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test_training_data.py
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test_training_data.py
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#!/usr/bin/env python
from __future__ import absolute_import
import numpy as np
import os
import pytest
import tempfile
import training_data
class TestTrainingData():
def test_add(self):
td = training_data.training_data()
assert np.array_equal(td.get_x(), np.empty([0, 4, 4], dtype=int))
assert np.array_equal(td.get_y_digit(), np.empty([0, 1], dtype=int))
assert np.allclose(td.get_reward(), np.empty([0, 1], dtype=float))
assert np.array_equal(td.get_next_x(), np.empty([0, 4, 4], dtype=int))
assert np.array_equal(td.get_done(), np.empty([0, 1], dtype=bool))
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]), True)
assert np.array_equal(td.get_x(), np.ones([1, 4, 4], dtype=int))
assert np.array_equal(td.get_y_digit(), np.array([[1]], dtype=int))
assert np.allclose(td.get_reward(), np.array([[4]], dtype=float))
assert np.array_equal(td.get_next_x(), np.zeros([1, 4, 4], dtype=int))
assert np.array_equal(td.get_done(), np.array([[1]], dtype=bool))
def test_get_x_stacked(self):
td = training_data.training_data()
td.add(np.full([4, 4], 2), 0, 4, np.zeros([4, 4]))
td.add(np.full([4, 4], 8), 1, 8, np.ones([4, 4]))
td.add(np.full([4, 4], 2048), 1, 8, np.ones([4, 4]))
expected_x_stacked = np.array([
[
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
],
[
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
],
[
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]
]
], dtype=int)
assert np.array_equal(td.get_x_stacked(), expected_x_stacked)
def test_get_y_one_hot(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 0, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 1, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 3, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 2, 8, np.ones([4, 4]))
expected_y_one_hot = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]
], dtype=int)
assert np.array_equal(td.get_y_one_hot(), expected_y_one_hot)
def test_get_total_reward(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 0, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 1, 8, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 3, 16, np.ones([4, 4]))
td.add(np.zeros([4, 4]), 2, 32, np.ones([4, 4]))
assert td.get_total_reward() == 60
def test_get_highest_tile(self):
td = training_data.training_data()
td.add(np.full((4, 4), 1), 0, 4, np.full((4, 4), 2))
td.add(np.full((4, 4), 2), 0, 4, np.full((4, 4), 4))
assert td.get_highest_tile() == 4
def test_get_n(self):
td = training_data.training_data()
td.add(np.ones([4, 4]), 1, 4, np.zeros([4, 4]))
td.add(np.zeros([4, 4]), 2, 8, np.ones([4, 4]))
(state, action, reward, next_state, done) = td.get_n(1)
assert np.array_equal(state, np.zeros([4, 4], dtype=int))
assert action == 2
assert reward == pytest.approx(8.)
assert np.array_equal(next_state, np.ones([4, 4], dtype=int))
def test_hflip(self):
td = training_data.training_data()
board1 = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
board2 = np.array([[0, 0, 0, 0],
[2, 4, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(board1, 1, 2, board2)
td.add(board2, 2, 0, board1)
td.hflip()
expected_x = np.array([
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 4, 2], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[3],
[2]
], dtype=int)
expected_reward = np.array([
[2],
[0],
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 4, 2], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.allclose(td.get_next_x(), expected_next_x)
def test_rotate(self):
td = training_data.training_data()
board1 = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
board2 = np.array([[0, 0, 0, 0],
[2, 4, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(board1, 1, 2, board2)
td.add(board2, 2, 0, board1)
td.rotate(3)
expected_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 4, 0, 0], [0, 2, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[0],
[1]
], dtype=int)
expected_reward = np.array([
[2],
[0],
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 4, 0, 0], [0, 2, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]]
], dtype=int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_augment(self):
td = training_data.training_data()
initial_board = np.array([[1, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
next_board = np.array([[0, 0, 0, 2],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
td.add(initial_board, 1, 4, next_board)
td.augment()
assert td.size() == 8
expected_x = np.array([
[[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
[[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[1],
[3],
[2],
[0],
[3],
[1],
[0],
[2]
], dtype=int)
expected_reward = np.array([
[4],
[4],
[4],
[4],
[4],
[4],
[4],
[4]
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 2], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], # Original
[[2, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0]], # Hflip'd
[[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 2]], # Original, rotated 90 degrees
