'''
Maximize the sum of logistic curves subject to linear and piecewise linear constraints
Approximate the logistic curves using piecewise linear functions
(c) 2020-2023 Fair Isaac Corporation
'''
import numpy as np
import xpress as xp
import matplotlib.pyplot as plt


def logistic(x, K, r, c):
    return K / (1 + np.exp(-r * (x - c)))


n_curves = 10
N = range(n_curves)
U = 10  # upper bound of the variables

# Create two numpy vectors of variables
x = xp.vars(N, ub=U, name='x')
y = xp.vars(N, name='y')

# Create a problem and add these two vectors
p = xp.problem(x, y)

n_intervals = 100
# define the breakpoints of the piecewise linear terms
breakpoints = np.array([(U / n_intervals) * i for i in range(n_intervals + 1)])

# compute the function values at breakpoints
y_vals = [logistic(breakpoints, U, np.random.uniform(0.5, 3), U / 2) for _ in N]

# Enable to visualize curves
for i in N:
    plt.plot(breakpoints, y_vals[i])

y_vals = np.array(y_vals).flatten().tolist()
x_vals = np.array([])
for i in N:
    x_vals = np.concatenate((x_vals, breakpoints))
x_vals = x_vals.tolist()

# Set the starting indices for the flattened piecewise linear function definitions
start = [i * (n_intervals + 1) for i in N]

# Add piecewise linear functions
p.addpwlcons(x, y, start, x_vals, y_vals)

# Add a constraint that limits the weighted sum of x variables
w = np.random.randint(1, 10, n_curves)
p.addConstraint(xp.Dot(w, x) <= 10)

# Maximize the sum of logistic functions
p.setObjective(xp.Dot(np.ones(n_curves), y), sense=xp.maximize)

p.write('test_logistic.mps', 'mps')

p.optimize()