
	if old < 0:
		old = 0
	if new < 0:
		new = 0

	# Compute gain
	delta = new - old

	# Treat negative gain as no gain
	if delta < 0:
		delta = 0

	# Compute the maximum of the sigmoid
	sig_max = sigmoid_max(old)

	# Compute the midpoint of the sigmoid
	sig_mp = sigmoid_midpoint(old)

	# Compute the steepness of the sigmoid
	sig_stp = sigmoid_steepness(old)

	# Calculate return
	factor = sigmoid(delta, sig_max, sig_mp, sig_stp)

	# Normalize between -1 and 1
	factor = factor - 1

	# Adjust based on net worth
	factor = factor * net_worth_coefficient(net_worth)

	# Return investment amount multiplier (change + 1)
	return factor + 1


def sigmoid(x, maxvalue, midpoint, steepness):
	arg = -(steepness * (x - midpoint))
	y = float(maxvalue) / (1 + math.exp(arg))
	return y


def sigmoid_max(old):
	return 1.2 + 0.6 / ((old / 10) + 1)


def sigmoid_midpoint(old):
	sig_mp_0 = 10
	sig_mp_1 = 500
	return linear_interpolate(old, 0, 25000, sig_mp_0, sig_mp_1)


def sigmoid_steepness(old):
	return 0.06 / ((old / 100) + 1)


def linear_interpolate(x, x_0, x_1, y_0, y_1):
	m = (y_1 - y_0) / float(x_1 - x_0)
	c = y_0
	y = (m * x) + c
	return y


def net_worth_coefficient(net_worth):
	return net_worth ** -0.155 * 6


def break_even(upvotes):
	y = upvotes
	x = y
	newFactor = calculate(x, y, 100)

	while newFactor < 1:
		x += 1
		newFactor = calculate(x, y, 100)

	return x