[[0, 0, 0, 2], [0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]], # Hflip, rotated 90 degrees
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 2, 0], [2, 0, 0, 0]], # Original, rotated 180 degrees
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 2]], # Hflip, rotated 180 degrees
[[2, 0, 0, 0], [0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0]], # Original, rotate 270 degrees
[[0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0], [2, 0, 0, 0]] # Hflip, rotated 270 degrees
], dtype=int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_merge(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 16, np.zeros([1, 4, 4]))
td2 = training_data.training_data()
td2.add(np.zeros([1, 4, 4]), 2, 0, np.ones([1, 4, 4]))
td.merge(td2)
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[1],
[2]
], dtype=int)
expected_reward = np.array([
[16],
[0]
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
def test_split(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 16, np.zeros([1, 4, 4]))
td2 = training_data.training_data()
td2.add(np.zeros([1, 4, 4]), 2, 0, np.ones([1, 4, 4]))
td.merge(td2)
a, b = td.split()
assert np.array_equal(a.get_x(), np.ones([1, 4, 4]))
assert np.array_equal(a.get_y_digit(), [[1]])
assert np.array_equal(a.get_reward(), [[16]])
assert np.array_equal(a.get_next_x(), np.zeros([1, 4, 4]))
assert np.array_equal(b.get_x(), np.zeros([1, 4, 4]))
assert np.array_equal(b.get_y_digit(), [[2]])
assert np.array_equal(b.get_reward(), [[0]])
assert np.array_equal(b.get_next_x(), np.ones([1, 4, 4]))
def test_sample(self):
td = training_data.training_data()
td.add(np.zeros([1, 4, 4]), 0, 0, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 1, np.ones([1, 4, 4]))
sample = td.sample([1])
assert sample.size() == 1
assert sample.get_y_digit() in [[[0]], [[1]]]
if sample.get_y_digit() == 0:
assert np.array_equal(sample.get_x(), np.zeros([1, 4, 4]))
if sample.get_y_digit() == 1:
assert np.array_equal(sample.get_x(), np.ones([1, 4, 4]))
def test_size(self):
td = training_data.training_data()
assert td.size() == 0
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
assert td.size() == 1
def test_log2_rewards(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 0, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 16, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 75, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2048, np.zeros([1, 4, 4]))
td.log2_rewards()
expected_reward = np.array([
[0], [1], [2], [4], [6.2288], [11]
], dtype=float)
assert np.allclose(td.get_reward(), expected_reward)
expected_action = np.array([
[0], [1], [2], [3], [0], [1]
], dtype=int)
assert np.allclose(td.get_y_digit(), expected_action)
def test_get_discounted_return(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 2, np.zeros([1, 4, 4]))
# Test using default gamma value of 0.9
td2 = td.copy()
discounted_return = td2.get_discounted_return()
expected_return = np.array([
[20.218], [18.02], [17.8], [2.0]
], dtype=float)
assert np.allclose(discounted_return, expected_return)
# Test using gamma value of 0, should have no effect on rewards
td2 = td.copy()
discounted_return = td2.get_discounted_return(gamma=0.0)
expected_return = np.array([
[4], [2], [16], [2]
], dtype=float)
assert np.allclose(discounted_return, expected_return)
# Test end of episode
td3 = training_data.training_data()
td3.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]), False)
td3.add(np.ones([1, 4, 4]), 1, 2, np.zeros([1, 4, 4]), True)
td3.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]), False)
td3.add(np.ones([1, 4, 4]), 3, 2, np.zeros([1, 4, 4]), True)
discounted_return = td3.get_discounted_return()
expected_return = np.array([
[5.8], [2.0], [17.8], [2.0]
], dtype=float)
assert np.allclose(discounted_return, expected_return)
def test_normalize_rewards(self):
# Test calculating mean and standard deviation
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 8, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 16, np.zeros([1, 4, 4]))
td.normalize_rewards()
expected_reward = np.array([
[-0.8165], [-0.8165], [0.], [1.633],
], dtype=float)
assert np.allclose(td.get_reward(), expected_reward)
# Test specifying mean and standard deviation
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 1, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 4, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 3, 8, np.zeros([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 0, 16, np.zeros([1, 4, 4]))
td.normalize_rewards(mean=8, sd=1)
expected_reward = np.array([
[-4.], [-4.], [0.], [8.],
], dtype=float)
assert np.allclose(td.get_reward(), expected_reward)
def test_normalize_boards(self):
# Test calculating mean and standard deviation
td = training_data.training_data()
td.add(np.full((1, 4, 4), 4), 1, 4, np.full((1, 4, 4), 8))
td.add(np.full((1, 4, 4), 8), 2, 4, np.full((1, 4, 4), 16))
td.add(np.full((1, 4, 4), 16), 3, 4, np.full((1, 4, 4), 32))
td.add(np.full((1, 4, 4), 32), 4, 4, np.full((1, 4, 4), 64))
td.normalize_boards()
mean = 15.
