from scipy import ndimage, misc
import image_segment
from environment import Env
from agent import R_Learner
import numpy as np
if __name__ == "__main__":
img = ndimage.imread('butterfly.jpg')
img = misc.imresize(img, size=0.0625)
search_env = img
patch_src = img
K = 50
labels = image_segment.segment(img, K)
label = 15
patch_indices = np.where(labels == label)
patch_pixels = img[patch_indices]
indices_at_origin = (patch_indices[0] - np.min(patch_indices[0]),
patch_indices[1] - np.min(patch_indices[1]))
env = Env(img)
agent = R_Learner(patch_pixels, indices_at_origin, env)
while True:
state = env.get_state()
action = agent.get_action(state)
reward, next_state = env.take_action(action)
agent.learn(state, action, reward, next_state)
assert len(np.concatenate(reg_periods)) is len(
np.unique(np.concatenate(reg_periods)))
# Annualized volatility on risky asset in different regimes:
for k, v in regimes.items():
ann_vol = np.squeeze(v["sigma"][1] * np.sqrt(252))
print(
"Annualized volatility of log-returns on risky asset "
"in regime", k, "=", "{0:.5f}".format(ann_vol))
# terminal wealth statistics for all risky allocation:
lw = []
for i in range(train_episodes + round(init_d_size / horizon)):
env.reset()
while not env.done:
_, _, _, _ = env.take_action(action=1.0)
lw.append(np.sum(env.p))
print("\nIf fully invested into the risky asset (over ",
train_episodes + round(init_d_size / horizon), "runs):")
print("-- Mean terminal wealth =", "{0:.2f}".format(np.mean(lw)))
print("-- Maximum terminal wealth =", "{0:.2f}".format(np.max(lw)))
print("-- Minimum terminal wealth =", "{0:.2f}".format(np.min(lw)))
print("-- Utility of mean terminal wealth =",
"{0:.5f}".format(crra_utility(np.mean(lw), env.theta)))
print("-- Mean utility of terminal wealth =",
"{0:.5f}".format(np.mean(crra_utility(lw, env.theta))))
print("-- For comparison: initial utility =",
"{0:.5f}".format(crra_utility(env.w, env.theta)))
del lw
