ActiveBOToytask/PolicyModel/GaussianModel.py

import numpy as np
import matplotlib.pyplot as plt


class GaussianPolicy:
    def __init__(self, nr_weights, nr_steps, seed=None, lowerb=-1.0, upperb=1.0):
        self.nr_weights = nr_weights
        self.nr_steps = nr_steps
        self.weights = None
        self.trajectory = None
        self.mean = np.linspace(0, self.nr_steps, self.nr_weights)
        if nr_weights > 1:
            self.std = self.mean[1] / (2 * np.sqrt(2 * np.log(2)))  # Full width at half maximum
        else:
            self.std = self.nr_steps / 2

        self.rng = np.random.default_rng(seed=seed)
        self.low = lowerb
        self.upper = upperb

        self.reset()

    def reset(self):
        self.weights = np.zeros((self.nr_weights, 1))
        self.trajectory = np.zeros((self.nr_steps, 1))

    def random_policy(self):
        self.weights = self.rng.uniform(self.low, self.upper, self.nr_weights)

    def policy_rollout(self):
        self.trajectory = np.zeros((self.nr_steps, 1))
        for i in range(self.nr_steps):
            for j in range(self.nr_weights):
                base_fun = np.exp(-0.5*(i - self.mean[j])**2 / self.std**2)
                self.trajectory[i] += base_fun * self.weights[j]

        return self.trajectory

    def plot_policy(self, finished=np.NAN):
        x = np.linspace(0, self.nr_steps, self.nr_steps)
        plt.plot(x, self.trajectory)
        if finished != np.NAN:
            plt.vlines(finished, -1, 1, colors='red')
        # for i in self.mean:
        #     gaussian = np.exp(-0.5 * (x - i)**2 / self.std**2)
        #     plt.plot(x, gaussian)


def main():
    policy = GaussianPolicy(1, 50)
    policy.random_policy()
    policy.policy_rollout()
    print(policy.weights)

    fig, (ax1, ax2) = plt.subplots(2, 1)

    x = np.linspace(0, policy.nr_steps, policy.nr_steps)
    ax1.plot(x, policy.trajectory)
    ax2.bar(policy.mean, policy.weights)

    plt.show()

if __name__ == "__main__":
    main()