sd = 10.7238052947636
a = (4. - mean) / sd
b = (8. - mean) / sd
c = (16. - mean) / sd
d = (32. - mean) / sd
e = (64. - mean) / sd
expected_x = np.array([
[[a, a, a, a], [a, a, a, a], [a, a, a, a], [a, a, a, a]],
[[b, b, b, b], [b, b, b, b], [b, b, b, b], [b, b, b, b]],
[[c, c, c, c], [c, c, c, c], [c, c, c, c], [c, c, c, c]],
[[d, d, d, d], [d, d, d, d], [d, d, d, d], [d, d, d, d]]
], dtype=float)
assert np.allclose(td.get_x(), expected_x)
expected_next_x = np.array([
[[b, b, b, b], [b, b, b, b], [b, b, b, b], [b, b, b, b]],
[[c, c, c, c], [c, c, c, c], [c, c, c, c], [c, c, c, c]],
[[d, d, d, d], [d, d, d, d], [d, d, d, d], [d, d, d, d]],
[[e, e, e, e], [e, e, e, e], [e, e, e, e], [e, e, e, e]]
], dtype=float)
assert np.allclose(td.get_next_x(), expected_next_x)
def test_save_restore(self):
# Set up training data
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 1, 2, np.ones([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 3, 2, np.ones([1, 4, 4]))
temp_dir = tempfile.mkdtemp()
temp_filename = os.path.join(temp_dir, 'data.csv')
td.export_csv(temp_filename)
td2 = training_data.training_data()
td2.import_csv(temp_filename)
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[0],
[1],
[2],
[3]
], dtype=int)
expected_reward = np.array([
[4],
[2],
[16],
[2]
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=int)
assert np.array_equal(td2.get_x(), expected_x)
assert np.array_equal(td2.get_y_digit(), expected_y_digit)
assert np.allclose(td2.get_reward(), expected_reward)
assert np.array_equal(td2.get_next_x(), expected_next_x)
os.remove(temp_filename)
os.rmdir(temp_dir)
def test_shuffle(self):
td = training_data.training_data()
n = 5
for i in range(n):
# Use "is odd" for done
td.add(np.full((1, 4, 4), i), i, i, np.full((1, 4, 4), i), (i % 2) == 1)
td.shuffle()
for i in range(n):
# Find where this has been shuffled too
index_of_val = np.where(td.get_y_digit() == i)[0].item(0)
# Check that all parts of this equal i
arrays = td.get_n(index_of_val)
for a in arrays:
if a.dtype is np.dtype(bool):
assert((a == ((i % 2) == 1)).all())
else:
assert((a == i).all())
def test_make_boards_unique(self):
td = training_data.training_data()
td.add(np.ones([1, 4, 4]), 0, 4, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 1, 2, np.ones([1, 4, 4]))
td.add(np.ones([1, 4, 4]), 2, 16, np.zeros([1, 4, 4]))
td.add(np.zeros([1, 4, 4]), 3, 2, np.ones([1, 4, 4]))
td.make_boards_unique()
expected_x = np.array([
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
], dtype=int)
expected_y_digit = np.array([
[0],
[1]
], dtype=int)
expected_reward = np.array([
[4],
[2]
], dtype=float)
expected_next_x = np.array([
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
], dtype=int)
assert np.array_equal(td.get_x(), expected_x)
assert np.array_equal(td.get_y_digit(), expected_y_digit)
assert np.allclose(td.get_reward(), expected_reward)
assert np.array_equal(td.get_next_x(), expected_next_x)
if __name__ == '__main__':
import pytest
pytest.main()